def compressImage(image_input, image_output_compress, inputBand4Found,
compress_type, predictor, zlevel, format_raster):
if debug >= 1:
print(cyan + "compressImage() : " + endC +
"Debut de la compression de %s" % (image_input))
# Preparation de la commande
command = ""
caseBand4 = ""
if inputBand4Found:
caseBand4 = "-colorinterp_4 undefined"
# Selon le type de compression
while switch(compress_type.upper()):
if case("DEFLATE"):
if debug >= 2:
print("Compression DEFLATE : ")
command = "gdal_translate -of %s %s -co TILED=YES -co COMPRESS=%s -co PREDICTOR=%s -co ZLEVEL=%s %s %s" % (
format_raster, caseBand4, compress_type, predictor, zlevel,
image_input, image_output_compress)
break
if case("LZW"):
if debug >= 2:
print("Compression LZW : ")
command = "gdal_translate -of %s %s -co TILED=YES -co COMPRESS=%s -co ZLEVEL=%s %s %s" % (
format_raster, caseBand4, compress_type, zlevel, image_input,
image_output_compress)
break
break
if command == "":
raise NameError(
bold + red +
"compressImage() : Le type de compression n'est pas reconu : " +
str(compress_type + endC))
if debug >= 1:
print(cyan + "compressImage() : " + endC +
"Algorithme de compressions : " + str(compress_type) + endC)
print(cyan + "compressImage() : " + endC + "Predicteur : " +
str(predictor) + endC)
if compress_type == "DEFLATE":
print(cyan + "compressImage() : " + endC +
"Taux de compression : " + str(zlevel) + endC)
print(cyan + "compressImage() : " + endC + "Fichier de sortie : " +
image_output_compress + endC)
exitCode = os.system(command)
if exitCode != 0:
print(command)
raise NameError(
bold + red +
"compressImage() : An error occured during gdal_translate command. See error message above."
+ endC)
print(bold + green + "FIN DE LA COMPRESSION DE " + image_input + endC)
return
def smoothGeomGrass(input_vector,
output_vector,
param_generalize_dico,
format_vector="ESRI_Shapefile",
overwrite=True):
if debug >= 2:
print(cyan + "smoothGeomGrass() : " + bold + green +
"Lancement de la fonction de lissage par GRASS v.generalize" +
endC)
format_vector = format_vector.replace(' ', '_')
# Import du vecteur d'entrée
leng_name_vector_input = len(
os.path.splitext(os.path.basename(input_vector))[0])
if leng_name_vector_input > 16:
leng_name_vector_input = 16
input_name = "VI" + os.path.splitext(
os.path.basename(input_vector))[0][0:leng_name_vector_input]
input_name = input_name.replace('-', '_')
leng_name_vector_output = len(
os.path.splitext(os.path.basename(output_vector))[0])
if leng_name_vector_output > 16:
leng_name_vector_output = 16
output_name = "VO" + os.path.splitext(
os.path.basename(output_vector))[0][0:leng_name_vector_output]
output_name = output_name.replace('-', '_')
importVectorOgr2Grass(input_vector, input_name, overwrite)
# Traitement avec la fonction v.generalize
method = None
threshold = None
for key in param_generalize_dico:
while switch(key):
if case("method"):
method = param_generalize_dico[key]
break
if case("threshold"):
threshold = param_generalize_dico[key]
break
grass.run_command('v.generalize',
input=input_name,
output=output_name,
method=method,
threshold=threshold,
overwrite=overwrite,
stderr=subprocess.PIPE)
# Export du jeu de données traité
exportVectorOgr2Grass(output_name, output_vector, format_vector, overwrite)
return
def convert2dot(command_doc, struct_cmd_dico, graph_name, dot_file, debug):
EXT_ERR = '.err'
# Definie les parametrres du graph
graph = pgv.AGraph(name=graph_name, directed=True)
graph.graph_attr['outputorder'] = 'edgesfirst'
graph.graph_attr['label'] = command_doc
#graph.graph_attr['ratio']='1.0'
graph.graph_attr['ratio'] = 'compress'
graph.graph_attr['rankdir'] = 'TB'
graph.node_attr['shape'] = 'ellipse'
graph.node_attr['fixedsize'] = 'false'
graph.node_attr['fontsize'] = '8'
graph.node_attr['style'] = 'filled'
graph.edge_attr['color'] = 'lightslategray'
graph.edge_attr['style'] = 'setlinewidth(2)'
graph.edge_attr['arrowhead'] = 'open'
graph.edge_attr.update(arrowhead='vee', arrowsize='2')
# Parcours du dictionaire de facon ordonnée
id_command_list = struct_cmd_dico.keys()
id_command_sorted_list = sorted(id_command_list)
# Pour toutes les lignes du fichier commande
for id_cmd in id_command_sorted_list:
# Recuperer les valeurs des coordonnees
info_cmd_list = struct_cmd_dico[id_cmd]
state = info_cmd_list[0]
dependency_list = info_cmd_list[1]
name_cmd = info_cmd_list[2]
start_date = info_cmd_list[3]
end_date = info_cmd_list[4]
name_task_list = info_cmd_list[5].split('.')
name_task = name_task_list[1] + '.' + name_task_list[2]
# Creation du graph
if debug >= 4:
print(cyan + "convert2dot() : " + endC + "Id Cmd = " +
str(id_cmd) + ", state : " + state)
graph.add_node(id_cmd)
node = graph.get_node(id_cmd)
# Dependances
for dependency in dependency_list:
graph.add_edge(id_cmd, dependency)
# Definir la couleur selon l'etat de la commande
value_color = 'white'
info = ""
while switch(state):
if case(TAG_STATE_MAKE):
# Etat A_Faire
value_color = 'lightgray'
break
if case(TAG_STATE_WAIT):
# Etat En_Attente
value_color = 'gray52'
break
if case(TAG_STATE_LOCK):
# Etat Bloqué
value_color = 'darkorange'
break
if case(TAG_STATE_RUN):
# Etat En_Cours
value_color = 'deepskyblue1'
info = "\n" + start_date
break
if case(TAG_STATE_END):
# Etat Termine
value_color = 'chartreuse'
start_date_time = datetime.strptime(start_date,
'%d/%m/%y %H:%M:%S')
end_date_time = datetime.strptime(end_date,
'%d/%m/%y %H:%M:%S')
during_time = end_date_time - start_date_time
info = "\n" + str(during_time)
break
if case(TAG_STATE_ERROR):
# Etat En_Erreur"
value_color = 'deeppink'
# Definir la boite du fichier d'erreur
error_file_name = os.path.splitext(
os.path.basename(command_doc))[0] + str(id_cmd) + EXT_ERR
id_err = str(int(id_cmd) + 10000)
graph.add_node(id_err)
graph.graph_attr['label'] = ''
graph.edge_attr['label'] = ''
node_err = graph.get_node(id_err)
graph.add_edge(id_err, id_cmd)
edge_err = graph.get_edge(id_err, id_cmd)
node_err.attr['shape'] = 'rectangle'
node_err.attr['fillcolor'] = 'red' #'lightpink'
node_err.attr['label'] = error_file_name
edge_err.attr['label'] = "See error file!"
edge_err.attr['color'] = 'black'
edge_err.attr['fontcolor'] = 'red'
edge_err.attr['style'] = 'dotted'
edge_err.attr['arrowhead'] = 'open'
break
break # Sortie du while
# Assign node color
node.attr['fillcolor'] = value_color
# Empty labels
node.attr['label'] = name_task + " - " + name_cmd + info + '\n' + str(
id_cmd)
# Ecriture dans le fichier
graph_reverse = graph.reverse()
graph_reverse.write(dot_file)
return
def executeCommand(ip_serveur,
port,
id_command,
command_to_execute,
type_execution,
error_management,
base_name_shell_command,
ip_remote="",
login="",
password=""):
EXT_SHELL = '.sh'
EXT_ERR = '.err'
EXT_LOG = '.log'
new_state = ''
# Preparation du fichier d'execution en background-local ou background-remote
if type_execution == TAG_ACTION_TO_MAKE_BG or type_execution == TAG_ACTION_TO_MAKE_RE:
# Pour les executions a faire en background ou en remote preparation des fichiers .sh et .err
shell_command = base_name_shell_command + str(id_command) + EXT_SHELL
error_file = base_name_shell_command + str(id_command) + EXT_ERR
log_file = base_name_shell_command + str(id_command) + EXT_LOG
# Creation du fichier shell
error_management_option = ""
if not error_management:
error_management_option = " -nem "
command_to_execute = command_to_execute.replace('\n', '')
if six.PY2:
cmd_tmp = command_to_execute + " 1> " + log_file.encode(
"utf-8") + " 2> " + error_file.encode("utf-8") + "\n"
else:
cmd_tmp = command_to_execute + " 1> " + log_file + " 2> " + error_file + "\n"
writeTextFile(shell_command, cmd_tmp)
appendTextFileCR(
shell_command, FUNCTION_PYTHON + "ReplyEndCommand -ip_serveur " +
str(ip_serveur) + " -port " + str(port) + " -id_command " +
str(id_command) + error_management_option + " -err " + error_file)
appendTextFileCR(shell_command, "rm " + shell_command)
os.chmod(shell_command, stat.S_IRWXU)
# Selon le type d'execution
while switch(type_execution):
if case(TAG_ACTION_TO_MAKE_NOW):
# Execution en direct (local)
exitCode = subprocess.call(command_to_execute, shell=True)
new_state = TAG_STATE_END
if exitCode != 0: # Si la commande command_to_execute a eu un probleme
new_state = TAG_STATE_ERROR
print(cyan + "executeCommand : " + endC + bold + red +
"ERREUR EXECUTION DE LA COMMANDE : " +
str(command_to_execute) + endC,
file=sys.stderr)
break
if case(TAG_ACTION_TO_MAKE_BG):
# Execution en back ground (local)
process = subprocess.Popen(shell_command,
shell=True,
stderr=subprocess.STDOUT)
time.sleep(0.1)
if process == None:
new_state = TAG_STATE_ERROR
print(cyan + "executeCommand : " + endC + bold + red +
"ERREUR EXECUTION DE LA COMMANDE EN BACKGROUND : " +
str(command_to_execute) + endC,
file=sys.stderr)
else:
print(cyan + "executeCommand : " + endC +
" background pid = " + str(process.pid))
break
if case(TAG_ACTION_TO_MAKE_RE):
# Test si la machine Remote est accesible
if ping(ip_remote):
# Execution en remote execution
try:
s = pxssh.pxssh()
s.login(ip_remote, login, password)
time.sleep(0.5)
s.sendline(shell_command + '&')
time.sleep(0.01)
s.logout()
except pxssh.ExceptionPxssh as e:
new_state = TAG_STATE_ERROR
print(
cyan + "executeCommand : " + endC + bold + red +
"ERREUR EXECUTION DE LA COMMANDE EN REMOTE (login failed) : "
+ str(command_to_execute) + endC,
file=sys.stderr)
print(e, file=sys.stderr)
else:
new_state = TAG_STATE_ERROR
print(
cyan + "executeCommand : " + endC + bold + red +
"ERREUR EXECUTION DE LA COMMANDE EN REMOTE (Computeur : " +
ip_remote + " non disponible) : " +
str(command_to_execute) + endC,
file=sys.stderr)
break
break # Sortie du while
return new_state
def selectSamples(image_input_list, sample_image_input, vector_output, table_statistics_output, sampler_strategy, select_ratio_floor, ratio_per_class_dico, name_column, no_data_value, path_time_log, rand_seed=0, ram_otb=0, epsg=2154, format_vector='ESRI Shapefile', extension_vector=".shp", save_results_intermediate=False, overwrite=True) :
# Mise à jour du Log
starting_event = "selectSamples() : Select points in raster mask macro input starting : "
timeLine(path_time_log, starting_event)
if debug >= 3:
print(cyan + "selectSamples() : " + endC + "image_input_list : " + str(image_input_list) + endC)
print(cyan + "selectSamples() : " + endC + "sample_image_input : " + str(sample_image_input) + endC)
print(cyan + "selectSamples() : " + endC + "vector_output : " + str(vector_output) + endC)
print(cyan + "selectSamples() : " + endC + "table_statistics_output : " + str(table_statistics_output) + endC)
print(cyan + "selectSamples() : " + endC + "sampler_strategy : " + str(sampler_strategy) + endC)
print(cyan + "selectSamples() : " + endC + "select_ratio_floor : " + str(select_ratio_floor) + endC)
print(cyan + "selectSamples() : " + endC + "ratio_per_class_dico : " + str(ratio_per_class_dico) + endC)
print(cyan + "selectSamples() : " + endC + "name_column : " + str(name_column) + endC)
print(cyan + "selectSamples() : " + endC + "no_data_value : " + str(no_data_value) + endC)
print(cyan + "selectSamples() : " + endC + "path_time_log : " + str(path_time_log) + endC)
print(cyan + "selectSamples() : " + endC + "rand_seed : " + str(rand_seed) + endC)
print(cyan + "selectSamples() : " + endC + "ram_otb : " + str(ram_otb) + endC)
print(cyan + "selectSamples() : " + endC + "epsg : " + str(epsg) + endC)
print(cyan + "selectSamples() : " + endC + "format_vector : " + str(format_vector) + endC)
print(cyan + "selectSamples() : " + endC + "extension_vector : " + str(extension_vector) + endC)
print(cyan + "selectSamples() : " + endC + "save_results_intermediate : " + str(save_results_intermediate) + endC)
print(cyan + "selectSamples() : " + endC + "overwrite : " + str(overwrite) + endC)
# Constantes
EXT_XML = ".xml"
SUFFIX_SAMPLE = "_sample"
SUFFIX_STATISTICS = "_statistics"
SUFFIX_POINTS = "_points"
SUFFIX_VALUE = "_value"
BAND_NAME = "band_"
COLUMN_CLASS = "class"
COLUMN_ORIGINFID = "originfid"
NB_POINTS = "nb_points"
AVERAGE = "average"
STANDARD_DEVIATION = "st_dev"
print(cyan + "selectSamples() : " + bold + green + "DEBUT DE LA SELECTION DE POINTS" + endC)
# Definition variables et chemins
repertory_output = os.path.dirname(vector_output)
filename = os.path.splitext(os.path.basename(vector_output))[0]
sample_points_output = repertory_output + os.sep + filename + SUFFIX_SAMPLE + extension_vector
file_statistic_points = repertory_output + os.sep + filename + SUFFIX_STATISTICS + SUFFIX_POINTS + EXT_XML
if debug >= 3:
print(cyan + "selectSamples() : " + endC + "file_statistic_points : " + str(file_statistic_points) + endC)
# 0. EXISTENCE DU FICHIER DE SORTIE
#----------------------------------
# Si le fichier vecteur points de sortie existe deja et que overwrite n'est pas activé
check = os.path.isfile(vector_output)
if check and not overwrite:
print(bold + yellow + "Samples points already done for file %s and will not be calculated again." %(vector_output) + endC)
else: # Si non ou si la vérification est désactivée : creation du fichier d'échantillons points
# Suppression de l'éventuel fichier existant
if check:
try:
removeVectorFile(vector_output)
except Exception:
pass # Si le fichier ne peut pas être supprimé, on suppose qu'il n'existe pas et on passe à la suite
if os.path.isfile(table_statistics_output) :
try:
removeFile(table_statistics_output)
except Exception:
pass # Si le fichier ne peut pas être supprimé, on suppose qu'il n'existe pas et on passe à la suite
# 1. STATISTIQUE SUR L'IMAGE DES ECHANTILLONS RASTEUR
#----------------------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start statistique sur l'image des echantillons rasteur..." + endC)
id_micro_list = identifyPixelValues(sample_image_input)
if 0 in id_micro_list :
id_micro_list.remove(0)
min_micro_class_nb_points = -1
min_micro_class_label = 0
infoStructPointSource_dico = {}
writeTextFile(file_statistic_points, '<?xml version="1.0" ?>\n')
appendTextFileCR(file_statistic_points, '<GeneralStatistics>')
appendTextFileCR(file_statistic_points, ' <Statistic name="pointsPerClassRaw">')
if debug >= 2:
print("Nombre de points par micro classe :" + endC)
for id_micro in id_micro_list :
nb_pixels = countPixelsOfValue(sample_image_input, id_micro)
if debug >= 2:
print("MicroClass : " + str(id_micro) + ", nb_points = " + str(nb_pixels))
appendTextFileCR(file_statistic_points, ' <StatisticPoints class="%d" value="%d" />' %(id_micro, nb_pixels))
if min_micro_class_nb_points == -1 or min_micro_class_nb_points > nb_pixels :
min_micro_class_nb_points = nb_pixels
min_micro_class_label = id_micro
infoStructPointSource_dico[id_micro] = StructInfoMicoClass()
infoStructPointSource_dico[id_micro].label_class = id_micro
infoStructPointSource_dico[id_micro].nb_points = nb_pixels
infoStructPointSource_dico[id_micro].info_points_list = []
del nb_pixels
if debug >= 2:
print("MicroClass min points find : " + str(min_micro_class_label) + ", nb_points = " + str(min_micro_class_nb_points))
appendTextFileCR(file_statistic_points, ' </Statistic>')
pending_event = cyan + "selectSamples() : " + bold + green + "End statistique sur l'image des echantillons rasteur. " + endC
if debug >= 3:
print(pending_event)
timeLine(path_time_log,pending_event)
# 2. CHARGEMENT DE L'IMAGE DES ECHANTILLONS
#------------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start chargement de l'image des echantillons..." + endC)
# Information image
cols, rows, bands = getGeometryImage(sample_image_input)
xmin, xmax, ymin, ymax = getEmpriseImage(sample_image_input)
pixel_width, pixel_height = getPixelWidthXYImage(sample_image_input)
projection_input = getProjectionImage(sample_image_input)
if projection_input == None or projection_input == 0 :
projection_input = epsg
else :
projection_input = int(projection_input)
pixel_width = abs(pixel_width)
pixel_height = abs(pixel_height)
# Lecture des données
raw_data = getRawDataImage(sample_image_input)
if debug >= 3:
print("projection = " + str(projection_input))
print("cols = " + str(cols))
print("rows = " + str(rows))
# Creation d'une structure dico contenent tous les points différents de zéro
progress = 0
pass_prog = False
for y_row in range(rows) :
for x_col in range(cols) :
value_class = raw_data[y_row][x_col]
if value_class != 0 :
infoStructPointSource_dico[value_class].info_points_list.append(x_col + (y_row * cols))
# Barre de progression
if debug >= 4:
if ((float(y_row) / rows) * 100.0 > progress) and not pass_prog :
progress += 1
pass_prog = True
print("Progression => " + str(progress) + "%")
if ((float(y_row) / rows) * 100.0 > progress + 1) :
pass_prog = False
del raw_data
pending_event = cyan + "selectSamples() : " + bold + green + "End chargement de l'image des echantillons. " + endC
if debug >= 3:
print(pending_event)
timeLine(path_time_log,pending_event)
# 3. SELECTION DES POINTS D'ECHANTILLON
#--------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start selection des points d'echantillon..." + endC)
appendTextFileCR(file_statistic_points, ' <Statistic name="pointsPerClassSelect">')
# Rendre deterministe la fonction aléatoire de random.sample
if rand_seed > 0:
random.seed( rand_seed )
# Pour toute les micro classes
for id_micro in id_micro_list :
# Selon la stategie de selection
nb_points_ratio = 0
while switch(sampler_strategy.lower()):
if case('all'):
# Le mode de selection 'all' est choisi
nb_points_ratio = infoStructPointSource_dico[id_micro].nb_points
infoStructPointSource_dico[id_micro].sample_points_list = range(nb_points_ratio)
break
if case('percent'):
# Le mode de selection 'percent' est choisi
id_macro_class = int(math.floor(id_micro / 100) * 100)
select_ratio_class = ratio_per_class_dico[id_macro_class]
nb_points_ratio = int(infoStructPointSource_dico[id_micro].nb_points * select_ratio_class / 100)
infoStructPointSource_dico[id_micro].sample_points_list = random.sample(range(infoStructPointSource_dico[id_micro].nb_points), nb_points_ratio)
break
if case('mixte'):
# Le mode de selection 'mixte' est choisi
nb_points_ratio = int(infoStructPointSource_dico[id_micro].nb_points * select_ratio_floor / 100)
if id_micro == min_micro_class_label :
# La plus petite micro classe est concervée intégralement
infoStructPointSource_dico[id_micro].sample_points_list = range(infoStructPointSource_dico[id_micro].nb_points)
nb_points_ratio = min_micro_class_nb_points
elif nb_points_ratio <= min_micro_class_nb_points :
# Les micro classes dont le ratio de selection est inferieur au nombre de points de la plus petite classe sont égement conservées intégralement
infoStructPointSource_dico[id_micro].sample_points_list = random.sample(range(infoStructPointSource_dico[id_micro].nb_points), min_micro_class_nb_points)
nb_points_ratio = min_micro_class_nb_points
else :
# Pour toutes les autres micro classes tirage aleatoire d'un nombre de points correspondant au ratio
infoStructPointSource_dico[id_micro].sample_points_list = random.sample(range(infoStructPointSource_dico[id_micro].nb_points), nb_points_ratio)
break
break
if debug >= 2:
print("MicroClass = " + str(id_micro) + ", nb_points_ratio " + str(nb_points_ratio))
appendTextFileCR(file_statistic_points, ' <StatisticPoints class="%d" value="%d" />' %(id_micro, nb_points_ratio))
appendTextFileCR(file_statistic_points, ' </Statistic>')
appendTextFileCR(file_statistic_points, '</GeneralStatistics>')
pending_event = cyan + "selectSamples() : " + bold + green + "End selection des points d'echantillon. " + endC
if debug >= 3:
print(pending_event)
timeLine(path_time_log,pending_event)
# 4. PREPARATION DES POINTS D'ECHANTILLON
#----------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start preparation des points d'echantillon..." + endC)
# Création du dico de points
points_random_value_dico = {}
index_dico_point = 0
for micro_class in infoStructPointSource_dico :
micro_class_struct = infoStructPointSource_dico[micro_class]
label_class = micro_class_struct.label_class
point_attr_dico = {name_column:int(label_class), COLUMN_CLASS:int(label_class), COLUMN_ORIGINFID:0}
for id_point in micro_class_struct.sample_points_list:
# Recuperer les valeurs des coordonnees des points
coor_x = float(xmin + (int(micro_class_struct.info_points_list[id_point] % cols) * pixel_width)) + (pixel_width / 2.0)
coor_y = float(ymax - (int(micro_class_struct.info_points_list[id_point] / cols) * pixel_height)) - (pixel_height / 2.0)
points_random_value_dico[index_dico_point] = [[coor_x, coor_y], point_attr_dico]
del coor_x
del coor_y
index_dico_point += 1
del point_attr_dico
del infoStructPointSource_dico
pending_event = cyan + "selectSamples() : " + bold + green + "End preparation des points d'echantillon. " + endC
if debug >=3:
print(pending_event)
timeLine(path_time_log,pending_event)
# 5. CREATION DU FICHIER SHAPE DE POINTS D'ECHANTILLON
#-----------------------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start creation du fichier shape de points d'echantillon..." + endC)
# Définir les attibuts du fichier résultat
attribute_dico = {name_column:ogr.OFTInteger, COLUMN_CLASS:ogr.OFTInteger, COLUMN_ORIGINFID:ogr.OFTInteger}
# Creation du fichier shape
createPointsFromCoordList(attribute_dico, points_random_value_dico, sample_points_output, projection_input, format_vector)
del attribute_dico
del points_random_value_dico
pending_event = cyan + "selectSamples() : " + bold + green + "End creation du fichier shape de points d'echantillon. " + endC
if debug >=3:
print(pending_event)
timeLine(path_time_log,pending_event)
# 6. EXTRACTION DES POINTS D'ECHANTILLONS
#-----------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start extraction des points d'echantillon dans l'image..." + endC)
# Cas ou l'on a une seule image
if len(image_input_list) == 1:
# Extract sample
image_input = image_input_list[0]
command = "otbcli_SampleExtraction -in %s -vec %s -outfield prefix -outfield.prefix.name %s -out %s -field %s" %(image_input, sample_points_output, BAND_NAME, vector_output, name_column)
if ram_otb > 0:
command += " -ram %d" %(ram_otb)
if debug >= 3:
print(command)
exitCode = os.system(command)
if exitCode != 0:
raise NameError(cyan + "selectSamples() : " + bold + red + "An error occured during otbcli_SampleExtraction command. See error message above." + endC)
# Cas de plusieurs imagettes
else :
# Le repertoire de sortie
repertory_output = os.path.dirname(vector_output)
# Initialisation de la liste pour le multi-threading et la liste de l'ensemble des echantions locaux
thread_list = []
vector_local_output_list = []
# Obtenir l'emprise des images d'entrées pour redecouper le vecteur d'echantillon d'apprentissage pour chaque image
for image_input in image_input_list :
# Definition des fichiers sur emprise local
file_name = os.path.splitext(os.path.basename(image_input))[0]
emprise_local_sample = repertory_output + os.sep + file_name + SUFFIX_SAMPLE + extension_vector
vector_sample_local_output = repertory_output + os.sep + file_name + SUFFIX_VALUE + extension_vector
vector_local_output_list.append(vector_sample_local_output)
# Gestion sans thread...
#SampleLocalExtraction(image_input, sample_points_output, emprise_local_sample, vector_sample_local_output, name_column, BAND_NAME, ram_otb, format_vector, extension_vector, save_results_intermediate)
# Gestion du multi threading
thread = threading.Thread(target=SampleLocalExtraction, args=(image_input, sample_points_output, emprise_local_sample, vector_sample_local_output, name_column, BAND_NAME, ram_otb, format_vector, extension_vector, save_results_intermediate))
thread.start()
thread_list.append(thread)
# Extraction des echantions points des images
try:
for thread in thread_list:
thread.join()
except:
print(cyan + "selectSamples() : " + bold + red + "Erreur lors de l'éextaction des valeurs d'echantion : impossible de demarrer le thread" + endC, file=sys.stderr)
# Fusion des multi vecteurs de points contenant les valeurs des bandes de l'image
fusionVectors(vector_local_output_list, vector_output, format_vector)
# Clean des vecteurs point sample local file
for vector_sample_local_output in vector_local_output_list :
removeVectorFile(vector_sample_local_output)
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "End extraction des points d'echantillon dans l'image." + endC)
# 7. CALCUL DES STATISTIQUES SUR LES VALEURS DES POINTS D'ECHANTILLONS SELECTIONNEES
#-----------------------------------------------------------------------------------
if debug >= 3:
print(cyan + "selectSamples() : " + bold + green + "Start calcul des statistiques sur les valeurs des points d'echantillons selectionnees..." + endC)
# Si le calcul des statistiques est demandé presence du fichier stat
if table_statistics_output != "":
# On récupère la liste de données
pending_event = cyan + "selectSamples() : " + bold + green + "Encours calcul des statistiques part1... " + endC
if debug >=4:
print(pending_event)
timeLine(path_time_log,pending_event)
attribute_name_dico = {}
name_field_value_list = []
names_attribut_list = getAttributeNameList(vector_output, format_vector)
if debug >=4:
print("names_attribut_list = " + str(names_attribut_list))
attribute_name_dico[name_column] = ogr.OFTInteger
for name_attribut in names_attribut_list :
if BAND_NAME in name_attribut :
attribute_name_dico[name_attribut] = ogr.OFTReal
name_field_value_list.append(name_attribut)
name_field_value_list.sort()
res_values_dico = getAttributeValues(vector_output, None, None, attribute_name_dico, format_vector)
del attribute_name_dico
# Trie des données par identifiant micro classes
pending_event = cyan + "selectSamples() : " + bold + green + "Encours calcul des statistiques part2... " + endC
if debug >=4:
print(pending_event)
timeLine(path_time_log,pending_event)
data_value_by_micro_class_dico = {}
stat_by_micro_class_dico = {}
# Initilisation du dico complexe
for id_micro in id_micro_list :
data_value_by_micro_class_dico[id_micro] = {}
stat_by_micro_class_dico[id_micro] = {}
for name_field_value in res_values_dico :
if name_field_value != name_column :
data_value_by_micro_class_dico[id_micro][name_field_value] = []
stat_by_micro_class_dico[id_micro][name_field_value] = {}
stat_by_micro_class_dico[id_micro][name_field_value][AVERAGE] = 0.0
stat_by_micro_class_dico[id_micro][name_field_value][STANDARD_DEVIATION] = 0.0
# Trie des valeurs
pending_event = cyan + "selectSamples() : " + bold + green + "Encours calcul des statistiques part3... " + endC
if debug >=4:
print(pending_event)
timeLine(path_time_log,pending_event)
for index in range(len(res_values_dico[name_column])) :
id_micro = res_values_dico[name_column][index]
for name_field_value in name_field_value_list :
data_value_by_micro_class_dico[id_micro][name_field_value].append(res_values_dico[name_field_value][index])
del res_values_dico
# Calcul des statistiques
pending_event = cyan + "selectSamples() : " + bold + green + "Encours calcul des statistiques part4... " + endC
if debug >=4:
print(pending_event)
timeLine(path_time_log,pending_event)
for id_micro in id_micro_list :
for name_field_value in name_field_value_list :
try :
stat_by_micro_class_dico[id_micro][name_field_value][AVERAGE] = average(data_value_by_micro_class_dico[id_micro][name_field_value])
except:
stat_by_micro_class_dico[id_micro][name_field_value][AVERAGE] = 0
try :
stat_by_micro_class_dico[id_micro][name_field_value][STANDARD_DEVIATION] = standardDeviation(data_value_by_micro_class_dico[id_micro][name_field_value])
except:
stat_by_micro_class_dico[id_micro][name_field_value][STANDARD_DEVIATION] = 0
try :
stat_by_micro_class_dico[id_micro][name_field_value][NB_POINTS] = len(data_value_by_micro_class_dico[id_micro][name_field_value])
except:
stat_by_micro_class_dico[id_micro][name_field_value][NB_POINTS] = 0
del data_value_by_micro_class_dico
# Creation du fichier statistique .csv
pending_event = cyan + "selectSamples() : " + bold + green + "Encours calcul des statistiques part5... " + endC
if debug >= 4:
print(pending_event)
timeLine(path_time_log,pending_event)
text_csv = " Micro classes ; Champs couche image ; Nombre de points ; Moyenne ; Ecart type \n"
writeTextFile(table_statistics_output, text_csv)
for id_micro in id_micro_list :
for name_field_value in name_field_value_list :
# Ecriture du fichier
text_csv = " %d " %(id_micro)
text_csv += " ; %s" %(name_field_value)
text_csv += " ; %d" %(stat_by_micro_class_dico[id_micro][name_field_value][NB_POINTS])
text_csv += " ; %f" %(stat_by_micro_class_dico[id_micro][name_field_value][AVERAGE])
text_csv += " ; %f" %(stat_by_micro_class_dico[id_micro][name_field_value][STANDARD_DEVIATION])
appendTextFileCR(table_statistics_output, text_csv)
del name_field_value_list
else :
if debug >=3:
print(cyan + "selectSamples() : " + bold + green + "Pas de calcul des statistiques sur les valeurs des points demander!!!." + endC)
del id_micro_list
pending_event = cyan + "selectSamples() : " + bold + green + "End calcul des statistiques sur les valeurs des points d'echantillons selectionnees. " + endC
if debug >= 3:
print(pending_event)
timeLine(path_time_log,pending_event)
# 8. SUPRESSION DES FICHIERS INTERMEDIAIRES
#------------------------------------------
if not save_results_intermediate:
if os.path.isfile(sample_points_output) :
removeVectorFile(sample_points_output)
print(cyan + "selectSamples() : " + bold + green + "FIN DE LA SELECTION DE POINTS" + endC)
# Mise à jour du Log
ending_event = "selectSamples() : Select points in raster mask macro input ending : "
timeLine(path_time_log,ending_event)
return
