diff --git a/.gitignore b/.gitignore
index 7e99e367f8443d86e5e8825b9fda39dfbb39630d..83658ec52733073084336058333361a36e6c8de6 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1 +1,2 @@
-*.pyc
\ No newline at end of file
+*.pyc
+*.log
diff --git a/Archive.py b/Archive.py
index 04d24254705a04dd56a335eec985a3089baa3947..55f948ee8fc367d2b00b6456fd05aed972e6d26e 100644
--- a/Archive.py
+++ b/Archive.py
@@ -18,43 +18,44 @@
# along with PHYMOBAT 3.0. If not, see <http://www.gnu.org/licenses/>.
import os, sys, glob, re, shutil, time
-import math, subprocess, json, urllib # urllib2
+import math, subprocess, json
+import urllib.request
import tarfile, zipfile
try :
import ogr
except :
- from osgeo import ogr
+ import osgeo.ogr as org
-# import UserDict
import numpy as np
-from lxml import etree
+import lxml
from collections import defaultdict, UserDict
+import Constantes, Satellites, Outils
-from RasterSat_by_date import RasterSat_by_date
+import RasterSat_by_date
class Archive():
"""
- Class to list, download and unpack Theia image archive because of a shapefile (box).
- This shapefile get extent of the area.
-
- :param captor: Name of the satellite (ex: Landsat or SpotWorldHeritage ...).
-
- Name used to the url on website Theia Land
- :type captor: str
- :param list_year: Processing's year (string for one year)
- :type list_year: list of str
- :param box: Path of the study area
- :type box: str
- :param folder: Path of the source folder
- :type folder: str
- :param repertory: Name of the archive's folder
- :type repertory: str
+ Class to list, download and unpack Theia image archive because of a shapefile (box).
+ This shapefile get extent of the area.
+
+ :param captor: Name of the satellite (ex: Landsat or SpotWorldHeritage ...).
+
+ Name used to the url on website Theia Land
+ :type captor: str
+ :param list_year: Processing's year (string for one year)
+ :type list_year: list of str
+ :param box: Path of the study area
+ :type box: str
+ :param folder: Path of the source folder
+ :type folder: str
+ :param repertory: Name of the archive's folder
+ :type repertory: str
"""
def __init__(self, captor, list_year, box, folder, repertory, proxy_enabled):
- """Create a new 'Archive' instance
-
+ """
+ Create a new 'Archive' instance
"""
self._captor = captor
self._list_year = list_year.split(";")
@@ -67,38 +68,31 @@ class Archive():
# 1. List of the website path archives
# 2. List of the local path archives
self.list_archive = []
- self.server = '' # Host
- self.resto =''
- #Â Info from Theia-land website or Olivier Hagolle blog
- if self._captor == 'SENTINEL2':
- self.server = 'https://theia.cnes.fr/atdistrib'
- self.resto = 'resto2'
- self.token_type = 'text'
- else:
- self.server = 'https://theia-landsat.cnes.fr'
- self.resto = 'resto'
- self.token_type = 'json'
+
+ self.logger = Outils.Log("Log", "archive")
+
+ self.server = Satellites.SATELLITE[self._captor]["server"]
+ self.resto = Satellites.SATELLITE[self._captor]["resto"]
+ self.token_type = Satellites.SATELLITE[self._captor]["token_type"]
+
self.url = '' # str : complete website JSON database
self.list_img = [] # List (dim 5) to get year, month, day, path of multispectral's images and path of cloud's images
self.single_date = [] # date list without duplication
-
- def __str__(self) :
- return 'Year\'s list : ', self._list_year
-
+
def set_list_archive_to_try(self, few_list_archive):
"""
- Test function to download a few archives
-
- :param few_list_archive: [archive_download, out_archive]
-
- with :
-
- * archive_dowload : Archives downloaded
-
- * out_archive : Output archives path
-
- :type few_list_archive: list dimension 2
+ Test function to download a few archives
+
+ :param few_list_archive: [archive_download, out_archive]
+
+ with :
+
+ * archive_dowload : Archives downloaded
+
+ * out_archive : Output archives path
+
+ :type few_list_archive: list dimension 2
"""
_few_list_archive = np.array(few_list_archive)
@@ -110,21 +104,20 @@ class Archive():
def utm_to_latlng(self, zone, easting, northing, northernHemisphere=True):
"""
- Function to convert UTM to geographic coordinates
-
- :param zone: UTM zone
- :type zone: int
- :param easting: Coordinates UTM in x
- :type easting: float
- :param northing: Coordinates UTM in y
- :type northing: float
- :param northernHemisphere: North hemisphere or not
- :type northernHemisphere: boolean
-
- :returns: tuple -- integer on the **longitude** and **latitude**
-
- Source : http://www.ibm.com/developerworks/java/library/j-coordconvert/index.html
-
+ Function to convert UTM to geographic coordinates
+
+ :param zone: UTM zone
+ :type zone: int
+ :param easting: Coordinates UTM in x
+ :type easting: float
+ :param northing: Coordinates UTM in y
+ :type northing: float
+ :param northernHemisphere: North hemisphere or not
+ :type northernHemisphere: boolean
+
+ :returns: tuple -- integer on the **longitude** and **latitude**
+
+ Source : http://www.ibm.com/developerworks/java/library/j-coordconvert/index.html
"""
if not northernHemisphere:
@@ -179,18 +172,18 @@ class Archive():
def coord_box_dd(self):
"""
- Function to get area's coordinates of shapefile
+ Function to get area's coordinates of shapefile
- :returns: str -- **area_coord_corner** : Area coordinates corner
-
- --> Left bottom on x, Left bottom on y, Right top on x, Right top on y
- :Example:
-
- >>> import Archive
- >>> test = Archive(captor, list_year, box, folder, repertory, proxy_enabled)
- >>> coor_test = test.coord_box_dd()
- >>> coor_test
- '45.52, 2.25, 46.71, 3.27'
+ :returns: str -- **area_coord_corner** : Area coordinates corner
+
+ --> Left bottom on x, Left bottom on y, Right top on x, Right top on y
+ :Example:
+
+ >>> import Archive
+ >>> test = Archive(captor, list_year, box, folder, repertory, proxy_enabled)
+ >>> coor_test = test.coord_box_dd()
+ >>> coor_test
+ '45.52, 2.25, 46.71, 3.27'
"""
# Processus to convert the UTM shapefile in decimal degrees shapefile with ogr2ogr in command line
@@ -208,7 +201,7 @@ class Archive():
data_source = driver.Open(utm_outfile, 0)
if data_source is None:
- print ('Could not open file')
+ self.logger.error('Could not open file')
sys.exit(1)
shp_ogr = data_source.GetLayer()
@@ -230,56 +223,45 @@ class Archive():
def listing(self):
"""
- Function to list available archive on plateform Theia Land, and on the area
-
+ Function to list available archive on plateform Theia Land, and on the area
"""
# Loop on the years
- print ("Images availables")
+ self.logger.info("Images availables")
for year in self._list_year:
first_date = year.split(',')[0]
#Â Tricks to put whether a year or a date (year-month-day)
try:
end_date = year.split(',')[1]
- print ("=============== " + str(first_date) + " to " + str(end_date) + " ===============")
- self.url = self.server + '/' + self.resto + '/api/collections/' + self._captor + '/search.json?lang=fr&_pretty=true&completionDate=' + str(end_date) + '&box=' + self.coord_box_dd() + '&maxRecord=500&startDate=' + str(first_date)
-
+ self.logger.info("=============== {0} to {1} ===============".format(first_date, end_date))
+ self.url = "{0}/{1}/api/collections/{2}/search.json?lang=fr&_pretty=true&completionDate={3}&box={4}&maxRecord=500&startDate={5}".format(self.server, self.resto, self._captor, end_date, self.coord_box_dd(), first_date)
except IndexError:
- print ("=============== " + str(first_date) + " ===============" )
- self.url = self.server + '/' + self.resto + '/api/collections/' + self._captor + '/search.json?lang=fr&_pretty=true&q=' + str(year) + '&box=' + self.coord_box_dd() + '&maxRecord=500'
+ self.logger.info("=============== {0} ===============".format(first_date))
+ self.url = "{0}/{1}/api/collections/{2}/search.json?lang=fr&_pretty=true&q={3}&box={4}&maxRecord=500".format(self.server, self.resto, self._captor, year, self.coord_box_dd())
- print (self.url)
- # Link to connect in the database JSON of the Theia plateform
-# self.url = r'https://theia.cnes.fr/resto/api/collections/' + self._captor + '/search.json?lang=fr&_pretty=true&q=' + str(year) + '&box=' + self.coord_box_dd() + '&maxRecord=500'
- # Temporary link
-# if self._captor == 'SENTINEL2':
-# self.url = r'https://theia.cnes.fr/atdistrib/resto2/api/collections/'
-# else:
-# self.url = r'https://theia-landsat.cnes.fr/resto/api/collections/'
-
# Initialisation variable for a next page
# There is a next page, next = 1
# There isn't next page, next = 0
next_ = 1
- if not os.path.exists(self._folder + '/' + self._repertory):
- os.mkdir(self._folder + '/' + self._repertory)
+ if not os.path.exists("{0}/{1}".format(self._folder ,self._repertory)):
+ os.mkdir("{0}/{1}".format(self._folder ,self._repertory))
# To know path to download images
while next_ == 1:
try :
- request_headers = {"User-Agent": "Firefox/48.0"}
- req = urllib.Request(str(self.url), headers = request_headers) # Connexion in the database
- data = urllib.urlopen(req).read() # Read in the database
-
- new_data = re.sub("null", "'null'", data) # Remove "null" because Python don't like
- new_data = re.sub("false", "False", new_data) # Remove "false" and replace by False (Python know False with a capital letter F)
-
+ request_headers = {"User-Agent": "Magic-browser"}
+ req = urllib.request.Request(self.url, headers = request_headers) # Connexion in the database
+ data = urllib.request.urlopen(req).read() # Read in the database
+
+ new_data = re.sub(b"null", b"'null'", data) # Remove "null" because Python don't like
+ new_data = re.sub(b"false", b"False", new_data) # Remove "false" and replace by False (Python know False with a capital letter F)
+
# Transform the data in dictionary
data_Dict = defaultdict(list)
- data_Dict = UserDict.UserDict(eval(new_data))
-
+ data_Dict = UserDict(eval(new_data))
+
# Select archives to download
for d in range(len(data_Dict['features'])):
name_archive = data_Dict['features'][d]['properties']['productIdentifier']
@@ -288,29 +270,31 @@ class Archive():
url_archive = link_archive.replace(self.resto, "rocket/#")
archive_download = url_archive.replace("/download", "") # Path to download
out_archive = self._folder + '/' + self._repertory + '/' + name_archive + '.tgz' # Name after download
- self.list_archive.append([archive_download, out_archive, feature_id])
+ if len(self.list_archive) == 0 : self.list_archive.append([archive_download, out_archive, feature_id])
# Verify if there is another page (next)
if data_Dict['properties']['links'][len(data_Dict['properties']['links'])-1]['title'] == 'suivant':
self.url = data_Dict['properties']['links'][len(data_Dict['properties']['links'])-1]['href'].replace("\\", "")
else:
next_ = 0
- except:
- print ("Error connexion or error variable !")
+
+ except Exception as e:
+ self.logger.error("Error connexion or error variable : {0}".format(e))
sys.exit(1)
- print ("There is " + str(len(self.list_archive)) + " images to download !")
+ self.logger.info("{0} image(s) matched the criteria.".format(len(self.list_archive)))
+
+ return len(self.list_archive)
def download_auto(self, user_theia, password_theia):
"""
- Function to download images archive automatically on Theia land data center.
- Source : https://github.com/olivierhagolle/theia_download
-
- :param user_theia: Username Theia Land data center
- :type user_theia: str
- :param password_theia: Password Theia Land data center
- :type password_theia: str
-
+ Function to download images archive automatically on Theia land data center.
+ Source : https://github.com/olivierhagolle/theia_download
+
+ :param user_theia: Username Theia Land data center
+ :type user_theia: str
+ :param password_theia: Password Theia Land data center
+ :type password_theia: str
"""
#=====================
# Proxy
@@ -331,7 +315,7 @@ class Archive():
#=============================================================
if os.path.exists('token.json'):
os.remove('token.json')
-# get_token='curl -k -s -X POST --data-urlencode "ident=%s" --data-urlencode "pass=%s" https://theia.cnes.fr/services/authenticate/>token.json'%(curl_proxy,user_theia, password_theia)
+
get_token='curl -k -s -X POST %s --data-urlencode "ident=%s" --data-urlencode "pass=%s" %s/services/authenticate/>token.json'%(curl_proxy, user_theia, password_theia, self.server)
os.system(get_token)
@@ -342,8 +326,8 @@ class Archive():
token = token_json["access_token"]
elif self.token_type=="text":
token=data_file.readline()
- except :
- print ("Authentification is probably wrong")
+ except Exception as e:
+ self.logger.error("Authentification is probably wrong : {0}".format(e))
sys.exit(-1)
#====================
@@ -353,25 +337,19 @@ class Archive():
for d in range(len(self.list_archive)):
# Download if not exist
if not os.path.exists(self.list_archive[d][1]):
-
- print (str(round(100*float(d)/len(self.list_archive),2)) + "%") # Print loading bar
- print (os.path.split(str(self.list_archive[d][1]))[1])
-
-# get_product='curl -o %s -k -H "Authorization: Bearer %s" https://theia.cnes.fr/resto/collections/Landsat/%s/download/?issuerId=theia'%(curl_proxy,self.list_archive[d][1], token, self.list_archive[d][2])
get_product='curl %s -o %s -k -H "Authorization: Bearer %s" %s/%s/collections/%s/%s/download/?issuerId=theia'%(curl_proxy, self.list_archive[d][1], token, self.server, self.resto, self._captor, self.list_archive[d][2])
- print (get_product)
+ self.logger.debug(get_product)
os.system(get_product)
os.remove('token.json')
- print ("100%")
- print ("END OF DOWNLOAD !" )
+ self.logger.info("All images have been downloaded !")
def decompress(self):
"""
- Function to unpack archives and store informations of the images (date, path, ...)
+ Function to unpack archives and store informations of the images (date, path, ...)
"""
- print ("Unpack archives")
+ self.logger.info("Unpack archives")
for annee in self._list_year:
@@ -379,29 +357,23 @@ class Archive():
#Â Tricks to put whether a year or a date (year-month-day)
try:
end_date = annee.split(',')[1]
- print ("=============== " + str(first_date) + " to " + str(end_date) + " ===============")
+ self.logger.info("=============== {0} to {1} ===============".format(first_date, end_date))
except IndexError:
- print ("=============== " + str(first_date) + " ===============")
+ self.logger.info ("=============== {0} ===============".format(first_date))
- img_in_glob = []
- img_in_glob = glob.glob(str(self._folder) + '/'+ str(self._repertory) + '/*gz')
+ img_in_glob = sorted(glob.glob("{0}/{1}/*gz".format(self._folder, self._repertory)))
- if img_in_glob == []:
- print ("There isn't tgzfile in the folder")
- sys.exit()
+ if not img_in_glob :
+ self.logger.error("There isn't tgzfile in the folder")
+ sys.exit(1)
else:
# Create a folder "Unpack"
- folder_unpack = self._folder + '/' + self._repertory + '/Unpack'
+ folder_unpack = "{0}/{1}/Unpack".format(self._folder, self._repertory)
- if os.path.isdir(folder_unpack):
- print('The folder already exists')
- # shutil.rmtree(FolderOut) # Remove the folder that it contains if exists ...
- else:
- process_tocall = ['mkdir', folder_unpack]
- subprocess.call(process_tocall)
+ if not os.path.isdir(folder_unpack):
+ os.mkdir(folder_unpack)
for img in img_in_glob:
-
# Unpack the archives if they aren't again!
if self._captor == 'Landsat':
out_folder_unpack = folder_unpack + '/' + os.path.split(img)[1][:len(os.path.split(img)[1])-4]
@@ -411,7 +383,7 @@ class Archive():
tfile.extractall(str(folder_unpack))
# On xmlfile, extract dates, path of images, cloud's images
- xmlfile = etree.parse(str(out_folder_unpack) + '/' + os.path.split(img)[1][:len(os.path.split(img)[1])-4] + '.xml')
+ xmlfile = lxml.etree.parse(str(out_folder_unpack) + '/' + os.path.split(img)[1][:len(os.path.split(img)[1])-4] + '.xml')
# Date images
# Exemple : '2015-09-27 10:41:25.956749'
@@ -442,7 +414,7 @@ class Archive():
tzip.extract(str(z_out), str(folder_unpack))
# On xmlfile, extract dates, path of images, cloud's images
- xmlfile = etree.parse(str(extraction_img[4]))
+ xmlfile = lxml.etree.parse(str(extraction_img[4]))
# Date images
di = xmlfile.xpath("/Muscate_Metadata_Document/Product_Characteristics/ACQUISITION_DATE")[0].text.split('T')[0].split('-')
# Multispectral images
@@ -450,7 +422,7 @@ class Archive():
if not os.path.exists(hi):
# For Sentinel2 from Theia plateform, we need to make a stack layer for rasters
# There is a function that make this in RasterSat_by_date class
- stack_img = RasterSat_by_date('', '', [0])
+ stack_img = RasterSat_by_date.RasterSat_by_date('', '', [0])
input_stack = extraction_img[:4]
input_stack.insert(0,'-separate')
stack_img.vrt_translate_gdal('vrt', input_stack, hi[:-4] + '.VRT')
diff --git a/Constantes.py b/Constantes.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ec418553205a57943cb28340dcc2a75ff634c19
--- /dev/null
+++ b/Constantes.py
@@ -0,0 +1,6 @@
+
+SIMPLE_MODE = 0
+EXPERT_MODE = 1
+
+MULTIPROCESSING_ENABLE = True
+MULTIPROCESSING_DISABLE = False
diff --git a/Log/.gitkeep b/Log/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/Outils.py b/Outils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7500da87ef663f921b7f527cd1d6bbc260a7906a
--- /dev/null
+++ b/Outils.py
@@ -0,0 +1,81 @@
+#!/bin/env python
+# -*- coding: utf-8 -*-
+
+import argparse
+import logging
+from logging.handlers import RotatingFileHandler
+import signal
+from contextlib import contextmanager
+
+class Fusion(argparse.Action):
+ def __init__(self, option_strings, dest, nargs=None, **kwargs):
+ super(Fusion, self).__init__(option_strings, dest, nargs, **kwargs)
+ def __call__(self, parser, namespace, values, option_string=None):
+ setattr(namespace, self.dest, ' '.join(values).lower())
+
+class Log(object):
+
+ def __init__(self, dossier, nomFichier, niveau=logging.DEBUG):
+ super(Log, self).__init__()
+
+ self.__logger__ = logging.getLogger(nomFichier)
+ self.__logger__.setLevel(niveau)
+
+ format = logging.Formatter('%(asctime)s :: %(levelname)s :: %(message)s', datefmt='%d-%m-%Y %H:%M:%S')
+ fichierLog = RotatingFileHandler("{0}/{1}.log".format(dossier, nomFichier), 'a', 1000000, 1)
+
+ fichierLog.setLevel(niveau)
+ fichierLog.setFormatter(format)
+ self.__logger__.addHandler(fichierLog)
+
+ console = logging.StreamHandler()
+ console.setLevel(niveau)
+ self.__logger__.addHandler(console)
+
+ def info(self, message):
+ self.__logger__.info(message)
+
+ def debug(self, message):
+ self.__logger__.debug(message)
+
+ def warning(self, message):
+ self.__logger__.warning(message)
+
+ def error(self, message):
+ self.__logger__.error(message)
+
+ def critical(self, message):
+ self.__logger__.critical(message)
+
+ def exception(self, message):
+ self.__logger__.exception(message)
+
+ def close(self):
+ for handler in self.__logger__.handlers[:] :
+ handler.close()
+ self.__logger__.removeHandler(handler)
+
+
+class TimeoutException(Exception): pass
+
+@contextmanager
+def limitation_temporelle(secondes):
+
+ def signal_handler(signum, frame):
+ raise TimeoutException
+
+ signal.signal(signal.SIGALRM, signal_handler)
+ signal.alarm(secondes)
+
+ try:
+ yield
+ finally:
+ signal.alarm(0)
+
+# Utilisation :
+#
+# try:
+# with limitation_temporelle(temps_en_seconde):
+# appel_fonction()
+# except TimeoutException:
+# pass
\ No newline at end of file
diff --git a/PHYMOBAT.py b/PHYMOBAT.py
index a6505a033d41ed948001bc4178eb0c3ad7c457db..8278ebf5c743fb8c5b910b352353f723e8ba7a89 100644
--- a/PHYMOBAT.py
+++ b/PHYMOBAT.py
@@ -36,1503 +36,1383 @@ from PyQt5 import QtWidgets, QtCore
from _collections import defaultdict
try :
- import ogr
+ import ogr
except :
- from osgeo import ogr
+ from osgeo import ogr
import webbrowser
import lxml.etree as ET
import os.path
-from ui_A_propos_PHYMOBAT_window import Ui_About
-from ui_Warming_study_area import Ui_Warming_study_area
-from ui_Warming_forgetting import Ui_Warming_forgetting
-from ui_Proxy_window import Ui_Proxy_window
+import Popup
from Processing import Processing
+import Constantes
+import Outils
class PHYMOBAT(QtWidgets.QMainWindow, Processing):
- """
- Interface main class. It makes to link ``ui_PHYMOBAT_tab`` and ``Processing``.
- """
-
- def __init__(self, mode = 0, parent=None):
- super(PHYMOBAT, self).__init__(parent)
- Processing.__init__(self)
+ """
+ Interface main class. It makes to link ``ui_PHYMOBAT_tab`` and ``Processing``.
+ """
+
+ def __init__(self, mode = Constantes.SIMPLE_MODE, parent=None):
+ super(PHYMOBAT, self).__init__(parent)
+ Processing.__init__(self)
- self.apropos = None # For the "About PHYMOBAT" window
- self.w_study_area = None # For the "Warming : forget study area" window
- self.w_forget = None # For the "Warming : forgetting" window
-
- self.simpli = None # For the "PHYMOBATs" window
- self.expert = None # For the "PHYMOBATe" window
- self.mode = mode # For the mode simple (0 by default) or expert (1)
-
- #Â To select interface's parameters
- if self.mode == 1:
- print ("Expert")
- global Ui_PHYMOBAT, _translate
- from ui_PHYMOBATe_tab import Ui_PHYMOBAT, _translate
- elif self.mode == 0:
- print ("Simple")
- global Ui_PHYMOBAT, _translate
- from ui_PHYMOBATs_tab import Ui_PHYMOBAT, _translate
-
- self.initUI()
-
- def initUI(self):
-
- """
- Get initial values from interface after a click button.
-
- There is :
-
- - Connect browser button to search a path
- * Main folder path
- * Study area shapefile path
- * VHRS image path
- * MNT image path
- * Segmentation shapefile path
- * Output classification shapefile path
- * Sample shapefile path
- * Image path for samples if the first processing image hasn't been launched
-
- - Connect button to add sample in the memory list
- - Connect button to clear sample record. Clear in the interface and in the memory list
- - Connect close|ok button
- - Connect menu bar tab (Open backup, save in a xml file, close, help, About PHYMOBAT, mode)
- - Initialize backup variable
-
- """
-
- #Â Initial interface
- self.ui = Ui_PHYMOBAT()
- self.ui.setupUi(self)
+ self.apropos = Popup.about() # For the "About PHYMOBAT" window
+ self.w_study_area = Popup.warming_study_area() # For the "Warming : forget study area" window
+ self.w_forget = Popup.warming_forgetting() # For the "Warming : forgetting" window
- #Â Connect browser button to search a path
- ##########################################
- #Â Main folder path
- self.ui.lineEdit_principal_folder.clear()
- self.ui.pushButton_browser_principal_folder.clicked.connect(self.f_path_folder_dpt)
-
- # Block other function if SpotWorldHeritage is chose
- try :
- self.ui.comboBox_captor.currentIndexChanged.connect(self.block_for_swh)
- except AttributeError:
- pass
-
- # VHRS image path
- self.ui.lineEdit_VHRS.clear()
- self.ui.pushButton_browser_VHRS.clicked.connect(self.f_path_ortho)
-
- # Study area shapefile path
- self.ui.lineEdit_area_path.clear()
- self.ui.pushButton_browser_area_path.clicked.connect(self.f_path_area)
-
- # Proxy
- self.ui.proxy.clicked.connect(self.f_proxy)
-
- #Â Segmentation shapefile path
- self.ui.lineEdit_segmentation.clear()
- self.ui.pushButton_browser_segmentation.clicked.connect(self.f_path_segm)
-
- #Â MNT image path
- self.ui.lineEdit_MNT.clear()
- self.ui.pushButton_browser_MNT.clicked.connect(self.f_path_mnt)
-
- # Output classification shapefile path
- self.ui.lineEdit_output.clear()
- self.ui.pushButton_browser_output.clicked.connect(self.f_output_name_moba)
-
- # Sample shapefile path.
- # For the simply mode, RPG file sample.
- self.ui.lineEdit_sample_path.clear()
- self.ui.pushButton_browser_sample_path.clicked.connect(self.enter_sample_name)
-
- if self.mode == 0:
- # For the simply mode, grass/wooden file sample.
- self.ui.lineEdit_sample_path_2.clear()
- self.ui.pushButton_browser_sample_path_2.clicked.connect(self.enter_sample_name_hl)
- # For the simply mode, wooden file sample.
- self.ui.lineEdit_sample_path_3.clear()
- self.ui.pushButton_browser_sample_path_3.clicked.connect(self.enter_sample_name_ll)
-
- #Â Image path for samples if the first processing image hasn't been launched
- try:
- self.ui.pushButton_img_sample.clicked.connect(self.img_sample_name)
- except AttributeError:
- pass # Simple mode
- ##########################################
+ self.window = None # The "PHYMOBAT" window
+ self.current_mode = mode
- #Â Connect button to add sample in the memory list
- try:
- self.ui.pushButton_add_sample.clicked.connect(self.add_sample)
- except AttributeError:
- pass # Simple mode
-
- # Connect button to clear sample record. Clear in the interface and in the memory list
- try:
- self.ui.pushButton_clear_sample.clicked.connect(self.clear_sample)
- except AttributeError:
- pass # Simple mode
-
- #Â Connect close|ok button
- self.ui.buttonBox.button(QtWidgets.QDialogButtonBox.Close).clicked.connect(self.close_button)
- self.ui.buttonBox.button(QtWidgets.QDialogButtonBox.Ok).clicked.connect(self.ok_button)
-
- #Â Connect Menu bar
- self.ui.actionOuvrir.triggered.connect(self.open_backup) # Open backup
- self.ui.actionSauver.triggered.connect(self.save_backup) #Â Save field name on the interface
- self.ui.actionQuiter.triggered.connect(self.close_button) #Â Close
- self.ui.actionAide_de_PHYMOBAT.triggered.connect(self.help_tools) #Â Help
- self.ui.actionA_propos_de_PHYMOBAT.triggered.connect(self.about_PHYMOBA) # About PHYMOBA
- self.ui.actionMode_Simplifi.triggered.connect(self.mode_simpli) # To open the simple apply
- self.ui.actionMode_expert.triggered.connect(self.mode_expert) # To open the expert apply
-
- self.rpg_tchek = [] #Â To backup rpg mode
- self.img_sample = [] # To backup
-
- #Â Connect change line edit on sample path to extract fieldnames. For the simply mode, this
- # is with RPG sample
- self.ui.lineEdit_select_sample_fieldname_1.textChanged.connect(self.field_display_1)
- self.ui.lineEdit_select_sample_fieldname_2.textChanged.connect(self.field_display_2)
-
- # To choose the classification method
- if self.mode == 1:
- if self.ui.radioButton_rf.isChecked():
- self.ui.checkBox_classifier_1.setEnabled(False)
- self.ui.checkBox_classifier_2.setEnabled(False)
-
- self.ui.radioButton_rf.toggled.connect(self.activate_level)
- self.ui.radioButton_s.toggled.connect(self.activate_level)
-
- if self.mode == 0:
- #Â Connect change line edit to grass/wooden sample
- self.ui.lineEdit_select_sample_fieldname_3.textChanged.connect(self.field_display_3)
- self.ui.lineEdit_select_sample_fieldname_4.textChanged.connect(self.field_display_4)
- self.ui.lineEdit_select_sample_fieldname_5.textChanged.connect(self.field_display_5)
- self.ui.lineEdit_select_sample_fieldname_6.textChanged.connect(self.field_display_6)
-
- #Â Change connect for classification checkboxes
- try:
- self.ui.checkBox_classifier_1.stateChanged.connect(self.display_one_level)
- self.ui.checkBox_classifier_2.stateChanged.connect(self.display_two_levels)
- self.ui.checkBox_classifier_3.stateChanged.connect(self.display_all_levels)
- except AttributeError:
- pass # Simple mode
-
- def get_variable(self):
-
- """
- Add a all system value like :
-
- - Main folder path by line edit
- - Satellite captor name by combo box
- - Classification year by line edit
- - Study area shapefile path by line edit
- - Connexion username and password by line edit
- - VHRS image path by line edit
- - MNT image path by line edit
- - Segmentation shapefile path path by line edit
- - Output classification shapefile path by line edit
- - Output shapefile field name by line edit and field type by combo box
-
- """
- #Â Main folder path by line edit.
- self.path_folder_dpt = "%s" % self.ui.lineEdit_principal_folder.text()
-
- # Satellite captor name by combo box
- try:
- self.captor_project = self.ui.comboBox_captor.currentText()
- except AttributeError:
- self.captor_project = "Landsat"
-
- # Classification year by line edit
- self.classif_year = "%s" % self.ui.lineEdit_year_images.text()
-
- # Study area shapefile path by line edit
- self.path_area = "%s" % self.ui.lineEdit_area_path.text()
-
- #Â Connexion username and password by line edit
- self.user = "%s" % self.ui.lineEdit_user.text()
- self.password = "%s" % self.ui.lineEdit_password.text()
-
- #Â VHRS image path by line edit
- self.path_ortho = "%s" % self.ui.lineEdit_VHRS.text()
-
- #Â MNT image path by line edit
- self.path_mnt = "%s" % self.ui.lineEdit_MNT.text()
-
- #Â Output shapefile field name by line edit and field type by combo box
- try:
- if self.ui.checkBox_classifier_1.isChecked() and self.ui.lineEdit_fieldname_1.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_1.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_1.currentText()))
-
- if self.ui.checkBox_classifier_2.isChecked() and self.ui.lineEdit_fieldname_12.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_12.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_12.currentText()))
-
- if self.ui.lineEdit_fieldname_2.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_2.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_2.currentText()))
-
- if self.ui.checkBox_classifier_3.isChecked() and self.ui.lineEdit_fieldname_13.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_13.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_13.currentText()))
-
- if self.ui.lineEdit_fieldname_23.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_23.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_23.currentText()))
-
- if self.ui.lineEdit_fieldname_3.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_3.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_3.currentText()))
-
- if self.ui.lineEdit_fieldname_4.text() != '':
- self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_4.text())
- self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_4.currentText()))
- except AttributeError:
- modes_fieldname = ["NIVEAU_1", "NIVEAU_2", "NIVEAU_3", "POURC"]
- modes_fieldtype = [eval("ogr.OFTString"), eval("ogr.OFTString"), eval("ogr.OFTString"), eval("ogr.OFTReal")]
-
- for mf in range(len(modes_fieldname)):
-
- self.out_fieldname_carto.append(modes_fieldname[mf])
- self.out_fieldtype_carto.append(modes_fieldtype[mf])
-
- # Segmentation shapefile path path by line edit
- self.path_segm = "%s" % self.ui.lineEdit_segmentation.text()
-
- #Â Output shapefile field name by line edit and field type by combo box
- self.output_name_moba = "%s" % self.ui.lineEdit_output.text()
-
- def set_variable(self):
- """
- Print number of available image from Theia's GeoJSON .
- """
- #Â self.ui.lineEdit_listing.setText(str(self.nb_avalaible_images))
- try:
- self.ui.label_listing.setText(str(self.nb_avalaible_images))
- except AttributeError:
- pass # Simple mode
-
- def f_path_folder_dpt(self):
- """
- Open a input browser box to select the main folder path by line edit.
- """
- infoldername = QtWidgets.QFileDialog.getExistingDirectory(self, "Principal folder path", os.getcwd(), QtWidgets.QFileDialog.ShowDirsOnly)
- self.ui.lineEdit_principal_folder.setText(str(infoldername).replace('[','').replace(']','').replace(' ',''))
-
- def block_for_swh(self):
- """
- Function to block others function when SportWorldHeritage is selected in the comboxbox captor.
- """
- ind_captor = int(self.ui.comboBox_captor.currentIndex())
- if ind_captor == 2:
- self.ui.checkBox_processing.setEnabled(False)
- self.ui.checkBox_MNT.setEnabled(False)
- self.ui.lineEdit_MNT.setEnabled(False)
- self.ui.pushButton_browser_MNT.setEnabled(False)
- self.ui.checkBox_VHRS.setEnabled(False)
- self.ui.lineEdit_VHRS.setEnabled(False)
- self.ui.pushButton_browser_VHRS.setEnabled(False)
- self.ui.tabWidget.setTabEnabled(1, False)
- self.ui.tabWidget.setTabEnabled(2, False)
- # If the user want, on the same moment, come back on other captor that SWH.
- else:
- self.ui.checkBox_processing.setEnabled(True)
- self.ui.checkBox_MNT.setEnabled(True)
- self.ui.lineEdit_MNT.setEnabled(True)
- self.ui.pushButton_browser_MNT.setEnabled(True)
- self.ui.checkBox_VHRS.setEnabled(True)
- self.ui.lineEdit_VHRS.setEnabled(True)
- self.ui.pushButton_browser_VHRS.setEnabled(True)
- self.ui.tabWidget.setTabEnabled(1, True)
- self.ui.tabWidget.setTabEnabled(2, True)
-
- def f_path_ortho(self):
- """
- Open a input browser box to select the VHRS image path by line edit.
- """
- orthofilename = QtWidgets.QFileDialog.getOpenFileName(self, "THRS image", self.ui.lineEdit_principal_folder.text(), '*.TIF *.tif')[0]
- self.ui.lineEdit_VHRS.setText(str(orthofilename).replace('[','').replace(']','').replace(' ',''))
-
- def f_path_mnt(self):
- """
- Open a input browser box to select the MNT image path by line edit.
- """
- mntfilename = QtWidgets.QFileDialog.getOpenFileName(self, "MNT image", self.ui.lineEdit_principal_folder.text(), '*.TIF *.tif')[0]
- self.ui.lineEdit_MNT.setText(str(mntfilename).replace('[','').replace(']','').replace(' ',''))
-
- def f_path_area(self):
- """
- Open a input browser box to select the study area shapefile path by line edit.
- """
- areafilename = QtWidgets.QFileDialog.getOpenFileName(self, "Area shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
- self.ui.lineEdit_area_path.setText(str(areafilename).replace('[','').replace(']','').replace(' ',''))
-
- def f_proxy(self):
- """
- Function to open a popup in order to enter proxy ID
- """
- if self.w_proxy is None:
- self.w_proxy = MyPopup_proxy_window()
- self.w_proxy.show()
-
- def f_path_segm(self):
- """
- Open a input browser box to select segmentation shapefile path path by line edit.
- """
- segmfilename = QtWidgets.QFileDialog.getOpenFileName(self, "Segmentation shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
- self.ui.lineEdit_segmentation.setText(str(segmfilename).replace('[','').replace(']','').replace(' ',''))
-
- def f_output_name_moba(self):
- """
- Set the output classification shapefile path by line edit.
- """
- outfilename = QtWidgets.QFileDialog.getSaveFileName(self, "FB file", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
- #Â if the user has forgotten to put .shp at the end of the output shapefile
- if outfilename[-4:] != '.shp':
- outfilename = outfilename + '.shp'
- self.ui.lineEdit_output.setText(outfilename)
-
- def enter_sample_name(self):
- """
- Open a input browser box to select the sample shapefile path by line edit. With :func:`add_sample` conditions for the expert mode.
- For the simply mode, this function is used for the RPG shapefile.
- """
- samplefilename = QtWidgets.QFileDialog.getOpenFileName(self, "Sample shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
- self.ui.lineEdit_sample_path.setText(str(samplefilename).replace('[','').replace(']','').replace(' ',''))
-
- def enter_sample_name_hl(self):
- """
- Open a input browser box to select the grass and wooden sample shapefile path by line edit. With :func:`add_sample` conditions
- for the simply mode.
- """
- samplefilename_hl = QtWidgets.QFileDialog.getOpenFileName(self, "Grass/Wooden sample shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
- self.ui.lineEdit_sample_path_2.setText(str(samplefilename_hl).replace('[','').replace(']','').replace(' ',''))
-
- def enter_sample_name_ll(self):
- """
- Open a input browser box to select the wooden sample shapefile path by line edit. With :func:`add_sample` conditions for the simply mode.
- """
- samplefilename_ll = QtWidgets.QFileDialog.getOpenFileName(self, "Wooden sample shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
- self.ui.lineEdit_sample_path_3.setText(str(samplefilename_ll).replace('[','').replace(']','').replace(' ',''))
-
- def img_sample_name(self):
- """
- Open a input browser box to select the image for samples path by line edit. With :func:`add_sample` conditions.
- """
- imgsamplefilename = QtWidgets.QFileDialog.getOpenFileName(self, "Sample image", self.ui.lineEdit_principal_folder.text(), '*.TIF')[0]
- self.ui.lineEdit_img_sample.setText(str(imgsamplefilename).replace('[','').replace(']','').replace(' ',''))
-
- def add_sample(self):
- """
- Add sample information and location to compute optimal threshold :
-
- For the expert mode (mode=1) :
-
- - Append a sample name by line Edit. *This is a check box* ``RPG``, *if the sample is RPG file. It launch the Rpg class. And append a other sample from Rpg class*.
- - Append two existent sample field names by combobox. It will be the same.
- - Append sample class names by line edit. One or more for every sample.
- - Append number of polygons for every samples by line edit.
- - Print in a plain text edit : sample name, two sample field names, sample class names and number of polygons.
- - *This check box* ``Image echantillonee``, *image path for samples if the first processing image hasn't been launched*.
- .. note:: This is for a image with one spectral band
- - Clear all widget field at the end.
-
- For the simply mode (mode=0):
-
- - Append a sample name by a different line Edit (a line Edit for each sample).
- - Append sample class names, existing sample fields and number of polygons (a different line Edit for each sample)
- """
-
- nb_sample = len(self.sample_name)#Â Compute number of samples added. Condition : max three.
- # Study area shapefile path by line edit if no processing other
- # Because the function "Vector" need study area
- self.path_area = "%s" % self.ui.lineEdit_area_path.text()
- if self.path_area == '':
- self.forget_study_area()
-
- if self.mode == 1:
- # Expert mode
- if nb_sample < 3 and not self.ui.lineEdit_sample_path.text().isEmpty() and \
- not self.ui.lineEdit_select_sample_fieldname_1.text().isEmpty() and not self.ui.lineEdit_select_sample_fieldname_2.text().isEmpty() and \
- not self.ui.lineEdit_select_sample_class_1.text().isEmpty() and not self.ui.lineEdit_select_sample_class_2.text().isEmpty() and \
- not self.ui.lineEdit_select_sample_nb_poly.text().isEmpty() and not self.ui.lineEdit_area_path.text().isEmpty():
-
- # Append a sample name by line Edit.
- if self.ui.checkBox_RPG.isChecked():
- # Check box, if the sample of the RPG file. It launch the Rpg class. And append a other sample from Rpg class
- self.sample_name.append(self.i_rpg("%s" % self.ui.lineEdit_sample_path.text()))
- self.rpg_tchek.append(1) # To backup
- self.ui.checkBox_RPG.setChecked(False)
- else:
- self.sample_name.append("%s" % self.ui.lineEdit_sample_path.text())
- self.rpg_tchek.append(0)
-
- #Â Append two sample field names by line edit. It must be the same.
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_1.text())
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_2.text())
- #Â Append sample class names by line edit. One or more for every sample
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_1.text())
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_2.text())
- # Append number of polygons for every samples by line edit.
- self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly.text())
-
- nb_sample = len(self.sample_name) # Number of samples added
- #Â Print in a plain text edit : sample name, two sample field names, sample class names and number of polygons.
- cursor = self.ui.plainTextEdit_sample.textCursor()
- cursor.movePosition(QTextCursor.End, QTextCursor.MoveAnchor)
- self.ui.plainTextEdit_sample.setTextCursor(cursor)
- self.ui.plainTextEdit_sample.insertPlainText(str(self.sample_name[nb_sample-1]) + "\n")
- cursor.movePosition(QTextCursor.Down, QTextCursor.MoveAnchor)
- self.ui.plainTextEdit_sample.setTextCursor(cursor)
- self.ui.plainTextEdit_sample.insertPlainText(str(self.fieldname_args[(nb_sample-1)*2]) + ' ' + str(self.fieldname_args[((nb_sample-1)*2)+1]) + "\n")
- cursor.movePosition(QTextCursor.Down, QTextCursor.MoveAnchor)
- self.ui.plainTextEdit_sample.setTextCursor(cursor)
- self.ui.plainTextEdit_sample.insertPlainText(str(self.class_args[(nb_sample-1)*2]) + ' ' + str(self.class_args[(nb_sample-1)*2+1]) + "\n")
- cursor.movePosition(QTextCursor.Down, QTextCursor.MoveAnchor)
- self.ui.plainTextEdit_sample.setTextCursor(cursor)
- self.ui.plainTextEdit_sample.insertPlainText(str(self.list_nb_sample[nb_sample-1]) + "\n")
-
- #Â Check box, image path for samples if the first processing image hasn't been launched
- # Warming : This is for a image with one spectral band
- if self.ui.checkBox_img_sample.isChecked():
- self.raster_path.append("%s" % self.ui.lineEdit_img_sample.text())
- self.list_band_outraster.append(1)
- self.ui.lineEdit_img_sample.clear()
- self.ui.checkBox_img_sample.setChecked(False)
- self.img_sample.append(1)
- else: #Â To backup
- self.img_sample.append(0)
-
- #Â Clear all line edit after addition, ie after click add button.
- self.ui.lineEdit_sample_path.clear()
- self.ui.lineEdit_select_sample_fieldname_1.clear()
- self.ui.lineEdit_select_sample_fieldname_2.clear()
- self.ui.lineEdit_select_sample_class_1.clear()
- self.ui.lineEdit_select_sample_class_2.clear()
- self.ui.lineEdit_select_sample_nb_poly.clear()
-
- elif self.mode == 0:
-
- # Simple mode
- # Append a sample name by line Edit.
- # For the sample of the RPG file. It launch the Rpg class. And append a other sample from Rpg class
- self.sample_name.append(self.i_rpg("%s" % self.ui.lineEdit_sample_path.text()))
-
- #Â Append two sample field names by line edit. It must be the same.
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_1.text())
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_2.text())
- #Â Append sample class names by line edit. One or more for every sample
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_1.text())
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_2.text())
- # Append number of polygons for every samples by line edit.
- self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly.text())
-
- # Same process that the RPG process except the start of the RPG class.
- # To Grass/wooden
- self.sample_name.append("%s" % self.ui.lineEdit_sample_path_2.text())
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_3.text())
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_4.text())
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_3.text())
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_4.text())
- self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly_2.text())
-
- # To wooden
- self.sample_name.append("%s" % self.ui.lineEdit_sample_path_3.text())
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_5.text())
- self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_6.text())
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_5.text())
- self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_6.text())
- self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly_3.text())
-
- nb_sample = 3
-
- def clear_sample(self):
- """
- Function to clear sample record. Clear in the interface and in the memory list.
- """
- self.sample_name = []
- self.fieldname_args = []
- self.class_args = []
- self.list_nb_sample = []
- self.rpg_tchek = []
- self.img_sample = []
-
- self.ui.lineEdit_sample_path.clear()
- self.ui.lineEdit_select_sample_fieldname_1.clear()
- self.ui.lineEdit_select_sample_fieldname_2.clear()
- self.ui.lineEdit_select_sample_class_1.clear()
- self.ui.lineEdit_select_sample_class_2.clear()
- self.ui.lineEdit_select_sample_nb_poly.clear()
- self.ui.checkBox_RPG.setChecked(False)
- self.ui.lineEdit_img_sample.clear()
- self.ui.checkBox_img_sample.setChecked(False)
- self.ui.plainTextEdit_sample.clear()
- self.ui.plainTextEdit_sample.insertPlainText("1 - Végétation non naturelle / Semi-naturelle\n")
- self.ui.plainTextEdit_sample.insertPlainText("2 - Herbacés / Ligneux\n")
- self.ui.plainTextEdit_sample.insertPlainText("3 - Lingeux mixtes / denses\n")
- self.ui.plainTextEdit_sample.insertPlainText("\n")
- self.ui.plainTextEdit_sample.insertPlainText("\n")
- self.ui.plainTextEdit_sample.insertPlainText("")
-
- def field_display_1(self):
- """
- Function to display fieldname class 1 in the other fieldname class 2 when text changed.
- For the simply mode, this is RPG sample.
- """
-
- self.ui.lineEdit_select_sample_fieldname_2.setText("%s" % self.ui.lineEdit_select_sample_fieldname_1.text())
-
- def field_display_2(self):
- """
- Function to display fieldname class 2 in the other fieldname class 2 when text changed.
- For the simply mode, this is RPG sample.
- """
-
- self.ui.lineEdit_select_sample_fieldname_1.setText("%s" % self.ui.lineEdit_select_sample_fieldname_2.text())
-
- def field_display_3(self):
- """
- For the grass/wooden sample, a function to display fieldname class 1 in the other fieldname class 2 when text changed.
- """
-
- self.ui.lineEdit_select_sample_fieldname_4.setText("%s" % self.ui.lineEdit_select_sample_fieldname_3.text())
-
- def field_display_4(self):
- """
- For the grass/wooden sample, a function to display fieldname class 2 in the other fieldname class 2 when text changed.
- """
-
- self.ui.lineEdit_select_sample_fieldname_3.setText("%s" % self.ui.lineEdit_select_sample_fieldname_4.text())
-
- def field_display_5(self):
- """
- For the wooden sample, a function to display fieldname class 1 in the other fieldname class 2 when text changed.
- """
-
- self.ui.lineEdit_select_sample_fieldname_6.setText("%s" % self.ui.lineEdit_select_sample_fieldname_5.text())
-
- def field_display_6(self):
- """
- For the wooden sample, a function to display fieldname class 2 in the other fieldname class 2 when text changed.
- """
-
- self.ui.lineEdit_select_sample_fieldname_5.setText("%s" % self.ui.lineEdit_select_sample_fieldname_6.text())
-
- def activate_level(self):
- """
- To activate the first levels with seath method. This is in pushing on the decision tree radio button.
- Else it activates the last level to random forest method.
- """
- if self.ui.radioButton_s.isChecked() and not self.ui.radioButton_rf.isChecked():
- self.ui.checkBox_classifier_1.setEnabled(True)
- self.ui.checkBox_classifier_2.setEnabled(True)
- else:
- self.ui.checkBox_classifier_1.setEnabled(False)
- self.ui.checkBox_classifier_2.setEnabled(False)
- self.ui.checkBox_classifier_3.setChecked(True)
-
-
- def display_one_level(self):
- """
- Function to display fieldnames option to classifier one level
- """
- if self.ui.checkBox_classifier_1.isChecked():
-
- # Don't checked others checkboxes
- self.ui.checkBox_classifier_2.setChecked(False)
- self.ui.checkBox_classifier_3.setChecked(False)
-
- #Â Display options filednames
- self.ui.label_chps_1 = QLabel(self.ui.tab_3)
- self.ui.label_chps_1.setObjectName("label_chps_1")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_1, 10, 0, 2, 2)
- self.ui.label_chps_name_1 = QLabel(self.ui.tab_3)
- self.ui.label_chps_name_1.setObjectName("label_chps_name_1")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_name_1, 10, 2, 1, 1)
- self.ui.label_chps_type_1 = QLabel(self.ui.tab_3)
- self.ui.label_chps_type_1.setObjectName("label_chps_type_1")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_type_1, 11, 2, 1, 1)
- self.ui.lineEdit_fieldname_1 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_1.setObjectName("lineEdit_fieldname_1")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_1, 10, 3, 1, 1)
- self.ui.comboBox_fieldname_1 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_1.setObjectName("comboBox_fieldname_1")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_1, 11, 3, 1, 1)
-
- self.ui.lineEdit_fieldname_1.setText(_translate("PHYMOBAT", "NIVEAU_1", None))
- self.ui.comboBox_fieldname_1.addItem("String")
- self.ui.comboBox_fieldname_1.addItem("Real")
-
- self.ui.label_chps_1.setText(_translate("PHYMOBAT", " Champs\n"+" des entités", None))
- self.ui.label_chps_name_1.setText(_translate("PHYMOBAT", "Nom :", None))
- self.ui.label_chps_type_1.setText(_translate("PHYMOBAT", "Type :", None))
-
- if not self.ui.checkBox_classifier_1.isChecked():
- #Â Clear options filednames
- try:
- self.ui.label_chps_1.deleteLater()
- self.ui.label_chps_name_1.deleteLater()
- self.ui.label_chps_type_1.deleteLater()
- self.ui.lineEdit_fieldname_1.deleteLater()
- self.ui.comboBox_fieldname_1.deleteLater()
- except AttributeError:
- pass
-
- def display_two_levels(self):
- """
- Function to display fieldnames option to classifier two first levels
- """
-
- if self.ui.checkBox_classifier_2.isChecked():
-
- # Don't checked others checkboxes
- self.ui.checkBox_classifier_1.setChecked(False)
- self.ui.checkBox_classifier_3.setChecked(False)
-
- self.ui.label_chps_2 = QLabel(self.ui.tab_3)
- self.ui.label_chps_2.setObjectName("label_chps_2")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_2, 13, 0, 2, 2)
- self.ui.lineEdit_fieldname_12 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_12.setObjectName("lineEdit_fieldname_12")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_12, 13, 3, 1, 1)
- self.ui.lineEdit_fieldname_2 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_2.setObjectName("lineEdit_fieldname_2")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_2, 13, 4, 1, 1)
- self.ui.label_chps_type_2 = QLabel(self.ui.tab_3)
- self.ui.label_chps_type_2.setObjectName("label_chps_type_2")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_type_2, 14, 2, 1, 1)
- self.ui.comboBox_fieldname_12 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_12.setObjectName("comboBox_fieldname_12")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_12, 14, 3, 1, 1)
- self.ui.comboBox_fieldname_2 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_2.setObjectName("comboBox_fieldname_2")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_2, 14, 4, 1, 1)
- self.ui.label_chps_name_2 = QLabel(self.ui.tab_3)
- self.ui.label_chps_name_2.setObjectName("label_chps_name_2")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_name_2, 13, 2, 1, 1)
-
- self.ui.lineEdit_fieldname_12.setText(_translate("PHYMOBAT", "NIVEAU_1", None))
- self.ui.comboBox_fieldname_12.addItem("String")
- self.ui.comboBox_fieldname_12.addItem("Real")
- self.ui.lineEdit_fieldname_2.setText(_translate("PHYMOBAT", "NIVEAU_2", None))
- self.ui.comboBox_fieldname_2.addItem("String")
- self.ui.comboBox_fieldname_2.addItem("Real")
-
- self.ui.label_chps_type_2.setText(_translate("PHYMOBAT", "Type :", None))
- self.ui.label_chps_2.setText(_translate("PHYMOBAT", " Champs\n"+" des entités", None))
- self.ui.label_chps_name_2.setText(_translate("PHYMOBAT", "Nom :", None))
-
- if not self.ui.checkBox_classifier_2.isChecked():
- #Â Clear options filednames
- try:
- self.ui.label_chps_2.deleteLater()
- self.ui.label_chps_name_2.deleteLater()
- self.ui.label_chps_type_2.deleteLater()
- self.ui.lineEdit_fieldname_12.deleteLater()
- self.ui.comboBox_fieldname_12.deleteLater()
- self.ui.lineEdit_fieldname_2.deleteLater()
- self.ui.comboBox_fieldname_2.deleteLater()
- except AttributeError:
- pass
-
- def display_all_levels(self):
- """
- Function to display fieldnames option to launch complete classification
- """
-
- if self.ui.checkBox_classifier_3.isChecked():
-
- # Don't checked others checkboxes
- self.ui.checkBox_classifier_1.setChecked(False)
- self.ui.checkBox_classifier_2.setChecked(False)
-
- self.ui.label_chps_name_3 = QLabel(self.ui.tab_3)
- self.ui.label_chps_name_3.setObjectName("label_chps_name_3")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_name_3, 16, 2, 1, 1)
- self.ui.label_chps_3 = QLabel(self.ui.tab_3)
- self.ui.label_chps_3.setObjectName("label_chps_3")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_3, 16, 0, 2, 2)
- self.ui.label_chps_type_3 = QLabel(self.ui.tab_3)
- self.ui.label_chps_type_3.setObjectName("label_chps_type_3")
- self.ui.gridLayout_2.addWidget(self.ui.label_chps_type_3, 17, 2, 1, 1)
- self.ui.lineEdit_fieldname_13 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_13.setObjectName("lineEdit_fieldname_13")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_13, 16, 3, 1, 1)
- self.ui.lineEdit_fieldname_23 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_23.setObjectName("lineEdit_fieldname_23")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_23, 16, 4, 1, 1)
- self.ui.lineEdit_fieldname_3 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_3.setObjectName("lineEdit_fieldname_3")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_3, 16, 5, 1, 1)
- self.ui.lineEdit_fieldname_4 = QLineEdit(self.ui.tab_3)
- self.ui.lineEdit_fieldname_4.setObjectName("lineEdit_fieldname_4")
- self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_4, 16, 6, 1, 1)
- self.ui.comboBox_fieldname_13 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_13.setObjectName("comboBox_fieldname_13")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_13, 17, 3, 1, 1)
- self.ui.comboBox_fieldname_23 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_23.setObjectName("comboBox_fieldname_23")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_23, 17, 4, 1, 1)
- self.ui.comboBox_fieldname_3 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_3.setObjectName("comboBox_fieldname_3")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_3, 17, 5, 1, 1)
- self.ui.comboBox_fieldname_4 = QComboBox(self.ui.tab_3)
- self.ui.comboBox_fieldname_4.setObjectName("comboBox_fieldname_4")
- self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_4, 17, 6, 1, 1)
-
- self.ui.lineEdit_fieldname_13.setText(_translate("PHYMOBAT", "NIVEAU_1", None))
- self.ui.comboBox_fieldname_13.addItem("String")
- self.ui.comboBox_fieldname_13.addItem("Real")
- self.ui.lineEdit_fieldname_23.setText(_translate("PHYMOBAT", "NIVEAU_2", None))
- self.ui.comboBox_fieldname_23.addItem("String")
- self.ui.comboBox_fieldname_23.addItem("Real")
- self.ui.lineEdit_fieldname_3.setText(_translate("PHYMOBAT", "NIVEAU_3", None))
- self.ui.comboBox_fieldname_3.addItem("String")
- self.ui.comboBox_fieldname_3.addItem("Real")
- self.ui.lineEdit_fieldname_4.setText(_translate("PHYMOBAT", "POURC", None))
- self.ui.comboBox_fieldname_4.addItem("Real")
- self.ui.comboBox_fieldname_4.addItem("String")
-
- self.ui.label_chps_3.setText(_translate("PHYMOBAT", " Champs\n"+" des entités", None))
- self.ui.label_chps_type_3.setText(_translate("PHYMOBAT", "Type :", None))
- self.ui.label_chps_name_3.setText(_translate("PHYMOBAT", "Nom :", None))
-
- if not self.ui.checkBox_classifier_3.isChecked():
- #Â Clear options filednames
- try:
- self.ui.label_chps_3.deleteLater()
- self.ui.label_chps_name_3.deleteLater()
- self.ui.label_chps_type_3.deleteLater()
- self.ui.lineEdit_fieldname_13.deleteLater()
- self.ui.comboBox_fieldname_13.deleteLater()
- self.ui.lineEdit_fieldname_23.deleteLater()
- self.ui.comboBox_fieldname_23.deleteLater()
- self.ui.lineEdit_fieldname_3.deleteLater()
- self.ui.comboBox_fieldname_3.deleteLater()
- self.ui.lineEdit_fieldname_4.deleteLater()
- self.ui.comboBox_fieldname_4.deleteLater()
- except AttributeError:
- pass
-
- def ok_button(self):
-
- """
- Function to launch the processing. This function take account :
-
- - The ``Multi-processing`` check box if the processing has launched with multi process. By default, this is checked. It need a computer with minimum 12Go memory.
- - Append a few system value with :func:`get_variable`.
- - There are 3 principal check boxes :
- - to get number download available images
- - for downloading and processing on theia platform
- - to compute optimal threshold.
- - to compute slope raster
- - for classification processing.
- """
-
- # Start the processus
- startTime = time.time()
-
- #Â To know if the processing must be launch on several thread
- if self.ui.checkBox_multiprocess.isChecked():
- self.mp = 1
- else:
- self.mp = 0
-
- self.get_variable() # Append a few system value
- vs = 0 # Variable to launch VHRS texture processing
- dd = 0 # Variable to launch image downloading
- ok = 1 #Â Variable to verify informations -> 0 not ok, 1 ok
-
- if self.mode == 1:
- #Â if download check box is checked only
- if not self.ui.checkBox_listing.isChecked() and self.ui.checkBox_download.isChecked():
-
- self.ui.checkBox_listing.setChecked(True)
-
- # #Â if processing check box is checked only
- # if not self.ui.checkBox_listing.isChecked() and not self.ui.checkBox_download.isChecked() and self.ui.checkBox_processing.isChecked():
- #
- # self.ui.checkBox_listing.setChecked(True)
- # self.ui.checkBox_download.setChecked(True)
-
- #Â Verify raster or sample to launch a good processing classification tab
- #Â If not ok, there will appear a message windows to enter the miss information
- # if self.ui.checkBox_classifier_1.isChecked():
- # if not self.ui.checkBox_processing.isChecked() or (len(self.raster_path) < 1 and len(self.sample_name) > 0) or \
- # len(self.sample_name) < 1:
- # self.forget_raster_sample()
- # ok = 0
- # if self.ui.checkBox_classifier_2.isChecked():
- # if (not self.ui.checkBox_processing.isChecked() and not self.ui.checkBox_MNT.isChecked() and \
- # not self.ui.checkBox_VHRS.isChecked()) or (len(self.raster_path) < 2 and len(self.sample_name) > 0) or \
- # len(self.sample_name) < 2:
- # self.forget_raster_sample()
- # ok = 0
- # if self.ui.checkBox_classifier_3.isChecked():
- # if (not self.ui.checkBox_processing.isChecked() and not self.ui.checkBox_MNT.isChecked() and \
- # not self.ui.checkBox_VHRS.isChecked()) or (len(self.raster_path) != 6 and len(self.sample_name) > 0) or \
- # len(self.sample_name) != 3:
- # self.forget_raster_sample()
- # ok = 0
-
- if ok == 1:
- # Compute a output slope raster
- if self.ui.checkBox_MNT.isChecked():
-
- self.i_slope()
-
- # Downloading and processing on theia platform
- # A check box to get number download available images
- if self.ui.checkBox_listing.isChecked():
-
- if self.ui.checkBox_download.isChecked():
- #Â To launch the downloading
- dd = 1
-
- self.i_download(dd) #Â Launch image listing and downloading if dd = 1
- self.set_variable() #Â to write in the line edit about number download available images
-
- #Â Check box to launch the image processing
- if self.ui.checkBox_processing.isChecked():
- # Another check box to launch VHRS texture processing. If checked, vs = 1.
- if self.ui.checkBox_VHRS.isChecked():
- vs = 1
- self.i_images_processing(vs) #Â function to launch the image processing
-
- # To launch the image processing without dowloading but with the images in a main folder
- # Without internet connection
- if not self.ui.checkBox_download.isChecked() and self.ui.checkBox_processing.isChecked():
-
- # Launch pre-processing without downloading
- self.i_glob()
- # Another check box to launch VHRS texture processing. If checked, vs = 1.
- if self.ui.checkBox_VHRS.isChecked():
- vs = 1
- self.i_images_processing(vs) #Â function to launch the image processing
-
- # To launch texture processing only
- if not self.ui.checkBox_listing.isChecked() and not self.ui.checkBox_processing.isChecked() and self.ui.checkBox_VHRS.isChecked():
-
- self.i_vhrs()
-
- #Â Compute optimal threshold
- if self.ui.checkBox_threshold.isChecked():
-
- if self.ui.radioButton_rf.isChecked():
- self.i_sample_rf()
- elif self.ui.radioButton_s.isChecked():
- self.i_sample()
-
- #Â Classification processing
- if self.ui.radioButton_rf.isChecked():
-
- if self.ui.checkBox_classifier_3.isChecked():
-
- self.out_fieldname_carto = self.out_fieldname_carto
- self.out_fieldtype_carto = self.out_fieldtype_carto
- self.i_classifier_rf()
- self.i_validate()
-
- elif self.ui.radioButton_s.isChecked():
-
- if self.ui.checkBox_classifier_1.isChecked() :
-
- self.out_fieldname_carto = self.out_fieldname_carto[:3]
- self.out_fieldtype_carto = self.out_fieldtype_carto[:3]
- self.i_classifier_s()
- self.i_validate()
-
- if self.ui.checkBox_classifier_2.isChecked() :
-
- self.out_fieldname_carto = self.out_fieldname_carto[:4]
- self.out_fieldtype_carto = self.out_fieldtype_carto[:4]
- self.i_classifier_s()
- self.i_validate()
-
- if self.ui.checkBox_classifier_3.isChecked():
-
- self.out_fieldname_carto = self.out_fieldname_carto
- self.out_fieldtype_carto = self.out_fieldtype_carto
- self.i_classifier_s()
- self.i_validate()
-
- if self.mode == 0:
-
- # Compute a output slope raster
- if self.path_mnt != '':
- self.i_slope()
-
- # Save the sample features
- self.add_sample()
- try:
- # Look at if the the images has been already downlaoded
- self.i_glob()
- except:
- # Else the processus download it
- self.i_download(1)
-
- #Â function to launch the image processing
- self.i_images_processing(1)
- # To launch texture processing only
- self.i_vhrs()
- #Â Compute optimal threshold
- self.i_sample_rf()
-
- #Â Classification processing
- self.out_fieldname_carto = self.out_fieldname_carto
- self.out_fieldtype_carto = self.out_fieldtype_carto
- self.i_classifier_rf()
- self.i_validate()
-
-
-# # Clear variables after processing
-# self.clear_sample()
- self.out_fieldname_carto = ['ID', 'AREA']
- self.out_fieldtype_carto = [ogr.OFTString, ogr.OFTReal]
- # Images after processing images
- self.out_ndvistats_folder_tab = defaultdict(list)
-
- Processing.__init__(self)#Â Initialize variable without to close and launch again the application
-
- # End of the processus
- endTime = time.time() # Tps : Terminé
- print ('...........' + ' Outputted to File in ' + str(endTime - startTime) + ' secondes')
- nb_day_processing = int(time.strftime('%d', time.gmtime(endTime - startTime))) - 1
- print ("That is, " + str(nb_day_processing) + ' day(s) ' + time.strftime('%Hh %Mmin%S', time.gmtime(endTime - startTime)))
-
- def open_backup(self):
- """
- Function to load input text in every fields. The input file must be a XML file.
- """
-
- in_backup = QtWidgets.QFileDialog.getOpenFileName(self, "Open backup", os.getcwd(), '*.xml')[0]
-
- if in_backup != None and len(in_backup) > 0 :
+ self.logger = Outils.Log("Log", "Phymobat")
+
+ #Â To select interface's parameters
+ if self.current_mode == Constantes.EXPERT_MODE:
+ self.logger.info("Expert mode.")
+ global Ui_PHYMOBAT, _translate
+ from ui_PHYMOBATe_tab import Ui_PHYMOBAT, _translate
+ elif self.current_mode == Constantes.SIMPLE_MODE:
+ self.logger.info("Simple mode.")
+ global Ui_PHYMOBAT, _translate
+ from ui_PHYMOBATs_tab import Ui_PHYMOBAT, _translate
+
+ self.initUI()
- # Parse the xml file if the user choose a file
- tree = ET.parse(str(in_backup))
-
- if tree.find("Multi_process").text == '1':
- self.ui.checkBox_multiprocess.setChecked(True)
- else:
- self.ui.checkBox_multiprocess.setChecked(False)
-
- pr = tree.find("Tab[@id='Processing_raster']")
+ # Chargement et lancement automatique pour les tests à supprimer
+ self.open_backup("/home/commandre/Documents/data_test_2/2018.xml")
+ # self.ok_button()
+
+ def initUI(self):
+
+ """
+ Get initial values from interface after a click button.
+
+ There is :
+
+ - Connect browser button to search a path
+ * Main folder path
+ * Study area shapefile path
+ * VHRS image path
+ * MNT image path
+ * Segmentation shapefile path
+ * Output classification shapefile path
+ * Sample shapefile path
+ * Image path for samples if the first processing image hasn't been launched
+
+ - Connect button to add sample in the memory list
+ - Connect button to clear sample record. Clear in the interface and in the memory list
+ - Connect close|ok button
+ - Connect menu bar tab (Open backup, save in a xml file, close, help, About PHYMOBAT, mode)
+ - Initialize backup variable
+
+ """
+
+ #Â Initial interface
+ self.ui = Ui_PHYMOBAT()
+ self.ui.setupUi(self)
- try:
- self.ui.lineEdit_principal_folder.setText(pr.find("Principal_folder").text)
- except:
- print('Not principal folder')
+ #Â Connect browser button to search a path
+ ##########################################
+ #Â Main folder path
+ self.ui.lineEdit_principal_folder.clear()
+ self.ui.pushButton_browser_principal_folder.clicked.connect(self.f_path_folder_dpt)
+
+ # Block other function if SpotWorldHeritage is chose
+ try :
+ self.ui.comboBox_captor.currentIndexChanged.connect(self.block_for_swh)
+ except AttributeError:
+ pass
+
+ # VHRS image path
+ self.ui.lineEdit_VHRS.clear()
+ self.ui.pushButton_browser_VHRS.clicked.connect(self.f_path_ortho)
+
+ # Study area shapefile path
+ self.ui.lineEdit_area_path.clear()
+ self.ui.pushButton_browser_area_path.clicked.connect(self.f_path_area)
+
+ # Proxy
+ self.ui.proxy.clicked.connect(self.f_proxy)
+
+ #Â Segmentation shapefile path
+ self.ui.lineEdit_segmentation.clear()
+ self.ui.pushButton_browser_segmentation.clicked.connect(self.f_path_segm)
+
+ #Â MNT image path
+ self.ui.lineEdit_MNT.clear()
+ self.ui.pushButton_browser_MNT.clicked.connect(self.f_path_mnt)
+
+ # Output classification shapefile path
+ self.ui.lineEdit_output.clear()
+ self.ui.pushButton_browser_output.clicked.connect(self.f_output_name_moba)
+
+ # Sample shapefile path.
+ # For the simply mode, RPG file sample.
+ self.ui.lineEdit_sample_path.clear()
+ self.ui.pushButton_browser_sample_path.clicked.connect(self.enter_sample_name)
+
+ if self.current_mode == Constantes.SIMPLE_MODE:
+ # For the simply mode, grass/wooden file sample.
+ self.ui.lineEdit_sample_path_2.clear()
+ self.ui.pushButton_browser_sample_path_2.clicked.connect(self.enter_sample_name_hl)
+ # For the simply mode, wooden file sample.
+ self.ui.lineEdit_sample_path_3.clear()
+ self.ui.pushButton_browser_sample_path_3.clicked.connect(self.enter_sample_name_ll)
+
+ #Â Image path for samples if the first processing image hasn't been launched
+ try:
+ self.ui.pushButton_img_sample.clicked.connect(self.img_sample_name)
+ except AttributeError:
+ pass # Simple mode
+ ##########################################
- if self.mode == 1:
- index_captor = self.ui.comboBox_captor.findText(pr.find("Captor").text) #Â To find combo box index
- self.ui.comboBox_captor.setCurrentIndex(index_captor)
+ #Â Connect button to add sample in the memory list
+ try:
+ self.ui.pushButton_add_sample.clicked.connect(self.add_sample)
+ except AttributeError:
+ pass # Simple mode
+
+ # Connect button to clear sample record. Clear in the interface and in the memory list
+ try:
+ self.ui.pushButton_clear_sample.clicked.connect(self.clear_sample)
+ except AttributeError:
+ pass # Simple mode
+
+ #Â Connect close|ok button
+ # self.ui.buttonBox.button(QtWidgets.QDialogButtonBox.Close).clicked.connect(self.close_button)
+ self.ui.buttonBox.button(QtWidgets.QDialogButtonBox.Close).clicked.connect(self.close_button)
+ self.ui.buttonBox.button(QtWidgets.QDialogButtonBox.Ok).clicked.connect(self.ok_button)
+
+ #Â Connect Menu bar
+ self.ui.actionOuvrir.triggered.connect(self.open_backup) # Open backup
+ self.ui.actionSauver.triggered.connect(self.save_backup) #Â Save field name on the interface
+ self.ui.actionQuiter.triggered.connect(self.close_button) #Â Close
+ self.ui.actionAide_de_PHYMOBAT.triggered.connect(self.help_tools) #Â Help
+ self.ui.actionA_propos_de_PHYMOBAT.triggered.connect(self.about_PHYMOBA) # About PHYMOBA
+ self.ui.actionMode_Simplifi.triggered.connect(lambda checked, mode=Constantes.SIMPLE_MODE: self.change_mode(mode)) # To open the simple apply
+ self.ui.actionMode_expert.triggered.connect(lambda checked, mode=Constantes.EXPERT_MODE: self.change_mode(mode)) # To open the expert apply
+
+ self.rpg_tchek = [] #Â To backup rpg mode
+ self.img_sample = [] # To backup
+
+ #Â Connect change line edit on sample path to extract fieldnames. For the simply mode, this
+ # is with RPG sample
+ self.ui.lineEdit_select_sample_fieldname_1.textChanged.connect(self.field_display_1)
+ self.ui.lineEdit_select_sample_fieldname_2.textChanged.connect(self.field_display_2)
+
+ # To choose the classification method
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if self.ui.radioButton_rf.isChecked():
+ self.ui.checkBox_classifier_1.setEnabled(False)
+ self.ui.checkBox_classifier_2.setEnabled(False)
+
+ self.ui.radioButton_rf.toggled.connect(self.activate_level)
+ self.ui.radioButton_s.toggled.connect(self.activate_level)
+
+ if self.current_mode == Constantes.SIMPLE_MODE:
+ #Â Connect change line edit to grass/wooden sample
+ self.ui.lineEdit_select_sample_fieldname_3.textChanged.connect(self.field_display_3)
+ self.ui.lineEdit_select_sample_fieldname_4.textChanged.connect(self.field_display_4)
+ self.ui.lineEdit_select_sample_fieldname_5.textChanged.connect(self.field_display_5)
+ self.ui.lineEdit_select_sample_fieldname_6.textChanged.connect(self.field_display_6)
+
+ #Â Change connect for classification checkboxes
+ try:
+ self.ui.checkBox_classifier_1.stateChanged.connect(self.display_one_level)
+ self.ui.checkBox_classifier_2.stateChanged.connect(self.display_two_levels)
+ self.ui.checkBox_classifier_3.stateChanged.connect(self.display_all_levels)
+ except AttributeError:
+ pass # Simple mode
+
+ def get_variable(self):
+ """
+ Add a all system value like :
+
+ - Main folder path by line edit
+ - Satellite captor name by combo box
+ - Classification year by line edit
+ - Study area shapefile path by line edit
+ - Connexion username and password by line edit
+ - VHRS image path by line edit
+ - MNT image path by line edit
+ - Segmentation shapefile path path by line edit
+ - Output classification shapefile path by line edit
+ - Output shapefile field name by line edit and field type by combo box
+
+ """
+ #Â Main folder path by line edit.
+ self.path_folder_dpt = "%s" % self.ui.lineEdit_principal_folder.text()
+
+ # Satellite captor name by combo box
+ try:
+ self.captor_project = self.ui.comboBox_captor.currentText()
+ except AttributeError:
+ self.captor_project = "Landsat"
+
+ # Classification year by line edit
+ self.classif_year = "%s" % self.ui.lineEdit_year_images.text()
+
+ # Study area shapefile path by line edit
+ self.path_area = "%s" % self.ui.lineEdit_area_path.text()
+
+ #Â Connexion username and password by line edit
+ self.user = "%s" % self.ui.lineEdit_user.text()
+ self.password = "%s" % self.ui.lineEdit_password.text()
+
+ #Â VHRS image path by line edit
+ self.path_ortho = "%s" % self.ui.lineEdit_VHRS.text()
+
+ #Â MNT image path by line edit
+ self.path_mnt = "%s" % self.ui.lineEdit_MNT.text()
+
+ #Â Output shapefile field name by line edit and field type by combo box
+ try:
+ if self.ui.checkBox_classifier_1.isChecked() and self.ui.lineEdit_fieldname_1.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_1.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_1.currentText()))
+
+ if self.ui.checkBox_classifier_2.isChecked() and self.ui.lineEdit_fieldname_12.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_12.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_12.currentText()))
+
+ if self.ui.lineEdit_fieldname_2.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_2.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_2.currentText()))
+
+ if self.ui.checkBox_classifier_3.isChecked() and self.ui.lineEdit_fieldname_13.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_13.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_13.currentText()))
+
+ if self.ui.lineEdit_fieldname_23.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_23.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_23.currentText()))
+
+ if self.ui.lineEdit_fieldname_3.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_3.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_3.currentText()))
+
+ if self.ui.lineEdit_fieldname_4.text() != '':
+ self.out_fieldname_carto.append("%s" % self.ui.lineEdit_fieldname_4.text())
+ self.out_fieldtype_carto.append(eval("ogr.OFT%s" % self.ui.comboBox_fieldname_4.currentText()))
+ except AttributeError:
+ modes_fieldname = ["NIVEAU_1", "NIVEAU_2", "NIVEAU_3", "POURC"]
+ modes_fieldtype = [eval("ogr.OFTString"), eval("ogr.OFTString"), eval("ogr.OFTString"), eval("ogr.OFTReal")]
+
+ for mf in range(len(modes_fieldname)):
+
+ self.out_fieldname_carto.append(modes_fieldname[mf])
+ self.out_fieldtype_carto.append(modes_fieldtype[mf])
+
+ # Segmentation shapefile path path by line edit
+ self.path_segm = "%s" % self.ui.lineEdit_segmentation.text()
+
+ #Â Output shapefile field name by line edit and field type by combo box
+ self.output_name_moba = "%s" % self.ui.lineEdit_output.text()
+
+ def set_variable(self):
+ """
+ Print number of available image from Theia's GeoJSON .
+ """
- try:
- self.ui.lineEdit_year_images.setText(pr.find("Year_images").text)
- except:
- print('Not year images')
+ #Â self.ui.lineEdit_listing.setText(str(self.nb_avalaible_images))
+ try:
+ self.ui.label_listing.setText(str(self.nb_avalaible_images))
+ except AttributeError:
+ pass # Simple mode
+
+ def f_path_folder_dpt(self):
+ """
+ Open a input browser box to select the main folder path by line edit.
+ """
+ infoldername = QtWidgets.QFileDialog.getExistingDirectory(self, "Principal folder path", os.getcwd(), QtWidgets.QFileDialog.ShowDirsOnly)
+ self.ui.lineEdit_principal_folder.setText(str(infoldername).replace('[','').replace(']','').replace(' ',''))
+
+ def block_for_swh(self):
+ """
+ Function to block others function when SportWorldHeritage is selected in the comboxbox captor.
+ """
+ ind_captor = int(self.ui.comboBox_captor.currentIndex())
+ if ind_captor == 2:
+ self.ui.checkBox_processing.setEnabled(False)
+ self.ui.checkBox_MNT.setEnabled(False)
+ self.ui.lineEdit_MNT.setEnabled(False)
+ self.ui.pushButton_browser_MNT.setEnabled(False)
+ self.ui.checkBox_VHRS.setEnabled(False)
+ self.ui.lineEdit_VHRS.setEnabled(False)
+ self.ui.pushButton_browser_VHRS.setEnabled(False)
+ self.ui.tabWidget.setTabEnabled(1, False)
+ self.ui.tabWidget.setTabEnabled(2, False)
+ # If the user want, on the same moment, come back on other captor that SWH.
+ else:
+ self.ui.checkBox_processing.setEnabled(True)
+ self.ui.checkBox_MNT.setEnabled(True)
+ self.ui.lineEdit_MNT.setEnabled(True)
+ self.ui.pushButton_browser_MNT.setEnabled(True)
+ self.ui.checkBox_VHRS.setEnabled(True)
+ self.ui.lineEdit_VHRS.setEnabled(True)
+ self.ui.pushButton_browser_VHRS.setEnabled(True)
+ self.ui.tabWidget.setTabEnabled(1, True)
+ self.ui.tabWidget.setTabEnabled(2, True)
+
+ def f_path_ortho(self):
+ """
+ Open a input browser box to select the VHRS image path by line edit.
+ """
+ orthofilename = QtWidgets.QFileDialog.getOpenFileName(self, "THRS image", self.ui.lineEdit_principal_folder.text(), '*.TIF *.tif')[0]
+ self.ui.lineEdit_VHRS.setText(str(orthofilename).replace('[','').replace(']','').replace(' ',''))
+
+ def f_path_mnt(self):
+ """
+ Open a input browser box to select the MNT image path by line edit.
+ """
+ mntfilename = QtWidgets.QFileDialog.getOpenFileName(self, "MNT image", self.ui.lineEdit_principal_folder.text(), '*.TIF *.tif')[0]
+ self.ui.lineEdit_MNT.setText(str(mntfilename).replace('[','').replace(']','').replace(' ',''))
+
+ def f_path_area(self):
+ """
+ Open a input browser box to select the study area shapefile path by line edit.
+ """
+ areafilename = QtWidgets.QFileDialog.getOpenFileName(self, "Area shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
+ self.ui.lineEdit_area_path.setText(str(areafilename).replace('[','').replace(']','').replace(' ',''))
+
+ def f_proxy(self):
+ """
+ Function to open a popup in order to enter proxy ID
+ """
+ if self.w_proxy is None:
+ self.w_proxy = Popup.proxy_window()
+ self.w_proxy.show()
+
+ def f_path_segm(self):
+ """
+ Open a input browser box to select segmentation shapefile path path by line edit.
+ """
+ segmfilename = QtWidgets.QFileDialog.getOpenFileName(self, "Segmentation shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
+ self.ui.lineEdit_segmentation.setText(str(segmfilename).replace('[','').replace(']','').replace(' ',''))
+
+ def f_output_name_moba(self):
+ """
+ Set the output classification shapefile path by line edit.
+ """
+ outfilename = QtWidgets.QFileDialog.getSaveFileName(self, "FB file", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
+ #Â if the user has forgotten to put .shp at the end of the output shapefile
+ if outfilename[-4:] != '.shp':
+ outfilename = outfilename + '.shp'
+ self.ui.lineEdit_output.setText(outfilename)
+
+ def enter_sample_name(self):
+ """
+ Open a input browser box to select the sample shapefile path by line edit. With :func:`add_sample` conditions for the expert mode.
+ For the simply mode, this function is used for the RPG shapefile.
+ """
+ samplefilename = QtWidgets.QFileDialog.getOpenFileName(self, "Sample shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
+ self.ui.lineEdit_sample_path.setText(str(samplefilename).replace('[','').replace(']','').replace(' ',''))
+
+ def enter_sample_name_hl(self):
+ """
+ Open a input browser box to select the grass and wooden sample shapefile path by line edit. With :func:`add_sample` conditions
+ for the simply mode.
+ """
+ samplefilename_hl = QtWidgets.QFileDialog.getOpenFileName(self, "Grass/Wooden sample shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
+ self.ui.lineEdit_sample_path_2.setText(str(samplefilename_hl).replace('[','').replace(']','').replace(' ',''))
+
+ def enter_sample_name_ll(self):
+ """
+ Open a input browser box to select the wooden sample shapefile path by line edit. With :func:`add_sample` conditions for the simply mode.
+ """
+ samplefilename_ll = QtWidgets.QFileDialog.getOpenFileName(self, "Wooden sample shapefile", self.ui.lineEdit_principal_folder.text(), '*.shp')[0]
+ self.ui.lineEdit_sample_path_3.setText(str(samplefilename_ll).replace('[','').replace(']','').replace(' ',''))
+
+ def img_sample_name(self):
+ """
+ Open a input browser box to select the image for samples path by line edit. With :func:`add_sample` conditions.
+ """
+ imgsamplefilename = QtWidgets.QFileDialog.getOpenFileName(self, "Sample image", self.ui.lineEdit_principal_folder.text(), '*.TIF')[0]
+ self.ui.lineEdit_img_sample.setText(str(imgsamplefilename).replace('[','').replace(']','').replace(' ',''))
+
+ def add_sample(self):
+ """
+ Add sample information and location to compute optimal threshold :
+
+ For the expert mode (mode=1) :
+
+ - Append a sample name by line Edit. *This is a check box* ``RPG``, *if the sample is RPG file. It launch the Rpg class. And append a other sample from Rpg class*.
+ - Append two existent sample field names by combobox. It will be the same.
+ - Append sample class names by line edit. One or more for every sample.
+ - Append number of polygons for every samples by line edit.
+ - Print in a plain text edit : sample name, two sample field names, sample class names and number of polygons.
+ - *This check box* ``Image echantillonee``, *image path for samples if the first processing image hasn't been launched*.
+ .. note:: This is for a image with one spectral band
+ - Clear all widget field at the end.
+
+ For the simply mode (mode=0):
+
+ - Append a sample name by a different line Edit (a line Edit for each sample).
+ - Append sample class names, existing sample fields and number of polygons (a different line Edit for each sample)
+ """
+
+ nb_sample = len(self.sample_name)#Â Compute number of samples added. Condition : max three.
+ # Study area shapefile path by line edit if no processing other
+ # Because the function "Vector" need study area
+ self.path_area = "%s" % self.ui.lineEdit_area_path.text()
+ if self.path_area == '':
+ self.forget_study_area()
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ # Expert mode
+ if nb_sample < 3 and not self.ui.lineEdit_sample_path.text().isEmpty() and \
+ not self.ui.lineEdit_select_sample_fieldname_1.text().isEmpty() and not self.ui.lineEdit_select_sample_fieldname_2.text().isEmpty() and \
+ not self.ui.lineEdit_select_sample_class_1.text().isEmpty() and not self.ui.lineEdit_select_sample_class_2.text().isEmpty() and \
+ not self.ui.lineEdit_select_sample_nb_poly.text().isEmpty() and not self.ui.lineEdit_area_path.text().isEmpty():
+
+ # Append a sample name by line Edit.
+ if self.ui.checkBox_RPG.isChecked():
+ # Check box, if the sample of the RPG file. It launch the Rpg class. And append a other sample from Rpg class
+ self.sample_name.append(self.i_rpg("%s" % self.ui.lineEdit_sample_path.text()))
+ self.rpg_tchek.append(1) # To backup
+ self.ui.checkBox_RPG.setChecked(False)
+ else:
+ self.sample_name.append("%s" % self.ui.lineEdit_sample_path.text())
+ self.rpg_tchek.append(0)
+
+ #Â Append two sample field names by line edit. It must be the same.
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_1.text())
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_2.text())
+ #Â Append sample class names by line edit. One or more for every sample
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_1.text())
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_2.text())
+ # Append number of polygons for every samples by line edit.
+ self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly.text())
+
+ nb_sample = len(self.sample_name) # Number of samples added
+ #Â Print in a plain text edit : sample name, two sample field names, sample class names and number of polygons.
+ cursor = self.ui.plainTextEdit_sample.textCursor()
+ cursor.movePosition(QTextCursor.End, QTextCursor.MoveAnchor)
+ self.ui.plainTextEdit_sample.setTextCursor(cursor)
+ self.ui.plainTextEdit_sample.insertPlainText(str(self.sample_name[nb_sample-1]) + "\n")
+ cursor.movePosition(QTextCursor.Down, QTextCursor.MoveAnchor)
+ self.ui.plainTextEdit_sample.setTextCursor(cursor)
+ self.ui.plainTextEdit_sample.insertPlainText(str(self.fieldname_args[(nb_sample-1)*2]) + ' ' + str(self.fieldname_args[((nb_sample-1)*2)+1]) + "\n")
+ cursor.movePosition(QTextCursor.Down, QTextCursor.MoveAnchor)
+ self.ui.plainTextEdit_sample.setTextCursor(cursor)
+ self.ui.plainTextEdit_sample.insertPlainText(str(self.class_args[(nb_sample-1)*2]) + ' ' + str(self.class_args[(nb_sample-1)*2+1]) + "\n")
+ cursor.movePosition(QTextCursor.Down, QTextCursor.MoveAnchor)
+ self.ui.plainTextEdit_sample.setTextCursor(cursor)
+ self.ui.plainTextEdit_sample.insertPlainText(str(self.list_nb_sample[nb_sample-1]) + "\n")
+
+ #Â Check box, image path for samples if the first processing image hasn't been launched
+ # Warming : This is for a image with one spectral band
+ if self.ui.checkBox_img_sample.isChecked():
+ self.raster_path.append("%s" % self.ui.lineEdit_img_sample.text())
+ self.list_band_outraster.append(1)
+ self.ui.lineEdit_img_sample.clear()
+ self.ui.checkBox_img_sample.setChecked(False)
+ self.img_sample.append(1)
+ else: #Â To backup
+ self.img_sample.append(0)
+
+ #Â Clear all line edit after addition, ie after click add button.
+ self.ui.lineEdit_sample_path.clear()
+ self.ui.lineEdit_select_sample_fieldname_1.clear()
+ self.ui.lineEdit_select_sample_fieldname_2.clear()
+ self.ui.lineEdit_select_sample_class_1.clear()
+ self.ui.lineEdit_select_sample_class_2.clear()
+ self.ui.lineEdit_select_sample_nb_poly.clear()
+
+ elif self.current_mode == Constantes.SIMPLE_MODE:
+
+ # Simple mode
+ # Append a sample name by line Edit.
+ # For the sample of the RPG file. It launch the Rpg class. And append a other sample from Rpg class
+ self.sample_name.append(self.i_rpg("%s" % self.ui.lineEdit_sample_path.text()))
+
+ #Â Append two sample field names by line edit. It must be the same.
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_1.text())
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_2.text())
+ #Â Append sample class names by line edit. One or more for every sample
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_1.text())
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_2.text())
+ # Append number of polygons for every samples by line edit.
+ self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly.text())
+
+ # Same process that the RPG process except the start of the RPG class.
+ # To Grass/wooden
+ self.sample_name.append("%s" % self.ui.lineEdit_sample_path_2.text())
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_3.text())
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_4.text())
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_3.text())
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_4.text())
+ self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly_2.text())
+
+ # To wooden
+ self.sample_name.append("%s" % self.ui.lineEdit_sample_path_3.text())
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_5.text())
+ self.fieldname_args.append("%s" % self.ui.lineEdit_select_sample_fieldname_6.text())
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_5.text())
+ self.class_args.append("%s" % self.ui.lineEdit_select_sample_class_6.text())
+ self.list_nb_sample.append("%s" % self.ui.lineEdit_select_sample_nb_poly_3.text())
+
+ nb_sample = 3
+
+ def clear_sample(self):
+ """
+ Function to clear sample record. Clear in the interface and in the memory list.
+ """
+ self.sample_name = []
+ self.fieldname_args = []
+ self.class_args = []
+ self.list_nb_sample = []
+ self.rpg_tchek = []
+ self.img_sample = []
+
+ self.ui.lineEdit_sample_path.clear()
+ self.ui.lineEdit_select_sample_fieldname_1.clear()
+ self.ui.lineEdit_select_sample_fieldname_2.clear()
+ self.ui.lineEdit_select_sample_class_1.clear()
+ self.ui.lineEdit_select_sample_class_2.clear()
+ self.ui.lineEdit_select_sample_nb_poly.clear()
+ self.ui.checkBox_RPG.setChecked(False)
+ self.ui.lineEdit_img_sample.clear()
+ self.ui.checkBox_img_sample.setChecked(False)
+ self.ui.plainTextEdit_sample.clear()
+ self.ui.plainTextEdit_sample.insertPlainText("1 - Végétation non naturelle / Semi-naturelle\n")
+ self.ui.plainTextEdit_sample.insertPlainText("2 - Herbacés / Ligneux\n")
+ self.ui.plainTextEdit_sample.insertPlainText("3 - Lingeux mixtes / denses\n")
+ self.ui.plainTextEdit_sample.insertPlainText("\n")
+ self.ui.plainTextEdit_sample.insertPlainText("\n")
+ self.ui.plainTextEdit_sample.insertPlainText("")
+
+ def field_display_1(self):
+ """
+ Function to display fieldname class 1 in the other fieldname class 2 when text changed.
+ For the simply mode, this is RPG sample.
+ """
+ self.ui.lineEdit_select_sample_fieldname_2.setText("%s" % self.ui.lineEdit_select_sample_fieldname_1.text())
+
+ def field_display_2(self):
+ """
+ Function to display fieldname class 2 in the other fieldname class 2 when text changed.
+ For the simply mode, this is RPG sample.
+ """
+ self.ui.lineEdit_select_sample_fieldname_1.setText("%s" % self.ui.lineEdit_select_sample_fieldname_2.text())
+
+ def field_display_3(self):
+ """
+ For the grass/wooden sample, a function to display fieldname class 1 in the other fieldname class 2 when text changed.
+ """
+ self.ui.lineEdit_select_sample_fieldname_4.setText("%s" % self.ui.lineEdit_select_sample_fieldname_3.text())
+
+ def field_display_4(self):
+ """
+ For the grass/wooden sample, a function to display fieldname class 2 in the other fieldname class 2 when text changed.
+ """
+ self.ui.lineEdit_select_sample_fieldname_3.setText("%s" % self.ui.lineEdit_select_sample_fieldname_4.text())
+
+ def field_display_5(self):
+ """
+ For the wooden sample, a function to display fieldname class 1 in the other fieldname class 2 when text changed.
+ """
+ self.ui.lineEdit_select_sample_fieldname_6.setText("%s" % self.ui.lineEdit_select_sample_fieldname_5.text())
+
+ def field_display_6(self):
+ """
+ For the wooden sample, a function to display fieldname class 2 in the other fieldname class 2 when text changed.
+ """
+ self.ui.lineEdit_select_sample_fieldname_5.setText("%s" % self.ui.lineEdit_select_sample_fieldname_6.text())
+
+ def activate_level(self):
+ """
+ To activate the first levels with seath method. This is in pushing on the decision tree radio button.
+ Else it activates the last level to random forest method.
+ """
+ if self.ui.radioButton_s.isChecked() and not self.ui.radioButton_rf.isChecked():
+ self.ui.checkBox_classifier_1.setEnabled(True)
+ self.ui.checkBox_classifier_2.setEnabled(True)
+ else:
+ self.ui.checkBox_classifier_1.setEnabled(False)
+ self.ui.checkBox_classifier_2.setEnabled(False)
+ self.ui.checkBox_classifier_3.setChecked(True)
+
+ def display_one_level(self):
+ """
+ Function to display fieldnames option to classifier one level
+ """
+ if self.ui.checkBox_classifier_1.isChecked():
+
+ # Don't checked others checkboxes
+ self.ui.checkBox_classifier_2.setChecked(False)
+ self.ui.checkBox_classifier_3.setChecked(False)
+
+ #Â Display options filednames
+ self.ui.label_chps_1 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_1.setObjectName("label_chps_1")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_1, 10, 0, 2, 2)
+ self.ui.label_chps_name_1 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_name_1.setObjectName("label_chps_name_1")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_name_1, 10, 2, 1, 1)
+ self.ui.label_chps_type_1 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_type_1.setObjectName("label_chps_type_1")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_type_1, 11, 2, 1, 1)
+ self.ui.lineEdit_fieldname_1 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_1.setObjectName("lineEdit_fieldname_1")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_1, 10, 3, 1, 1)
+ self.ui.comboBox_fieldname_1 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_1.setObjectName("comboBox_fieldname_1")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_1, 11, 3, 1, 1)
+
+ self.ui.lineEdit_fieldname_1.setText(_translate("PHYMOBAT", "NIVEAU_1", None))
+ self.ui.comboBox_fieldname_1.addItem("String")
+ self.ui.comboBox_fieldname_1.addItem("Real")
+
+ self.ui.label_chps_1.setText(_translate("PHYMOBAT", " Champs\n"+" des entités", None))
+ self.ui.label_chps_name_1.setText(_translate("PHYMOBAT", "Nom :", None))
+ self.ui.label_chps_type_1.setText(_translate("PHYMOBAT", "Type :", None))
+
+ if not self.ui.checkBox_classifier_1.isChecked():
+ #Â Clear options filednames
+ try:
+ self.ui.label_chps_1.deleteLater()
+ self.ui.label_chps_name_1.deleteLater()
+ self.ui.label_chps_type_1.deleteLater()
+ self.ui.lineEdit_fieldname_1.deleteLater()
+ self.ui.comboBox_fieldname_1.deleteLater()
+ except AttributeError:
+ pass
+
+ def display_two_levels(self):
+ """
+ Function to display fieldnames option to classifier two first levels
+ """
+ if self.ui.checkBox_classifier_2.isChecked():
+
+ # Don't checked others checkboxes
+ self.ui.checkBox_classifier_1.setChecked(False)
+ self.ui.checkBox_classifier_3.setChecked(False)
+
+ self.ui.label_chps_2 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_2.setObjectName("label_chps_2")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_2, 13, 0, 2, 2)
+ self.ui.lineEdit_fieldname_12 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_12.setObjectName("lineEdit_fieldname_12")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_12, 13, 3, 1, 1)
+ self.ui.lineEdit_fieldname_2 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_2.setObjectName("lineEdit_fieldname_2")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_2, 13, 4, 1, 1)
+ self.ui.label_chps_type_2 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_type_2.setObjectName("label_chps_type_2")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_type_2, 14, 2, 1, 1)
+ self.ui.comboBox_fieldname_12 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_12.setObjectName("comboBox_fieldname_12")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_12, 14, 3, 1, 1)
+ self.ui.comboBox_fieldname_2 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_2.setObjectName("comboBox_fieldname_2")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_2, 14, 4, 1, 1)
+ self.ui.label_chps_name_2 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_name_2.setObjectName("label_chps_name_2")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_name_2, 13, 2, 1, 1)
+
+ self.ui.lineEdit_fieldname_12.setText(_translate("PHYMOBAT", "NIVEAU_1", None))
+ self.ui.comboBox_fieldname_12.addItem("String")
+ self.ui.comboBox_fieldname_12.addItem("Real")
+ self.ui.lineEdit_fieldname_2.setText(_translate("PHYMOBAT", "NIVEAU_2", None))
+ self.ui.comboBox_fieldname_2.addItem("String")
+ self.ui.comboBox_fieldname_2.addItem("Real")
+
+ self.ui.label_chps_type_2.setText(_translate("PHYMOBAT", "Type :", None))
+ self.ui.label_chps_2.setText(_translate("PHYMOBAT", " Champs\n"+" des entités", None))
+ self.ui.label_chps_name_2.setText(_translate("PHYMOBAT", "Nom :", None))
+
+ if not self.ui.checkBox_classifier_2.isChecked():
+ #Â Clear options filednames
+ try:
+ self.ui.label_chps_2.deleteLater()
+ self.ui.label_chps_name_2.deleteLater()
+ self.ui.label_chps_type_2.deleteLater()
+ self.ui.lineEdit_fieldname_12.deleteLater()
+ self.ui.comboBox_fieldname_12.deleteLater()
+ self.ui.lineEdit_fieldname_2.deleteLater()
+ self.ui.comboBox_fieldname_2.deleteLater()
+ except AttributeError:
+ pass
+
+ def display_all_levels(self):
+ """
+ Function to display fieldnames option to launch complete classification
+ """
+
+ if self.ui.checkBox_classifier_3.isChecked():
+
+ # Don't checked others checkboxes
+ self.ui.checkBox_classifier_1.setChecked(False)
+ self.ui.checkBox_classifier_2.setChecked(False)
+
+ self.ui.label_chps_name_3 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_name_3.setObjectName("label_chps_name_3")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_name_3, 16, 2, 1, 1)
+ self.ui.label_chps_3 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_3.setObjectName("label_chps_3")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_3, 16, 0, 2, 2)
+ self.ui.label_chps_type_3 = QtWidgets.QLabel(self.ui.tab_3)
+ self.ui.label_chps_type_3.setObjectName("label_chps_type_3")
+ self.ui.gridLayout_2.addWidget(self.ui.label_chps_type_3, 17, 2, 1, 1)
+ self.ui.lineEdit_fieldname_13 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_13.setObjectName("lineEdit_fieldname_13")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_13, 16, 3, 1, 1)
+ self.ui.lineEdit_fieldname_23 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_23.setObjectName("lineEdit_fieldname_23")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_23, 16, 4, 1, 1)
+ self.ui.lineEdit_fieldname_3 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_3.setObjectName("lineEdit_fieldname_3")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_3, 16, 5, 1, 1)
+ self.ui.lineEdit_fieldname_4 = QtWidgets.QLineEdit(self.ui.tab_3)
+ self.ui.lineEdit_fieldname_4.setObjectName("lineEdit_fieldname_4")
+ self.ui.gridLayout_2.addWidget(self.ui.lineEdit_fieldname_4, 16, 6, 1, 1)
+ self.ui.comboBox_fieldname_13 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_13.setObjectName("comboBox_fieldname_13")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_13, 17, 3, 1, 1)
+ self.ui.comboBox_fieldname_23 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_23.setObjectName("comboBox_fieldname_23")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_23, 17, 4, 1, 1)
+ self.ui.comboBox_fieldname_3 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_3.setObjectName("comboBox_fieldname_3")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_3, 17, 5, 1, 1)
+ self.ui.comboBox_fieldname_4 = QtWidgets.QComboBox(self.ui.tab_3)
+ self.ui.comboBox_fieldname_4.setObjectName("comboBox_fieldname_4")
+ self.ui.gridLayout_2.addWidget(self.ui.comboBox_fieldname_4, 17, 6, 1, 1)
+
+ self.ui.lineEdit_fieldname_13.setText(_translate("PHYMOBAT", "NIVEAU_1", None))
+ self.ui.comboBox_fieldname_13.addItem("String")
+ self.ui.comboBox_fieldname_13.addItem("Real")
+ self.ui.lineEdit_fieldname_23.setText(_translate("PHYMOBAT", "NIVEAU_2", None))
+ self.ui.comboBox_fieldname_23.addItem("String")
+ self.ui.comboBox_fieldname_23.addItem("Real")
+ self.ui.lineEdit_fieldname_3.setText(_translate("PHYMOBAT", "NIVEAU_3", None))
+ self.ui.comboBox_fieldname_3.addItem("String")
+ self.ui.comboBox_fieldname_3.addItem("Real")
+ self.ui.lineEdit_fieldname_4.setText(_translate("PHYMOBAT", "POURC", None))
+ self.ui.comboBox_fieldname_4.addItem("Real")
+ self.ui.comboBox_fieldname_4.addItem("String")
+
+ self.ui.label_chps_3.setText(_translate("PHYMOBAT", " Champs\n"+" des entités", None))
+ self.ui.label_chps_type_3.setText(_translate("PHYMOBAT", "Type :", None))
+ self.ui.label_chps_name_3.setText(_translate("PHYMOBAT", "Nom :", None))
+
+ if not self.ui.checkBox_classifier_3.isChecked():
+ #Â Clear options filednames
+ try:
+ self.ui.label_chps_3.deleteLater()
+ self.ui.label_chps_name_3.deleteLater()
+ self.ui.label_chps_type_3.deleteLater()
+ self.ui.lineEdit_fieldname_13.deleteLater()
+ self.ui.comboBox_fieldname_13.deleteLater()
+ self.ui.lineEdit_fieldname_23.deleteLater()
+ self.ui.comboBox_fieldname_23.deleteLater()
+ self.ui.lineEdit_fieldname_3.deleteLater()
+ self.ui.comboBox_fieldname_3.deleteLater()
+ self.ui.lineEdit_fieldname_4.deleteLater()
+ self.ui.comboBox_fieldname_4.deleteLater()
+ except AttributeError:
+ pass
+
+ def ok_button(self):
+ """
+ Function to launch the processing. This function take account :
+
+ - The ``Multi-processing`` check box if the processing has launched with multi process. By default, this is checked. It need a computer with minimum 12Go memory.
+ - Append a few system value with :func:`get_variable`.
+ - There are 3 principal check boxes :
+ - to get number download available images
+ - for downloading and processing on theia platform
+ - to compute optimal threshold.
+ - to compute slope raster
+ - for classification processing.
+ """
+
+ # Start the processus
+ startTime = time.time()
+
+ #Â To know if the processing must be launch on several thread
+ self.mp = Constantes.MULTIPROCESSING_ENABLE if self.ui.checkBox_multiprocess.isChecked() else Constantes.MULTIPROCESSING_DISABLE
+
+ self.get_variable() # Append a few system value
+ vs = 0 # Variable to launch VHRS texture processing
+ dd = 0 # Variable to launch image downloading
+ ok = 1 #Â Variable to verify informations -> 0 not ok, 1 ok
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ #Â if download check box is checked only
+ if not self.ui.checkBox_listing.isChecked() and self.ui.checkBox_download.isChecked():
+ self.ui.checkBox_listing.setChecked(True)
+
+ if ok == 1:
+ # Compute a output slope raster
+ if self.ui.checkBox_MNT.isChecked():
+
+ self.i_slope()
+
+ # Downloading and processing on theia platform
+ # A check box to get number download available images
+ if self.ui.checkBox_listing.isChecked():
+
+ if self.ui.checkBox_download.isChecked():
+ #Â To launch the downloading
+ dd = 1
+
+ self.i_download(dd) #Â Launch image listing and downloading if dd = 1
+ self.set_variable() #Â to write in the line edit about number download available images
+
+ #Â Check box to launch the image processing
+ if self.ui.checkBox_processing.isChecked():
+ # Another check box to launch VHRS texture processing. If checked, vs = 1.
+ if self.ui.checkBox_VHRS.isChecked():
+ vs = 1
+ self.i_images_processing(vs) #Â function to launch the image processing
+
+ # To launch the image processing without dowloading but with the images in a main folder
+ # Without internet connection
+ if not self.ui.checkBox_download.isChecked() and self.ui.checkBox_processing.isChecked():
+
+ # Launch pre-processing without downloading
+ self.i_glob()
+ # Another check box to launch VHRS texture processing. If checked, vs = 1.
+ if self.ui.checkBox_VHRS.isChecked():
+ vs = 1
+ self.i_images_processing(vs) #Â function to launch the image processing
+
+ # To launch texture processing only
+ if not self.ui.checkBox_listing.isChecked() and not self.ui.checkBox_processing.isChecked() and self.ui.checkBox_VHRS.isChecked():
+
+ self.i_vhrs()
+
+ #Â Compute optimal threshold
+ if self.ui.checkBox_threshold.isChecked():
+
+ if self.ui.radioButton_rf.isChecked():
+ self.i_sample_rf()
+ elif self.ui.radioButton_s.isChecked():
+ self.i_sample()
+
+ #Â Classification processing
+ if self.ui.radioButton_rf.isChecked():
+
+ if self.ui.checkBox_classifier_3.isChecked():
+
+ self.out_fieldname_carto = self.out_fieldname_carto
+ self.out_fieldtype_carto = self.out_fieldtype_carto
+ self.i_classifier_rf()
+ self.i_validate()
+
+ elif self.ui.radioButton_s.isChecked():
+
+ if self.ui.checkBox_classifier_1.isChecked() :
+
+ self.out_fieldname_carto = self.out_fieldname_carto[:3]
+ self.out_fieldtype_carto = self.out_fieldtype_carto[:3]
+ self.i_classifier_s()
+ self.i_validate()
+
+ if self.ui.checkBox_classifier_2.isChecked() :
+
+ self.out_fieldname_carto = self.out_fieldname_carto[:4]
+ self.out_fieldtype_carto = self.out_fieldtype_carto[:4]
+ self.i_classifier_s()
+ self.i_validate()
+
+ if self.ui.checkBox_classifier_3.isChecked():
+
+ self.out_fieldname_carto = self.out_fieldname_carto
+ self.out_fieldtype_carto = self.out_fieldtype_carto
+ self.i_classifier_s()
+ self.i_validate()
+
+ if self.current_mode == Constantes.SIMPLE_MODE:
+
+ # Compute a output slope raster
- self.ui.lineEdit_area_path.setText(pr.find("Area_path").text)
-
- if self.mode == 1:
- if pr.find("Images_available").text == '1':
- self.ui.checkBox_listing.setChecked(True)
- else:
- self.ui.checkBox_listing.setChecked(False)
- if pr.find("Download").text == '1':
- self.ui.checkBox_download.setChecked(True)
- else:
- self.ui.checkBox_download.setChecked(False)
-
- try:
- self.ui.lineEdit_user.setText(pr.find("Username").text)
- self.ui.lineEdit_password.setText(pr.find("Password").text)
- except:
- print('Not username or password Theia')
-
- self.f_proxy()
- try:
- pp = pr.find("Proxy[@id='Proxy']")
- self.w_proxy.w_proxy.lineEdit_proxy.setText(pp.find("Proxy_adress").text)
- try:
- self.w_proxy.w_proxy.lineEdit_password_proxy.setText(pp.find("Proxy_login").text)
- except TypeError:
- pass
- try:
- self.w_proxy.w_proxy.lineEdit_login_proxy.setText(pp.find("Proxy_password").text)
- except TypeError:
- pass
- self.w_proxy.id_proxy()
- except AttributeError:
- print('Not proxy')
- self.w_proxy.close_window()
-
- if self.mode == 1:
- if pr.find("Img_processing").text == '1':
- self.ui.checkBox_processing.setChecked(True)
- else:
- self.ui.checkBox_processing.setChecked(False)
- if pr.find("VHRS_checked").text == '1':
- self.ui.checkBox_VHRS.setChecked(True)
- else:
- self.ui.checkBox_VHRS.setChecked(False)
-
- try:
- self.ui.lineEdit_VHRS.setText(pr.find("VHRS").text)
- except:
- print('Not VHRS image')
-
- if self.mode == 1:
- if pr.find("MNT_checked").text == '1':
- self.ui.checkBox_MNT.setChecked(True)
- else:
- self.ui.checkBox_MNT.setChecked(False)
-
- try:
- self.ui.lineEdit_MNT.setText(pr.find("MNT").text)
- except:
- print('Not MNT')
-
- ps = tree.find("Tab[@id='Processing_sample']")
- if self.mode == 1:
- try:
- for sple_n in ps.iter("Sample"):
- self.ui.lineEdit_sample_path.setText(sple_n.find("Sample_path").text)
- self.ui.lineEdit_select_sample_fieldname_1.setText(sple_n.find("Fieldname_1").text)
- self.ui.lineEdit_select_sample_fieldname_2.setText(sple_n.find("Fieldname_2").text)
- self.ui.lineEdit_select_sample_class_1.setText(sple_n.find("Classname_1").text)
- self.ui.lineEdit_select_sample_class_2.setText(sple_n.find("Classname_2").text)
- self.ui.lineEdit_select_sample_nb_poly.setText(sple_n.find("Nb_polygones").text)
- #Â Launch rpg method if the box is checked and if the shapefile hasn't go through rpg method (with prefix MONO_)
- if sple_n.find("RPG").text == '1' and os.path.split(sple_n.find("Sample_path").text)[1][:5] != 'MONO_':
- self.ui.checkBox_RPG.setChecked(True)
- try:
- if sple_n.find("Img_sample").text != "":
- self.ui.lineEdit_img_sample.setText(sple_n.find("Img_sample").text)
- self.ui.checkBox_img_sample.setChecked(True)
- except:
- print('Not sample raster only !')
- self.add_sample()
- except:
- print('Not sample')
- elif self.mode ==0:
- # RPG
- sple_n = ps[0]
- self.ui.lineEdit_sample_path.setText(sple_n.find("Sample_path").text)
- self.ui.lineEdit_select_sample_fieldname_1.setText(sple_n.find("Fieldname_1").text)
- self.ui.lineEdit_select_sample_fieldname_2.setText(sple_n.find("Fieldname_2").text)
- self.ui.lineEdit_select_sample_class_1.setText(sple_n.find("Classname_1").text)
- self.ui.lineEdit_select_sample_class_2.setText(sple_n.find("Classname_2").text)
- self.ui.lineEdit_select_sample_nb_poly.setText(sple_n.find("Nb_polygones").text)
-
- # To Grass/wooden
- sple_n = ps[1]
- self.ui.lineEdit_sample_path_2.setText(sple_n.find("Sample_path").text)
- self.ui.lineEdit_select_sample_fieldname_3.setText(sple_n.find("Fieldname_1").text)
- self.ui.lineEdit_select_sample_fieldname_4.setText(sple_n.find("Fieldname_2").text)
- self.ui.lineEdit_select_sample_class_3.setText(sple_n.find("Classname_1").text)
- self.ui.lineEdit_select_sample_class_4.setText(sple_n.find("Classname_2").text)
- self.ui.lineEdit_select_sample_nb_poly_2.setText(sple_n.find("Nb_polygones").text)
-
- # To wooden
- sple_n = ps[2]
- self.ui.lineEdit_sample_path_3.setText(sple_n.find("Sample_path").text)
- self.ui.lineEdit_select_sample_fieldname_5.setText(sple_n.find("Fieldname_1").text)
- self.ui.lineEdit_select_sample_fieldname_6.setText(sple_n.find("Fieldname_2").text)
- self.ui.lineEdit_select_sample_class_5.setText(sple_n.find("Classname_1").text)
- self.ui.lineEdit_select_sample_class_6.setText(sple_n.find("Classname_2").text)
- self.ui.lineEdit_select_sample_nb_poly_3.setText(sple_n.find("Nb_polygones").text)
-
- if self.mode == 1:
- if ps.find("Threshold_checked").text == '1':
- self.ui.checkBox_threshold.setChecked(True)
- else:
- self.ui.checkBox_threshold.setChecked(False)
-
- c = tree.find("Tab[@id='Classification']")
- try:
- self.ui.lineEdit_segmentation.setText(c.find("Segmentation_path").text)
- except:
- print('Not segmentation')
- try:
- self.ui.lineEdit_output.setText(c.find("Output_path").text)
- except:
- print('Not output file')
- if self.mode == 1:
- if len(c) == 5:
- self.ui.checkBox_classifier_1.setChecked(True)
- self.ui.lineEdit_fieldname_1.setText(c.find("Output_fieldname_1").text)
- index_fieldname_1 = self.ui.comboBox_fieldname_1.findText(c.find("Output_type_1").text)
- self.ui.comboBox_fieldname_1.setCurrentIndex(index_fieldname_1)
- elif len(c) == 7:
- self.ui.checkBox_classifier_2.setChecked(True)
- self.ui.lineEdit_fieldname_12.setText(c.find("Output_fieldname_1").text)
- self.ui.lineEdit_fieldname_2.setText(c.find("Output_fieldname_2").text)
- index_fieldname_12 = self.ui.comboBox_fieldname_12.findText(c.find("Output_type_1").text)
- self.ui.comboBox_fieldname_12.setCurrentIndex(index_fieldname_12)
- index_fieldname_2 = self.ui.comboBox_fieldname_2.findText(c.find("Output_type_2").text)
- self.ui.comboBox_fieldname_2.setCurrentIndex(index_fieldname_2)
- elif len(c) == 11:
- self.ui.checkBox_classifier_3.setChecked(True)
- self.ui.lineEdit_fieldname_13.setText(c.find("Output_fieldname_1").text)
- self.ui.lineEdit_fieldname_23.setText(c.find("Output_fieldname_2").text)
- self.ui.lineEdit_fieldname_3.setText(c.find("Output_fieldname_3").text)
- self.ui.lineEdit_fieldname_4.setText(c.find("Output_fieldname_4").text)
- index_fieldname_13 = self.ui.comboBox_fieldname_13.findText(c.find("Output_type_1").text)
- self.ui.comboBox_fieldname_13.setCurrentIndex(index_fieldname_13)
- index_fieldname_23 = self.ui.comboBox_fieldname_23.findText(c.find("Output_type_2").text)
- self.ui.comboBox_fieldname_23.setCurrentIndex(index_fieldname_23)
- index_fieldname_3 = self.ui.comboBox_fieldname_3.findText(c.find("Output_type_3").text)
- self.ui.comboBox_fieldname_3.setCurrentIndex(index_fieldname_3)
- index_fieldname_4 = self.ui.comboBox_fieldname_4.findText(c.find("Output_type_4").text)
- self.ui.comboBox_fieldname_4.setCurrentIndex(index_fieldname_4)
-
- # Classification mode
- if c.find("Classification_method").text == '1':
- self.ui.radioButton_rf.setChecked(True)
- else:
- self.ui.radioButton_s.setChecked(True)
-
- def save_backup(self):
- """
- Function to save input text in every fields. The output file must be a XML file.
- """
-
- out_backup = QtWidgets.QFileDialog.getSaveFileName(self, "Save backup", os.getcwd(), '*.xml')[0]
+ if self.path_mnt != "":
+ self.i_slope()
+
+ # Save the sample features
+ self.add_sample()
+
+ # Look at if the the images has been already downloaded, download them otherwise
+ # Commenté pour ne traiter qu'une image
+ self.i_download()
+
+ #Â function to launch the image processing
+ self.i_images_processing()
- #Â if the user has forgotten to put .shp at the end of the output xml
- if out_backup[-4:] != '.xml':
- out_backup = out_backup + '.xml'
-
- root = ET.Element("Data_filled")
-
- if self.ui.checkBox_multiprocess.isChecked():
- ET.SubElement(root, "Multi_process", type = "int").text = str(1)
- else:
- ET.SubElement(root, "Multi_process", type = "int").text = str(0)
-
- doc = ET.SubElement(root, "Tab", id="Processing_raster")
- ET.SubElement(doc, "Principal_folder", type = "str").text = "%s" % self.ui.lineEdit_principal_folder.text()
- if self.mode == 1:
- ET.SubElement(doc, "Captor", type = "str").text = "%s" % self.ui.comboBox_captor.currentText()
- else:
- ET.SubElement(doc, "Captor", type = "str").text = "Landsat"
- ET.SubElement(doc, "Year_images", type = "str").text = "%s" % self.ui.lineEdit_year_images.text()
- ET.SubElement(doc, "Area_path", type = "str").text = "%s" % self.ui.lineEdit_area_path.text()
-
- if self.mode == 1:
- if self.ui.checkBox_listing.isChecked():
- ET.SubElement(doc, "Images_available", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "Images_available", type = "int").text = str(0)
- if self.ui.checkBox_download.isChecked():
- ET.SubElement(doc, "Download", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "Download", type = "int").text = str(0)
- else:
- ET.SubElement(doc, "Images_available", type = "int").text = str(0)
- ET.SubElement(doc, "Download", type = "int").text = str(0)
-
- ET.SubElement(doc, "Username", type = "str").text = "%s" % self.ui.lineEdit_user.text()
- ET.SubElement(doc, "Password", type = "str").text = "%s" % self.ui.lineEdit_password.text()
- if self.w_proxy is None:
- sub_proxy = ET.SubElement(doc, "Proxy", id="No_proxy")
- ET.SubElement(sub_proxy, "Proxy_adress", type = "str").text = ""
- ET.SubElement(sub_proxy, "Proxy_login", type = "str").text = ""
- ET.SubElement(sub_proxy, "Proxy_password", type = "str").text = ""
- elif self.w_proxy.proxy == "":
- sub_proxy = ET.SubElement(doc, "Proxy", id="No_proxy")
- ET.SubElement(sub_proxy, "Proxy_adress", type = "str").text = ""
- ET.SubElement(sub_proxy, "Proxy_login", type = "str").text = ""
- ET.SubElement(sub_proxy, "Proxy_password", type = "str").text = ""
- else:
- sub_proxy = ET.SubElement(doc, "Proxy", id="Proxy")
- ET.SubElement(sub_proxy, "Proxy_adress", type = "str").text = "%s" % self.w_proxy.proxy
- ET.SubElement(sub_proxy, "Proxy_login", type = "str").text = "%s" % self.w_proxy.login_proxy
- ET.SubElement(sub_proxy, "Proxy_password", type = "str").text = "%s" % self.w_proxy.password_proxy
-
- if self.mode == 1:
- if self.ui.checkBox_processing.isChecked():
- ET.SubElement(doc, "Img_processing", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "Img_processing", type = "int").text = str(0)
-
- if self.ui.checkBox_VHRS.isChecked():
- ET.SubElement(doc, "VHRS_checked", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "VHRS_checked", type = "int").text = str(0)
- else:
- ET.SubElement(doc, "Img_processing", type = "int").text = str(1)
- ET.SubElement(doc, "VHRS_checked", type = "int").text = str(1)
-
- ET.SubElement(doc, "VHRS", type = "str").text = "%s" % self.ui.lineEdit_VHRS.text()
-
- if self.mode == 1:
- if self.ui.checkBox_MNT.isChecked():
- ET.SubElement(doc, "MNT_checked", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "MNT_checked", type = "int").text = str(0)
- else:
- if "%s" % self.ui.lineEdit_MNT.text() != '':
- ET.SubElement(doc, "MNT_checked", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "MNT_checked", type = "int").text = str(0)
-
- ET.SubElement(doc, "MNT", type = "str").text = "%s" % self.ui.lineEdit_MNT.text()
-
- doc = ET.SubElement(root, "Tab", id="Processing_sample")
- if self.mode == 1:
- for sa in range(len(self.sample_name)):
- sub_doc = ET.SubElement(doc, "Sample", id="Sample_" + str(sa))
- ET.SubElement(sub_doc, "Sample_path", type = "str").text = self.sample_name[sa]
- ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = self.fieldname_args[2*sa]
- ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = self.fieldname_args[2*sa+1]
- ET.SubElement(sub_doc, "Classname_1", type = "str").text = self.class_args[2*sa]
- ET.SubElement(sub_doc, "Classname_2", type = "str").text = self.class_args[2*sa+1]
- ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = self.list_nb_sample[sa]
- ET.SubElement(sub_doc, "RPG", type = "int").text = str(self.rpg_tchek[sa])
- try:
- #Â To enter a sample raster if the first tab doesn't launch before
- if self.img_sample[sa] == 1:
- ET.SubElement(sub_doc, "Img_sample", type = "str").text = self.raster_path[sa]
- except:
- print('Not sample raster only !')
- else:
- # RPG
- sub_doc = ET.SubElement(doc, "Sample", id="Sample_0")
- ET.SubElement(sub_doc, "Sample_path", type = "str").text = "%s" % self.ui.lineEdit_sample_path.text()
- ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_1.text()
- ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_2.text()
- ET.SubElement(sub_doc, "Classname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_1.text()
- ET.SubElement(sub_doc, "Classname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_2.text()
- ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = "%s" % self.ui.lineEdit_select_sample_nb_poly.text()
- ET.SubElement(sub_doc, "RPG", type = "int").text = str(1)
-
- # To Grass/wooden
- sub_doc = ET.SubElement(doc, "Sample", id="Sample_1")
- ET.SubElement(sub_doc, "Sample_path", type = "str").text = "%s" % self.ui.lineEdit_sample_path_2.text()
- ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_3.text()
- ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_4.text()
- ET.SubElement(sub_doc, "Classname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_3.text()
- ET.SubElement(sub_doc, "Classname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_4.text()
- ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = "%s" % self.ui.lineEdit_select_sample_nb_poly_2.text()
- ET.SubElement(sub_doc, "RPG", type = "int").text = str(0)
-
- # To wooden
- sub_doc = ET.SubElement(doc, "Sample", id="Sample_2")
- ET.SubElement(sub_doc, "Sample_path", type = "str").text = "%s" % self.ui.lineEdit_sample_path_3.text()
- ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_5.text()
- ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_6.text()
- ET.SubElement(sub_doc, "Classname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_5.text()
- ET.SubElement(sub_doc, "Classname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_6.text()
- ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = "%s" % self.ui.lineEdit_select_sample_nb_poly_3.text()
- ET.SubElement(sub_doc, "RPG", type = "int").text = str(0)
-
- if self.mode == 1:
- if self.ui.checkBox_threshold.isChecked():
- ET.SubElement(doc, "Threshold_checked", type = "int").text = str(1)
- else:
- ET.SubElement(doc, "Threshold_checked", type = "int").text = str(0)
- else:
- ET.SubElement(doc, "Threshold_checked", type = "int").text = str(1)
-
- doc = ET.SubElement(root, "Tab", id="Classification")
- ET.SubElement(doc, "Segmentation_path", type = "str").text = "%s" % self.ui.lineEdit_segmentation.text()
- ET.SubElement(doc, "Output_path", type = "str").text = "%s" % self.ui.lineEdit_output.text()
-
- if self.mode == 1:
-
- if self.ui.checkBox_classifier_1.isChecked():
- ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_fieldname_1.text()
- ET.SubElement(doc, "Output_type_1", type = "str").text = "%s" % self.ui.comboBox_fieldname_1.currentText()
-
- if self.ui.checkBox_classifier_2.isChecked():
- ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_fieldname_12.text()
- ET.SubElement(doc, "Output_type_1", type = "str").text = "%s" % self.ui.comboBox_fieldname_12.currentText()
- ET.SubElement(doc, "Output_fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_fieldname_2.text()
- ET.SubElement(doc, "Output_type_2", type = "str").text = "%s" % self.ui.comboBox_fieldname_2.currentText()
-
- if self.ui.checkBox_classifier_3.isChecked():
- ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_fieldname_13.text()
- ET.SubElement(doc, "Output_type_1", type = "str").text = "%s" % self.ui.comboBox_fieldname_13.currentText()
- ET.SubElement(doc, "Output_fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_fieldname_23.text()
- ET.SubElement(doc, "Output_type_2", type = "str").text = "%s" % self.ui.comboBox_fieldname_23.currentText()
- ET.SubElement(doc, "Output_fieldname_3", type = "str").text = "%s" % self.ui.lineEdit_fieldname_3.text()
- ET.SubElement(doc, "Output_type_3", type = "str").text = "%s" % self.ui.comboBox_fieldname_3.currentText()
- ET.SubElement(doc, "Output_fieldname_4", type = "str").text = "%s" % self.ui.lineEdit_fieldname_4.text()
- ET.SubElement(doc, "Output_type_4", type = "str").text = "%s" % self.ui.comboBox_fieldname_4.currentText()
-
- # Classification mode
- if self.ui.radioButton_rf.isChecked():
- ET.SubElement(doc, "Classification_method", type = "int").text = str(1)
- elif self.ui.radioButton_s.isChecked():
- ET.SubElement(doc, "Classification_method", type = "int").text = str(0)
- else:
- ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "NIVEAU_1"
- ET.SubElement(doc, "Output_type_1", type = "str").text = "String"
- ET.SubElement(doc, "Output_fieldname_2", type = "str").text = "NIVEAU_2"
- ET.SubElement(doc, "Output_type_2", type = "str").text = "String"
- ET.SubElement(doc, "Output_fieldname_3", type = "str").text = "NIVEAU_3"
- ET.SubElement(doc, "Output_type_3", type = "str").text = "String"
- ET.SubElement(doc, "Output_fieldname_4", type = "str").text = "POURC"
- ET.SubElement(doc, "Output_type_4", type = "str").text = "Real"
- ET.SubElement(doc, "Classification_method", type = "int").text = str(1)
-
- tree = ET.ElementTree(root)
- #Â Write in a xml file
- tree.write(str(out_backup), encoding="UTF-8",xml_declaration=True, pretty_print=True)
-
- def about_PHYMOBA(self):
- """
- Function to open a new window "About PHYMOBAT"
- """
- if self.apropos is None:
- self.apropos = MyPopup_about()
- self.apropos.show()
-
- def help_tools(self):
- """
- Function to open html help
- """
- webbrowser.open('file://' + os.getcwd() + '/Documentation/methode_tuto.html#tutoriels-interface')
-
- def mode_simpli(self):
- """
- Function to open a new window in simple mode "PHYMOBATs"
- """
- if self.simpli is None:
- print ("Simplify mode")
- self.close()
- self.simpli = PHYMOBAT(mode = 0)
-
- self.simpli.show()
-
- def mode_expert(self):
- """
- Function to open a new window in expert mode "PHYMOBATe"
- """
- if self.expert is None:
- print ("Expert mode")
- self.close()
- self.expert = PHYMOBAT(mode = 1)
-
- self.expert.show()
-
- def forget_study_area(self):
- """
- Function to open a new window 'Alert' because user forgotten to declare study area.
- """
- if self.w_study_area is None:
- self.w_study_area = MyPopup_warming_study_area()
- self.w_study_area.show()
-
- def forget_raster_sample(self):
- """
- Function to open a new window 'Alert' because user forgotten to declare rasters or samples.
- """
- if self.w_forget is None:
- self.w_forget = MyPopup_warming_forgetting()
- self.w_forget.show()
-
- def close_button(self):
- """
- Function to close the interface.
- """
- self.close()
+ # To launch texture processing only
+ self.i_vhrs()
-class MyPopup_about(QtWidgets.QWidget):
- """
- Popup to display "About PHYMOBAT". In this windows, it prints informations on the processing, license
- and version.
- """
- def __init__(self, parent=None):
- QtWidgets.QWidget.__init__(self, parent)
- self.prop = Ui_About()
- self.prop.setupUi(self)
-
- self.prop.close_newWindow.clicked.connect(self.close_window)
-
- def close_window(self):
- """
- Function to close the "A propos PHYMOBAT".
- """
- self.close()
-
-class MyPopup_warming_study_area(QtWidgets.QWidget):
- """
- Popup to display a message to say there isn't declared study area file.
- """
- def __init__(self, parent=None):
- QtWidgets.QWidget.__init__(self, parent)
- self.w_study_a = Ui_Warming_study_area()
- self.w_study_a.setupUi(self)
-
- self.w_study_a.pushButton_ok_window_warning_study_area.clicked.connect(self.close_window)
-
- def close_window(self):
- """
- Function to close the popup.
- """
- self.close()
-
-class MyPopup_warming_forgetting(QtWidgets.QWidget):
- """
- Popup to display a message to tell you if you fogotten to enter a raster or a sample.
- """
- def __init__(self, parent=None):
- QtWidgets.QWidget.__init__(self, parent)
- self.w_forget = Ui_Warming_forgetting()
- self.w_forget.setupUi(self)
-
- self.w_forget.pushButton_ok_forget.clicked.connect(self.close_window)
-
- def close_window(self):
- """
- Function to close the popup.
- """
- self.close()
-
-class MyPopup_proxy_window(QtWidgets.QWidget):
- """
- Popup to display a message to tell you if you fogotten to enter a raster or a sample.
- """
- def __init__(self, parent=None):
- QtWidgets.QWidget.__init__(self, parent)
- self.w_proxy = Ui_Proxy_window()
- self.w_proxy.setupUi(self)
-
- #Â Proxy ID
- self.proxy = ""
- self.login_proxy = ""
- self.password_proxy = ""
-
- #Â Connect Apply|Close button
- self.w_proxy.buttonBox_proxy.button(QtWidgets.QDialogButtonBox.Close).clicked.connect(self.close_window)
- self.w_proxy.buttonBox_proxy.button(QtWidgets.QDialogButtonBox.Apply).clicked.connect(self.id_proxy)
-
- def id_proxy(self):
- """
- Function to use input proxy id
- """
- self.login_proxy = "%s" % self.w_proxy.lineEdit_login_proxy.text()
- self.password_proxy = "%s" % self.w_proxy.lineEdit_password_proxy.text()
- self.proxy = "%s" % self.w_proxy.lineEdit_proxy.text()
-
- def close_window(self):
- """
- Function to close the popup.
- """
- self.close()
-
+ #Â Compute optimal threshold
+ self.i_sample_rf()
+
+ #Â Classification processing
+ self.out_fieldname_carto = self.out_fieldname_carto
+ self.out_fieldtype_carto = self.out_fieldtype_carto
+ self.i_classifier_rf()
+ self.i_validate()
+
+ # Clear variables after processing
+ #self.clear_sample()
+ self.out_fieldname_carto = ['ID', 'AREA']
+ self.out_fieldtype_carto = [ogr.OFTString, ogr.OFTReal]
+ # Images after processing images
+ self.out_ndvistats_folder_tab = defaultdict(list)
+
+ Processing.__init__(self)#Â Initialize variable without to close and launch again the application
+
+ # End of the processus
+ endTime = time.time() # Tps : Terminé
+ self.logger.info('...........' + ' Outputted to File in ' + str(endTime - startTime) + ' secondes')
+ nb_day_processing = int(time.strftime('%d', time.gmtime(endTime - startTime))) - 1
+ self.logger.info("That is, " + str(nb_day_processing) + ' day(s) ' + time.strftime('%Hh %Mmin%S', time.gmtime(endTime - startTime)))
+
+ def open_backup(self, test=None):
+ """
+ Function to load input text in every fields. The input file must be a XML file.
+ """
+ if test is None :
+ in_backup = QtWidgets.QFileDialog.getOpenFileName(self, "Open backup", os.getcwd(), '*.xml')[0]
+ else :
+ in_backup = test
+
+ if in_backup != None and len(in_backup) > 0 :
+
+ # Parse the xml file if the user choose a file
+ tree = ET.parse(str(in_backup))
+
+ if tree.find("Multi_process").text == '1':
+ self.ui.checkBox_multiprocess.setChecked(True)
+ else:
+ self.ui.checkBox_multiprocess.setChecked(False)
+
+ pr = tree.find("Tab[@id='Processing_raster']")
+
+ try:
+ self.ui.lineEdit_principal_folder.setText(pr.find("Principal_folder").text)
+ except:
+ self.info.debug('Not principal folder')
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ index_captor = self.ui.comboBox_captor.findText(pr.find("Captor").text) #Â To find combo box index
+ self.ui.comboBox_captor.setCurrentIndex(index_captor)
+
+ try:
+ self.ui.lineEdit_year_images.setText(pr.find("Year_images").text)
+ except:
+ self.logger.debug('Not year images')
+
+ self.ui.lineEdit_area_path.setText(pr.find("Area_path").text)
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if pr.find("Images_available").text == '1':
+ self.ui.checkBox_listing.setChecked(True)
+ else:
+ self.ui.checkBox_listing.setChecked(False)
+ if pr.find("Download").text == '1':
+ self.ui.checkBox_download.setChecked(True)
+ else:
+ self.ui.checkBox_download.setChecked(False)
+
+ try:
+ self.ui.lineEdit_user.setText(pr.find("Username").text)
+ self.ui.lineEdit_password.setText(pr.find("Password").text)
+ except:
+ self.logger.debug('Not username or password Theia')
+
+ self.f_proxy()
+ try:
+ pp = pr.find("Proxy[@id='Proxy']")
+ self.w_proxy.w_proxy.lineEdit_proxy.setText(pp.find("Proxy_adress").text)
+ try:
+ self.w_proxy.w_proxy.lineEdit_password_proxy.setText(pp.find("Proxy_login").text)
+ except TypeError:
+ pass
+ try:
+ self.w_proxy.w_proxy.lineEdit_login_proxy.setText(pp.find("Proxy_password").text)
+ except TypeError:
+ pass
+ self.w_proxy.id_proxy()
+ except AttributeError:
+ self.logger.debug('Not proxy')
+ self.w_proxy.close_window()
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if pr.find("Img_processing").text == '1':
+ self.ui.checkBox_processing.setChecked(True)
+ else:
+ self.ui.checkBox_processing.setChecked(False)
+ if pr.find("VHRS_checked").text == '1':
+ self.ui.checkBox_VHRS.setChecked(True)
+ else:
+ self.ui.checkBox_VHRS.setChecked(False)
+
+ try:
+ self.ui.lineEdit_VHRS.setText(pr.find("VHRS").text)
+ except:
+ self.logger.debug('Not VHRS image')
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if pr.find("MNT_checked").text == '1':
+ self.ui.checkBox_MNT.setChecked(True)
+ else:
+ self.ui.checkBox_MNT.setChecked(False)
+
+ try:
+ self.ui.lineEdit_MNT.setText(pr.find("MNT").text)
+ except:
+ self.logger.debug('Not MNT')
+
+ ps = tree.find("Tab[@id='Processing_sample']")
+ if self.current_mode == Constantes.EXPERT_MODE:
+ try:
+ for sple_n in ps.iter("Sample"):
+ self.ui.lineEdit_sample_path.setText(sple_n.find("Sample_path").text)
+ self.ui.lineEdit_select_sample_fieldname_1.setText(sple_n.find("Fieldname_1").text)
+ self.ui.lineEdit_select_sample_fieldname_2.setText(sple_n.find("Fieldname_2").text)
+ self.ui.lineEdit_select_sample_class_1.setText(sple_n.find("Classname_1").text)
+ self.ui.lineEdit_select_sample_class_2.setText(sple_n.find("Classname_2").text)
+ self.ui.lineEdit_select_sample_nb_poly.setText(sple_n.find("Nb_polygones").text)
+ #Â Launch rpg method if the box is checked and if the shapefile hasn't go through rpg method (with prefix MONO_)
+ if sple_n.find("RPG").text == '1' and os.path.split(sple_n.find("Sample_path").text)[1][:5] != 'MONO_':
+ self.ui.checkBox_RPG.setChecked(True)
+ try:
+ if sple_n.find("Img_sample").text != "":
+ self.ui.lineEdit_img_sample.setText(sple_n.find("Img_sample").text)
+ self.ui.checkBox_img_sample.setChecked(True)
+ except:
+ self.logger.debug('Not sample raster only !')
+
+ self.add_sample()
+ except:
+ self.logger.debug('Not sample')
+
+ elif self.current_mode == Constantes.SIMPLE_MODE:
+ # RPG
+ sple_n = ps[0]
+ self.ui.lineEdit_sample_path.setText(sple_n.find("Sample_path").text)
+ self.ui.lineEdit_select_sample_fieldname_1.setText(sple_n.find("Fieldname_1").text)
+ self.ui.lineEdit_select_sample_fieldname_2.setText(sple_n.find("Fieldname_2").text)
+ self.ui.lineEdit_select_sample_class_1.setText(sple_n.find("Classname_1").text)
+ self.ui.lineEdit_select_sample_class_2.setText(sple_n.find("Classname_2").text)
+ self.ui.lineEdit_select_sample_nb_poly.setText(sple_n.find("Nb_polygones").text)
+
+ # To Grass/wooden
+ sple_n = ps[1]
+ self.ui.lineEdit_sample_path_2.setText(sple_n.find("Sample_path").text)
+ self.ui.lineEdit_select_sample_fieldname_3.setText(sple_n.find("Fieldname_1").text)
+ self.ui.lineEdit_select_sample_fieldname_4.setText(sple_n.find("Fieldname_2").text)
+ self.ui.lineEdit_select_sample_class_3.setText(sple_n.find("Classname_1").text)
+ self.ui.lineEdit_select_sample_class_4.setText(sple_n.find("Classname_2").text)
+ self.ui.lineEdit_select_sample_nb_poly_2.setText(sple_n.find("Nb_polygones").text)
+
+ # To wooden
+ sple_n = ps[2]
+ self.ui.lineEdit_sample_path_3.setText(sple_n.find("Sample_path").text)
+ self.ui.lineEdit_select_sample_fieldname_5.setText(sple_n.find("Fieldname_1").text)
+ self.ui.lineEdit_select_sample_fieldname_6.setText(sple_n.find("Fieldname_2").text)
+ self.ui.lineEdit_select_sample_class_5.setText(sple_n.find("Classname_1").text)
+ self.ui.lineEdit_select_sample_class_6.setText(sple_n.find("Classname_2").text)
+ self.ui.lineEdit_select_sample_nb_poly_3.setText(sple_n.find("Nb_polygones").text)
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if ps.find("Threshold_checked").text == '1':
+ self.ui.checkBox_threshold.setChecked(True)
+ else:
+ self.ui.checkBox_threshold.setChecked(False)
+
+ c = tree.find("Tab[@id='Classification']")
+
+ try:
+ self.ui.lineEdit_segmentation.setText(c.find("Segmentation_path").text)
+ except:
+ self.logger.debug('Not segmentation')
+
+ try:
+ self.ui.lineEdit_output.setText(c.find("Output_path").text)
+ except:
+ self.logger.debug('Not output file')
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if len(c) == 5:
+ self.ui.checkBox_classifier_1.setChecked(True)
+ self.ui.lineEdit_fieldname_1.setText(c.find("Output_fieldname_1").text)
+ index_fieldname_1 = self.ui.comboBox_fieldname_1.findText(c.find("Output_type_1").text)
+ self.ui.comboBox_fieldname_1.setCurrentIndex(index_fieldname_1)
+ elif len(c) == 7:
+ self.ui.checkBox_classifier_2.setChecked(True)
+ self.ui.lineEdit_fieldname_12.setText(c.find("Output_fieldname_1").text)
+ self.ui.lineEdit_fieldname_2.setText(c.find("Output_fieldname_2").text)
+ index_fieldname_12 = self.ui.comboBox_fieldname_12.findText(c.find("Output_type_1").text)
+ self.ui.comboBox_fieldname_12.setCurrentIndex(index_fieldname_12)
+ index_fieldname_2 = self.ui.comboBox_fieldname_2.findText(c.find("Output_type_2").text)
+ self.ui.comboBox_fieldname_2.setCurrentIndex(index_fieldname_2)
+ elif len(c) == 11:
+ self.ui.checkBox_classifier_3.setChecked(True)
+ self.ui.lineEdit_fieldname_13.setText(c.find("Output_fieldname_1").text)
+ self.ui.lineEdit_fieldname_23.setText(c.find("Output_fieldname_2").text)
+ self.ui.lineEdit_fieldname_3.setText(c.find("Output_fieldname_3").text)
+ self.ui.lineEdit_fieldname_4.setText(c.find("Output_fieldname_4").text)
+ index_fieldname_13 = self.ui.comboBox_fieldname_13.findText(c.find("Output_type_1").text)
+ self.ui.comboBox_fieldname_13.setCurrentIndex(index_fieldname_13)
+ index_fieldname_23 = self.ui.comboBox_fieldname_23.findText(c.find("Output_type_2").text)
+ self.ui.comboBox_fieldname_23.setCurrentIndex(index_fieldname_23)
+ index_fieldname_3 = self.ui.comboBox_fieldname_3.findText(c.find("Output_type_3").text)
+ self.ui.comboBox_fieldname_3.setCurrentIndex(index_fieldname_3)
+ index_fieldname_4 = self.ui.comboBox_fieldname_4.findText(c.find("Output_type_4").text)
+ self.ui.comboBox_fieldname_4.setCurrentIndex(index_fieldname_4)
+
+ # Classification mode
+ if c.find("Classification_method").text == '1':
+ self.ui.radioButton_rf.setChecked(True)
+ else:
+ self.ui.radioButton_s.setChecked(True)
+
+ def save_backup(self):
+ """
+ Function to save input text in every fields. The output file must be a XML file.
+ """
+
+ out_backup = QtWidgets.QFileDialog.getSaveFileName(self, "Save backup", os.getcwd(), '*.xml')[0]
+
+ #Â if the user has forgotten to put .shp at the end of the output xml
+ if out_backup[-4:] != '.xml':
+ out_backup = out_backup + '.xml'
+
+ root = ET.Element("Data_filled")
+
+ if self.ui.checkBox_multiprocess.isChecked():
+ ET.SubElement(root, "Multi_process", type = "int").text = str(1)
+ else:
+ ET.SubElement(root, "Multi_process", type = "int").text = str(0)
+
+ doc = ET.SubElement(root, "Tab", id="Processing_raster")
+ ET.SubElement(doc, "Principal_folder", type = "str").text = "%s" % self.ui.lineEdit_principal_folder.text()
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ ET.SubElement(doc, "Captor", type = "str").text = "%s" % self.ui.comboBox_captor.currentText()
+ else:
+ ET.SubElement(doc, "Captor", type = "str").text = "Landsat"
+
+ ET.SubElement(doc, "Year_images", type = "str").text = "%s" % self.ui.lineEdit_year_images.text()
+ ET.SubElement(doc, "Area_path", type = "str").text = "%s" % self.ui.lineEdit_area_path.text()
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if self.ui.checkBox_listing.isChecked():
+ ET.SubElement(doc, "Images_available", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "Images_available", type = "int").text = str(0)
+ if self.ui.checkBox_download.isChecked():
+ ET.SubElement(doc, "Download", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "Download", type = "int").text = str(0)
+ else:
+ ET.SubElement(doc, "Images_available", type = "int").text = str(0)
+ ET.SubElement(doc, "Download", type = "int").text = str(0)
+
+ ET.SubElement(doc, "Username", type = "str").text = "%s" % self.ui.lineEdit_user.text()
+ ET.SubElement(doc, "Password", type = "str").text = "%s" % self.ui.lineEdit_password.text()
+ if self.w_proxy is None:
+ sub_proxy = ET.SubElement(doc, "Proxy", id="No_proxy")
+ ET.SubElement(sub_proxy, "Proxy_adress", type = "str").text = ""
+ ET.SubElement(sub_proxy, "Proxy_login", type = "str").text = ""
+ ET.SubElement(sub_proxy, "Proxy_password", type = "str").text = ""
+ elif self.w_proxy.proxy == "":
+ sub_proxy = ET.SubElement(doc, "Proxy", id="No_proxy")
+ ET.SubElement(sub_proxy, "Proxy_adress", type = "str").text = ""
+ ET.SubElement(sub_proxy, "Proxy_login", type = "str").text = ""
+ ET.SubElement(sub_proxy, "Proxy_password", type = "str").text = ""
+ else:
+ sub_proxy = ET.SubElement(doc, "Proxy", id="Proxy")
+ ET.SubElement(sub_proxy, "Proxy_adress", type = "str").text = "%s" % self.w_proxy.proxy
+ ET.SubElement(sub_proxy, "Proxy_login", type = "str").text = "%s" % self.w_proxy.login_proxy
+ ET.SubElement(sub_proxy, "Proxy_password", type = "str").text = "%s" % self.w_proxy.password_proxy
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if self.ui.checkBox_processing.isChecked():
+ ET.SubElement(doc, "Img_processing", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "Img_processing", type = "int").text = str(0)
+
+ if self.ui.checkBox_VHRS.isChecked():
+ ET.SubElement(doc, "VHRS_checked", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "VHRS_checked", type = "int").text = str(0)
+ else:
+ ET.SubElement(doc, "Img_processing", type = "int").text = str(1)
+ ET.SubElement(doc, "VHRS_checked", type = "int").text = str(1)
+
+ ET.SubElement(doc, "VHRS", type = "str").text = "%s" % self.ui.lineEdit_VHRS.text()
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if self.ui.checkBox_MNT.isChecked():
+ ET.SubElement(doc, "MNT_checked", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "MNT_checked", type = "int").text = str(0)
+ else:
+ if "%s" % self.ui.lineEdit_MNT.text() != '':
+ ET.SubElement(doc, "MNT_checked", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "MNT_checked", type = "int").text = str(0)
+
+ ET.SubElement(doc, "MNT", type = "str").text = "%s" % self.ui.lineEdit_MNT.text()
+
+ doc = ET.SubElement(root, "Tab", id="Processing_sample")
+ if self.current_mode == Constantes.EXPERT_MODE:
+ for sa in range(len(self.sample_name)):
+ sub_doc = ET.SubElement(doc, "Sample", id="Sample_" + str(sa))
+ ET.SubElement(sub_doc, "Sample_path", type = "str").text = self.sample_name[sa]
+ ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = self.fieldname_args[2*sa]
+ ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = self.fieldname_args[2*sa+1]
+ ET.SubElement(sub_doc, "Classname_1", type = "str").text = self.class_args[2*sa]
+ ET.SubElement(sub_doc, "Classname_2", type = "str").text = self.class_args[2*sa+1]
+ ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = self.list_nb_sample[sa]
+ ET.SubElement(sub_doc, "RPG", type = "int").text = str(self.rpg_tchek[sa])
+ try:
+ #Â To enter a sample raster if the first tab doesn't launch before
+ if self.img_sample[sa] == 1:
+ ET.SubElement(sub_doc, "Img_sample", type = "str").text = self.raster_path[sa]
+ except:
+ self.logger.debug('Not sample raster only !')
+ else:
+ # RPG
+ sub_doc = ET.SubElement(doc, "Sample", id="Sample_0")
+ ET.SubElement(sub_doc, "Sample_path", type = "str").text = "%s" % self.ui.lineEdit_sample_path.text()
+ ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_1.text()
+ ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_2.text()
+ ET.SubElement(sub_doc, "Classname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_1.text()
+ ET.SubElement(sub_doc, "Classname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_2.text()
+ ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = "%s" % self.ui.lineEdit_select_sample_nb_poly.text()
+ ET.SubElement(sub_doc, "RPG", type = "int").text = str(1)
+
+ # To Grass/wooden
+ sub_doc = ET.SubElement(doc, "Sample", id="Sample_1")
+ ET.SubElement(sub_doc, "Sample_path", type = "str").text = "%s" % self.ui.lineEdit_sample_path_2.text()
+ ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_3.text()
+ ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_4.text()
+ ET.SubElement(sub_doc, "Classname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_3.text()
+ ET.SubElement(sub_doc, "Classname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_4.text()
+ ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = "%s" % self.ui.lineEdit_select_sample_nb_poly_2.text()
+ ET.SubElement(sub_doc, "RPG", type = "int").text = str(0)
+
+ # To wooden
+ sub_doc = ET.SubElement(doc, "Sample", id="Sample_2")
+ ET.SubElement(sub_doc, "Sample_path", type = "str").text = "%s" % self.ui.lineEdit_sample_path_3.text()
+ ET.SubElement(sub_doc, "Fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_5.text()
+ ET.SubElement(sub_doc, "Fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_fieldname_6.text()
+ ET.SubElement(sub_doc, "Classname_1", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_5.text()
+ ET.SubElement(sub_doc, "Classname_2", type = "str").text = "%s" % self.ui.lineEdit_select_sample_class_6.text()
+ ET.SubElement(sub_doc, "Nb_polygones", type = "str").text = "%s" % self.ui.lineEdit_select_sample_nb_poly_3.text()
+ ET.SubElement(sub_doc, "RPG", type = "int").text = str(0)
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+ if self.ui.checkBox_threshold.isChecked():
+ ET.SubElement(doc, "Threshold_checked", type = "int").text = str(1)
+ else:
+ ET.SubElement(doc, "Threshold_checked", type = "int").text = str(0)
+ else:
+ ET.SubElement(doc, "Threshold_checked", type = "int").text = str(1)
+
+ doc = ET.SubElement(root, "Tab", id="Classification")
+ ET.SubElement(doc, "Segmentation_path", type = "str").text = "%s" % self.ui.lineEdit_segmentation.text()
+ ET.SubElement(doc, "Output_path", type = "str").text = "%s" % self.ui.lineEdit_output.text()
+
+ if self.current_mode == Constantes.EXPERT_MODE:
+
+ if self.ui.checkBox_classifier_1.isChecked():
+ ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_fieldname_1.text()
+ ET.SubElement(doc, "Output_type_1", type = "str").text = "%s" % self.ui.comboBox_fieldname_1.currentText()
+
+ if self.ui.checkBox_classifier_2.isChecked():
+ ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_fieldname_12.text()
+ ET.SubElement(doc, "Output_type_1", type = "str").text = "%s" % self.ui.comboBox_fieldname_12.currentText()
+ ET.SubElement(doc, "Output_fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_fieldname_2.text()
+ ET.SubElement(doc, "Output_type_2", type = "str").text = "%s" % self.ui.comboBox_fieldname_2.currentText()
+
+ if self.ui.checkBox_classifier_3.isChecked():
+ ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "%s" % self.ui.lineEdit_fieldname_13.text()
+ ET.SubElement(doc, "Output_type_1", type = "str").text = "%s" % self.ui.comboBox_fieldname_13.currentText()
+ ET.SubElement(doc, "Output_fieldname_2", type = "str").text = "%s" % self.ui.lineEdit_fieldname_23.text()
+ ET.SubElement(doc, "Output_type_2", type = "str").text = "%s" % self.ui.comboBox_fieldname_23.currentText()
+ ET.SubElement(doc, "Output_fieldname_3", type = "str").text = "%s" % self.ui.lineEdit_fieldname_3.text()
+ ET.SubElement(doc, "Output_type_3", type = "str").text = "%s" % self.ui.comboBox_fieldname_3.currentText()
+ ET.SubElement(doc, "Output_fieldname_4", type = "str").text = "%s" % self.ui.lineEdit_fieldname_4.text()
+ ET.SubElement(doc, "Output_type_4", type = "str").text = "%s" % self.ui.comboBox_fieldname_4.currentText()
+
+ # Classification mode
+ if self.ui.radioButton_rf.isChecked():
+ ET.SubElement(doc, "Classification_method", type = "int").text = str(1)
+ elif self.ui.radioButton_s.isChecked():
+ ET.SubElement(doc, "Classification_method", type = "int").text = str(0)
+ else:
+ ET.SubElement(doc, "Output_fieldname_1", type = "str").text = "NIVEAU_1"
+ ET.SubElement(doc, "Output_type_1", type = "str").text = "String"
+ ET.SubElement(doc, "Output_fieldname_2", type = "str").text = "NIVEAU_2"
+ ET.SubElement(doc, "Output_type_2", type = "str").text = "String"
+ ET.SubElement(doc, "Output_fieldname_3", type = "str").text = "NIVEAU_3"
+ ET.SubElement(doc, "Output_type_3", type = "str").text = "String"
+ ET.SubElement(doc, "Output_fieldname_4", type = "str").text = "POURC"
+ ET.SubElement(doc, "Output_type_4", type = "str").text = "Real"
+ ET.SubElement(doc, "Classification_method", type = "int").text = str(1)
+
+ tree = ET.ElementTree(root)
+ #Â Write in a xml file
+ tree.write(str(out_backup), encoding="UTF-8",xml_declaration=True, pretty_print=True)
+
+ def help_tools(self):
+ """
+ Function to open html help
+ """
+ webbrowser.open('file://' + os.getcwd() + '/Documentation/methode_tuto.html#tutoriels-interface')
+
+
+ def change_mode(self, new_mode):
+ """
+ Function to switch between SIMPLE and EXPERT mode of "PHYMOBAT"
+ """
+ if new_mode != self.current_mode :
+ if new_mode == Constantes.SIMPLE_MODE :
+ self.logger.info("Simplify mode")
+ self.close()
+ self.window = PHYMOBAT(mode = Constantes.SIMPLE_MODE)
+ else :
+ self.logger.info("Expert mode")
+ self.close()
+ self.window = PHYMOBAT(mode = Constantes.EXPERT_MODE)
+
+ self.window.show()
+
+ def about_PHYMOBA(self):
+ """
+ Function to open a new window "About PHYMOBAT"
+ """
+ self.apropos.show()
+
+ def forget_study_area(self):
+ """
+ Function to open a new window 'Alert' because user forgotten to declare study area.
+ """
+ self.w_study_area.show()
+
+ def forget_raster_sample(self):
+ """
+ Function to open a new window 'Alert' because user forgotten to declare rasters or samples.
+ """
+ self.w_forget.show()
+
+ def close_button(self):
+ """
+ Function to close the interface.
+ """
+ self.close()
+
if __name__ == "__main__":
-
- app = QtWidgets.QApplication(sys.argv)
- myapp = PHYMOBAT()
- myapp.show()
-
- sys.exit(app.exec_())
+
+ app = QtWidgets.QApplication(sys.argv)
+ myapp = PHYMOBAT()
+ myapp.show()
+
+ sys.exit(app.exec_())
diff --git a/Popup.py b/Popup.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea9e1a04a70842d0d7c6f343fa26af285babfb36
--- /dev/null
+++ b/Popup.py
@@ -0,0 +1,94 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os, sys, time
+from PyQt5 import QtWidgets, QtCore
+
+from ui_A_propos_PHYMOBAT_window import Ui_About
+from ui_Warming_study_area import Ui_Warming_study_area
+from ui_Warming_forgetting import Ui_Warming_forgetting
+from ui_Proxy_window import Ui_Proxy_window
+
+class about(QtWidgets.QWidget):
+ """
+ Popup to display "About PHYMOBAT". In this windows, it prints informations on the processing, license
+ and version.
+ """
+ def __init__(self, parent=None):
+ QtWidgets.QWidget.__init__(self, parent)
+ self.prop = Ui_About()
+ self.prop.setupUi(self)
+
+ self.prop.close_newWindow.clicked.connect(self.close_window)
+
+ def close_window(self):
+ """
+ Function to close the "A propos PHYMOBAT".
+ """
+ self.close()
+
+class warming_study_area(QtWidgets.QWidget):
+ """
+ Popup to display a message to say there isn't declared study area file.
+ """
+ def __init__(self, parent=None):
+ QtWidgets.QWidget.__init__(self, parent)
+ self.w_study_a = Ui_Warming_study_area()
+ self.w_study_a.setupUi(self)
+
+ self.w_study_a.pushButton_ok_window_warning_study_area.clicked.connect(self.close_window)
+
+ def close_window(self):
+ """
+ Function to close the popup.
+ """
+ self.close()
+
+class warming_forgetting(QtWidgets.QWidget):
+ """
+ Popup to display a message to tell you if you fogotten to enter a raster or a sample.
+ """
+ def __init__(self, parent=None):
+ QtWidgets.QWidget.__init__(self, parent)
+ self.w_forget = Ui_Warming_forgetting()
+ self.w_forget.setupUi(self)
+
+ self.w_forget.pushButton_ok_forget.clicked.connect(self.close_window)
+
+ def close_window(self):
+ """
+ Function to close the popup.
+ """
+ self.close()
+
+class proxy_window(QtWidgets.QWidget):
+ """
+ Popup to display a message to tell you if you fogotten to enter a raster or a sample.
+ """
+ def __init__(self, parent=None):
+ QtWidgets.QWidget.__init__(self, parent)
+ self.w_proxy = Ui_Proxy_window()
+ self.w_proxy.setupUi(self)
+
+ #Â Proxy ID
+ self.proxy = ""
+ self.login_proxy = ""
+ self.password_proxy = ""
+
+ #Â Connect Apply|Close button
+ self.w_proxy.buttonBox_proxy.button(QtWidgets.QDialogButtonBox.Close).clicked.connect(self.close_window)
+ self.w_proxy.buttonBox_proxy.button(QtWidgets.QDialogButtonBox.Apply).clicked.connect(self.id_proxy)
+
+ def id_proxy(self):
+ """
+ Function to use input proxy id
+ """
+ self.login_proxy = "%s" % self.w_proxy.lineEdit_login_proxy.text()
+ self.password_proxy = "%s" % self.w_proxy.lineEdit_password_proxy.text()
+ self.proxy = "%s" % self.w_proxy.lineEdit_proxy.text()
+
+ def close_window(self):
+ """
+ Function to close the popup.
+ """
+ self.close()
diff --git a/Precision_moba.py b/Precision_moba.py
index 244ab4e17513da38c58ae5fb211ba3098b115e61..ff3e1ddb7b6eeddac0bad4469f9dc1d4e378ebc0 100644
--- a/Precision_moba.py
+++ b/Precision_moba.py
@@ -115,53 +115,52 @@ class Precision_moba():
data_val = data_val.flatten()
# Add pixel value in a list without no data
- fb_stats_2 = [[data_class[x],data_val[x]] for x in range(len(data_val)) if data_val[x] != -10000]
- fb_stats_2 = map(list, zip(*fb_stats_2))# transpose list
+ fb_stats_2 = [[int(data_class[x]),int(data_val[x])] for x in range(len(data_val)) if data_val[x] != -10000]
+ fb_stats_2 = list(map(list, zip(*fb_stats_2)))# transpose list
+
fb_stats_in = fb_stats_2[0]
fb_stats_val = fb_stats_2[1]
# Open a file (txt) to save results
- f = open(self.path_save + "/ConfusionMatrix.txt", "wb")
-
- # Compute statistics on the classification
- cm = confusion_matrix(fb_stats_val, fb_stats_in)
- f.write("Confusion Matrix :\n")
- for out in cm:
- f.write(str(out) + "\n")
-
- all_pr = classification_report(fb_stats_val, fb_stats_in)
- f.write("\n")
- f.write(all_pr)
-
- accur = accuracy_score(fb_stats_val, fb_stats_in)
- f.write("\n")
- f.write("Accuracy : " + str(accur) + "\n")
-
- recall = recall_score(fb_stats_val, fb_stats_in)
- f.write("\n")
- f.write("Recall : " + str(recall) + "\n")
-
- pr = precision_score(fb_stats_val, fb_stats_in)
- f.write("\n")
- f.write("Precision : " + str(pr) + "\n")
-
- f_scor = f1_score(fb_stats_val, fb_stats_in)
- f.write("\n")
- f.write("F_score : " + str(f_scor) + "\n")
-
- kappa = self.cohen_kappa_score(fb_stats_val, fb_stats_in)
- f.write("\n")
- f.write("Kappa : " + str(kappa) + "\n")
- print('')
- print('')
- print('Accuracy : %f, Kappa : %f, Recall : %f, Precision : %f, F_score : %f' % (accur, kappa, recall, pr, f_scor))
- print('')
- print (all_pr)
- print('')
- print('Confusion matrix :')
- print (cm)
-
- f.close()
+ with open(self.path_save + "/ConfusionMatrix.txt", "w") as f :
+ # Compute statistics on the classification
+ cm = confusion_matrix(fb_stats_val, fb_stats_in)
+ f.write("Confusion Matrix :\n")
+ for out in cm:
+ f.write(str(out) + "\n")
+
+ all_pr = classification_report(fb_stats_val, fb_stats_in)
+ f.write("\n")
+ f.write(all_pr)
+
+ accur = accuracy_score(fb_stats_val, fb_stats_in)
+ f.write("\n")
+ f.write("Accuracy : " + str(accur) + "\n")
+
+ recall = recall_score(fb_stats_val, fb_stats_in)
+ f.write("\n")
+ f.write("Recall : " + str(recall) + "\n")
+
+ pr = precision_score(fb_stats_val, fb_stats_in)
+ f.write("\n")
+ f.write("Precision : " + str(pr) + "\n")
+
+ f_scor = f1_score(fb_stats_val, fb_stats_in)
+ f.write("\n")
+ f.write("F_score : " + str(f_scor) + "\n")
+
+ kappa = self.cohen_kappa_score(fb_stats_val, fb_stats_in)
+ f.write("\n")
+ f.write("Kappa : " + str(kappa) + "\n")
+ print('')
+ print('')
+ print('Accuracy : %f, Kappa : %f, Recall : %f, Precision : %f, F_score : %f' % (accur, kappa, recall, pr, f_scor))
+ print('')
+ print (all_pr)
+ print('')
+ print('Confusion matrix :')
+ print (cm)
+
def cohen_kappa_score(self, y1, y2, labels=None):
"""
diff --git a/Processing.py b/Processing.py
index 5ffbbbf19b72f7def198d66c2be44a8ac44e8bbe..af90bb46500dc869cabdbab24830fd405f64934d 100644
--- a/Processing.py
+++ b/Processing.py
@@ -23,13 +23,14 @@ import subprocess
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
try :
- import ogr, gdal
+ import ogr, gdal
except :
- from osgeo import ogr, gdal
+ from osgeo import ogr, gdal
from Toolbox import Toolbox
from Seath import Seath
from Precision_moba import Precision_moba
+
# Class group image
from Archive import Archive
from RasterSat_by_date import RasterSat_by_date
@@ -42,835 +43,815 @@ from Sample import Sample
from Segmentation import Segmentation
from Rpg import Rpg
+import Constantes
+
from collections import defaultdict
from multiprocessing import Process
from multiprocessing.managers import BaseManager, DictProxy
class Processing():
-
- """
- Main processing. This class launch the others system classes. It take into account
- CarHab classification method MOBA.
-
- This way is broken down into 3 parts :
- - Image Processing (Search, download and processing)
- - Vector Processing (Optimal threshold, Sample processing)
- - Classification
- - Validation
-
- **Main parameters**
-
- :param captor_project: Satellite captor name
- :type captor_project: str
- :param classif_year: Classification year
- :type classif_year: str
- :param nb_avalaible_images: Number download available images
- :type nb_avalaible_images: int
- :param path_folder_dpt: Main folder path
- :type path_folder_dpt: str
- :param folder_archive: Archive downloaded folder path
- :type folder_archive: str
- :param folder_processing: Processing folder name. By default : 'Traitement'
- :type folder_processing: str
- :param path_area: Study area shapefile
- :type path_area: str
- :param path_ortho: VHRS image path
- :type path_ortho: str
- :param path_mnt: MNT image path
- :type path_mnt: str
- :param path_segm: Segmentation shapefile
- :type path_segm: str
-
- **Id information to download on theia platform**
-
- :param user: Connexion Username
- :type user: str
- :param password: Connexion Password
- :type password: str
-
- **Output parameters**
-
- :param output_name_moba: Output classification shapefile
- :type output_name_moba: str
- :param out_fieldname_carto: Output shapefile field name
- :type out_fieldname_carto: list of str
- :param out_fieldtype_carto: Output shapefile field type
- :type out_fieldtype_carto: list of str (eval ogr pointer)
-
- **Sample parameters**
-
- :param fieldname_args: Sample field names 2 by 2
- :type fieldname_args: list of str
- :param class_args: Sample class names 2 by 2
- :type class_args: list of str
- :param sample_name: List of sample name (path)
- :type sample_name: list of str
- :param list_nb_sample: Number of polygons for every sample
- :type list_nb_sample: list of int
-
- **Multi-processing parameters**
-
- :param mp: Boolean variable -> 0 or 1.
-
- - 0 means, not multi-processing
- - 1 means, launch process with multi-processing
- :type mp: int
- """
-
- def __init__(self):
-
- # Used variables
- self.captor_project = ''
- self.classif_year = ''
- self.path_folder_dpt = ''
- self.folder_archive = ''
- self.folder_processing = 'Traitement'
- self.path_area = ''
- self.path_ortho = ''
- self.path_mnt = ''
- self.path_segm = ''
- self.output_name_moba = ''
-
- self.w_proxy = None # For the "Proxy window"
-
- # Id information to download on theia platform
- self.user = ''
- self.password = ''
+
+ """
+ Main processing. This class launch the others system classes. It take into account
+ CarHab classification method MOBA.
+
+ This way is broken down into 3 parts :
+ - Image Processing (Search, download and processing)
+ - Vector Processing (Optimal threshold, Sample processing)
+ - Classification
+ - Validation
+
+ **Main parameters**
+
+ :param captor_project: Satellite captor name
+ :type captor_project: str
+ :param classif_year: Classification year
+ :type classif_year: str
+ :param nb_avalaible_images: Number download available images
+ :type nb_avalaible_images: int
+ :param path_folder_dpt: Main folder path
+ :type path_folder_dpt: str
+ :param folder_processing: Processing folder name. By default : 'Traitement'
+ :type folder_processing: str
+ :param path_area: Study area shapefile
+ :type path_area: str
+ :param path_ortho: VHRS image path
+ :type path_ortho: str
+ :param path_mnt: MNT image path
+ :type path_mnt: str
+ :param path_segm: Segmentation shapefile
+ :type path_segm: str
+
+ **Id information to download on theia platform**
+
+ :param user: Connexion Username
+ :type user: str
+ :param password: Connexion Password
+ :type password: str
+
+ **Output parameters**
+
+ :param output_name_moba: Output classification shapefile
+ :type output_name_moba: str
+ :param out_fieldname_carto: Output shapefile field name
+ :type out_fieldname_carto: list of str
+ :param out_fieldtype_carto: Output shapefile field type
+ :type out_fieldtype_carto: list of str (eval ogr pointer)
+
+ **Sample parameters**
+
+ :param fieldname_args: Sample field names 2 by 2
+ :type fieldname_args: list of str
+ :param class_args: Sample class names 2 by 2
+ :type class_args: list of str
+ :param sample_name: List of sample name (path)
+ :type sample_name: list of str
+ :param list_nb_sample: Number of polygons for every sample
+ :type list_nb_sample: list of int
+
+ **Multi-processing parameters**
+
+ :param mp: Boolean variable -> 0 or 1.
+
+ - 0 means, not multi-processing
+ - 1 means, launch process with multi-processing
+ :type mp: int
+ """
+
+ def __init__(self):
+
+ # Used variables
+ self.captor_project = ''
+ self.classif_year = ''
+ self.path_folder_dpt = ''
+ self.folder_processing = 'Traitement'
+ self.path_area = ''
+ self.path_ortho = ''
+ self.path_mnt = ''
+ self.path_segm = ''
+ self.output_name_moba = ''
+
+ self.w_proxy = None # For the "Proxy window"
+
+ # Id information to download on theia platform
+ self.user = ''
+ self.password = ''
+
+ # List of output raster path
+ self.raster_path = []
+ self.list_band_outraster = []
+
+ # Class name
+
+ # TODO : Change index of the classes -> Harbacées 6 / Ligneux 7 by Agriculuture 4 / Eboulis 5
+
+ self.in_class_name = ['Non Vegetation semi-naturelle', 'Vegetation semi-naturelle',\
+ 'Herbacees', 'Ligneux', \
+ 'Ligneux mixtes', 'Ligneux denses',\
+ 'Agriculture', 'Eboulis', \
+ 'Forte phytomasse', 'Moyenne phytomasse', 'Faible phytomasse']
+ # Sample field names 2 by 2
+ self.fieldname_args = []
+# 'CODE_GROUP', 'CODE_GROUP',\
+# 'echant', 'echant',\
+# 'echant', 'echant']
+ # Sample class names 2 by 2
+ self.class_args = []
+# '1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 20, 21, 22, 23, 24, 26, 28', '18',\
+# 'H','LF, LO',\
+# 'LF', 'LO']
+
+ # Decision tree combination
+ self.tree_direction = [[0],\
+ [0],\
+ [1, 3, 4],\
+ [1, 3, 5],\
+ [1, 2, 8],\
+ [1, 2, 9],\
+ [1, 2, 10]] # [['Cultures'],['Semi-naturelles', 'Herbacees', 'Forte phytomasse'], ...
+ # ..., ['Semi-naturelles', 'Ligneux', 'Ligneux denses']]
+ # Slope degrees
+ self.slope_degree = 30
+
+ # Output shapefile field name
+ self.out_fieldname_carto = ['ID', 'AREA'] #, 'NIVEAU_1', 'NIVEAU_2', 'NIVEAU_3', 'POURC']
+ # Output shapefile field type
+ self.out_fieldtype_carto = [ogr.OFTString, ogr.OFTReal] #, ogr.OFTString, ogr.OFTString, ogr.OFTString, ogr.OFTReal]
+
+ # List of sample name path
+ self.sample_name = []
+ # List of number sample
+ self.list_nb_sample = []
+ # Number download available images
+ self.nb_avalaible_images = 0
+
+ # Multi-processing variable
+ self.mp = Constantes.MULTIPROCESSING_DISABLE
+
+ # Function followed
+ self.check_download = ''
+ self.decis = {}
+ # Images after processing images
+ self.out_ndvistats_folder_tab = defaultdict(list)
+
+ # Validation shapefiles information
+ self.valid_shp = []
+
+ # Radom Forest Model
+ # Set the parameters of this random forest from the estimator
+ self.rf = RandomForestClassifier(n_estimators=500, criterion='gini', max_depth=None, min_samples_split=2, \
+ min_samples_leaf=1, max_features='auto', \
+ bootstrap=True, oob_score=True)
+
+ def i_tree_direction(self):
+ """
+ Interface function to can extract one level or two levels of the final classification
+ """
+
+ if len(self.out_fieldname_carto) == 3:
+ self.tree_direction = [[0], [1]]
+
+ if len(self.out_fieldname_carto) == 4:
+ self.tree_direction = [[0], [0], [1, 2], [1, 3]]
+
+ def i_download(self):
+ """
+ Interface function to download archives on the website Theia Land. This function extract
+ the number of downloadable image with :func:`Archive.Archive.listing`.
+
+ Then, this function download :func:`Archive.Archive.download` and unzip :func:`Archive.Archive.decompress` images
+ in the archive folder (**folder_archive**).
+ """
+
+ folder_archive = self.captor_project + '_PoleTheia'
- # List of output raster path
- self.raster_path = []
- self.list_band_outraster = []
-
- # Class name
-
- # TODO : Change index of the classes -> Harbacées 6 / Ligneux 7 by Agriculuture 4 / Eboulis 5
-
- self.in_class_name = ['Non Vegetation semi-naturelle', 'Vegetation semi-naturelle',\
- 'Herbacees', 'Ligneux', \
- 'Ligneux mixtes', 'Ligneux denses',\
- 'Agriculture', 'Eboulis', \
- 'Forte phytomasse', 'Moyenne phytomasse', 'Faible phytomasse']
- # Sample field names 2 by 2
- self.fieldname_args = []
-# 'CODE_GROUP', 'CODE_GROUP',\
-# 'echant', 'echant',\
-# 'echant', 'echant']
- # Sample class names 2 by 2
- self.class_args = []
-# '1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 20, 21, 22, 23, 24, 26, 28', '18',\
-# 'H','LF, LO',\
-# 'LF', 'LO']
-
- # Decision tree combination
- self.tree_direction = [[0],\
- [0],\
- [1, 3, 4],\
- [1, 3, 5],\
- [1, 2, 8],\
- [1, 2, 9],\
- [1, 2, 10]] # [['Cultures'],['Semi-naturelles', 'Herbacees', 'Forte phytomasse'], ...
- # ..., ['Semi-naturelles', 'Ligneux', 'Ligneux denses']]
- # Slope degrees
- self.slope_degree = 30
-
- # Output shapefile field name
- self.out_fieldname_carto = ['ID', 'AREA'] #, 'NIVEAU_1', 'NIVEAU_2', 'NIVEAU_3', 'POURC']
- # Output shapefile field type
- self.out_fieldtype_carto = [ogr.OFTString, ogr.OFTReal] #, ogr.OFTString, ogr.OFTString, ogr.OFTString, ogr.OFTReal]
-
- # List of sample name path
- self.sample_name = []
- # List of number sample
- self.list_nb_sample = []
- # Number download available images
- self.nb_avalaible_images = 0
- # Multi-processing variable
- self.mp = 1
-
- # Function followed
- self.check_download = ''
- self.decis = {}
- # Images after processing images
- self.out_ndvistats_folder_tab = defaultdict(list)
-
- # Validation shapefiles information
- self.valid_shp = []
-
- # Radom Forest Model
- # Set the parameters of this random forest from the estimator
- self.rf = RandomForestClassifier(n_estimators=500, criterion='gini', max_depth=None, min_samples_split=2, \
- min_samples_leaf=1, max_features='auto', \
- bootstrap=True, oob_score=True)
-
- def i_tree_direction(self):
-
- """
- Interface function to can extract one level or two levels of the final classification
- """
+ self.check_download = Archive(self.captor_project, self.classif_year, self.path_area, self.path_folder_dpt, folder_archive, self.w_proxy)
+ self.nb_avalaible_images = self.check_download.listing()
+ self.check_download.download_auto(self.user, self.password)
+ self.check_download.decompress()
+
+ def i_img_sat(self):
+
+ """
+ Interface function to processing satellite images:
+
+ 1. Clip archive images and modify Archive class to integrate clip image path.
+ With :func:`Toolbox.clip_raster` in ``Toolbox`` module.
+
+ 2. Search cloud's percentage :func:`RasterSat_by_date.RasterSat_by_date.pourc_cloud`, select
+ image and compute ndvi index :func:`RasterSat_by_date.RasterSat_by_date.calcul_ndvi`. If cloud's percentage is
+ greater than 40%, then not select and compute ndvi index.
+
+ 3. Compute temporal stats on ndvi index [min, max, std, min-max]. With :func:`Toolbox.calc_serie_stats`
+ in ``Toolbox`` module.
+
+ 4. Create stats ndvi raster and stats cloud raster.
+
+ >>> import RasterSat_by_date
+ >>> stats_test = RasterSat_by_date(class_archive, big_folder, one_date)
+ >>> stats_test.complete_raster(stats_test.create_raster(in_raster, stats_data, in_ds), stats_data)
+ """
- if len(self.out_fieldname_carto) == 3:
- self.tree_direction = [[0], [1]]
-
- if len(self.out_fieldname_carto) == 4:
- self.tree_direction = [[0], [0], [1, 2], [1, 3]]
-
- def i_download(self, dd):
-
- """
- Interface function to download archives on the website Theia Land. This function extract
- the number of downloadable image with :func:`Archive.Archive.listing`.
-
- Then, this function download :func:`Archive.Archive.download` and unzip :func:`Archive.Archive.decompress` images
- in the archive folder (**folder_archive**).
-
- :param dd: Boolean variable to launch download images -> 0 or 1.
-
- - 0 means, not downloading
- - 1 means, launch downloading
- :type dd: int
- """
-
- self.folder_archive = self.captor_project + '_PoleTheia'
- self.check_download = Archive(self.captor_project, self.classif_year, self.path_area, self.path_folder_dpt, self.folder_archive, self.w_proxy)
- self.check_download.listing()
- self.nb_avalaible_images = len(self.check_download.list_archive)
- # check_download.set_list_archive_to_try(check_download.list_archive[:3])
- if dd == 1:
-# self.check_download.download_auto(self.user, self.password)
- self.check_download.download_auto(self.user, self.password)
- self.check_download.decompress()
-
- def i_glob(self):
- """
- Function to load existing images to launch the processing.
- It need to archives. Then, to select processing images, select archives
-
- """
-
- self.folder_archive = self.captor_project + '_PoleTheia'
- self.check_download = Archive(self.captor_project, self.classif_year, self.path_area, self.path_folder_dpt, self.folder_archive, self.w_proxy)
- self.check_download.decompress()
-
- def i_img_sat(self):
-
- """
- Interface function to processing satellite images:
-
- 1. Clip archive images and modify Archive class to integrate clip image path.
- With :func:`Toolbox.clip_raster` in ``Toolbox`` module.
-
- 2. Search cloud's percentage :func:`RasterSat_by_date.RasterSat_by_date.pourc_cloud`, select
- image and compute ndvi index :func:`RasterSat_by_date.RasterSat_by_date.calcul_ndvi`. If cloud's percentage is
- greater than 40%, then not select and compute ndvi index.
-
- 3. Compute temporal stats on ndvi index [min, max, std, min-max]. With :func:`Toolbox.calc_serie_stats`
- in ``Toolbox`` module.
-
- 4. Create stats ndvi raster and stats cloud raster.
-
- >>> import RasterSat_by_date
- >>> stats_test = RasterSat_by_date(class_archive, big_folder, one_date)
- >>> stats_test.complete_raster(stats_test.create_raster(in_raster, stats_data, in_ds), stats_data)
- """
+ # Clip archive images and modify Archive class to integrate clip image path
+ for clip in self.check_download.list_img:
+ clip_index = self.check_download.list_img.index(clip)
+
+ current_list = Toolbox()
+ current_list.imag = clip[3]
+ current_list.vect = self.path_area
+ current_list.check_proj() # Check if projection is RFG93
+ self.check_download.list_img[clip_index][3] = current_list.clip_raster() # Multispectral images
+
+ current_list.imag = clip[4]
+ current_list.check_proj() # Check if projection is RFG93
+ self.check_download.list_img[clip_index][4] = current_list.clip_raster() # Cloud images
+
+ # Images pre-processing
+ spectral_out = []
+ for date in self.check_download.single_date:
+
+ check_L8 = RasterSat_by_date(self.check_download, self.folder_processing, date)
+ check_L8.mosaic_by_date()
+
+ # Search cloud's percentage, select image and compute ndvi index if > cl
+ cl = check_L8.pourc_cloud(check_L8._one_date[3], check_L8._one_date[4])
+ if cl > 0.60:
+ check_L8.calcul_ndvi(check_L8._one_date[3])
+ spectral_out.append(check_L8._one_date)
- # Clip archive images and modify Archive class to integrate clip image path
- for clip in self.check_download.list_img:
- clip_index = self.check_download.list_img.index(clip)
-
- current_list = Toolbox()
- current_list.imag = clip[3]
- current_list.vect = self.path_area
- current_list.check_proj() # Check if projection is RFG93
- self.check_download.list_img[clip_index][3] = current_list.clip_raster() # Multispectral images
-
- current_list.imag = clip[4]
- current_list.check_proj() # Check if projection is RFG93
- self.check_download.list_img[clip_index][4] = current_list.clip_raster() # Cloud images
-
- # Images pre-processing
- spectral_out = []
- for date in self.check_download.single_date:
-
- check_L8 = RasterSat_by_date(self.check_download, self.folder_processing, date)
- check_L8.mosaic_by_date()
-
- # Search cloud's percentage, select image and compute ndvi index if > cl
- cl = check_L8.pourc_cloud(check_L8._one_date[3], check_L8._one_date[4])
- if cl > 0.60:
- check_L8.calcul_ndvi(check_L8._one_date[3])
- spectral_out.append(check_L8._one_date)
+ # Compute temporal stats on ndvi index [min, max, std, min-max]
+ spectral_trans = np.transpose(np.array(spectral_out, dtype=object))
+ stats_name = ['Min', 'Date', 'Max', 'Std', 'MaxMin']
+ stats_ndvi, stats_cloud = current_list.calc_serie_stats(spectral_trans)
+
+ # Create stats ndvi raster and stats cloud raster
+ stats_L8 = RasterSat_by_date(self.check_download, self.folder_processing, [0])
+
+ # Stats cloud raster
+ out_cloud_folder = stats_L8._class_archive._folder + '/' + stats_L8._big_folder + '/' + self.captor_project + '/Cloud_number_' + self.captor_project + '.TIF'
+
+ stats_L8.complete_raster(*stats_L8.create_raster(out_cloud_folder, stats_cloud, stats_L8.raster_data(self.check_download.list_img[0][4])[1]), stats_cloud)
+
+ # Stats ndvi rasters
+ for stats_index in range(len(stats_ndvi)):
+ out_ndvistats_folder = stats_L8._class_archive._folder + '/' + stats_L8._big_folder + '/' + self.captor_project + '/' + stats_name[stats_index] + '_' + self.captor_project + '.TIF'
+ self.out_ndvistats_folder_tab[stats_index] = out_ndvistats_folder
+ stats_L8.complete_raster(*stats_L8.create_raster(out_ndvistats_folder, stats_ndvi[stats_index], stats_L8.raster_data(self.check_download.list_img[0][4])[1]), stats_ndvi[stats_index])
+
+ def i_slope(self):
+ """
+ Interface function to processing slope raster. From a MNT, and with a command line gdal,
+ this function compute slope in degrees :func:`Slope.Slope`.
+ """
+
+ current_path_mnt = Toolbox()
+ current_path_mnt.imag = self.path_mnt
+ current_path_mnt.vect = self.path_area
+ path_mnt = current_path_mnt.clip_raster()
+
+ study_slope = Slope(path_mnt)
+ study_slope.extract_slope()# Call this function to compute slope raster
+ self.path_mnt = study_slope.out_mnt
+
+ def i_vhrs(self):#, vs):
+ """
+ Interface function to processing VHRS images. It create two OTB texture images :func:`Vhrs.Vhrs` : SFS Texture and Haralick Texture
+ """
+
+ # Create texture image
+ # Clip orthography image
+ current_path_ortho = Toolbox()
+ current_path_ortho.imag = self.path_ortho
+ current_path_ortho.vect = self.path_area
+ path_ortho = current_path_ortho.clip_raster()
+
+ texture_irc = Vhrs(path_ortho, self.mp)
+ self.out_ndvistats_folder_tab['sfs'] = texture_irc.out_sfs
+ self.out_ndvistats_folder_tab['haralick'] = texture_irc.out_haralick
+
+ def i_images_processing(self, vs=1):
+
+ """
+ Interface function to launch processing VHRS images :func:`i_vhrs` and satellite images :func:`i_img_sat` in multi-processing.
+
+ :param vs: Boolean variable to launch texture processing because of interface checkbox -> 0 or 1.
+
+ - 0 means, not texture processing
+ - 1 means, launch texture processing
+ :type vs: int
+ """
+
+ ######################### Multiprocessing #########################
+
+ mgr = BaseManager()
+ mgr.register('defaultdict', defaultdict, DictProxy)
+ mgr.start()
- # Compute temporal stats on ndvi index [min, max, std, min-max]
- spectral_trans = np.transpose(np.array(spectral_out, dtype=object))
- stats_name = ['Min', 'Date', 'Max', 'Std', 'MaxMin']
- stats_ndvi, stats_cloud = current_list.calc_serie_stats(spectral_trans)
-
- # Create stats ndvi raster and stats cloud raster
- stats_L8 = RasterSat_by_date(self.check_download, self.folder_processing, [0])
- # Stats cloud raster
- out_cloud_folder = stats_L8._class_archive._folder + '/' + stats_L8._big_folder + '/' + self.captor_project + \
- '/Cloud_number_' + self.captor_project + '.TIF'
- stats_L8.complete_raster(stats_L8.create_raster(out_cloud_folder, stats_cloud, \
- stats_L8.raster_data(self.check_download.list_img[0][4])[1]), \
- stats_cloud)
-
- # Stats ndvi rasters
- for stats_index in range(len(stats_ndvi)):
- out_ndvistats_folder = stats_L8._class_archive._folder + '/' + stats_L8._big_folder + '/' + self.captor_project + \
- '/' + stats_name[stats_index] + '_' + self.captor_project + '.TIF'
- self.out_ndvistats_folder_tab[stats_index] = out_ndvistats_folder
- stats_L8.complete_raster(stats_L8.create_raster(out_ndvistats_folder, stats_ndvi[stats_index], \
- stats_L8.raster_data(self.check_download.list_img[0][4])[1]), \
- stats_ndvi[stats_index])
-
- def i_slope(self):
- """
- Interface function to processing slope raster. From a MNT, and with a command line gdal,
- this function compute slope in degrees :func:`Slope.Slope`.
-
- """
-
- current_path_mnt = Toolbox()
- current_path_mnt.imag = self.path_mnt
- current_path_mnt.vect = self.path_area
- path_mnt = current_path_mnt.clip_raster()
-
- study_slope = Slope(path_mnt)
- study_slope.extract_slope()# Call this function to compute slope raster
- self.path_mnt = study_slope.out_mnt
-
- def i_vhrs(self):#, vs):
- """
- Interface function to processing VHRS images. It create two OTB texture images :func:`Vhrs.Vhrs` : SFS Texture and Haralick Texture
+ self.out_ndvistats_folder_tab = mgr.defaultdict(list)
+
+ p_img_sat = Process(target=self.i_img_sat)
+ p_img_sat.start()
+
+ if self.mp == Constantes.MULTIPROCESSING_DISABLE:
+ p_img_sat.join()
+
+ if vs == 1:
+ p_vhrs = Process(target=self.i_vhrs)
+ p_vhrs.start()
+ p_vhrs.join()
+
+ if self.mp == Constantes.MULTIPROCESSING_ENABLE:
+ p_img_sat.join()
+
+ ###################################################################
+
+ # List of output raster path
+ self.raster_path.append(self.out_ndvistats_folder_tab[0])
+ # List of output raster band
+ self.list_band_outraster.append(1)
+
+ if vs == 1:
+ self.raster_path.append(self.out_ndvistats_folder_tab['sfs'])
+ self.list_band_outraster.append(4)
+ self.raster_path.append(self.out_ndvistats_folder_tab['haralick'])
+ self.list_band_outraster.append(2)
+
+ # To slope, to extract scree
+ if self.path_mnt != '':
+ self.raster_path.append(self.path_mnt)
+ self.list_band_outraster.append(1)
+
+ self.raster_path.append(self.out_ndvistats_folder_tab[2])
+ # example raster path tab :
+ # [path_folder_dpt + '/' + folder_processing + '/' + classif_year + '/Min_2014.TIF',\
+ # os.path.dirname(path_ortho) + '/Clip_buffer_surface_dep_18_IRCOrtho65_2m_sfs.TIF',\
+ # os.path.dirname(path_ortho) + '/Clip_buffer_surface_dep_18_IRCOrtho65_2m_haralick.TIF',\
+ # path_folder_dpt + '/' + folder_processing + '/' + classif_year + '/Max_2014.TIF']
+
+ # List of output raster band
+ self.list_band_outraster.append(1) #[1, 4, 2, 1]
+
+ self.logger.info("End of images processing !")
+
+ def i_rpg(self, path_rpg):
+ """
+ Interface function to extract mono rpg crops.
+
+ :param path_rpg: Input RPG shapefile.
+ :type path_rpg: str
+
+ :returns: str -- variable **Rpg.vector_used**, output no duplicated crops shapefile (path).
+ """
+
+ # Extract mono rpg crops
+ mono_sample = Rpg(path_rpg, self.path_area)
+ # If exists, do not create a mono rpg file
+ if os.path.basename(str(path_rpg))[:5]!='MONO_':
+ mono_sample.mono_rpg()
+ mono_sample.create_new_rpg_files()
+ else:
+ print('MONO RPG file exists already !!!')
+ print('End of RPG processing')
+
+ return mono_sample.vector_used
+
+ def i_sample(self):
+ """
+ Interface function to compute threshold with various sample. It also extract a list of validation layer (shapefile)
+ to compute the precision of the next classification :func:`i_validate`.
+
+ It create samples 2 by 2 with kwargs field names and class :func:`Sample.Sample.create_sample`.
+ Then, it compute zonal statistics by polygons :func:`Vector.Sample.zonal_stats`.
+
+ With zonal statistics computed, a optimal threshold is determined :func:`Seath.Seath.separability_and_threshold` that
+ will print in a text file .lg in the main folder.
+
+ .. warning:: :func:`Seath.Seath.separability_and_threshold` does not always allow to discriminate optimal threshold.
+ Then, this function will be launch at least ten time until it reaches a optimal threshold.
+ """
+
+ # Compute threshold with various sample
+ i_s = 0
+ while i_s < 10:
+ try :
+ self.valid_shp = []
+ sample_rd = {}
+ for sple in range(len(self.sample_name) * 2):
+ kwargs = {}
+ kwargs['fieldname'] = self.fieldname_args[sple]
+ kwargs['class'] = self.class_args[sple]
+ sample_rd[sple] = Sample(self.sample_name[sple/2], self.path_area, self.list_nb_sample[sple/2])
+ sample_rd[sple].create_sample(**kwargs)
+ sample_rd[sple].zonal_stats((self.raster_path[sple/2], self.list_band_outraster[sple/2]))
+
+ # Add the validation shapefile
+ self.valid_shp.append([sample_rd[sple].vector_val, kwargs['fieldname'], kwargs['class']])
+
+ # Search the optimal threshold by class
+ # Open a text file to print stats of Seath method
+ file_J = self.path_folder_dpt + '/log_J.lg'
+ f = open(file_J, "wb")
+ for th_seath in range(len(self.sample_name)):
+ self.decis[th_seath] = Seath()
+ self.decis[th_seath].value_1 = sample_rd[th_seath*2].stats_dict
+ self.decis[th_seath].value_2 = sample_rd[th_seath*2 + 1].stats_dict
+ self.decis[th_seath].separability_and_threshold()
+
+ # Print the J value in the text file .lg
+ f.write('For ' + str(self.sample_name[th_seath]) + ' :\n')
+ f.write('J = ' + str(self.decis[th_seath].J[0]) +'\n')
+ f.write('The class 1 ' + str(self.decis[th_seath].threshold[0]) +'\n')
+
+ f.close()
+ i_s = 20
+ except:
+ i_s = i_s + 1
+ # Method to stop the processus if there is not found a valid threshold
+ if i_s != 20:
+ print ('Problem in the sample processing !!!')
+ sys.exit(1)
- """
+ def i_sample_rf(self):
+ """
+ This function build a random forest trees like model to create a final classification.
+ All of This using the method described in the :func:`i_validate` function and because
+ of sklearn module.
+ """
+
+ X_rf = []
+ y_rf = []
+ sample_rd = {}
+ # Tricks to add all textural indexes
+ rm_index = 1
+ self.raster_path.remove(self.raster_path[rm_index]) # Remove SFS layer
+ self.raster_path.remove(self.raster_path[rm_index]) # Remove Haralick layer
+ self.list_band_outraster.remove(self.list_band_outraster[rm_index]) # Remove band of the layer
+ self.list_band_outraster.remove(self.list_band_outraster[rm_index]) # Remove band of the layer
+ # Add all layers in the simple index haralick
+ for add_layer in range(8):
+ self.raster_path.insert(add_layer+1, self.out_ndvistats_folder_tab['haralick'])
+ self.list_band_outraster.insert(add_layer+1, add_layer+1)
+ # Add all layers in the SFS index
+ for add_layer in range(6):
+ self.raster_path.insert(add_layer+1, self.out_ndvistats_folder_tab['sfs'])
+ self.list_band_outraster.insert(add_layer+1, add_layer+1)
+
+ # Extract value mean from polygons
+ for sple in range(len(self.sample_name) * 2):
+ kwargs = {}
+ kwargs['fieldname'] = self.fieldname_args[sple]
+ kwargs['class'] = self.class_args[sple]
+ sample_rd[sple] = Sample(self.sample_name[round(sple/2)], self.path_area, self.list_nb_sample[round(sple/2)])
+
+ sample_rd[sple].create_sample(**kwargs)
+
+ # Add the validation shapefile
+ self.valid_shp.append([sample_rd[sple].vector_val, kwargs['fieldname'], kwargs['class']])
- # Create texture image
- # Clip orthography image
- current_path_ortho = Toolbox()
- current_path_ortho.imag = self.path_ortho
- current_path_ortho.vect = self.path_area
- path_ortho = current_path_ortho.clip_raster()
-
- texture_irc = Vhrs(path_ortho, self.mp)
- self.out_ndvistats_folder_tab['sfs'] = texture_irc.out_sfs
- self.out_ndvistats_folder_tab['haralick'] = texture_irc.out_haralick
-
- def i_images_processing(self, vs):
-
- """
- Interface function to launch processing VHRS images :func:`i_vhrs` and satellite images :func:`i_img_sat` in multi-processing.
-
- :param vs: Boolean variable to launch processing because of interface checkbox -> 0 or 1.
-
- - 0 means, not texture processing
- - 1 means, launch texture processing
- :type vs: int
- """
-
- # Multiprocessing
- mgr = BaseManager()
- mgr.register('defaultdict', defaultdict, DictProxy)
- mgr.start()
- self.out_ndvistats_folder_tab = mgr.defaultdict(list)
-
- p_img_sat = Process(target=self.i_img_sat)
- p_img_sat.start()
- if self.mp == 0:
- p_img_sat.join()
-
- if vs == 1:
- p_vhrs = Process(target=self.i_vhrs)#, args=(vs, ))
- p_vhrs.start()
- p_vhrs.join()
-
- if self.mp == 1:
- p_img_sat.join()
-
- # List of output raster path
- self.raster_path.append(self.out_ndvistats_folder_tab[0])
- # List of output raster band
- self.list_band_outraster.append(1)
-
- if vs == 1:
- self.raster_path.append(self.out_ndvistats_folder_tab['sfs'])
- self.list_band_outraster.append(4)
- self.raster_path.append(self.out_ndvistats_folder_tab['haralick'])
- self.list_band_outraster.append(2)
-
- # To slope, to extract scree
- if self.path_mnt != '':
- self.raster_path.append(self.path_mnt)
- self.list_band_outraster.append(1)
-
- self.raster_path.append(self.out_ndvistats_folder_tab[2])
- # example raster path tab :
- # [path_folder_dpt + '/' + folder_processing + '/' + classif_year + '/Min_2014.TIF',\
- # os.path.dirname(path_ortho) + '/Clip_buffer_surface_dep_18_IRCOrtho65_2m_sfs.TIF',\
- # os.path.dirname(path_ortho) + '/Clip_buffer_surface_dep_18_IRCOrtho65_2m_haralick.TIF',\
- # path_folder_dpt + '/' + folder_processing + '/' + classif_year + '/Max_2014.TIF']
-
- # List of output raster band
- self.list_band_outraster.append(1) #[1, 4, 2, 1]
-
- print("End of images processing !")
-
- def i_rpg(self, path_rpg):
- """
- Interface function to extract mono rpg crops.
-
- :param path_rpg: Input RPG shapefile.
- :type path_rpg: str
-
- :returns: str -- variable **Rpg.vector_used**, output no duplicated crops shapefile (path).
- """
-
- # Extract mono rpg crops
- mono_sample = Rpg(path_rpg, self.path_area)
- # If exists, do not create a mono rpg file
- if os.path.basename(str(path_rpg))[:5]!='MONO_':
- mono_sample.mono_rpg()
- mono_sample.create_new_rpg_files()
- else:
- print('MONO RPG file exists already !!!')
- print('End of RPG processing')
-
- return mono_sample.vector_used
-
- def i_sample(self):
- """
- Interface function to compute threshold with various sample. It also extract a list of validation layer (shapefile)
- to compute the precision of the next classification :func:`i_validate`.
-
- It create samples 2 by 2 with kwargs field names and class :func:`Sample.Sample.create_sample`.
- Then, it compute zonal statistics by polygons :func:`Vector.Sample.zonal_stats`.
-
- With zonal statistics computed, a optimal threshold is determined :func:`Seath.Seath.separability_and_threshold` that
- will print in a text file .lg in the main folder.
-
- .. warning:: :func:`Seath.Seath.separability_and_threshold` does not always allow to discriminate optimal threshold.
- Then, this function will be launch at least ten time until it reaches a optimal threshold.
- """
-
- # Compute threshold with various sample
- i_s = 0
- while i_s < 10:
- try :
- self.valid_shp = []
- sample_rd = {}
- for sple in range(len(self.sample_name) * 2):
- kwargs = {}
- kwargs['fieldname'] = self.fieldname_args[sple]
- kwargs['class'] = self.class_args[sple]
- sample_rd[sple] = Sample(self.sample_name[sple/2], self.path_area, self.list_nb_sample[sple/2])
- sample_rd[sple].create_sample(**kwargs)
- sample_rd[sple].zonal_stats((self.raster_path[sple/2], self.list_band_outraster[sple/2]))
-
- # Add the validation shapefile
- self.valid_shp.append([sample_rd[sple].vector_val, kwargs['fieldname'], kwargs['class']])
-
- # Search the optimal threshold by class
- # Open a text file to print stats of Seath method
- file_J = self.path_folder_dpt + '/log_J.lg'
- f = open(file_J, "wb")
- for th_seath in range(len(self.sample_name)):
- self.decis[th_seath] = Seath()
- self.decis[th_seath].value_1 = sample_rd[th_seath*2].stats_dict
- self.decis[th_seath].value_2 = sample_rd[th_seath*2 + 1].stats_dict
- self.decis[th_seath].separability_and_threshold()
-
- # Print the J value in the text file .lg
- f.write('For ' + str(self.sample_name[th_seath]) + ' :\n')
- f.write('J = ' + str(self.decis[th_seath].J[0]) +'\n')
- f.write('The class 1 ' + str(self.decis[th_seath].threshold[0]) +'\n')
-
- f.close()
- i_s = 20
- except:
- i_s = i_s + 1
- # Method to stop the processus if there is not found a valid threshold
- if i_s != 20:
- print ('Problem in the sample processing !!!')
- sys.exit(1)
-
- def i_sample_rf(self):
- """
- This function build a random forest trees like model to create a final classification.
- All of This using the method described in the :func:`i_validate` function and because
- of sklearn module.
- """
-
- X_rf = []
- y_rf = []
- sample_rd = {}
- # Tricks to add all textural indexes
- rm_index = 1
- self.raster_path.remove(self.raster_path[rm_index]) # Remove SFS layer
- self.raster_path.remove(self.raster_path[rm_index]) # Remove Haralick layer
- self.list_band_outraster.remove(self.list_band_outraster[rm_index]) # Remove band of the layer
- self.list_band_outraster.remove(self.list_band_outraster[rm_index]) # Remove band of the layer
- # Add all layers in the simple index haralick
- for add_layer in range(8):
- self.raster_path.insert(add_layer+1, self.out_ndvistats_folder_tab['haralick'])
- self.list_band_outraster.insert(add_layer+1, add_layer+1)
- # Add all layers in the SFS index
- for add_layer in range(6):
- self.raster_path.insert(add_layer+1, self.out_ndvistats_folder_tab['sfs'])
- self.list_band_outraster.insert(add_layer+1, add_layer+1)
-
- # Extract value mean from polygons
- for sple in range(len(self.sample_name) * 2):
- kwargs = {}
- kwargs['fieldname'] = self.fieldname_args[sple]
- kwargs['class'] = self.class_args[sple]
- sample_rd[sple] = Sample(self.sample_name[sple/2], self.path_area, self.list_nb_sample[sple/2])
- sample_rd[sple].create_sample(**kwargs)
-
- # Add the validation shapefile
- self.valid_shp.append([sample_rd[sple].vector_val, kwargs['fieldname'], kwargs['class']])
+ for lbo in range(len(self.raster_path)):
+ kwargs['rank'] = lbo
+ kwargs['nb_img'] = len(self.raster_path)
+ sample_rd[sple].zonal_stats(self.raster_path[lbo], self.list_band_outraster[lbo], **kwargs)
+
+ # To convert the dictionnary in a list
+ # for key, value in sample_rd[sple].stats_dict.iteritems():
+ for key, value in sample_rd[sple].stats_dict.items():
+# X_rf.append([-10000 if (math.isnan(x) or math.isinf(x)) else x for x in value])
+ # To set the grassland class of the RPG and PIAO (same class)
+ if sple == 2:
+# y_rf.append(1)
+ pass
+ elif sple == 3:
+# y_rf.append(4)
+ pass
+ else:
+ y_rf.append(sple)
+ X_rf.append([-10000 if (x is None or math.isnan(x) or math.isinf(x)) else x for x in value])
+
+ #Â Build a forest of trees from the samples
+ self.rf = self.rf.fit(X_rf, y_rf)
+
+ #Â Print in a file feature important
+ importance = self.rf.feature_importances_
+ importance = [(importance[x],x+1) for x in range(len(importance))]
+ importance.sort()
+
+ file_feat_import = os.path.dirname(str(self.raster_path[0])) + '/Feature_important_RF.ft'
+ if os.path.exists(file_feat_import):
+ os.remove(file_feat_import)
+
+ with open(file_feat_import, "w") as f_out :
+ f_out.write(str(importance))
+
+ #Â Print in a file decision tree
+ file_decisiontree = os.path.dirname(str(self.raster_path[0])) + '/Decision_tree.dot'
+
+ if os.path.exists(file_decisiontree):
+ os.remove(file_decisiontree)
+
+ tree_in_forest = self.rf.estimators_[499]
+ with open(file_decisiontree, 'w') as my_file:
+ my_file = tree.export_graphviz(tree_in_forest, out_file = my_file)
- for lbo in range(len(self.raster_path)):
- kwargs['rank'] = lbo
- kwargs['nb_img'] = len(self.raster_path)
- sample_rd[sple].zonal_stats((self.raster_path[lbo], self.list_band_outraster[lbo]), **kwargs)
-
- # To convert the dictionnary in a list
- for key, value in sample_rd[sple].stats_dict.iteritems():
-# X_rf.append([-10000 if (math.isnan(x) or math.isinf(x)) else x for x in value])
- # To set the grassland class of the RPG and PIAO (same class)
- if sple == 2:
-# y_rf.append(1)
- pass
- elif sple == 3:
-# y_rf.append(4)
- pass
- else:
- y_rf.append(sple)
- X_rf.append([-10000 if (math.isnan(x) or math.isinf(x)) else x for x in value])
-
- #Â Build a forest of trees from the samples
- self.rf = self.rf.fit(X_rf, y_rf)
-
- #Â Print in a file feature important
- importance = self.rf.feature_importances_
- importance = [(importance[x],x+1) for x in range(len(importance))]
- importance.sort()
-
- file_feat_import = os.path.dirname(str(self.raster_path[0])) + '/Feature_important_RF.ft'
- if os.path.exists(file_feat_import):
- os.remove(file_feat_import)
- f_out = open(file_feat_import, "wb")
- f_out.write(str(importance))
- # Close the output file
- f_out.close()
-
- #Â Print in a file decision tree
- file_decisiontree = os.path.dirname(str(self.raster_path[0])) + '/Decision_tree.dot'
- if os.path.exists(file_decisiontree):
- os.remove(file_decisiontree)
-
- tree_in_forest = self.rf.estimators_[499]
- with open(file_decisiontree, 'w') as my_file:
- my_file = tree.export_graphviz(tree_in_forest, out_file = my_file)
+ def i_classifier_rf(self):
+ """
+ Interface function to launch random forest classification with a input segmentation :func:`Segmentation.Segmentation`.
+
+ This function use the sklearn module to build the best of decision tree to extract classes.
+ The optimal threshold are stored by class **rf** variable in :func:`Processing.i_sample_rf`. Then it computes zonal statistics by polygons
+ for every images in multi-processing (if **mp** = 1).
+ """
+
+ # Multiprocessing
+ mgr = BaseManager()
+ mgr.register('defaultdict', defaultdict, DictProxy)
+ mgr.start()
+ multi_process_var = [] # Multi processing variable
+
+ # Extract final cartography
+ out_carto = Segmentation(self.path_segm, self.path_area)
+ out_carto.output_file = self.output_name_moba
+ out_carto.out_class_name = self.in_class_name
+# out_carto.out_threshold = []
- def i_classifier_rf(self):
- """
- Interface function to launch random forest classification with a input segmentation :func:`Segmentation.Segmentation`.
-
- This function use the sklearn module to build the best of decision tree to extract classes.
- The optimal threshold are stored by class **rf** variable in :func:`Processing.i_sample_rf`. Then it computes zonal statistics by polygons
- for every images in multi-processing (if **mp** = 1).
- """
-
- # Multiprocessing
- mgr = BaseManager()
- mgr.register('defaultdict', defaultdict, DictProxy)
- mgr.start()
- multi_process_var = [] # Multi processing variable
-
- # Extract final cartography
- out_carto = Segmentation(self.path_segm, self.path_area)
- out_carto.output_file = self.output_name_moba
- out_carto.out_class_name = self.in_class_name
-# out_carto.out_threshold = []
- for ind_th in range(len(self.raster_path)):
- multi_process_var.append([self.raster_path[ind_th], self.list_band_outraster[ind_th]])
+ for ind_th in range(len(self.raster_path)):
+ multi_process_var.append([self.raster_path[ind_th], self.list_band_outraster[ind_th]])
- # Compute zonal stats with multi processing
- exist_stats = 1 # By default, the stats file exists already
- file_stats = os.path.dirname(str(self.raster_path[0])) + '/Stats_raster_spectral_texture.stats' # Stats backup file
- if not os.path.exists(file_stats):
- exist_stats = 0 # The sats file doesn't exist
- # Open a stats backup to avoid computing again (Gain of time)
- f_out = open(file_stats, "wb")
-
- p = []
- kwargs = {}
- X_out_rf = [] # Variable list to compute decision tree with random forest method
- if exist_stats == 0:
- out_carto.stats_dict = mgr.defaultdict(list)
- for i in range(len(multi_process_var)):
- kwargs['rank'] = i
- kwargs['nb_img'] = len(multi_process_var)
- p.append(Process(target=out_carto.zonal_stats, args=(multi_process_var[i], ), kwargs=kwargs))
- p[i].start()
-
- if self.mp == 0:
- p[i].join()
-
- if self.mp == 1:
- for i in range(len(multi_process_var)):
- p[i].join()
-
- for key, value_seg in out_carto.stats_dict.items():
- true_value = [-10000 if (math.isnan(x) or math.isinf(x)) else x for x in value_seg]
- X_out_rf.append(true_value)
-
- # Print rasters stats value in the text file .lg
- f_out.write(str(true_value) + '\n')
-
- # Close the output file
- f_out.close()
-
- else:
- # If the stats file exists already, open this file and append in the stats_dict variable
- out_carto.stats_dict = defaultdict(list)
- with open(file_stats, "r") as f_in:
- index_in_stats=-1
- for x_in in f_in.readlines():
- index_in_stats = index_in_stats + 1
- out_carto.stats_dict[index_in_stats] = eval(x_in.strip('\n'))
- X_out_rf.append(eval(x_in.strip('\n')))
-
- predicted_rf = self.rf.predict(X_out_rf)
-
- # For the higher than level 1
- if len(self.sample_name) > 2:
- # Compute the biomass and density distribution
- # Use 'out_carto.out_threshold' to konw predicted in the segmentation class
- out_carto.out_threshold = predicted_rf
- # In the compute_biomass_density function, this variable used normally to define
- #Â threshold of the classification with SEATH method is initialized
- out_carto.compute_biomass_density('RF')
-
- out_carto.class_tab_final = defaultdict(list)
- for i_polyg in range(len(predicted_rf)):
- i_final = 0
- class_final = []
- #Â Initialize the predicted output format
- # For example : predicted => 4, formatted => [1,3,4]
- while i_final < len(self.tree_direction):
- if self.tree_direction[i_final][len(self.tree_direction[i_final])-1] == predicted_rf[i_polyg]:
- class_final = self.tree_direction[i_final]
- i_final = len(self.tree_direction)
- i_final = i_final + 1
-
- if class_final == []:
- class_final = [1, 2]
- # Set the class name because of predicted output formatted
- out_carto.class_tab_final[i_polyg] = [self.in_class_name[f] for f in class_final] + \
- [predicted_rf[i_polyg]] + [predicted_rf[i_polyg]]
-
- #Â For the output line with one level, add a phantom level
- #Â TODO : Replace constant by a variable in the condition 'while'
- while len(out_carto.class_tab_final[i_polyg]) < 5:
- out_carto.class_tab_final[i_polyg].insert(len(out_carto.class_tab_final[i_polyg])-2,'')
-
- # If there is more one fieldnames line edit fulled in classification tab
- if len(self.sample_name) > 2:
- # Compute biomass and density scale
- out_carto.append_scale(self.in_class_name[2], 'self.stats_dict[ind_stats][3]/self.max_bio')
- out_carto.append_scale(self.in_class_name[3], 'self.stats_dict[ind_stats][2]/self.max_wood_idm')
-
- #Â Rasterize RPG shapefile to complete the final shapefile
- opt = {}
- opt['Remove'] = 1
- rpg_tif = Vector(self.sample_name[0], self.path_area, **opt)
-# if not os.path.exists(str(rpg_tif.vector_used[:-3]+'TIF')):
- kwargs['choice_nb_b'] = 1
- out_carto.stats_rpg_tif = out_carto.zonal_stats_pp(rpg_tif.layer_rasterization(self.path_ortho, 'CODE_GROUP', **kwargs))
+ # Compute zonal stats with multi processing
+ exist_stats = 1 # By default, the stats file exists already
+ file_stats = os.path.dirname(str(self.raster_path[0])) + '/Stats_raster_spectral_texture.stats' # Stats backup file
+ if not os.path.exists(file_stats):
+ exist_stats = 0 # The sats file doesn't exist
+ # Open a stats backup to avoid computing again (Gain of time)
+ f_out = open(file_stats, "w")
+
+ p = []
+ kwargs = {}
+ X_out_rf = [] # Variable list to compute decision tree with random forest method
+ if exist_stats == 0:
+ out_carto.stats_dict = mgr.defaultdict(list)
+
+ for i in range(len(multi_process_var)):
+ kwargs['rank'] = i
+ kwargs['nb_img'] = len(multi_process_var)
+ p.append(Process(target=out_carto.zonal_stats, args=(*multi_process_var[i], ), kwargs=kwargs))
+ p[i].start()
+
+ if self.mp == Constantes.MULTIPROCESSING_DISABLE:
+ p[i].join()
+
+ if self.mp == Constantes.MULTIPROCESSING_ENABLE:
+ for i in range(len(multi_process_var)):
+ p[i].join()
+
+ for key, value_seg in out_carto.stats_dict.items():
+ true_value = [-10000 if (x == None or math.isnan(x) or math.isinf(x)) else x for x in value_seg]
+ X_out_rf.append(true_value)
+
+ # Print rasters stats value in the text file .lg
+ f_out.write(str(true_value) + '\n')
+
+ # Close the output file
+ f_out.close()
+
+ else:
+ # If the stats file exists already, open this file and append in the stats_dict variable
+ out_carto.stats_dict = defaultdict(list)
+ with open(file_stats, "r") as f_in:
+ index_in_stats=-1
+ for x_in in f_in.readlines():
+ index_in_stats = index_in_stats + 1
+ out_carto.stats_dict[index_in_stats] = eval(x_in.strip('\n'))
+ X_out_rf.append(eval(x_in.strip('\n')))
+
+ predicted_rf = self.rf.predict(X_out_rf)
+
+ # For the higher than level 1
+ if len(self.sample_name) > 2:
+ # Compute the biomass and density distribution
+ # Use 'out_carto.out_threshold' to konw predicted in the segmentation class
+ out_carto.out_threshold = predicted_rf
+ # In the compute_biomass_density function, this variable used normally to define
+ #Â threshold of the classification with SEATH method is initialized
+ out_carto.compute_biomass_density('RF')
+
+ out_carto.class_tab_final = defaultdict(list)
+ for i_polyg in range(len(predicted_rf)):
+ i_final = 0
+ class_final = []
+ #Â Initialize the predicted output format
+ # For example : predicted => 4, formatted => [1,3,4]
+ while i_final < len(self.tree_direction):
+ if self.tree_direction[i_final][len(self.tree_direction[i_final])-1] == predicted_rf[i_polyg]:
+ class_final = self.tree_direction[i_final]
+ i_final = len(self.tree_direction)
+ i_final = i_final + 1
+
+ if class_final == []:
+ class_final = [1, 2]
+ # Set the class name because of predicted output formatted
+ out_carto.class_tab_final[i_polyg] = [self.in_class_name[f] for f in class_final] + \
+ [predicted_rf[i_polyg]] + [predicted_rf[i_polyg]]
+
+ #Â For the output line with one level, add a phantom level
+ #Â TODO : Replace constant by a variable in the condition 'while'
+ while len(out_carto.class_tab_final[i_polyg]) < 5:
+ out_carto.class_tab_final[i_polyg].insert(len(out_carto.class_tab_final[i_polyg])-2,'')
+
+ # If there is more one fieldnames line edit fulled in classification tab
+ if len(self.sample_name) > 2:
+ # Compute biomass and density scale
+ out_carto.append_scale(self.in_class_name[2], 'self.stats_dict[ind_stats][3]/self.max_bio')
+ out_carto.append_scale(self.in_class_name[3], 'self.stats_dict[ind_stats][2]/self.max_wood_idm')
+
+ #Â Rasterize RPG shapefile to complete the final shapefile
+ opt = {}
+ opt['Remove'] = 1
+ rpg_tif = Vector(self.sample_name[0], self.path_area, **opt)
+# if not os.path.exists(str(rpg_tif.vector_used[:-3]+'TIF')):
+ kwargs['choice_nb_b'] = 1
+ out_carto.stats_rpg_tif = out_carto.zonal_stats_pp(rpg_tif.layer_rasterization(self.path_ortho, 'CODE_GROUP', **kwargs))
- # Final cartography
- out_carto.create_cartography(self.out_fieldname_carto, self.out_fieldtype_carto)
-
- def i_classifier_s(self):
- """
- Interface function to launch decision tree classification with a input segmentation :func:`Segmentation.Segmentation`.
-
- This function store optimal threshold by class **Segmentation.out_threshold**. Then it computes zonal statistics by polygons
- for every images in multi-processing (if **mp** = 1).
- """
-
- # Multiprocessing
- mgr = BaseManager()
- mgr.register('defaultdict', defaultdict, DictProxy)
- mgr.start()
- multi_process_var = [] # Multi processing variable
-
- # Extract final cartography
- out_carto = Segmentation(self.path_segm, self.path_area)
- out_carto.output_file = self.output_name_moba
- out_carto.out_class_name = self.in_class_name
- out_carto.out_threshold = []
- for ind_th in range(len(self.sample_name)):
- out_carto.out_threshold.append(self.decis[ind_th].threshold[0])
- if '>' in self.decis[ind_th].threshold[0]:
- out_carto.out_threshold.append(self.decis[ind_th].threshold[0].replace('>', '<='))
- elif '<' in self.decis[ind_th].threshold[0]:
- out_carto.out_threshold.append(self.decis[ind_th].threshold[0].replace('<', '>='))
- # out_carto.zonal_stats((raster_path[ind_th], list_band_outraster[ind_th]))
- multi_process_var.append([self.raster_path[ind_th], self.list_band_outraster[ind_th]])
-
- # Compute zonal stats on slope raster
- multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
- out_carto.out_threshold.append('<'+str(self.slope_degree)) # To agriculture
- out_carto.out_threshold.append('>='+str(self.slope_degree)) # To scree
- if self.path_mnt != '':
- # Add class indexes
- self.tree_direction[0].append(6)
- self.tree_direction[0].append(7)
-
- # Compute zonal stats on Max NDVI raster
- try:
- # out_carto.zonal_stats((raster_path[ind_th+1], list_band_outraster[ind_th+1]))
- multi_process_var.append([self.raster_path[ind_th+2], self.list_band_outraster[ind_th+2]])
- # Compute stats twice, because there is 3 classes and not 2
- # out_carto.zonal_stats((raster_path[ind_th+1], list_band_outraster[ind_th+1]))
- multi_process_var.append([self.raster_path[ind_th+2], self.list_band_outraster[ind_th+2]])
- except:
- print('Not MNT on the 3rd step')
- multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
- multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
+ # Final cartography
+ out_carto.create_cartography(self.out_fieldname_carto, self.out_fieldtype_carto)
+
+ def i_classifier_s(self):
+ """
+ Interface function to launch decision tree classification with a input segmentation :func:`Segmentation.Segmentation`.
+
+ This function store optimal threshold by class **Segmentation.out_threshold**. Then it computes zonal statistics by polygons
+ for every images in multi-processing (if **mp** = 1).
+ """
+
+ # Multiprocessing
+ mgr = BaseManager()
+ mgr.register('defaultdict', defaultdict, DictProxy)
+ mgr.start()
+ multi_process_var = [] # Multi processing variable
+
+ # Extract final cartography
+ out_carto = Segmentation(self.path_segm, self.path_area)
+ out_carto.output_file = self.output_name_moba
+ out_carto.out_class_name = self.in_class_name
+ out_carto.out_threshold = []
+ for ind_th in range(len(self.sample_name)):
+ out_carto.out_threshold.append(self.decis[ind_th].threshold[0])
+ if '>' in self.decis[ind_th].threshold[0]:
+ out_carto.out_threshold.append(self.decis[ind_th].threshold[0].replace('>', '<='))
+ elif '<' in self.decis[ind_th].threshold[0]:
+ out_carto.out_threshold.append(self.decis[ind_th].threshold[0].replace('<', '>='))
+ # out_carto.zonal_stats((raster_path[ind_th], list_band_outraster[ind_th]))
+ multi_process_var.append([self.raster_path[ind_th], self.list_band_outraster[ind_th]])
+
+ # Compute zonal stats on slope raster
+ multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
+ out_carto.out_threshold.append('<'+str(self.slope_degree)) # To agriculture
+ out_carto.out_threshold.append('>='+str(self.slope_degree)) # To scree
+ if self.path_mnt != '':
+ # Add class indexes
+ self.tree_direction[0].append(6)
+ self.tree_direction[0].append(7)
+
+ # Compute zonal stats on Max NDVI raster
+ try:
+ # out_carto.zonal_stats((raster_path[ind_th+1], list_band_outraster[ind_th+1]))
+ multi_process_var.append([self.raster_path[ind_th+2], self.list_band_outraster[ind_th+2]])
+ # Compute stats twice, because there is 3 classes and not 2
+ # out_carto.zonal_stats((raster_path[ind_th+1], list_band_outraster[ind_th+1]))
+ multi_process_var.append([self.raster_path[ind_th+2], self.list_band_outraster[ind_th+2]])
+ except:
+ print('Not MNT on the 3rd step')
+ multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
+ multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
- # Compute zonal stats with multi processing
- exist_stats = 1 # By default, the stats file exists already
- file_stats = os.path.dirname(str(self.raster_path[0])) + '/Stats_raster_spectral_texture.stats' # Stats backup file
- if not os.path.exists(file_stats):
- exist_stats = 0 # The sats file doesn't exist
- # Open a stats backup to avoid computing again (Gain of time)
- f_out = open(file_stats, "wb")
-
- p = []
- kwargs = {}
- X_out_rf = [] # Variable list to compute decision tree with random forest method
- if exist_stats == 0:
- out_carto.stats_dict = mgr.defaultdict(list)
- for i in range(len(multi_process_var)):
- kwargs['rank'] = i
- kwargs['nb_img'] = len(multi_process_var)
- p.append(Process(target=out_carto.zonal_stats, args=(multi_process_var[i], ), kwargs=kwargs))
- p[i].start()
-
- if self.mp == 0:
- p[i].join()
-
- if self.mp == 1:
- for i in range(len(multi_process_var)):
- p[i].join()
-
- for key, value_seg in out_carto.stats_dict.items():
-
- true_value = [-10000 if (math.isnan(x) or math.isinf(x)) else x for x in value_seg]
- # Print rasters stats value in the text file .lg
- f_out.write(str(true_value) + '\n')
-
- # Close the output file
- f_out.close()
-
- else:
- # If the stats file exists already, open this file and append in the stats_dict variable
- out_carto.stats_dict = defaultdict(list)
- with open(file_stats, "r") as f_in:
- index_in_stats=-1
- for x_in in f_in.readlines():
- index_in_stats = index_in_stats + 1
- out_carto.stats_dict[index_in_stats] = eval(x_in.strip('\n'))
- X_out_rf.append(eval(x_in.strip('\n')))
-
- # For the higher than level 1
- if len(self.sample_name) > 2:
- # Compute the biomass and density distribution
- out_carto.compute_biomass_density()
-
- out_carto.class_tab_final = defaultdict(list)
- self.i_tree_direction()
- out_carto.decision_tree(self.tree_direction)
-
- # If there is more one fieldnames line edit fulled in classification tab
- if len(self.sample_name) > 2:
- # Compute biomass and density scale
- out_carto.append_scale(self.in_class_name[2], 'self.stats_dict[ind_stats][3]/self.max_bio')
- out_carto.append_scale(self.in_class_name[3], 'self.stats_dict[ind_stats][2]/self.max_wood_idm')
-
- #Â Rasterize RPG shapefile to complete the final shapefile
- opt = {}
- opt['Remove'] = 1
- rpg_tif = Vector(self.sample_name[0], self.path_area, **opt)
- rpg_tif.layer_rasterization(self.path_ortho, 'CODE_GROUP')
-
- # Final cartography
- out_carto.mono_rpg_tif = self.sample_name[0][:-4] + '.TIF'
- out_carto.create_cartography(self.out_fieldname_carto, self.out_fieldtype_carto)
-
- def i_validate(self):
- """
- Interface to validate a classification. It going to rasterize the validation shapefile and the
- classification shapefile with :func:`layer_rasterization`. Next, to compare pixel by pixel, the classification
- quality to built a confusion matrix in a csv file.
-
- """
- # Variable to convert the input classname to an individual interger
- # Only for the validate sample
- class_validate = 0
- complete_validate_shp = os.path.dirname(str(self.valid_shp[0][0])) + '/validate.shp'
+ # Compute zonal stats with multi processing
+ exist_stats = 1 # By default, the stats file exists already
+ file_stats = os.path.dirname(str(self.raster_path[0])) + '/Stats_raster_spectral_texture.stats' # Stats backup file
+ if not os.path.exists(file_stats):
+ exist_stats = 0 # The sats file doesn't exist
+ # Open a stats backup to avoid computing again (Gain of time)
+ f_out = open(file_stats, "wb")
+
+ p = []
+ kwargs = {}
+ X_out_rf = [] # Variable list to compute decision tree with random forest method
+ if exist_stats == 0:
+ out_carto.stats_dict = mgr.defaultdict(list)
+ for i in range(len(multi_process_var)):
+ kwargs['rank'] = i
+ kwargs['nb_img'] = len(multi_process_var)
+ p.append(Process(target=out_carto.zonal_stats, args=(multi_process_var[i], ), kwargs=kwargs))
+ p[i].start()
+
+ if self.mp == Constantes.MULTIPROCESSING_DISABLE:
+ p[i].join()
+
+ if self.mp == Constantes.MULTIPROCESSING_ENABLE:
+ for i in range(len(multi_process_var)):
+ p[i].join()
+
+ for key, value_seg in out_carto.stats_dict.items():
+
+ true_value = [-10000 if (math.isnan(x) or math.isinf(x)) else x for x in value_seg]
+ # Print rasters stats value in the text file .lg
+ f_out.write(str(true_value) + '\n')
+
+ # Close the output file
+ f_out.close()
+
+ else:
+ # If the stats file exists already, open this file and append in the stats_dict variable
+ out_carto.stats_dict = defaultdict(list)
+ with open(file_stats, "r") as f_in:
+ index_in_stats=-1
+ for x_in in f_in.readlines():
+ index_in_stats = index_in_stats + 1
+ out_carto.stats_dict[index_in_stats] = eval(x_in.strip('\n'))
+ X_out_rf.append(eval(x_in.strip('\n')))
+
+ # For the higher than level 1
+ if len(self.sample_name) > 2:
+ # Compute the biomass and density distribution
+ out_carto.compute_biomass_density()
+
+ out_carto.class_tab_final = defaultdict(list)
+ self.i_tree_direction()
+ out_carto.decision_tree(self.tree_direction)
+
+ # If there is more one fieldnames line edit fulled in classification tab
+ if len(self.sample_name) > 2:
+ # Compute biomass and density scale
+ out_carto.append_scale(self.in_class_name[2], 'self.stats_dict[ind_stats][3]/self.max_bio')
+ out_carto.append_scale(self.in_class_name[3], 'self.stats_dict[ind_stats][2]/self.max_wood_idm')
+
+ #Â Rasterize RPG shapefile to complete the final shapefile
+ opt = {}
+ opt['Remove'] = 1
+ rpg_tif = Vector(self.sample_name[0], self.path_area, **opt)
+ rpg_tif.layer_rasterization(self.path_ortho, 'CODE_GROUP')
+
+ # Final cartography
+ out_carto.mono_rpg_tif = self.sample_name[0][:-4] + '.TIF'
+ out_carto.create_cartography(self.out_fieldname_carto, self.out_fieldtype_carto)
+
+ def i_validate(self):
+ """
+ Interface to validate a classification. It going to rasterize the validation shapefile and the
+ classification shapefile with :func:`layer_rasterization`. Next, to compare pixel by pixel, the classification
+ quality to built a confusion matrix in a csv file.
+
+ """
+ # Variable to convert the input classname to an individual interger
+ # Only for the validate sample
+ class_validate = 0
+ complete_validate_shp = os.path.dirname(str(self.valid_shp[0][0])) + '/validate.shp'
- #Â TODO: Set this method in the Precision_moba class
+ #Â TODO: Set this method in the Precision_moba class
-
- # Processing to rasterize the validate shapefile. 1) Merge sahpefiles 2) Rasterization
- for val in self.valid_shp:
- if class_validate != 2:
- # Grassland to 1
- if (class_validate !=3 and len(self.out_fieldname_carto) != 4+2) or len(self.out_fieldname_carto) == 4+2:
- # To the level 3 with woodeen to 4 and 5
- #
- # Self.valid_shp is a list of list. In this variable there is :
- # [Shapefile path, fieldname classes, classnames]
- opt = {}
- opt['Remove'] = 1 # To overwrite
-
- # Create a raster to valide the classification
- # First time, create a new shapefile with a new field integer
- sample_val = Sample(val[0], self.path_area, 1, **opt)
- opt['add_fieldname'] = 1
- opt['fieldname'] = 'CLASS_CODE'
- opt['class'] = str(class_validate) # Add integer classes
- # Set the new shapefile
- val[0] = val[0][:-4] + '_.shp'
- val[1] = opt['fieldname']
- val[2] = opt['class']
- # Complete the new shapefile
- sample_val.fill_sample(val[0], 0, **opt)
- # Second time, merge the validate shapefile
- if class_validate == 0:
- process_tocall_merge = ['ogr2ogr', '-overwrite', complete_validate_shp, val[0]]
- elif class_validate > 0:
- process_tocall_merge = ['ogr2ogr', '-update', '-append', complete_validate_shp, \
- val[0], '-nln', os.path.basename(str(complete_validate_shp[:-4]))]
- subprocess.call(process_tocall_merge)
- # Increrment variable
- class_validate = self.valid_shp.index(val) + 1
-
- # Compute precision of the classification
- valid = Precision_moba(self.path_area, self.path_folder_dpt)
- valid.complete_validation_shp = complete_validate_shp
- valid.ex_raster = self.raster_path[0]
-
- # TODO: Call the RasterSat_by_Date class here instead of the Precision_moba class
-
- valid.preprocess_to_raster_precision(self.output_name_moba, 'FBPHY_CODE') # To the classification's data
- valid.preprocess_to_raster_precision(complete_validate_shp, val[1]) # To the validation's data
-
- # Compute precision on the output classification
- valid.confus_matrix(valid.complete_img[0].raster_data(valid.img_pr[0])[0], \
- valid.complete_img[1].raster_data(valid.img_pr[1])[0])
+
+ # Processing to rasterize the validate shapefile. 1) Merge sahpefiles 2) Rasterization
+ for val in self.valid_shp:
+ if class_validate != 2:
+ # Grassland to 1
+ if (class_validate !=3 and len(self.out_fieldname_carto) != 4+2) or len(self.out_fieldname_carto) == 4+2:
+ # To the level 3 with woodeen to 4 and 5
+ #
+ # Self.valid_shp is a list of list. In this variable there is :
+ # [Shapefile path, fieldname classes, classnames]
+ opt = {}
+ opt['Remove'] = 1 # To overwrite
+
+ # Create a raster to valide the classification
+ # First time, create a new shapefile with a new field integer
+ sample_val = Sample(val[0], self.path_area, 1, **opt)
+ opt['add_fieldname'] = 1
+ opt['fieldname'] = 'CLASS_CODE'
+ opt['class'] = str(class_validate) # Add integer classes
+ # Set the new shapefile
+ val[0] = val[0][:-4] + '_.shp'
+ val[1] = opt['fieldname']
+ val[2] = opt['class']
+ # Complete the new shapefile
+ sample_val.fill_sample(val[0], 0, **opt)
+ # Second time, merge the validate shapefile
+ if class_validate == 0:
+ process_tocall_merge = ['ogr2ogr', '-overwrite', complete_validate_shp, val[0]]
+ elif class_validate > 0:
+ process_tocall_merge = ['ogr2ogr', '-update', '-append', complete_validate_shp, \
+ val[0], '-nln', os.path.basename(str(complete_validate_shp[:-4]))]
+ subprocess.call(process_tocall_merge)
+ # Increrment variable
+ class_validate = self.valid_shp.index(val) + 1
+
+ # Compute precision of the classification
+ valid = Precision_moba(self.path_area, self.path_folder_dpt)
+ valid.complete_validation_shp = complete_validate_shp
+ valid.ex_raster = self.raster_path[0]
+
+ # TODO: Call the RasterSat_by_Date class here instead of the Precision_moba class
+
+ valid.preprocess_to_raster_precision(self.output_name_moba, 'FBPHY_CODE') # To the classification's data
+ valid.preprocess_to_raster_precision(complete_validate_shp, val[1]) # To the validation's data
+
+ # Compute precision on the output classification
+ valid.confus_matrix(valid.complete_img[0].raster_data(valid.img_pr[0])[0], valid.complete_img[1].raster_data(valid.img_pr[1])[0])
diff --git a/RasterSat_by_date.py b/RasterSat_by_date.py
index ef9489f417b9b7e4c556635abba9aaed4f147cbb..0f44f9670154e6c30dee28bc602eef592c70c06b 100644
--- a/RasterSat_by_date.py
+++ b/RasterSat_by_date.py
@@ -20,389 +20,389 @@
import os, sys
import math, subprocess
try :
- import gdal
+ import gdal
except :
- from osgeo import gdal
+ from osgeo import gdal
import numpy as np
class RasterSat_by_date():
- """
- Satellite image processing's class. This class include several processes to group images by date, mosaic images by date,
- extract images information, compute ndvi, compute cloud pourcent and create new rasters.
-
- :param class_archive: Archive class name with every information on downloaded images
- :type class_archive: class
- :param big_folder: Image processing folder
- :type big_folder: str
- :param one_date: [year, month, day] ...
- This variable is modified in the function :func:`mosaic_by_date()`.
- To append mosaic image path, mosaic cloud image path, cloud pixel value table, mosaic ndvi image path and ndvi pixel value table.
- :type one_date: list of str
- :param choice_nb_b: A option to choice output image number of band :func:`layer_rasterization` in Vector's class. If this option is 0, it take input band. By default 0.
- :type choice_nb_b: int
-
- """
- def __init__(self, class_archive, big_folder, one_date):
- """Create a new 'Landsat_by_date' instance
-
- """
-
- self._class_archive = class_archive
- self._big_folder = big_folder
- self._one_date = one_date
- # Verify list of list of str
- if one_date == []:
- print ("Enter dates to mosaic images like [[str(year), str(month), str(day)], [str(year), str(month), str(day)], ...]")
- sys.exit(1)
- else:
- self._one_date = one_date
-
- self.out_ds = None
- self.choice_nb_b = 0
-
- def group_by_date(self, d_uni):
- """
- Function to extract images on a single date
-
- :param d_uni: [year, month, day]
- :type d_uni: list of str
-
- :returns: list of str -- variable **group** = [year, month, day, multispectral image path, cloud image path]
- """
-
- # Group of images with the same date
- group = []
-
- # Take images with the same date
- for d_dup in self._class_archive.list_img:
- if d_dup[:3] == d_uni:
- group.append(d_dup)
-
- return group
-
- def vrt_translate_gdal(self, vrt_translate, src_data, dst_data):
- """
- Function to launch gdal tools in command line. With ``gdalbuildvrt`` and ``gdal_translate``.
- This function is used :func:`mosaic_by_date` to mosaic image by date.
-
- :param vrt_translate: ``vrt`` or ``translate``
- :type vrt_translate: str
- :param src_data: Data source. Several data for vrt process and one data (vrt data) for gdal_translate
- :type src_data: list (process ``vrt``) or str (process ``translate``)
- :param dst_data: Output path
- :type dst_data: str
- """
-
- if os.path.exists(dst_data):
- os.remove(dst_data)
-
- # Select process
- if vrt_translate == 'vrt':
- # Verify input data
- if type(src_data) is not np.ndarray and type(src_data) is not list:
- print ('VRT file ! The data source should be composed of several data. A list minimal of 2 dimensions')
- sys.exit(1)
-
- print ('Build VRT file')
- if not os.path.exists(dst_data):
- process_tocall = ['gdalbuildvrt', '-srcnodata', '-10000', dst_data]
-
- # Copy rasters
- for cp_image in src_data:
- process_tocall.append(cp_image)
-
- elif vrt_translate == 'translate':
- # Verify input data
- try :
- src_data = str(src_data)
- except:#Â if type(src_data) is not str:
- print ('Geotiff file ! The data source should be composed of path file. A character string !')
- sys.exit(1)
-
- print ('Build Geotiff file')
- if not os.path.exists(dst_data):
- process_tocall = ['gdal_translate', '-a_nodata', '-10000', src_data, dst_data]
-
- # Launch vrt process
- subprocess.call(process_tocall)
-
- def mosaic_by_date(self):
- """
- Function to merge images of the same date in a image group :func:`group_by_date`.
- """
-
- # Create the processing images folder if not exists
- if not os.path.exists(self._class_archive._folder + '/' + self._big_folder):
- os.mkdir(self._class_archive._folder + '/' + self._big_folder)
-
- # Matrix multi images for a single date
- group = self.group_by_date(self._one_date) # Every images [year, month, day, multispectral image, cloud image]
- group_ = np.transpose(np.array(group)) # Transpose matrix to extract path of images
-
- # Create a folder with images year if it doesn't exist
- index_repertory_img = self._class_archive._captor
- if not os.path.exists(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img):
- os.mkdir(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img)
-
- index_repertory_img = index_repertory_img + '/'
- # Create a folder with images date if it doesn't exist
- for d_ in self._one_date:
- index_repertory_img = index_repertory_img + d_
-
- if not os.path.exists(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img):
- os.mkdir(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img)
-
- #Â Build VRT file with data images required
- vrt_out = self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img + '/' \
- + self._class_archive._captor + index_repertory_img.split("/")[1] + '.VRT' # Multispectral VRT outfile
- if not os.path.exists(vrt_out):
- self.vrt_translate_gdal('vrt', group_[3], vrt_out)
-
- vrtcloud_out = self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img + '/' \
- + self._class_archive._captor + index_repertory_img.split("/")[1] + '_' + group_[4][0][-7:-4] + '.VRT' # Cloud TIF outfile
- if not os.path.exists(vrtcloud_out):
- self.vrt_translate_gdal('vrt', group_[4], vrtcloud_out)
-
- #Â Build Geotiff file with data images required
- gtif_out = vrt_out[:-4] + '.TIF' # Multispectral VRT outfile
- if not os.path.exists(gtif_out):
- self.vrt_translate_gdal('translate', vrt_out, gtif_out)
- self._one_date.append(gtif_out)
-
- gtifcloud_out = vrtcloud_out[:-4] + '.TIF' # Cloud TIF outfile
- if not os.path.exists(gtifcloud_out):
- self.vrt_translate_gdal('translate', vrtcloud_out, gtifcloud_out)
- self._one_date.append(gtifcloud_out)
-
- def raster_data(self, img):
- """
- Function to extract raster information.
- Return table of pixel values and raster information like line number, pixel size, ... (gdal pointer)
-
- :param img: Raster path
- :type img: str
+ """
+ Satellite image processing's class. This class include several processes to group images by date, mosaic images by date,
+ extract images information, compute ndvi, compute cloud pourcent and create new rasters.
+
+ :param class_archive: Archive class name with every information on downloaded images
+ :type class_archive: class
+ :param big_folder: Image processing folder
+ :type big_folder: str
+ :param one_date: [year, month, day] ...
+ This variable is modified in the function :func:`mosaic_by_date()`.
+ To append mosaic image path, mosaic cloud image path, cloud pixel value table, mosaic ndvi image path and ndvi pixel value table.
+ :type one_date: list of str
+ :param choice_nb_b: A option to choice output image number of band :func:`layer_rasterization` in Vector's class. If this option is 0, it take input band. By default 0.
+ :type choice_nb_b: int
+
+ """
+ def __init__(self, class_archive, big_folder, one_date):
+ """Create a new 'Landsat_by_date' instance
+
+ """
+
+ self._class_archive = class_archive
+ self._big_folder = big_folder
+ self._one_date = one_date
+ # Verify list of list of str
+ if one_date == []:
+ print ("Enter dates to mosaic images like [[str(year), str(month), str(day)], [str(year), str(month), str(day)], ...]")
+ sys.exit(1)
+ else:
+ self._one_date = one_date
+
+ self.out_ds = None
+ self.choice_nb_b = 0
+
+ def group_by_date(self, d_uni):
+ """
+ Function to extract images on a single date
+
+ :param d_uni: [year, month, day]
+ :type d_uni: list of str
+
+ :returns: list of str -- variable **group** = [year, month, day, multispectral image path, cloud image path]
+ """
+
+ # Group of images with the same date
+ group = []
+
+ # Take images with the same date
+ for d_dup in self._class_archive.list_img:
+ if d_dup[:3] == d_uni:
+ group.append(d_dup)
+
+ return group
+
+ def vrt_translate_gdal(self, vrt_translate, src_data, dst_data):
+ """
+ Function to launch gdal tools in command line. With ``gdalbuildvrt`` and ``gdal_translate``.
+ This function is used :func:`mosaic_by_date` to mosaic image by date.
+
+ :param vrt_translate: ``vrt`` or ``translate``
+ :type vrt_translate: str
+ :param src_data: Data source. Several data for vrt process and one data (vrt data) for gdal_translate
+ :type src_data: list (process ``vrt``) or str (process ``translate``)
+ :param dst_data: Output path
+ :type dst_data: str
+ """
+
+ if os.path.exists(dst_data):
+ os.remove(dst_data)
+
+ # Select process
+ if vrt_translate == 'vrt':
+ # Verify input data
+ if type(src_data) is not np.ndarray and type(src_data) is not list:
+ print ('VRT file ! The data source should be composed of several data. A list minimal of 2 dimensions')
+ sys.exit(1)
+
+ print ('Build VRT file')
+ if not os.path.exists(dst_data):
+ process_tocall = ['gdalbuildvrt', '-srcnodata', '-10000', dst_data]
+
+ # Copy rasters
+ for cp_image in src_data:
+ process_tocall.append(cp_image)
+
+ elif vrt_translate == 'translate':
+ # Verify input data
+ try :
+ src_data = str(src_data)
+ except:#Â if type(src_data) is not str:
+ print ('Geotiff file ! The data source should be composed of path file. A character string !')
+ sys.exit(1)
+
+ print ('Build Geotiff file')
+ if not os.path.exists(dst_data):
+ process_tocall = ['gdal_translate', '-a_nodata', '-10000', src_data, dst_data]
+
+ # Launch vrt process
+ subprocess.call(process_tocall)
+
+ def mosaic_by_date(self):
+ """
+ Function to merge images of the same date in a image group :func:`group_by_date`.
+ """
+
+ # Create the processing images folder if not exists
+ if not os.path.exists(self._class_archive._folder + '/' + self._big_folder):
+ os.mkdir(self._class_archive._folder + '/' + self._big_folder)
+
+ # Matrix multi images for a single date
+ group = self.group_by_date(self._one_date) # Every images [year, month, day, multispectral image, cloud image]
+ group_ = np.transpose(np.array(group)) # Transpose matrix to extract path of images
+
+ # Create a folder with images year if it doesn't exist
+ index_repertory_img = self._class_archive._captor
+ if not os.path.exists(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img):
+ os.mkdir(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img)
+
+ index_repertory_img = index_repertory_img + '/'
+ # Create a folder with images date if it doesn't exist
+ for d_ in self._one_date:
+ index_repertory_img = index_repertory_img + d_
+
+ if not os.path.exists(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img):
+ os.mkdir(self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img)
+
+ #Â Build VRT file with data images required
+ vrt_out = self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img + '/' \
+ + self._class_archive._captor + index_repertory_img.split("/")[1] + '.VRT' # Multispectral VRT outfile
+ if not os.path.exists(vrt_out):
+ self.vrt_translate_gdal('vrt', group_[3], vrt_out)
+
+ vrtcloud_out = self._class_archive._folder + '/' + self._big_folder + '/' + index_repertory_img + '/' \
+ + self._class_archive._captor + index_repertory_img.split("/")[1] + '_' + group_[4][0][-7:-4] + '.VRT' # Cloud TIF outfile
+ if not os.path.exists(vrtcloud_out):
+ self.vrt_translate_gdal('vrt', group_[4], vrtcloud_out)
+
+ #Â Build Geotiff file with data images required
+ gtif_out = vrt_out[:-4] + '.TIF' # Multispectral VRT outfile
+ if not os.path.exists(gtif_out):
+ self.vrt_translate_gdal('translate', vrt_out, gtif_out)
+ self._one_date.append(gtif_out)
+
+ gtifcloud_out = vrtcloud_out[:-4] + '.TIF' # Cloud TIF outfile
+ if not os.path.exists(gtifcloud_out):
+ self.vrt_translate_gdal('translate', vrtcloud_out, gtifcloud_out)
+ self._one_date.append(gtifcloud_out)
+
+ def raster_data(self, img):
+ """
+ Function to extract raster information.
+ Return table of pixel values and raster information like line number, pixel size, ... (gdal pointer)
+
+ :param img: Raster path
+ :type img: str
- :returns: numpy.array -- variable **data**, Pixel value matrix of a raster.
-
- gdal pointer -- variable **_in_ds**, Raster information.
- """
-
- # Load Gdal's drivers
- gdal.AllRegister()
-
- # Loading input raster
- print ('Loading input raster :', os.path.split(str(img))[1][:-4])
- in_ds = gdal.Open(str(img), gdal.GA_ReadOnly)
-
- # if it doesn't exist
- if in_ds is None:
- print('could not open ')
- sys.exit(1)
-
- # Information on the input raster
- if self.choice_nb_b == 0:
- nbband = in_ds.RasterCount # Spectral band number
- else:
- nbband = self.choice_nb_b
- rows = in_ds.RasterYSize # Rows number
- cols = in_ds.RasterXSize # Columns number
-
- # Table's declaration
- data = [] # np.float32([[0]*cols for i in xrange(rows)])
- for band in range(nbband):
-
- canal = in_ds.GetRasterBand(band + 1) # Select a band
- if nbband == 1:
- data = canal.ReadAsArray(0, 0, cols, rows).astype(np.float32) # Assign pixel values at the data
- else:
- data.append(canal.ReadAsArray(0, 0, cols, rows).astype(np.float32))
-# print('Copie des pixels du raster ! Bande :', (band + 1))
-
- ###################################
- # Close input raster
- _in_ds = in_ds
- in_ds = None
-
- return data, _in_ds
-
- def pourc_cloud(self, img_spec, img_cloud):
- """
- Return clear pixel percentage on the image **img_spec** because of a cloud image **img_cloud**.
-
- :param img_spec: Spectral image path
- :type img_spec: str
- :param img_cloud: Cloud image path
- :type img_cloud: str
-
- :returns: float -- variable **nb0**, clear pixel percentage.
- :Example:
-
- >>> import RasterSat_by_date
- >>> Landsat_test = RasterSat_by_date(class_archive, big_folder, one_date)
- >>> nb0_test = Landsat_test.pourc_cloud(Landsat_test._one_date[3], Landsat_test._one_date[4])
- >>> nb0_test
- 98
- """
-
- # Extract raster's information
- data_spec, info_spec = self.raster_data(img_spec)
- data_cloud, info_cloud = self.raster_data(img_cloud)
- self._one_date.append(data_cloud) # Add cloud pixel value table
-
- # Extent of the images
- mask_spec = np.in1d(data_spec[0], [-10000, math.isnan], invert=True) # ex : array([ True, True, True, True, False, True, True, True, True], dtype=bool) -> False where there is -10000 ou NaN
-
- # Print area account of the pixel size 'info_spec.GetGeoTransform()'
- print ('Area = ' + str(float((np.sum(mask_spec) * info_spec.GetGeoTransform()[1] * abs(info_spec.GetGeoTransform()[-1]) )/10000)) + 'ha' )
-
- # Cloud mask
- mask_cloud = np.in1d(data_cloud, 0) # This is the same opposite False where there is 0
- cloud = np.choose(mask_cloud, (False, mask_spec)) # If True in cloud mask, it take spectral image else False
- dist = np.sum(cloud) # Sum of True. True is cloud
-
- # Computer cloud's percentage with dist (sum of cloud) by sum of the image's extent
- try :
- nb0 = float(dist)/(np.sum(mask_spec))
- print('For ' + os.path.split(str(img_spec))[1][:-4] + ', cloudy cover ' + str(100 - round(nb0*100, 2)) + "%")
- except ZeroDivisionError:
- nb0 = 0
- print("The raster isn\'t in the area !")
-
- return nb0
-
- def calcul_ndvi(self, img_spec):
- """
- Computer NDVI index for a Landsat image.
-
- NDVI = band4 - band3 / band4 + band3 with nb_captor = 0
-
- or for Sentinel 2 image (2A) : NDVI = band3 - band2 / band3 + band2 with nb_captor = -1
-
- :param img_spec: Spectral image path
- :type img_spec: str
+ :returns: numpy.array -- variable **data**, Pixel value matrix of a raster.
+
+ gdal pointer -- variable **_in_ds**, Raster information.
+ """
+
+ # Load Gdal's drivers
+ gdal.AllRegister()
+
+ # Loading input raster
+ print ('Loading input raster :', os.path.split(str(img))[1][:-4])
+ in_ds = gdal.Open(str(img), gdal.GA_ReadOnly)
+
+ # if it doesn't exist
+ if in_ds is None:
+ print('could not open ')
+ sys.exit(1)
+
+ # Information on the input raster
+ if self.choice_nb_b == 0:
+ nbband = in_ds.RasterCount # Spectral band number
+ else:
+ nbband = self.choice_nb_b
+ rows = in_ds.RasterYSize # Rows number
+ cols = in_ds.RasterXSize # Columns number
+
+ # Table's declaration
+ data = [] # np.float32([[0]*cols for i in xrange(rows)])
+ for band in range(nbband):
+
+ canal = in_ds.GetRasterBand(band + 1) # Select a band
+ if nbband == 1:
+ data = canal.ReadAsArray(0, 0, cols, rows).astype(np.float32) # Assign pixel values at the data
+ else:
+ data.append(canal.ReadAsArray(0, 0, cols, rows).astype(np.float32))
+# print('Copie des pixels du raster ! Bande :', (band + 1))
+
+ ###################################
+ # Close input raster
+ _in_ds = in_ds
+ in_ds = None
+
+ return data, _in_ds
+
+ def pourc_cloud(self, img_spec, img_cloud):
+ """
+ Return clear pixel percentage on the image **img_spec** because of a cloud image **img_cloud**.
+
+ :param img_spec: Spectral image path
+ :type img_spec: str
+ :param img_cloud: Cloud image path
+ :type img_cloud: str
+
+ :returns: float -- variable **nb0**, clear pixel percentage.
+ :Example:
+
+ >>> import RasterSat_by_date
+ >>> Landsat_test = RasterSat_by_date(class_archive, big_folder, one_date)
+ >>> nb0_test = Landsat_test.pourc_cloud(Landsat_test._one_date[3], Landsat_test._one_date[4])
+ >>> nb0_test
+ 98
+ """
+
+ # Extract raster's information
+ data_spec, info_spec = self.raster_data(img_spec)
+ data_cloud, info_cloud = self.raster_data(img_cloud)
+ self._one_date.append(data_cloud) # Add cloud pixel value table
+
+ # Extent of the images
+ mask_spec = np.in1d(data_spec[0], [-10000, math.isnan], invert=True) # ex : array([ True, True, True, True, False, True, True, True, True], dtype=bool) -> False where there is -10000 ou NaN
+
+ # Print area account of the pixel size 'info_spec.GetGeoTransform()'
+ print ('Area = ' + str(float((np.sum(mask_spec) * info_spec.GetGeoTransform()[1] * abs(info_spec.GetGeoTransform()[-1]) )/10000)) + 'ha' )
+
+ # Cloud mask
+ mask_cloud = np.in1d(data_cloud, 0) # This is the same opposite False where there is 0
+ cloud = np.choose(mask_cloud, (False, mask_spec)) # If True in cloud mask, it take spectral image else False
+ dist = np.sum(cloud) # Sum of True. True is cloud
+
+ # Computer cloud's percentage with dist (sum of cloud) by sum of the image's extent
+ try :
+ nb0 = float(dist)/(np.sum(mask_spec))
+ print('For ' + os.path.split(str(img_spec))[1][:-4] + ', cloudy cover ' + str(100 - round(nb0*100, 2)) + "%")
+ except ZeroDivisionError:
+ nb0 = 0
+ print("The raster isn\'t in the area !")
+
+ return nb0
+
+ def calcul_ndvi(self, img_spec):
+ """
+ Computer NDVI index for a Landsat image.
+
+ NDVI = (band4 - band3) / (band4 + band3) with nb_captor = 0
+
+ or for Sentinel 2 image (2A) : NDVI = band3 - band2 / band3 + band2 with nb_captor = -1
+
+ :param img_spec: Spectral image path
+ :type img_spec: str
- """
-
- # NDVI formula index for 2 captor (L8 or S2A Theia)
- if self._class_archive._captor == 'SENTINEL2':
- n_captor = -1
- else :
- n_captor = 0
-
- # Extract raster's information
- data, in_ds = self.raster_data(img_spec)
-
- # Computer NDVI
- mask = np.greater(data[0], -10000)
- ndvi = np.choose(mask, (-10000, eval('(data[4+n_captor]-data[3+n_captor])') / eval('(data[4+n_captor]+data[3+n_captor])'))) # If True, -10000 (NaN) else compute mathematical operation
-
- # Outfile name
- img_ndvi = img_spec[:-4] + '_ndvi.TIF'
- self._one_date.append(img_ndvi) # Add ndvi image path
- self._one_date.append(ndvi) # Add ndvi pixel value table
- self.create_raster(img_ndvi, ndvi, in_ds)
-
- def create_raster(self, out_raster, data, in_ds):
- """
- Create a raster empty with the input raster property
-
- :param out_raster: Output image path
- :type out_raster: str
- :param data: Pixel value matrix. Matrix size equal to that of a raster.
- :type data: numpy.array
- :param in_ds: Raster information
- :type in_ds: gdal pointer
-
- :returns: gdal pointer -- variable **out_ds**, Raster out information.
-
- int -- variable **nbband**, Band number of the out layer.
-
- int -- variable **e**, Index to know if the raster exists. If it doesn't exists e = 0 else e = 1 (by default).
- """
-
-# if os.path.exists(str(out_raster)):
-# os.remove(str(out_raster))
- e = 1 # Raster out exists by default
- #Â Verify if the processing take input band or one spectral band
- if data.ndim == 2 or self.choice_nb_b == 1:
- nbband = 1
- else:
- nbband = in_ds.RasterCount
-
- driver = gdal.GetDriverByName('GTiff')
- if not os.path.exists(str(out_raster)):
- e = 0
- # Create outfile
- self.out_ds = driver.Create(str(out_raster), in_ds.RasterXSize, in_ds.RasterYSize, nbband, gdal.GDT_Float32)
- if self.out_ds is None:
- print ('Could not create ' + os.path.split(str(out_raster))[1])
- sys.exit(1)
-
- # Get extent coordinates and raster resolution
- transform = in_ds.GetGeoTransform()
- # print transform
-
- minX = transform[0]
- maxY = transform[3]
- pixelWidth = transform[1]
- pixelHeight = transform[5]
-
- geotransform = [minX, pixelWidth, 0, maxY, 0, pixelHeight]
-
- # Record projection
- def_projection = in_ds.GetProjection()
+ """
+
+ # NDVI formula index for 2 captor (L8 or S2A Theia)
+ if self._class_archive._captor == 'SENTINEL2':
+ n_captor = -1
+ else :
+ n_captor = 0
+
+ # Extract raster's information
+ data, in_ds = self.raster_data(img_spec)
+
+ # Computer NDVI
+ mask = np.greater(data[0], -10000)
+ ndvi = np.choose(mask, (-10000, eval('(data[4+n_captor]-data[3+n_captor])') / eval('(data[4+n_captor]+data[3+n_captor])'))) # If True, -10000 (NaN) else compute mathematical operation
+
+ # Outfile name
+ img_ndvi = img_spec[:-4] + '_ndvi.TIF'
+ self._one_date.append(img_ndvi) # Add ndvi image path
+ self._one_date.append(ndvi) # Add ndvi pixel value table
+ self.create_raster(img_ndvi, ndvi, in_ds)
+
+ def create_raster(self, out_raster, data, in_ds):
+ """
+ Create a raster empty with the input raster property
+
+ :param out_raster: Output image path
+ :type out_raster: str
+ :param data: Pixel value matrix. Matrix size equal to that of a raster.
+ :type data: numpy.array
+ :param in_ds: Raster information
+ :type in_ds: gdal pointer
+
+ :returns: gdal pointer -- variable **out_ds**, Raster out information.
+
+ int -- variable **nbband**, Band number of the out layer.
+
+ int -- variable **e**, Index to know if the raster exists. If it doesn't exists e = 0 else e = 1 (by default).
+ """
+
+# if os.path.exists(str(out_raster)):
+# os.remove(str(out_raster))
+ e = 1 # Raster out exists by default
+ #Â Verify if the processing take input band or one spectral band
+ if data.ndim == 2 or self.choice_nb_b == 1:
+ nbband = 1
+ else:
+ nbband = in_ds.RasterCount
+
+ driver = gdal.GetDriverByName('GTiff')
+ if not os.path.exists(str(out_raster)):
+ e = 0
+ # Create outfile
+ self.out_ds = driver.Create(str(out_raster), in_ds.RasterXSize, in_ds.RasterYSize, nbband, gdal.GDT_Float32)
+ if self.out_ds is None:
+ print ('Could not create ' + os.path.split(str(out_raster))[1])
+ sys.exit(1)
+
+ # Get extent coordinates and raster resolution
+ transform = in_ds.GetGeoTransform()
+ # print transform
+
+ minX = transform[0]
+ maxY = transform[3]
+ pixelWidth = transform[1]
+ pixelHeight = transform[5]
+
+ geotransform = [minX, pixelWidth, 0, maxY, 0, pixelHeight]
+
+ # Record projection
+ def_projection = in_ds.GetProjection()
- # Set the geo-traking and outfile projection
- self.out_ds.SetGeoTransform(geotransform)
- self.out_ds.SetProjection(def_projection)
-
- else:
-
- self.out_ds = gdal.Open(str(out_raster), gdal.GA_ReadOnly)
-
- return nbband, e
-
- def complete_raster(self, nbband, e, data):
- """
- This function complete the function above :func:`create_raster()`. It
- fills the raster table and close the layer.
-
- :param out_ds: Raster out information
- :type out_ds: gdal pointer
- :param nbband: Band number of the out layer
- :type nbband: int
- :param e: Index to know if the raster existed. If it didn't exist e = 0.
- :type e: int
- :param data: Pixel value matrix. Matrix size equal to that of a raster.
- :type data: numpy.array
- """
-
- # The e index to verify if the layer existed already because of the
- # function :func:`create_raster()`
- if e == 0 :
- p = 0 #Â Increment for the number band
- while p < nbband:
- #Incrementation
- p = p + 1
-
- print ("Copy on the band ", p)
-
- # Loading spectral band of outfile
- out_band = self.out_ds.GetRasterBand(p)
- # write the data
- if data.ndim == 2:
- out_band.WriteArray(data, 0, 0)
- else:
- out_band.WriteArray(data[p-1], 0, 0)
-
- # Closing and statistics on output raster
- out_band.FlushCache()
- out_band.SetNoDataValue(-10000)
- out_band.GetStatistics(-1, 1)
- out_band = None
-
- # Close open data
- self.out_ds = None
+ # Set the geo-traking and outfile projection
+ self.out_ds.SetGeoTransform(geotransform)
+ self.out_ds.SetProjection(def_projection)
+
+ else:
+
+ self.out_ds = gdal.Open(str(out_raster), gdal.GA_ReadOnly)
+
+ return nbband, e
+
+ def complete_raster(self, nbband, e, data):
+ """
+ This function complete the function above :func:`create_raster()`. It
+ fills the raster table and close the layer.
+
+ :param out_ds: Raster out information
+ :type out_ds: gdal pointer
+ :param nbband: Band number of the out layer
+ :type nbband: int
+ :param e: Index to know if the raster existed. If it didn't exist e = 0.
+ :type e: int
+ :param data: Pixel value matrix. Matrix size equal to that of a raster.
+ :type data: numpy.array
+ """
+
+ # The e index to verify if the layer existed already because of the
+ # function :func:`create_raster()`
+ if e == 0 :
+ p = 0 #Â Increment for the number band
+ while p < nbband:
+ #Incrementation
+ p = p + 1
+
+ print ("Copy on the band ", p)
+
+ # Loading spectral band of outfile
+ out_band = self.out_ds.GetRasterBand(p)
+ # write the data
+ if data.ndim == 2:
+ out_band.WriteArray(data, 0, 0)
+ else:
+ out_band.WriteArray(data[p-1], 0, 0)
+
+ # Closing and statistics on output raster
+ out_band.FlushCache()
+ out_band.SetNoDataValue(-10000)
+ out_band.GetStatistics(-1, 1)
+ out_band = None
+
+ # Close open data
+ self.out_ds = None
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/Rpg.py b/Rpg.py
index 580f1ec306249411b1f4e92ab1d3485111be6ffb..18c722c92d8e1d523826012528c0a145067339f8 100644
--- a/Rpg.py
+++ b/Rpg.py
@@ -150,7 +150,7 @@ class Rpg(Vector):
self.head_in_read.append(y)
else:
self.head_in_read.append(y[:10])
- body_in_read = map(list, zip(*in_read[1:])) #Â Transpose table [[e,e,e],[a,a,a]] -> [[e,a],[e,a],[e,a]]
+ body_in_read = list(map(list, zip(*in_read[1:]))) #Â Transpose table [[e,e,e],[a,a,a]] -> [[e,a],[e,a],[e,a]]
# self.rm_dupli = [[x, body_in_read[1][body_in_read[0].index(x)], body_in_read[2][body_in_read[0].index(x)]] \
# for x in body_in_read[0] if body_in_read[0].count(x) == 1]
diff --git a/Sample.py b/Sample.py
index 9b5409dad2722a4258e6d89ae4380c50de41a3c3..91ed7bc595b74c684b89ff2f7c8ffe215dca486a 100644
--- a/Sample.py
+++ b/Sample.py
@@ -22,195 +22,194 @@ import random
import numpy as np
from Vector import Vector
try :
- import ogr
+ import ogr
except :
- from osgeo import ogr
+ from osgeo import ogr
class Sample(Vector):
-
- """
- Vector class inherits the super vector class properties. This class create training sample.
-
- :param vector_used: Input/Output shapefile to clip (path)
- :type vector_used: str
- :param vector_cut: Area shapefile (path)
- :type vector_cut: str
- :param nb_sample: Number of polygons for every sample
- :type nb_sample: int
- :param vector_val: Output shapefile to validate the futur classification
- :type vector_val: str
-
- :opt: Refer to the Vector class
- """
-
- def __init__(self, used, cut, nb_sample, **opt):
- """Create a new 'Sample' instance
-
- """
- Vector.__init__(self, used, cut, **opt)
-
- self._nb_sample = nb_sample
- self.vector_val = ''
-
- def create_sample(self, **kwargs):
- """
- Function to create a sample shapefile of a specific class
-
- :kwargs: **fieldname** (list of str) - Fieldname in the input shapefile (if the user want select polygons of the class names specific)
-
- **class** (list of str) - class names in the input shapefile (with fieldname index).
- Can use one or several classes like this --> example : [classname1, classname2, ...]
- """
-
- kw_field = kwargs['fieldname'] if kwargs.get('fieldname') else ''
- kw_classes = kwargs['class'] if kwargs.get('class') else ''
-
- # If the users want select polygons with a certain class name
- if kw_field and kw_classes:
- # The random sample with class name selected only
- random_sample = np.array(random.sample(self.select_random_sample(kw_field, kw_classes), int(self._nb_sample)))
- else:
- # The random sample without class name selected
- random_sample = np.array(random.sample(range(self.data_source.GetLayer().GetFeatureCount()), self._nb_sample))
-
- # Output shapefile of the sample's polygons (path)
- self.vector_used = self.vector_used[:-4] + '_' + kw_classes.replace(',','').replace(' ','') + 'rd.shp'
- # Fill and create the sample shapefile
- self.fill_sample(self.vector_used, random_sample[:len(random_sample)/2])
- # Output shapefile of the validate polygon (path)
- self.vector_val = self.vector_used[:-6] + 'val.shp'
- # Fill and create the validate polygons shapefile
- self.fill_sample(self.vector_val, random_sample[len(random_sample)/2:])
-
- def select_random_sample(self, kw_field, kw_classes):
- """
- Function to select id with class name specific only. This function is used in :func:`create_sample`
+
+ """
+ Vector class inherits the super vector class properties. This class create training sample.
+
+ :param vector_used: Input/Output shapefile to clip (path)
+ :type vector_used: str
+ :param vector_cut: Area shapefile (path)
+ :type vector_cut: str
+ :param nb_sample: Number of polygons for every sample
+ :type nb_sample: int
+ :param vector_val: Output shapefile to validate the futur classification
+ :type vector_val: str
+
+ :opt: Refer to the Vector class
+ """
+
+ def __init__(self, used, cut, nb_sample, **opt):
+ """
+ Create a new 'Sample' instance
+ """
+ Vector.__init__(self, used, cut, **opt)
+
+ self._nb_sample = nb_sample
+ self.vector_val = ''
+
+ def create_sample(self, **kwargs):
+ """
+ Function to create a sample shapefile of a specific class
+
+ :kwargs: **fieldname** (list of str) - Fieldname in the input shapefile (if the user want select polygons of the class names specific)
+
+ **class** (list of str) - class names in the input shapefile (with fieldname index).
+ Can use one or several classes like this --> example : [classname1, classname2, ...]
+ """
+
+ kw_field = kwargs['fieldname'] if kwargs.get('fieldname') else ''
+ kw_classes = kwargs['class'] if kwargs.get('class') else ''
+
+ # If the users want select polygons with a certain class name
+ if kw_field and kw_classes:
+ # The random sample with class name selected only
+ random_sample = np.array(random.sample(self.select_random_sample(kw_field, kw_classes), int(self._nb_sample)))
+ else:
+ # The random sample without class name selected
+ random_sample = np.array(random.sample(range(self.data_source.GetLayer().GetFeatureCount()), self._nb_sample))
+
+ # Output shapefile of the sample's polygons (path)
+ self.vector_used = self.vector_used[:-4] + '_' + kw_classes.replace(',','').replace(' ','') + 'rd.shp'
+ # Fill and create the sample shapefile
+ self.fill_sample(self.vector_used, random_sample[:round(len(random_sample)/2)])
+ # Output shapefile of the validate polygon (path)
+ self.vector_val = self.vector_used[:-6] + 'val.shp'
+ # Fill and create the validate polygons shapefile
+ self.fill_sample(self.vector_val, random_sample[round(len(random_sample)/2):])
- :param kw_field: Field name in the input shapefile
- :type kw_field: str
- :param kw_classes: Class names in the input shapefile like this --> 'classname1, classname2'
- :type kw_classes: str
- :returns: list -- variable **select_id**, List of id with a class name specific.
- """
-
- # Convert string in a list. For that, it remove
- # space and clip this string with comma (Add everywhere if the script modified
- # because the process work with a input string chain)
- kw_classes = kw_classes.replace(' ','').split(',')
-
- #Â List of class name id
- select_id = []
-
- shp_ogr = self.data_source.GetLayer()
-
- #Â Loop on input polygons
- in_feature = shp_ogr.SetNextByIndex(0) # Initialization
- in_feature = shp_ogr.GetNextFeature()
- while in_feature:
-
- #Â if polygon is a defined class name
- ## .replace('0','') to remove '0' in front of for example '1' (RPG -> '01')
- table_name_class = in_feature.GetField(self.field_names[self.field_names.index(kw_field)])
- # To avoid that the process crashed this part of the algorithm will be launch if the field is contains characters
- if table_name_class != None :
- if in_feature.GetField(self.field_names[self.field_names.index(kw_field)]).replace('0','') in kw_classes:
-
- #Â Add id in the extract list
- select_id.append(in_feature.GetFID())
-
- in_feature.Destroy()
-
- in_feature = shp_ogr.GetNextFeature()
- return select_id
-
- def fill_sample(self, output_sample, polygon, **opt):
-
- """
- Function to fill and create the output sample shapefile. This function is used in :func:`create_sample`
- to create samples polygons and validated polygons (to the take out the precision of the classification)
+ def select_random_sample(self, kw_field, kw_classes):
+ """
+ Function to select id with class name specific only. This function is used in :func:`create_sample`
- :param output_sample: Path of the output shapefile
- :type output_sample: str
- :param polygon: Identity of the selected random polygons. If this variable = 0, the processing will take all polygons
- :type polygon: list or int
-
- :opt: **add_fieldname** (int) - Variable to kown if add a field. By default non (0), if it have to add (1)
-
- **fieldname** (str) - Fieldname to add in the input shapefile
-
- **class** (int) - class names in integer to add in the input shapefile
- """
-
- # In option to add a integer field
- add_field = opt['add_fieldname'] if opt.get('add_fieldname') else 0
- opt_field = opt['fieldname'] if opt.get('fieldname') else ''
- opt_class = opt['class'] if opt.get('class') else 0
-
- shp_ogr = self.data_source.GetLayer()
-
- # To take all polygon
- if type(polygon) == int:
- polygon = range(shp_ogr.GetFeatureCount())
-
- # Projection
- # Import input shapefile projection
- srsObj = shp_ogr.GetSpatialRef()
- # Conversion to syntax ESRI
- srsObj.MorphToESRI()
-
- ## Remove the output shapefile if it exists
- if os.path.exists(output_sample):
- self.data_source.GetDriver().DeleteDataSource(output_sample)
- out_ds = self.data_source.GetDriver().CreateDataSource(output_sample)
-
- if out_ds is None:
- print('Could not create file')
- sys.exit(1)
-
- # Specific output layer
- out_layer = out_ds.CreateLayer(str(output_sample), srsObj, geom_type=ogr.wkbMultiPolygon)
-
- # Add existing fields
- for i in range(0, len(self.field_names)):
- # use the input FieldDefn to add a field to the output
- fieldDefn = shp_ogr.GetFeature(0).GetFieldDefnRef(self.field_names[i])
- out_layer.CreateField(fieldDefn)
-
- # In Option : Add a integer field
- if add_field == 1:
- new_field = ogr.FieldDefn(opt_field, 0)
- out_layer.CreateField(new_field)
-
- # Feature for the ouput shapefile
- featureDefn = out_layer.GetLayerDefn()
-
- # Loop on the input elements
- # Create a existing polygons in random list
- for cnt in polygon:
-
- # Select input polygon by id
- in_feature = shp_ogr.SetNextByIndex(cnt)
- in_feature = shp_ogr.GetNextFeature()
-
- geom = in_feature.GetGeometryRef() # Extract input geometry
+ :param kw_field: Field name in the input shapefile
+ :type kw_field: str
+ :param kw_classes: Class names in the input shapefile like this --> 'classname1, classname2'
+ :type kw_classes: str
+ :returns: list -- variable **select_id**, List of id with a class name specific.
+ """
+
+ # Convert string in a list. For that, it remove
+ # space and clip this string with comma (Add everywhere if the script modified
+ # because the process work with a input string chain)
+ kw_classes = kw_classes.replace(' ','').split(',')
+
+ #Â List of class name id
+ select_id = []
- # Create a new polygon
- out_feature = ogr.Feature(featureDefn)
+ shp_ogr = self.data_source.GetLayer()
- # Set the polygon geometry and attribute
- out_feature.SetGeometry(geom)
- for i in range(0, len(self.field_names)):
- out_feature.SetField(self.field_names[i], in_feature.GetField(self.field_names[i]))
- # In Option : Add a integer field
- if add_field == 1:
- out_feature.SetField(opt_field, opt_class[0])
-
- # Append polygon to the output shapefile
- out_layer.CreateFeature(out_feature)
-
- # Destroy polygons
- out_feature.Destroy()
- in_feature.Destroy()
-
- # Close data
- out_ds.Destroy()
\ No newline at end of file
+ #Â Loop on input polygons
+ in_feature = shp_ogr.SetNextByIndex(0) # Initialization
+ in_feature = shp_ogr.GetNextFeature()
+ while in_feature:
+ #Â if polygon is a defined class name
+ ## .replace('0','') to remove '0' in front of for example '1' (RPG -> '01')
+ table_name_class = in_feature.GetField(self.field_names[self.field_names.index(kw_field)])
+ # To avoid that the process crashed this part of the algorithm will be launch if the field is contains characters
+ if table_name_class != None :
+ if in_feature.GetField(self.field_names[self.field_names.index(kw_field)]).replace('0','') in kw_classes:
+
+ #Â Add id in the extract list
+ select_id.append(in_feature.GetFID())
+
+ in_feature.Destroy()
+
+ in_feature = shp_ogr.GetNextFeature()
+ return select_id
+
+ def fill_sample(self, output_sample, polygon, **opt):
+
+ """
+ Function to fill and create the output sample shapefile. This function is used in :func:`create_sample`
+ to create samples polygons and validated polygons (to the take out the precision of the classification)
+
+ :param output_sample: Path of the output shapefile
+ :type output_sample: str
+ :param polygon: Identity of the selected random polygons. If this variable = 0, the processing will take all polygons
+ :type polygon: list or int
+
+ :opt: **add_fieldname** (int) - Variable to kown if add a field. By default non (0), if it have to add (1)
+
+ **fieldname** (str) - Fieldname to add in the input shapefile
+
+ **class** (int) - class names in integer to add in the input shapefile
+ """
+
+ # In option to add a integer field
+ add_field = opt['add_fieldname'] if opt.get('add_fieldname') else 0
+ opt_field = opt['fieldname'] if opt.get('fieldname') else ''
+ opt_class = opt['class'] if opt.get('class') else 0
+
+ shp_ogr = self.data_source.GetLayer()
+
+ # To take all polygon
+ if type(polygon) == int:
+ polygon = range(shp_ogr.GetFeatureCount())
+
+ # Projection
+ # Import input shapefile projection
+ srsObj = shp_ogr.GetSpatialRef()
+ # Conversion to syntax ESRI
+ srsObj.MorphToESRI()
+
+ ## Remove the output shapefile if it exists
+ if os.path.exists(output_sample):
+ self.data_source.GetDriver().DeleteDataSource(output_sample)
+ out_ds = self.data_source.GetDriver().CreateDataSource(output_sample)
+
+ if out_ds is None:
+ print('Could not create file')
+ sys.exit(1)
+
+ # Specific output layer
+ out_layer = out_ds.CreateLayer(str(output_sample), srsObj, geom_type=ogr.wkbMultiPolygon)
+
+ # Add existing fields
+ for i in range(0, len(self.field_names)):
+ # use the input FieldDefn to add a field to the output
+ fieldDefn = shp_ogr.GetFeature(0).GetFieldDefnRef(self.field_names[i])
+ out_layer.CreateField(fieldDefn)
+
+ # In Option : Add a integer field
+ if add_field == 1:
+ new_field = ogr.FieldDefn(opt_field, 0)
+ out_layer.CreateField(new_field)
+
+ # Feature for the ouput shapefile
+ featureDefn = out_layer.GetLayerDefn()
+
+ # Loop on the input elements
+ # Create a existing polygons in random list
+ for cnt in polygon:
+
+ # Select input polygon by id
+ in_feature = shp_ogr.SetNextByIndex(cnt)
+ in_feature = shp_ogr.GetNextFeature()
+
+ geom = in_feature.GetGeometryRef() # Extract input geometry
+
+ # Create a new polygon
+ out_feature = ogr.Feature(featureDefn)
+
+ # Set the polygon geometry and attribute
+ out_feature.SetGeometry(geom)
+ for i in range(0, len(self.field_names)):
+ out_feature.SetField(self.field_names[i], in_feature.GetField(self.field_names[i]))
+ # In Option : Add a integer field
+ if add_field == 1:
+ out_feature.SetField(opt_field, opt_class[0])
+
+ # Append polygon to the output shapefile
+ out_layer.CreateFeature(out_feature)
+
+ # Destroy polygons
+ out_feature.Destroy()
+ in_feature.Destroy()
+
+ # Close data
+ out_ds.Destroy()
\ No newline at end of file
diff --git a/Satellites.py b/Satellites.py
new file mode 100644
index 0000000000000000000000000000000000000000..814082a83beba3b69af67512f979e330cafd51f6
--- /dev/null
+++ b/Satellites.py
@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import collections
+
+SATELLITE = collections.defaultdict(dict)
+
+ #Â Info from Theia-land website or Olivier Hagolle blog
+
+########################## SENTINEL2 ###############################
+
+SATELLITE["SENTINEL2"]["server"] = "https://theia.cnes.fr/atdistrib"
+SATELLITE["SENTINEL2"]["resto"] = "resto2"
+SATELLITE["SENTINEL2"]["token_type"] = "text"
+
+########################## LANDSAT #################################
+
+SATELLITE["Landsat"]["server"] = "https://theia-landsat.cnes.fr"
+SATELLITE["Landsat"]["resto"] = "resto"
+SATELLITE["Landsat"]["token_type"] = "json"
+
+
+
diff --git a/Segmentation.py b/Segmentation.py
index 7e53b0768afaafcfaee54ecae1b6ceb836ceed10..18c3d8007a792f1e12ee6b0ed01fd754581d43d0 100644
--- a/Segmentation.py
+++ b/Segmentation.py
@@ -257,6 +257,7 @@ class Segmentation(Vector):
:type method: str
"""
+ print("Calcul densité : ")
if method == 'SEATH':
distri = [v[1:] for k, v in self.stats_dict.items() if eval('v[0]' + self.out_threshold[1])]
@@ -268,6 +269,7 @@ class Segmentation(Vector):
else:
distri_den.append(b)
elif method == 'RF':
+
distri = [v[1:] for k, v in self.stats_dict.items() if not self.out_threshold[k] in [0,6,7]]
distri_bio = []
@@ -280,21 +282,31 @@ class Segmentation(Vector):
# Set this variable used normally to define threshold of the classification with SEATH method
self.out_threshold = []
+
#Â Transpose table
t_distri_bio = list(map(list, zip(*distri_bio)))
t_distri_den = list(map(list, zip(*distri_den)))
-
- #Â Biomass threshold
- stdmore = (np.mean(t_distri_bio[2]) + np.std(t_distri_bio[2]))/np.max(t_distri_bio[2])
- stdless = (np.mean(t_distri_bio[2]) - np.std(t_distri_bio[2]))/np.max(t_distri_bio[2])
- self.out_threshold.append('>'+str(stdmore))
- self.out_threshold.append('')
- self.out_threshold.append('<'+str(stdless))
-
- # Compute density and biomass maximum
- self.max_wood_idm = np.max(t_distri_den[1])
- self.max_wood_sfs = np.max(t_distri_den[0])
- self.max_bio = np.max(t_distri_bio[2])
+
+ try:
+ #Â Biomass threshold
+ stdmore = (np.mean(t_distri_bio[2]) + np.std(t_distri_bio[2]))/np.max(t_distri_bio[2])
+ stdless = (np.mean(t_distri_bio[2]) - np.std(t_distri_bio[2]))/np.max(t_distri_bio[2])
+ self.out_threshold.append('>'+str(stdmore))
+ self.out_threshold.append('')
+ self.out_threshold.append('<'+str(stdless))
+
+ except Exception as e:
+ print("Bio : ", t_distri_bio)
+ print("Den : ", t_distri_den)
+
+ try:
+ # Compute density and biomass maximum
+ self.max_wood_idm = np.max(t_distri_den[1])
+ self.max_wood_sfs = np.max(t_distri_den[0])
+ self.max_bio = np.max(t_distri_bio[2])
+ except Exception as e:
+ print(e)
+
def append_scale(self, select_class, form):
"""
@@ -307,7 +319,7 @@ class Segmentation(Vector):
:type form: str
"""
-
+ print(self.max_wood_idm)
for ind_stats in range(len(self.stats_dict)):
# Only valid on the second level
try:
diff --git a/Toolbox.py b/Toolbox.py
index 7f675bbb96e1473c1f26fd3560513be2f5c3b3ac..3ef37903cdfbce532851c822918fd7dd3700418f 100644
--- a/Toolbox.py
+++ b/Toolbox.py
@@ -24,30 +24,29 @@ import json
class Toolbox():
"""
- Class used to grouped small tools to cut raster or compute statistics on a raster.
-
- :param imag: Input image (path)
- :type imag: str
- :param vect: Extent shapefile (path)
- :type vect: str
+ Class used to grouped small tools to cut raster or compute statistics on a raster.
+
+ :param imag: Input image (path)
+ :type imag: str
+ :param vect: Extent shapefile (path)
+ :type vect: str
"""
def __init__(self):
"""
- Create a new 'Toolbox' instance
+ Create a new 'Toolbox' instance
"""
-
self.imag = ''
self.vect = ''
def clip_raster(self, **kwargs):
"""
- Function to clip a raster with a vector. The raster created will be in the same folder than the input raster.
- With a prefix *Clip_*.
-
- :kwargs: **rm_rast** (int) - 0 (by default) or 1. Variable to remove the output raster. 0 to keep and 1 to remove.
-
- :returns: str -- variable **outclip**, output raster clip (path).
+ Function to clip a raster with a vector. The raster created will be in the same folder than the input raster.
+ With a prefix *Clip_*.
+
+ :kwargs: **rm_rast** (int) - 0 (by default) or 1. Variable to remove the output raster. 0 to keep and 1 to remove.
+
+ :returns: str -- variable **outclip**, output raster clip (path).
"""
rm_rast = kwargs['rm_rast'] if kwargs.get('rm_rast') else 0
@@ -78,14 +77,14 @@ class Toolbox():
def calc_serie_stats(self, table):
"""
- Function to compute stats on temporal cloud and ndvi spectral table
- Ndvi stats : min max std max-min
-
- :param table: Spectral data, cloud raster and ndvi raster
- :type table: numpy.ndarray
- :returns: list of numpy.ndarray -- variable **account_stats**, list of temporal NDVI stats.
-
- numpy.ndarray -- variable **account_cloud**, pixel number clear on the area.
+ Function to compute stats on temporal cloud and ndvi spectral table
+ Ndvi stats : min max std max-min
+
+ :param table: Spectral data, cloud raster and ndvi raster
+ :type table: numpy.ndarray
+ :returns: list of numpy.ndarray -- variable **account_stats**, list of temporal NDVI stats.
+
+ numpy.ndarray -- variable **account_cloud**, pixel number clear on the area.
"""
# Compute stats on these indexes
@@ -129,8 +128,8 @@ class Toolbox():
def check_proj(self):
"""
- Function to check if raster's projection is RFG93.
- For the moment, PHYMOBAT works with one projection only Lambert 93 EPSG:2154
+ Function to check if raster's projection is RFG93.
+ For the moment, PHYMOBAT works with one projection only Lambert 93 EPSG:2154
"""
# Projection for PHYMOBAT
diff --git a/Vector.py b/Vector.py
index 737ea71e9a6f4e91cddfae500f478a68eafcb8b4..c97590cea85cb9159a2b409b9fd68c9075e9a736 100644
--- a/Vector.py
+++ b/Vector.py
@@ -25,7 +25,8 @@ try :
except :
from osgeo import ogr, gdal
-from rasterstats import *
+# from rasterstats import *
+import rasterstats
from collections import *
from RasterSat_by_date import RasterSat_by_date
@@ -106,8 +107,7 @@ class Vector():
def close_data(self):
"""
- Function to remove allocate memory
-
+ Function to remove allocate memory
"""
# Close data sources
@@ -117,7 +117,7 @@ class Vector():
def zonal_stats(self, inraster, band, **kwargs):
"""
- Function to compute the average in every polygons for a raster
+ Function to compute the mean in every polygons for a raster
because of package ``rasterstats`` in */usr/local/lib/python2.7/dist-packages/rasterstats-0.3.2-py2.7.egg/rasterstats/*
:param (inraster,band): inraster -> Input image path, and band -> band number
@@ -130,9 +130,8 @@ class Vector():
ranking = kwargs['rank'] if kwargs.get('rank') else 0
nb_img = kwargs['nb_img'] if kwargs.get('nb_img') else 1
- print('Compute ' + os.path.split(str(self.vector_used))[1] + ' stats on ' + os.path.split(str(inraster))[1])
- stats = raster_stats(str(self.vector_used), str(inraster), stats =['mean'], band_num=band)
-
+ print('Compute ' + os.path.split(self.vector_used)[1] + ' stats on ' + os.path.split(inraster)[1])
+ stats = rasterstats.zonal_stats(self.vector_used, inraster, stats =['mean'], nodata=float('nan'), band=band)
for i in range(len(stats)):
temp = defaultdict(lambda : [0]*nb_img)
for j in range(nb_img):
@@ -140,22 +139,24 @@ class Vector():
temp[0][j] = self.stats_dict[i][j]
except IndexError:
pass
- temp[0][ranking] = stats[i].values()[1]
+
+ # temp[0][ranking] = stats[i].values()[1]
+ temp[0][ranking] = list(stats)[i]['mean']
self.stats_dict[i] = temp[0]
print('End of stats on ' + os.path.split(str(inraster))[1])
def zonal_stats_pp(self, inraster):
"""
- A zonal statistics ++ to dertermine pxl percent in every polygon
+ A zonal statistics ++ to dertermine pxl percent in every polygon
- :param inraster: Input image path
- :type inraster: str
- :returns: dict -- **p_stats** : dictionnary with pxl percent in every polygon. Mainly 'Maj_count' (Majority Value) and 'Maj_count_perc' (Majority Percent)
+ :param inraster: Input image path
+ :type inraster: str
+ :returns: dict -- **p_stats** : dictionnary with pxl percent in every polygon. Mainly 'Maj_count' (Majority Value) and 'Maj_count_perc' (Majority Percent)
"""
- p_stats = raster_stats(str(self.vector_used), str(inraster), stats=['count'], copy_properties=True, categorical=True)
+ p_stats = rasterstats.zonal_stats(self.vector_used, inraster, stats=['count'], copy_properties=True, categorical=True)
for i in range(len(p_stats)):
percent = 0.0
@@ -216,7 +217,7 @@ class Vector():
self.raster_ds = None
# Complete the raster creation
- example_raster.complete_raster(info_out, new_data)
+ example_raster.complete_raster(*info_out, new_data)
return valid_raster
diff --git a/ui_PHYMOBATs_tab.py b/ui_PHYMOBATs_tab.py
index 9c512b1b40b2a0d738fe195f670d2d6fa343a793..bc5911b9ae0d5b083206e3b7d6ba770b3e8facf8 100644
--- a/ui_PHYMOBATs_tab.py
+++ b/ui_PHYMOBATs_tab.py
@@ -19,7 +19,7 @@ except AttributeError:
class Ui_PHYMOBAT(object):
"""
- Class to display “Simplify PHYMOBAT windowâ€.
+ Class to display “Simplify PHYMOBAT windowâ€.
"""
def setupUi(self, PHYMOBAT):
@@ -36,7 +36,7 @@ class Ui_PHYMOBAT(object):
self.gridLayout_4.addWidget(self.buttonBox, 5, 1, 1, 1)
self.checkBox_multiprocess = QtWidgets.QCheckBox(self.centralwidget)
self.checkBox_multiprocess.setCheckable(True)
- self.checkBox_multiprocess.setChecked(True)
+ self.checkBox_multiprocess.setChecked(False)
self.checkBox_multiprocess.setAutoRepeat(False)
self.checkBox_multiprocess.setObjectName("checkBox_multiprocess")
self.gridLayout_4.addWidget(self.checkBox_multiprocess, 5, 0, 1, 1)