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SPeillet authoredbf38621e
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Gaetano Raffaele / moringa
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import ogr
import sys
import subprocess
import platform
import numpy as np
import os
import csv
from mtdUtils import queuedProcess, cloneVectorDataStructure, fieldToArray, mergeShapefiles
def getFeaturesFields(shp,flds_pref):
ds = ogr.Open(shp, 0)
ly = ds.GetLayer(0)
flds = []
ldfn = ly.GetLayerDefn()
for n in range(ldfn.GetFieldCount()):
fn = ldfn.GetFieldDefn(n).name
if fn.startswith(tuple(flds_pref)):
flds.append(fn)
ds = None
return flds
def roughFix(shp,flds):
ds = ogr.Open(shp,1)
ly = ds.GetLayer(0)
arr = np.empty([ly.GetFeatureCount(), len(flds)])
i = 0
for f in ly:
j = 0
for fld in flds:
arr[i,j] = f.GetFieldAsDouble(fld)
j += 1
i += 1
#R-like rough fix
arr[np.where(arr==-9999.0)] = np.nan
mns = np.tile(np.nanmean(arr,axis=0),[ly.GetFeatureCount(),1])
arr[np.isnan(arr)] = mns[np.isnan(arr)]
ly.ResetReading()
i = 0
for f in ly:
j = 0
for fld in flds:
f.SetField(fld,arr[i, j])
ly.SetFeature(f)
j += 1
i += 1
ds = None
return
def baseTrainingCmd(shp,stat_file,code,flds,params,model_file,confmat_file):
# Platform dependent parameters
if platform.system() == 'Linux':
sh = False
elif platform.system() == 'Windows':
sh = True
else:
sys.exit("Platform not supported!")
if type(shp) == str:
shp = [shp]
if platform.system() == 'Linux':
cmd = ['otbcli_TrainVectorClassifier', '-io.vd'] + shp + ['-io.stats', stat_file, '-io.confmatout', confmat_file, '-cfield', code, '-io.out', model_file, '-feat'] + flds + params
subprocess.call(cmd,shell=sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('TrainVectorClassifier')
app.SetParameterStringList('io.vd', shp)
app.SetParameterString('io.stats', stat_file)
app.SetParameterString('io.confmatout', confmat_file)
app.SetParameterString('io.out', model_file)
app.UpdateParameters()
app.SetParameterStringList('cfield', [code])
app.SetParameterStringList('feat', flds)
# Parse classification parameters string
# WARNING: works for all classifier with single value parameters
# (surely hangs on <string list> classification parameter types - e.g. -classifier.ann.sizes)
cl_param_keys = params[0::2]
cl_param_vals = params[1::2]
for prk,prv in zip(cl_param_keys,cl_param_vals):
app.SetParameterString(prk[1:],prv)
app.UpdateParameters()
app.ExecuteAndWriteOutput()
else:
sys.exit("Platform not supported!")
def baseClassifyCmd(shp,stat_file,model_file,code,flds,out_file,compute_confidence=False):
# Platform dependent parameters
if platform.system() == 'Linux':
sh = False
elif platform.system() == 'Windows':
sh = True
else:
sys.exit("Platform not supported!")
if platform.system() == 'Linux':
cmd = ['otbcli_VectorClassifier', '-in', shp, '-instat', stat_file, '-model', model_file, '-cfield', code, '-feat'] + flds
if out_file is not None:
cmd += ['-out', out_file]
if compute_confidence:
cmd += ['-confmap', 1]
subprocess.call(cmd,sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('VectorClassifier')
app.SetParameterString('in', shp)
app.SetParameterString('instat', stat_file)
app.SetParameterString('model', model_file)
if out_file is not None:
app.SetParameterString('out', out_file)
if compute_confidence:
app.SetParameterInt('confmap', 1)
app.SetParameterString('cfield', code)
app.UpdateParameters()
app.SetParameterStringList('feat', flds)
app.UpdateParameters()
app.ExecuteAndWriteOutput()
else:
sys.exit('Platform not supported!')
def training(shp,code,model_fld,params,feat,feat_mode = 'list'):
# Platform dependent parameters
if platform.system() == 'Linux':
sh = False
elif platform.system() == 'Windows':
sh = True
else:
sys.exit("Platform not supported!")
if '-classifier' in params:
classifier = params[params.index('-classifier') + 1]
else:
classifier = 'libsvm'
model_file = model_fld + '/' + classifier + '_' + code + '.model'
confmat_file = model_fld + '/' + classifier + '_' + code + '.confmat.txt'
stat_file = model_fld + '/GT_stats.xml'
if feat_mode == 'prefix':
flds = getFeaturesFields(shp, feat)
elif feat_mode == 'list':
flds = feat
else:
sys.exit('ERROR: mode ' + feat_mode + ' not valid.')
if platform.system() == 'Linux':
cmd = ['otbcli_ComputeVectorFeaturesStatistics','-io.vd',shp,'-io.stats',stat_file,'-feat'] + flds
subprocess.call(cmd,shell=sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('ComputeVectorFeaturesStatistics')
app.SetParameterStringList('io.vd', [shp])
app.SetParameterString('io.stats', model_fld + '/GT_stats.xml')
app.UpdateParameters()
app.SetParameterStringList('feat',flds)
app.ExecuteAndWriteOutput()
else:
sys.exit("Platform not supported!")
if platform.system() == 'Linux':
cmd = ['otbcli_TrainVectorClassifier', '-io.vd', shp, '-io.stats', model_fld + '/GT_stats.xml', '-io.confmatout', confmat_file, '-cfield', code, '-io.out', model_file, '-feat'] + flds + params
subprocess.call(cmd,shell=sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('TrainVectorClassifier')
app.SetParameterStringList('io.vd', [shp])
app.SetParameterString('io.stats', model_fld + '/GT_stats.xml')
app.SetParameterString('io.confmatout', confmat_file)
app.SetParameterString('io.out', model_file)
app.UpdateParameters()
app.SetParameterStringList('cfield', [code])
app.SetParameterStringList('feat', flds)
# Parse classification parameters string
# WARNING: works for all classifier with single value parameters
# (surely hangs on <string list> classification parameter types - e.g. -classifier.ann.sizes)
cl_param_keys = params[0::2]
cl_param_vals = params[1::2]
for prk,prv in zip(cl_param_keys,cl_param_vals):
app.SetParameterString(prk[1:],prv)
app.UpdateParameters()
app.ExecuteAndWriteOutput()
else:
sys.exit("Platform not supported!")
return stat_file, model_file
def classify(shp_list,code,stat_file,model_file,out_fld,out_ext,feat,feat_mode = 'list',Nproc=1,compute_confidence=False):
# Platform dependent parameters
if platform.system() == 'Linux':
sh = False
elif platform.system() == 'Windows':
sh = True
else:
sys.exit("Platform not supported!")
if feat_mode == 'prefix':
flds = getFeaturesFields(shp_list[0], feat)
elif feat_mode == 'list':
flds = feat
else:
sys.exit('ERROR: mode ' + feat_mode + ' not valid.')
'''
for shp in shp_list:
#roughFix(shp, flds)
if platform.system() == 'Linux':
cmd = ['otbcli_VectorClassifier','-in',shp,'-instat',stat_file,'-model',model_file,'-out',out_file,'-cfield',code,'-feat'] + flds
subprocess.call(cmd,shell=sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('VectorClassifier')
app.SetParameterString('in',shp)
app.SetParameterString('instat', stat_file)
app.SetParameterString('model', model_file)
app.SetParameterString('out', out_file)
app.SetParameterString('cfield', code)
app.UpdateParameters()
app.SetParameterStringList('feat',flds)
app.UpdateParameters()
app.ExecuteAndWriteOutput()
else:
sys.exit('Platform not supported!')
return
'''
if platform.system() == 'Linux':
cmd_list = []
out_file_list = []
for shp in shp_list:
out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
cmd = ['otbcli_VectorClassifier', '-in', shp, '-instat', stat_file, '-model', model_file, '-out', out_file,
'-cfield', code, '-feat'] + flds
if compute_confidence:
cmd += ['-confmap','1']
cmd_list.append(cmd)
out_file_list.append(out_file)
queuedProcess(cmd_list,Nproc,shell=sh)
return out_file_list
elif platform.system() == 'Windows':
out_file_list = []
for shp in shp_list:
out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
import otbApplication
app = otbApplication.Registry.CreateApplication('VectorClassifier')
app.SetParameterString('in', shp)
app.SetParameterString('instat', stat_file)
app.SetParameterString('model', model_file)
app.SetParameterString('out', out_file)
app.SetParameterString('cfield', code)
app.UpdateParameters()
app.SetParameterStringList('feat', flds)
if compute_confidence:
app.SetParameterInt('confmap', 1)
app.UpdateParameters()
app.ExecuteAndWriteOutput()
out_file_list.append(out_file)
return out_file_list
else:
sys.exit('Platform not supported!')
def addField(filein, nameField, valueField, valueType=None,
driver_name="ESRI Shapefile", fWidth=None):
driver = ogr.GetDriverByName(driver_name)
source = driver.Open(filein, 1)
layer = source.GetLayer()
layer_name = layer.GetName()
layer_defn = layer.GetLayerDefn()
field_names = [layer_defn.GetFieldDefn(i).GetName() for i in range(layer_defn.GetFieldCount())]
if not valueType:
try :
int(valueField)
new_field1 = ogr.FieldDefn(nameField, ogr.OFTInteger)
except :
new_field1 = ogr.FieldDefn(nameField, ogr.OFTString)
elif valueType == str:
new_field1 = ogr.FieldDefn(nameField, ogr.OFTString)
sqlite_type = 'varchar'
elif valueType == int:
new_field1 = ogr.FieldDefn(nameField, ogr.OFTInteger)
sqlite_type = 'int'
elif valueType == float:
new_field1 = ogr.FieldDefn(nameField, ogr.OFTFLOAT)
sqlite_type = 'float'
if fWidth:
new_field1.SetWidth(fWidth)
layer.CreateField(new_field1)
for feat in layer:
layer.SetFeature(feat)
feat.SetField(nameField, valueField)
layer.SetFeature(feat)
def splitShapefileByClasses(shp,code):
ds = ogr.Open(shp)
ly = ds.GetLayer(0)
sep = {}
for f in ly:
cl = f.GetField(code)
if cl not in sep.keys():
sep[cl] = []
sep[cl].append(f)
ds_dict = {}
for cl in sep.keys():
fn = shp.replace('.shp','_' + code + '_' + str(cl) + '.shp')
ds_dict[cl] = fn
dsi = cloneVectorDataStructure(ds,fn)
lyi = dsi.GetLayer(0)
for f in sep[cl]:
lyi.CreateFeature(f)
dsi = None
ds = None
return ds_dict
def retrieveClassHierarchy(shp,code_list):
nomen = []
for code in code_list:
nomen.append(fieldToArray(shp,code).astype(int))
ds_dict = splitShapefileByClasses(shp, code_list[-1])
h_dict = {}
h_dict[code_list[0]] = ([0],[[ds_dict[x] for x in list(np.unique(nomen[-1]))]])
for i in range(1,len(code_list)):
cls = np.unique(nomen[i - 1])
h_dict[code_list[i]] = (list(cls),[])
for cl in cls:
clist = list(np.unique(nomen[-1][np.where(nomen[i - 1] == cl)[0]]))
h_dict[code_list[i]][1].append([ds_dict[x] for x in clist])
return h_dict,ds_dict.values()
def Htraining(shp,code_list,model_fld,params,feat,feat_mode = 'list'):
'''
Hierarchical classification
---------------------------
Input
-----
shp: str
path of the training shapefile
code_list: list
list of the fields names for the different classification levels
model_fld: str
path to the folder where model files will be saved
params: list
list of parameter model to use with otbVectorTraining application
feat: list or str
features to use for classification
feat_mode: list/prefix
selection mode of the features
----------------------------------------------
Output
------
stat_file: str
path to a stat_file to use to unskew model
h_model_fld: str
path to the folder where model files are
'''
# Platform dependent parameters
if platform.system() == 'Linux':
sh = False
elif platform.system() == 'Windows':
sh = True
else:
sys.exit("Platform not supported!")
if '-classifier' in params:
classifier = params[params.index('-classifier') + 1]
else:
classifier = 'libsvm'
h_model_fld = model_fld + '/' + 'H-MODEL_' + '_'.join(code_list)
if not os.path.exists(h_model_fld):
os.mkdir(h_model_fld)
stat_file = model_fld + '/GT_stats.xml'
if feat_mode == 'prefix':
flds = getFeaturesFields(shp, feat)
elif feat_mode == 'list':
flds = feat
else:
sys.exit('ERROR: mode ' + feat_mode + ' not valid.')
# Statistics unskew
if platform.system() == 'Linux':
cmd = ['otbcli_ComputeVectorFeaturesStatistics','-io.vd',shp,'-io.stats',stat_file,'-feat'] + flds
subprocess.call(cmd,shell=sh)
elif platform.system() == 'Windows':
import otbApplication
app = otbApplication.Registry.CreateApplication('ComputeVectorFeaturesStatistics')
app.SetParameterStringList('io.vd', [shp])
app.SetParameterString('io.stats', stat_file)
app.UpdateParameters()
app.SetParameterStringList('feat',flds)
app.ExecuteAndWriteOutput()
else:
sys.exit("Platform not supported!")
# Computes classes hierarchy in a dictionnary
h_dict, ds_list = retrieveClassHierarchy(shp,code_list)
# Create models for each level
with open(h_model_fld + '/h-model.csv',mode='wb') as h_model_file:
writer = csv.writer(h_model_file)
level = 'ROOT'
last = h_dict.keys()[-1]
for code in h_dict.keys():
Nsb = len(h_dict[code][0])
for i in range(Nsb):
model_file = h_model_fld + '/' + classifier + '_' + code + '_' + str(h_dict[code][0][i]) + '.model'
confmat_file = h_model_fld + '/' + classifier + '_' + code + '_' + str(h_dict[code][0][i]) + '.confmat.txt'
baseTrainingCmd(h_dict[code][1][i],stat_file,code,feat,params,model_file,confmat_file)
writer.writerow([level,str(h_dict[code][0][i]),model_file,code,str(code!=last)])
level = code
drv = ogr.GetDriverByName('ESRI Shapefile')
for fn in ds_list:
drv.DeleteDataSource(fn)
return stat_file, h_model_fld
def Hclassify(shp_list,stat_file,h_model_fld,feat,out_fld,out_ext,feat_mode = 'list'):
'''
Hierarchical classification
---------------------------
Input
-----
shp_list: list
list of shapefile path to classify
stat_file: str
path to a stat_file to use to unskew model
h_model_fld: str
path to the folder where model files are
feat: list or str
features to use for classification
out_fld: str
path to the output folder
out_ext: str
output suffix
feat_mode: list/prefix
selection mode of the features
----------------------------------------------
Output
------
out_file_list : list
list of the output shapefile paths
'''
if feat_mode == 'prefix':
flds = getFeaturesFields(shp_list[0], feat)
elif feat_mode == 'list':
flds = feat
else:
sys.exit('ERROR: mode ' + feat_mode + ' not valid.')
if not os.path.exists(h_model_fld):
sys.exit('Folder ' + h_model_fld + ' not exists!')
out_file_list = []
for shp in shp_list:
with open(h_model_fld + '/h-model.csv', mode='rb') as h_model_file:
toProcess = []
toDelete = []
toMerge = []
rdr = csv.reader(h_model_file)
for row in rdr:
if row[0] == 'ROOT':
in_shp = shp
elif len(toProcess) > 0:
for i,process in enumerate(toProcess):
if process.endswith('_p' + row[0] + '_' + row[1] + '.shp') :
in_shp = toProcess.pop(i)
else:
continue
if os.path.exists(in_shp):
out_shp = in_shp.replace('.shp','_ROOT.shp') if row[0] == 'ROOT' else None
split_shp = out_shp if row[0] == 'ROOT' else in_shp
# Classification attempt with only one class raised an error,
# if so, apply the value class in consequence
with open(row[2]) as model:
lines = model.readlines()
to_classify = int(lines[1].split(' ')[0]) != 1
if to_classify :
baseClassifyCmd(in_shp,stat_file,row[2],'p'+row[3],flds,out_shp)
else :
addField(in_shp,'p'+row[3],int(lines[1].split(' ')[1]))
if out_shp is not None:
toDelete.append(out_shp)
#if there is a more detailed level than the current one, split the current
#classification by classes to use as bases for next level
if row[4] == 'True':
ds_dict = splitShapefileByClasses(split_shp,'p'+row[3])
[toProcess.insert(0,x) for x in ds_dict.values()]
toDelete.extend(ds_dict.values())
#last level of hierarchy classes, to merge
elif row[4] == 'False':
toMerge.append(in_shp)
# Merge all resulting shapefiles, or rename if there is only one
if len(toMerge) > 1 :
out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
mergeShapefiles(toMerge,out_file)
out_file_list.append(out_file)
elif len(toMerge) == 1 :
out_file = out_fld + '/' + os.path.basename(shp).replace('.shp', out_ext + '.shp')
os.rename(toMerge[0],out_file)
out_file_list.append(out_file)
drv = ogr.GetDriverByName('ESRI Shapefile')
# Drop tmp files
for fn in toDelete:
drv.DeleteDataSource(fn)
return out_file_list