diff --git a/Segmentation.py b/Segmentation.py
index e796a25a1cd9fff1aa809476ecb2278289fe4f3f..40ea191dfdd1c565055eef5607e11f57b45defd1 100644
--- a/Segmentation.py
+++ b/Segmentation.py
@@ -1,19 +1,19 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# This file is part of PHYMOBAT 1.2.
# Copyright 2016 Sylvio Laventure (IRSTEA - UMR TETIS)
-#
+#
# PHYMOBAT 1.2 is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
-#
+#
# PHYMOBAT 1.2 is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
-#
+#
# You should have received a copy of the GNU General Public License
# along with PHYMOBAT 1.2. If not, see <http://www.gnu.org/licenses/>.
@@ -31,9 +31,9 @@ from collections import *
class Segmentation(Vector):
"""
- Vector class inherits the super vector class properties. This class create the final shapefile : Cartography
+ Vector class inherits the super vector class properties. This class create the final shapefile : Cartography
on a input segmentation by decision tree.
-
+
The output classname are (**out_class_name** variable):
- Vegetation non naturelle
- Vegetation semi-naturelle
@@ -44,7 +44,7 @@ class Segmentation(Vector):
- Forte phytomasse
- Moyenne phytomasse
- Faible phytomasse
-
+
@param vector_used: Input/Output shapefile to clip (path)
@type vector_used: str
@@ -70,7 +70,7 @@ class Segmentation(Vector):
Mainly 'Maj_count' (Majority Value) and 'Maj_count_perc' (Majority Percent)
@type stats_rpg_tif: dict
"""
-
+
def __init__(self, used, cut):
"""
Create a new 'Clustering' instance
@@ -79,10 +79,10 @@ class Segmentation(Vector):
self.logger = Outils.Log("Log", "Segmentation")
Vector.__init__(self, used, cut)
-
+
self.stats_rpg_tif = {}
self.output_file = 'Final_classification.shp'
-
+
self.out_class_name = []
self.out_threshold = dict()
@@ -91,7 +91,7 @@ class Segmentation(Vector):
self.max_wood_idm = 0
self.max_wood_sfs = 0
self.max_bio = 0
-
+
def create_cartography(self, out_fieldnames, out_fieldtype):
"""
Function to create a output shapefile. In this output file,
@@ -103,30 +103,31 @@ class Segmentation(Vector):
:param out_fieldtype: List of outpu field type
:type out_fieldtype: list of str
"""
-
+
+ self.logger.debug("Create output shapefile : {0}".format(self.output_file))
shp_ogr_ds = self.data_source
shp_ogr = self.data_source.GetLayer()
-
+
# Projection
# Import input shapefile projection
srsObj = shp_ogr.GetSpatialRef()
# Conversion to syntax ESRI
srsObj.MorphToESRI()
-
+
## Remove the output final shapefile if it exists
self.vector_used = self.output_file
if os.path.exists(self.vector_used):
self.data_source.GetDriver().DeleteDataSource(self.vector_used)
-
+
out_ds = self.data_source.GetDriver().CreateDataSource(self.vector_used)
-
+
if out_ds is None:
self.logger.error('Could not create file')
sys.exit(1)
-
+
# Specific output layer
out_layer = out_ds.CreateLayer(self.vector_used, srsObj, geom_type=ogr.wkbMultiPolygon)
-
+
# Add new fields
#Â To add RPG_CODE field
out_fieldnames.insert(2,'RPG_CODE')
@@ -143,45 +144,43 @@ class Segmentation(Vector):
fieldDefn = ogr.FieldDefn('FBPHY_SUB', ogr.OFTInteger)
out_layer.CreateField(fieldDefn)
out_fieldnames.append('FBPHY_SUB')
-
+
# Feature for the ouput shapefile
featureDefn = out_layer.GetLayerDefn()
-
- in_feature = shp_ogr.SetNextByIndex(0) # Polygons initialisation
- in_feature = shp_ogr.GetNextFeature()
-
+
#Â Loop on input polygons
- while in_feature:
-
+ for idx in range(shp_ogr.GetFeatureCount()) :
+
+ in_feature = shp_ogr.GetFeature(idx)
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)
- # Set the existing ID
+ # Set the existing ID
out_feature.SetField(out_fieldnames[0], in_feature.GetField(self.field_names[2]))
# Set the area
out_feature.SetField(out_fieldnames[1], geom.GetArea()/10000)
-
#Â Set the RPG column
recouv_crops_RPG = 0
pourc_inter = self.stats_rpg_tif[in_feature.GetFID()]['Maj_count_perc']
if pourc_inter >= 85:
recouv_crops_RPG = self.stats_rpg_tif[in_feature.GetFID()]['Maj_count']
-
+
out_feature.SetField('RPG_CODE', int(recouv_crops_RPG))
-
+
# Set the others polygons fields with the decision tree dictionnary
for i in range(3, len(out_fieldnames)):
+
# If list stopped it on the second level, complete by empty case
if len(self.class_tab_final[in_feature.GetFID()]) < len(out_fieldnames)-3 and \
self.class_tab_final[in_feature.GetFID()] != []:
self.class_tab_final[in_feature.GetFID()].insert(len(self.class_tab_final[in_feature.GetFID()])-3,'') # To 3rd level
self.class_tab_final[in_feature.GetFID()].insert(len(self.class_tab_final[in_feature.GetFID()])-3,0) # To degree
-
+
try:
# To the confusion matrix, replace level ++ by level --
if i == len(out_fieldnames)-1:
@@ -191,48 +190,44 @@ class Segmentation(Vector):
# if self.class_tab_final[in_feature.GetFID()][i-3] == 2:
# Grassland to natural vegetation
# self.class_tab_final[in_feature.GetFID()][i-3] = 1
+
if self.class_tab_final[in_feature.GetFID()][i-3] > 7:
# Phytomass to natural vegetation
self.class_tab_final[in_feature.GetFID()][i-4] = 1
-
+
out_feature.SetField(str(out_fieldnames[i]), self.class_tab_final[in_feature.GetFID()][i-3])
except:
-
for i in range(3, len(out_fieldnames)-3):
out_feature.SetField(str(out_fieldnames[i]), 'Undefined')
-
+
out_feature.SetField('FBPHY_CODE', 255)
out_feature.SetField('FBPHY_SUB', 255)
# Append polygon to the output shapefile
out_layer.CreateFeature(out_feature)
-
+
# Destroy polygons
- out_feature.Destroy()
- in_feature.Destroy()
-
- # Next polygon
- in_feature = shp_ogr.GetNextFeature()
-
+ out_feature.Destroy()
+
# Close data
- out_ds.Destroy()
+ out_ds.Destroy()
def decision_tree(self, combin_tree):
"""
- Function to build the decision tree. Taking account output threshold and input
+ Function to build the decision tree. Taking account output threshold and input
class name.
-
+
@param combin_tree: Decision tree combination
@type combin_tree: list of number class name
"""
-
+
# Combination tree on a sentence. Every sentence will be in a table.
cond_tab = []
for ct in combin_tree:
cond_a = '' #Â Condition Term
c = 0
- while c < len(ct):
- # Loop on tree combinaison
+ while c < len(ct):
+ # Loop on tree combinaison
if self.out_threshold[ct[c]] == '' :
#Â For interval condition
cond_a = cond_a + 'self.stats_dict[ind_stats][' + str(ct[c]/2) + ']' +\
@@ -245,7 +240,7 @@ class Segmentation(Vector):
c = c + 1
cond_tab.append(cond_a[:-5]) # Remove the last 'and'
- # Loop on every value
+ # Loop on every value
for ind_stats in range(len(self.stats_dict)):
# Loop on decision tree combination.
for cond in cond_tab:
@@ -263,19 +258,19 @@ class Segmentation(Vector):
for s in combin_tree[cond_tab.index(cond)]] + \
[combin_tree[cond_tab.index(cond)][len(combin_tree[cond_tab.index(cond)])-1]] + \
[combin_tree[cond_tab.index(cond)][len(combin_tree[cond_tab.index(cond)])-1]]
-
+
def compute_biomass_density(self, method='SEATH'):
"""
Function to compute the biomass and density distribution.
It returns threshold of biomass level.
-
+
@param method: Classification method used. It can set 'SEATH' (by default) or 'RF'
@type method: str
"""
-
+
if method == 'SEATH':
distri = [v[1:] for k, v in self.stats_dict.items() if eval('v[0]' + self.out_threshold[1])]
-
+
distri_bio = []
distri_den = []
for b in distri:
@@ -283,11 +278,11 @@ class Segmentation(Vector):
distri_bio.append(b)
else:
distri_den.append(b)
-
+
elif method == 'RF':
distri = [v[1:] for k, v in self.stats_dict.items() if not self.out_threshold["PREDICTION"][k] in [0,6,7]]
-
+
distri_bio = []
distri_den = []
@@ -299,10 +294,10 @@ class Segmentation(Vector):
else:
distri_den.append(b)
- #Â Transpose table
+ #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])
@@ -314,21 +309,21 @@ class Segmentation(Vector):
wood_sfs = np.array(t_distri_den[0], dtype=np.float64)
wood_idm = np.array(t_distri_den[1], dtype=np.float64)
max_bio = np.array(t_distri_den[2], dtype=np.float64)
-
+
self.max_wood_sfs = np.nanmax(wood_sfs)
self.max_wood_idm = np.nanmax(wood_idm)
self.max_bio = np.nanmax(max_bio)
-
+
def append_scale(self, select_class, form):
"""
Function to complete the 'class_tab_final' list with density and biomass information.
This list will be used to build the final shapefile.
-
+
@param select_class: Class name to add degree
@type select_class: str
@param form: Formula to add degree
- @type form: str
+ @type form: str
"""
for ind_stats in range(len(self.stats_dict)):
@@ -356,5 +351,3 @@ class Segmentation(Vector):
pass
except IndexError:
pass
-
-
\ No newline at end of file