def prepareData(input_buffer_tdc, input_paysage, output_dir, input_repertories_list, id_paysage, id_name_sub_rep, epsg, optimization_zone, no_cover, zone_date, separ_name, pos_date, nb_char_date, separ_date, path_time_log, format_raster='GTiff', format_vector="ESRI Shapefile", extension_raster=".tif", extension_vector=".shp", save_results_intermediate=True, overwrite=True):
# Mise à jour du Log
starting_event = "prepareData() : Select prepare data starting : "
timeLine(path_time_log,starting_event)
# Affichage des paramètres
if debug >= 3:
print(bold + green + "Variables dans le prepareData - Variables générales" + endC)
print(cyan + "PrepareData() : " + endC + "input_buffer_tdc : " + str(input_buffer_tdc) + endC)
print(cyan + "PrepareData() : " + endC + "input_paysage : " + str(input_paysage) + endC)
print(cyan + "PrepareData() : " + endC + "output_dir : " + str(output_dir) + endC)
print(cyan + "PrepareData() : " + endC + "input_repertories_list : " + str(input_repertories_list) + endC)
print(cyan + "PrepareData() : " + endC + "id_paysage : " + str(id_paysage) + endC)
print(cyan + "PrepareData() : " + endC + "id_name_sub_rep : " + str(id_name_sub_rep) + endC)
print(cyan + "PrepareData() : " + endC + "epsg : " + str(epsg) +endC)
print(cyan + "PrepareData() : " + endC + "optimization_zone : " + str(optimization_zone) + endC)
print(cyan + "PrepareData() : " + endC + "no_cover : " + str(no_cover) + endC)
print(cyan + "PrepareData() : " + endC + "zone_date : " + str(zone_date) + endC)
print(cyan + "PrepareData() : " + endC + "separ_name : " + str(separ_name) + endC)
print(cyan + "PrepareData() : " + endC + "pos_date : " + str(pos_date) + endC)
print(cyan + "PrepareData() : " + endC + "nb_char_date : " + str(nb_char_date) + endC)
print(cyan + "PrepareData() : " + endC + "separ_date : " + str(separ_date) + endC)
print(cyan + "PrepareData() : " + endC + "path_time_log : " + str(path_time_log) + endC)
print(cyan + "PrepareData() : " + endC + "format_raster : " + str(format_raster) + endC)
print(cyan + "PrepareData() : " + endC + "format_vector : " + str(format_vector) + endC)
print(cyan + "PrepareData() : " + endC + "extension_raster : " + str(extension_raster) + endC)
print(cyan + "PrepareData() : " + endC + "extension_vector : " + str(extension_vector) + endC)
print(cyan + "PrepareData() : " + endC + "save_results_inter : " + str(save_results_intermediate) + endC)
print(cyan + "PrepareData() : " + endC + "overwrite : " + str(overwrite) + endC)
REPERTORY_PAYSAGES = "Paysages"
REPERTORY_IMAGES = "Images"
ID_P = "id_p"
SUFFIX_OPTI = "_opti"
SUFFIX_CUT = "_cut"
SUFFIX_ERROR = "_error"
SUFFIX_MERGE = "_merge"
SUFFIX_CLEAN = "_clean"
SUFFIX_STACK = "_stack"
output_dir_paysages = output_dir + os.sep + REPERTORY_PAYSAGES
output_dir_images = output_dir + os.sep + REPERTORY_IMAGES
# Création du répertoire de sortie s'il n'existe pas déjà
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Création du répertoire de sortie pour les paysages s'il n'existe pas
if not os.path.exists(output_dir_paysages):
os.makedirs(output_dir_paysages)
# Création du répertoire de sortie pour les images s'il n'existe pas
if not os.path.exists(output_dir_images):
os.makedirs(output_dir_images)
# Recuperer l'epsg du fichier d'emprise
if epsg == 0 :
epsg = getProjection(input_paysage, format_vector)
# Création du paysage optimal
optimPaysage(input_buffer_tdc, input_paysage, optimization_zone, SUFFIX_OPTI, output_dir_paysages, id_paysage, format_vector)
# Création un shapefile par polygone
paysage_opti = output_dir_paysages + os.sep + os.path.splitext(os.path.basename(input_paysage))[0] + SUFFIX_OPTI + os.path.splitext(input_paysage)[1]
if id_paysage != "" :
paysages_list = splitVector(paysage_opti, str(output_dir_paysages), str(id_paysage), epsg, format_vector, extension_vector)
else:
paysages_list = splitVector(paysage_opti, str(output_dir_paysages), ID_P, epsg, format_vector, extension_vector)
if debug >= 3:
print(cyan + "PrepareData() : " + endC + "Liste des fichiers en entrée de imagesAssembly() : " + str(paysages_list))
# Création du fichier de sortie des images s'il n'existe pas dejà
if not os.path.exists(output_dir_images):
os.makedirs(output_dir_images)
# Assemblage des images dans les paysages optimisés
# Si on choisit pas de recouvrement entre les images
if no_cover:
# Récupération des noms de sortie
image_output_list = []
id_paysage_list = []
for shape in paysages_list :
attribute_name_dico = {}
attribute_name_dico[id_name_sub_rep] = ogr.OFTString
attribute_name_dico[id_paysage] = ogr.OFTInteger
res_values_dico = getAttributeValues(shape, None, None, attribute_name_dico, format_vector)
id_name_sub_rep_value = res_values_dico[id_name_sub_rep][0]
id_paysage_value = res_values_dico[id_paysage][0]
image_output_list.append(id_name_sub_rep_value)
id_paysage_list.append(id_paysage_value)
if debug >= 3:
print("image_output_list " + str(image_output_list))
# Récupération de tous les (sous-)répertoires
repertory_images_sources_list_temp = []
for input_dir in input_repertories_list:
sub_rep_list = getSubRepRecursifList(input_dir)
if sub_rep_list != []:
for sub_rep in sub_rep_list:
repertory_images_sources_list_temp.append(sub_rep)
else:
repertory_images_sources_list_temp.append(input_dir)
# On réorganise pour avoir le même ordre dans les 2 listes 'repertory_images_sources_list' et 'image_output_list'
repertory_images_sources_list = []
for paysage in id_paysage_list:
for repertory_images_source in repertory_images_sources_list_temp:
if str(paysage) in repertory_images_source.split(os.sep)[-1]:
repertory_images_sources_list.append(repertory_images_source)
if debug >= 3:
print("repertory_images_sources_list " + str(repertory_images_sources_list))
if len(repertory_images_sources_list) != len(image_output_list):
raise Exception(bold + red + "Error: not same number of input repertories and output files." + endC)
# Commande ImagesAssembly sur les éléments des 2 listes
for i in range(len(paysages_list)):
image_output = output_dir_images + os.sep + image_output_list[i]
if debug >= 3:
print(cyan + "PrepareData() : " + endC + bold + green + "image_output : " + endC + image_output)
try:
# ~ selectAssembyImagesByHold(paysages_list[i], [repertory_images_sources_list[i]], image_output, False, True, epsg, False, False, False, False, 0, 0, 0, 0, separ_name, pos_date, nb_char_date, separ_date, path_time_log, SUFFIX_ERROR, SUFFIX_MERGE, SUFFIX_CLEAN, SUFFIX_STACK, format_raster, format_vector, extension_raster, extension_vector, save_results_intermediate, overwrite)
selectAssembyImagesByHold(paysages_list[i], [repertory_images_sources_list[i]], image_output, False, zone_date, epsg, False, False, False, False, 0, 0, 0, 0, separ_name, pos_date, nb_char_date, separ_date, path_time_log, SUFFIX_ERROR, SUFFIX_MERGE, SUFFIX_CLEAN, SUFFIX_STACK, format_raster, format_vector, extension_raster, extension_vector, save_results_intermediate, overwrite)
except Exception:
pass
else:
for shape in paysages_list :
# ~ filename = os.path.basename(shape)
# ~ info_name = filename.split(separ_name, 2)[POS]
# ~ name_shape = info_name[:NB_CHAR]
# ~ image_output = output_dir_images + os.sep + name_shape + SUFFIX_ASS + extension_raster
# ~ for shape in sub_repertory_images_paysages_list :
image_output = output_dir_images + os.sep + os.path.splitext(os.path.basename(shape)) + extension_raster
if optimization_zone :
shape_cut = os.path.splitext(shape)[0] + SUFFIX_CUT + os.path.splitext(shape)[1]
cutVector(input_buffer_tdc, shape, shape_cut, overwrite, format_vector)
else :
shape_cut = shape
selectAssembyImagesByHold(shape_cut, input_repertories_list, image_output, False, zone_date, epsg, False, False, False, False, 0, 0, 0, 0, separ_name, pos_date, nb_char_date, separ_date, path_time_log, SUFFIX_ERROR, SUFFIX_MERGE, SUFFIX_CLEAN, SUFFIX_STACK, format_raster, format_vector, save_results_intermediate, overwrite)
if optimization_zone and os.path.exists(shape_cut):
removeVectorFile(shape_cut, format_vector)
# Mise à jour du Log
ending_event = "prepareData() : Select prepare data ending : "
timeLine(path_time_log,ending_event)
if debug >= 3:
print(cyan + "PrepareData() : " + endC + bold + green + "Fin de traitement")
return
def runTDCSeuil(input_im_seuils_dico,
output_dir,
input_sea_points,
input_cut_vector,
input_emprise_vector,
simplif,
is_calc_indice_image,
attribute_val_limite,
attribute_val_proced,
attribute_val_datepr,
attribute_val_precis,
attribute_val_contac,
attribute_val_type,
no_data_value,
path_time_log,
channel_order=['Red', 'Green', 'Blue', 'NIR'],
epsg=2154,
format_raster='GTiff',
format_vector="ESRI Shapefile",
extension_raster=".tif",
extension_vector=".shp",
save_results_intermediate=True,
overwrite=True):
# Mise à jour du Log
starting_event = "runTDCSeuil() : Select TDC Seuil starting : "
timeLine(path_time_log, starting_event)
# Affichage des paramètres
if debug >= 3:
print(bold + green +
"Variables dans runTDCSeuil - Variables générales" + endC)
print(cyan + "runTDCSeuil() : " + endC + "input_im_seuils_dico : " +
str(input_im_seuils_dico) + endC)
print(cyan + "runTDCSeuil() : " + endC + "output_dir : " +
str(output_dir) + endC)
print(cyan + "runTDCSeuil() : " + endC + "input_sea_points : " +
str(input_sea_points) + endC)
print(cyan + "runTDCSeuil() : " + endC + "input_cut_vector : " +
str(input_cut_vector) + endC)
print(cyan + "runTDCSeuil() : " + endC + "input_emprise_vector : " +
str(input_emprise_vector) + endC)
print(cyan + "runTDCSeuil() : " + endC + "simplif : " + str(simplif) +
endC)
print(cyan + "runTDCSeuil() : " + endC + "is_calc_indice_image : " +
str(is_calc_indice_image) + endC)
print(cyan + "runTDCSeuil() : " + endC + "attribute_val_limite : " +
str(attribute_val_limite) + endC)
print(cyan + "runTDCSeuil() : " + endC + "attribute_val_proced : " +
str(attribute_val_proced) + endC)
print(cyan + "runTDCSeuil() : " + endC + "attribute_val_datepr : " +
str(attribute_val_datepr) + endC)
print(cyan + "runTDCSeuil() : " + endC + "attribute_val_precis : " +
str(attribute_val_precis) + endC)
print(cyan + "runTDCSeuil() : " + endC + "attribute_val_contac : " +
str(attribute_val_contac) + endC)
print(cyan + "runTDCSeuil() : " + endC + "attribute_val_type : " +
str(attribute_val_type) + endC)
print(cyan + "runTDCSeuil() : " + endC + "no_data_value : " +
str(no_data_value) + endC)
print(cyan + "runTDCSeuil() : " + endC + "path_time_log : " +
str(path_time_log) + endC)
print(cyan + "runTDCSeuil() : " + endC + "channel_order: " +
str(channel_order) + endC)
print(cyan + "runTDCSeuil() : " + endC + "epsg : " + str(epsg) + endC)
print(cyan + "runTDCSeuil() : " + endC + "format_raster : " +
str(format_raster) + endC)
print(cyan + "runTDCSeuil() : " + endC + "format_vector : " +
str(format_vector) + endC)
print(cyan + "runTDCSeuil() : " + endC + "extension_raster : " +
str(extension_raster) + endC)
print(cyan + "runTDCSeuil() : " + endC + "extension_vector : " +
str(extension_vector) + endC)
print(cyan + "runTDCSeuil() : " + endC +
"save_results_intermediate : " + str(save_results_intermediate) +
endC)
print(cyan + "runTDCSeuil() : " + endC + "overwrite : " +
str(overwrite) + endC)
# Initialisation des constantes
AUTO = "auto"
POS_NUMERO_DOSSIER = 2
REP_NDVI_TDC_SEUIL = "ndvi_TDCSeuil"
REP_TEMP_BIN_MASK_V = "Temp_Binary_Mask_Vector_"
ATTR_NAME_REFDOSSIER = "RefDossier"
ATTR_NAME_NOMIMAGE = "NomImage"
ATTR_NAME_DATEACQUI = "DateAcqui"
ATTR_NAME_HEUREACQUI = "HeureAcqui"
ATTR_NAME_LIMITE = "TdcLimite"
ATTR_NAME_PROCED = "TdcProced"
ATTR_NAME_DATEPR = "TdcDatepro"
ATTR_NAME_PRECIS = "TdcPrecis"
ATTR_NAME_CONTAC = "TdcContact"
ATTR_NAME_TYPE = "Type"
# Repertoire NDVI à conserver!!!
repertory_ndvi = output_dir + os.sep + REP_NDVI_TDC_SEUIL
repertory_temp_list = []
# Création du répertoire de sortie s'il n'existe pas déjà
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Création du répertoire de sortie temporaire s'il n'existe pas déjà
if not os.path.exists(repertory_ndvi):
os.makedirs(repertory_ndvi)
# Exploitation du fichier emprise pour renseigner les informations des attribues
res_values_dico = {}
if input_emprise_vector != "":
# Lecture des attributs de fichier vecteur
names_attribut_list = getAttributeNameList(input_emprise_vector,
format_vector)
attribute_name_dico = {}
for name_attribut in names_attribut_list:
attribute_name_dico[name_attribut] = getAttributeType(
input_emprise_vector, name_attribut, format_vector)
res_values_dico = getAttributeValues(input_emprise_vector, None, None,
attribute_name_dico,
format_vector)
# On calcule plusieurs seuils par image, mais différents selon les images
bin_mask_list = []
images_list = []
nb_images = len(input_im_seuils_dico.split())
image_first_and_seuils = input_im_seuils_dico.split()[0]
# Création d'une liste d'image
for elt in input_im_seuils_dico.split():
images_list.append(elt.split(":")[0])
if ":" not in image_first_and_seuils:
print(
cyan + "runTDCSeuil() : " + red + bold +
"Aucun seuil spécifié ! (Nécessité d'au moins un pour la 1ère image)"
+ endC,
file=sys.stderr)
sys.exit(1)
else:
seuils_first_image_list = image_first_and_seuils.split(":")[1].split(
",")
for i in range(nb_images):
# Chaque image + seuils (exemple : /path/image_xx.tif:0.1,0,-0.1)
image_index_and_seuils = input_im_seuils_dico.split()[i]
seuils_index_image_list = image_index_and_seuils.split(":")[1].split(
",")
# L'image à traiter
input_image = image_index_and_seuils.split(":")[0]
image_name = os.path.splitext(os.path.basename(input_image))[0]
# Création du répertoire temporaire de calcul
repertory_temp = output_dir + os.sep + REP_TEMP_BIN_MASK_V + image_name
if not os.path.exists(repertory_temp):
os.makedirs(repertory_temp)
repertory_temp_list.append(repertory_temp)
# Initialisation des champs des attributs
num_dossier = image_name.split("_")[POS_NUMERO_DOSSIER]
attribute_val_refdossier = num_dossier
attribute_val_nomimage = image_name
attribute_val_datecqui = " "
attribute_val_heureacqui = " "
if attribute_val_limite == "":
attribute_val_limite = " "
if attribute_val_proced == "":
attribute_val_proced = " "
if attribute_val_datepr == "":
now = datetime.datetime.now()
attribute_val_datepr = now.strftime("%Y-%m-%d")
if attribute_val_precis == "":
attribute_val_precis = " "
if attribute_val_contac == "":
attribute_val_contac = " "
if attribute_val_type == "":
attribute_val_type = " "
# Cas ou un fichier d'emprise contenant des données des attributs est present et contient un champs "RefDossier"
if ATTR_NAME_REFDOSSIER in res_values_dico:
if num_dossier in res_values_dico[ATTR_NAME_REFDOSSIER]:
index_dossier = res_values_dico[ATTR_NAME_REFDOSSIER].index(
num_dossier)
if ATTR_NAME_NOMIMAGE in res_values_dico:
attribute_val_nomimage = res_values_dico[
ATTR_NAME_NOMIMAGE][index_dossier]
if ATTR_NAME_DATEACQUI in res_values_dico:
datecqui_list = res_values_dico[ATTR_NAME_DATEACQUI][
index_dossier]
attribute_val_datecqui = str(datecqui_list[0]) + "-" + str(
datecqui_list[1]) + "-" + str(datecqui_list[2])
if ATTR_NAME_HEUREACQUI in res_values_dico:
attribute_val_heureacqui = res_values_dico[
ATTR_NAME_HEUREACQUI][index_dossier]
# Initialisation de StructAttribute pour la création des champs
attributes_list = [StructAttribute(ATTR_NAME_REFDOSSIER, ogr.OFTString, 20, attribute_val_refdossier), \
StructAttribute(ATTR_NAME_NOMIMAGE, ogr.OFTString, 20, attribute_val_nomimage), \
StructAttribute(ATTR_NAME_DATEACQUI, ogr.OFTDate, None,attribute_val_datecqui), \
StructAttribute(ATTR_NAME_HEUREACQUI, ogr.OFTString, 14, attribute_val_heureacqui), \
StructAttribute(ATTR_NAME_LIMITE, ogr.OFTString, 20, attribute_val_limite), \
StructAttribute(ATTR_NAME_PROCED, ogr.OFTString, 30, attribute_val_proced), \
StructAttribute(ATTR_NAME_DATEPR, ogr.OFTString, 14, attribute_val_datepr), \
StructAttribute(ATTR_NAME_PRECIS, ogr.OFTString, 20, attribute_val_precis), \
StructAttribute(ATTR_NAME_CONTAC, ogr.OFTString, 20, attribute_val_contac), \
StructAttribute(ATTR_NAME_TYPE, ogr.OFTString, 14, attribute_val_type)]
# Calcul de l'image NDVI si is_calc_indice_image est à True
if is_calc_indice_image:
image_index = repertory_ndvi + os.sep + "image_NDVI_" + os.path.splitext(
os.path.basename(images_list[i]))[0] + extension_raster
if not os.path.exists(input_image):
print(cyan + "runTDCSeuil() : " + red + bold +
"L'image renseignée en entrée : " + input_image +
" n'existe pas. Vérifiez le chemin !" + endC,
file=sys.stderr)
sys.exit(1)
createNDVI(input_image, image_index, channel_order)
else:
image_index = seuils_index_image_list[0]
if os.path.splitext(image_index)[1] != extension_raster:
print(
cyan + "runTDCSeuil() : " + red + bold +
"Si vous choisissez de calculer l'image NDVI, mettre l'option -c. Sinon, le 1er paramètre derrière \":\" dans -isd doit être l'image indice (.tif)"
+ endC,
file=sys.stderr)
sys.exit(1)
if ":" not in image_index_and_seuils:
if is_calc_indice_image:
for t in seuils_first_image_list:
if t == AUTO:
seuils_list = runCalculSeuil(
image_index, output_dir, save_results_intermediate)
# Masque centre classe
bin_mask_cc = binaryMaskVect(
image_index, repertory_temp, float(seuils_list[0]),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
# Masque borne inf
bin_mask_bi = binaryMaskVect(
image_index, repertory_temp, float(v[1]),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
# Ajout des masques à la liste
bin_mask_list.append(bin_mask_cc)
bin_mask_list.append(bin_mask_bi)
else:
bin_mask = binaryMaskVect(
image_index, repertory_temp, float(t),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
bin_mask_list.append(bin_mask)
else:
print(cyan + "runTDCSeuil() : " + red + +bold +
"Renseignez les images NDVI associées et les seuils !" +
endC,
file=sys.stderr)
sys.exit(1)
else:
if is_calc_indice_image:
for t in seuils_index_image_list:
if t == AUTO:
seuils_list = runCalculSeuil(
image_index, output_dir, save_results_intermediate)
# Masque centre classe
bin_mask_cc = binaryMaskVect(
image_index, repertory_temp, float(seuils_list[0]),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
# Masque borne inf
bin_mask_bi = binaryMaskVect(
image_index, repertory_temp, float(seuils_list[1]),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
# Ajout des masques à la liste
bin_mask_list.append(bin_mask_cc)
bin_mask_list.append(bin_mask_bi)
else:
bin_mask = binaryMaskVect(
image_index, repertory_temp, float(t),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
bin_mask_list.append(bin_mask)
else:
for j in range(1, len(seuils_index_image_list)):
t = seuils_index_image_list[j]
if t == AUTO:
seuils_list = runCalculSeuil(
image_index, output_dir, save_results_intermediate)
# Masque centre classe
bin_mask_cc = binaryMaskVect(
image_index, repertory_temp, float(seuils_list[0]),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
# Masque borne inf
bin_mask_bi = binaryMaskVect(
image_index, repertory_temp, float(seuils_list[1]),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
# Ajout des masques à la liste
bin_mask_list.append(bin_mask_cc)
bin_mask_list.append(bin_mask_bi)
else:
bin_mask = binaryMaskVect(
image_index, repertory_temp, float(t),
input_cut_vector, attributes_list, no_data_value,
epsg, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
bin_mask_list.append(bin_mask)
# Constitution du dictionnaire associant chaque image aux vecteurs NDVI associés, pour l'entrée dans PolygonMerToTDC
im_ndvivect_dico = ""
if is_calc_indice_image:
ndvi_mask_index = 0
for i in range(nb_images):
# Chaque image + seuils (exemple : /path/image_xx.tif:0.1,0,-0.1)
image_index_and_seuils = input_im_seuils_dico.split()[i]
input_image = image_index_and_seuils.split(":")[0]
seuils_index_image_list = image_index_and_seuils.split(
":")[1].split(",")
is_presence_auto = False
im_ndvivect_dico += input_image + ":"
# Si des seuils sont renseignés seulement pour la 1ère image
if ":" not in image_index_and_seuils:
# Parcours des seuils de la première image
for seuil in seuils_first_image_list:
if seuil == AUTO:
is_presence_auto = True
# S'il y a un seuil à "auto" dans la boucle, on parcourt un tour de plus (auto = borneinf + centre classe)
if is_presence_auto == True:
nb_iter = len(seuils_first_image_list)
else:
nb_iter = len(seuils_first_image_list) - 1
for s in range(nb_iter):
im_ndvivect_dico += bin_mask_list[ndvi_mask_index] + ","
ndvi_mask_index = ndvi_mask_index + 1
im_ndvivect_dico += bin_mask_list[ndvi_mask_index] + " "
# Si au moins un seuil est renseigné pour chacune des autres images
else:
# Parcours des seuils de l'image
for seuil in seuils_index_image_list:
if seuil == AUTO:
is_presence_auto = True
# S'il y a un seuil à "auto" dans la boucle, on parcourt un tour de plus (auto = borneinf + centre classe)
if is_presence_auto:
nb_iter = len(seuils_index_image_list)
else:
nb_iter = len(seuils_index_image_list) - 1
for s in range(nb_iter):
im_ndvivect_dico += bin_mask_list[ndvi_mask_index] + ","
ndvi_mask_index = ndvi_mask_index + 1
im_ndvivect_dico += bin_mask_list[ndvi_mask_index] + " "
ndvi_mask_index = ndvi_mask_index + 1
else:
ndvi_mask_index = 0
for i in range(nb_images):
# Chaque image + seuils (exemple : /path/image_xx.tif:0.1,0,-0.1)
image_index_and_seuils = input_im_seuils_dico.split()[i]
input_image = image_index_and_seuils.split(":")[0]
seuils_index_image_list = image_index_and_seuils.split(
":")[1].split(",")
is_presence_auto = False
im_ndvivect_dico += input_image + ":"
if ":" not in image_index_and_seuils:
print(cyan + "runTDCSeuil() : " + red + bold +
"Renseignez les images NDVI associées et les seuils !" +
endC,
file=sys.stderr)
sys.exit(1)
# Si au moins un seuil est renseigné pour chacune des autres images
else:
# Parcours des seuils de l'image
for seuil in seuils_index_image_list:
if seuil == AUTO:
is_presence_auto = True
# S'il y a un seuil à "auto" dans la boucle, on parcourt un tour de plus (auto = borneinf + centre classe)
if is_presence_auto:
nb_iter = len(seuils_index_image_list)
else:
nb_iter = len(seuils_index_image_list) - 1
for s in range(1, nb_iter):
im_ndvivect_dico += bin_mask_list[ndvi_mask_index] + ","
ndvi_mask_index = ndvi_mask_index + 1
im_ndvivect_dico += bin_mask_list[ndvi_mask_index] + " "
ndvi_mask_index = ndvi_mask_index + 1
im_ndvivect_dico = im_ndvivect_dico[:-1]
tdc_shp = polygonMerToTDC(im_ndvivect_dico, output_dir, input_sea_points,
True, simplif, input_cut_vector, 3.5, -3.5,
no_data_value, path_time_log, epsg,
format_vector, extension_raster,
extension_vector, save_results_intermediate,
overwrite)
# Suppression des répertoires temporaires
for repertory_temp in repertory_temp_list:
if not save_results_intermediate and os.path.exists(repertory_temp):
shutil.rmtree(repertory_temp)
# Mise à jour du Log
ending_event = "runTDCSeuil() : Select TDC Seuil ending : "
timeLine(path_time_log, ending_event)
return tdc_shp
def createMnh(image_mns_input, image_mnt_input, image_threshold_input, vector_emprise_input, image_mnh_output, automatic, bd_road_vector_input_list, bd_road_buff_list, sql_road_expression_list, bd_build_vector_input_list, height_bias, threshold_bd_value, threshold_delta_h, mode_interpolation, method_interpolation, interpolation_bco_radius, simplify_vector_param, epsg, no_data_value, ram_otb, path_time_log, format_raster='GTiff', format_vector='ESRI Shapefile', extension_raster=".tif", extension_vector=".shp", save_results_intermediate=False, overwrite=True):
# Mise à jour du Log
starting_event = "createMnh() : MNH creation starting : "
timeLine(path_time_log,starting_event)
print(endC)
print(bold + green + "## START : MNH CREATION" + endC)
print(endC)
if debug >= 2:
print(bold + green + "createMnh() : Variables dans la fonction" + endC)
print(cyan + "createMnh() : " + endC + "image_mns_input : " + str(image_mns_input) + endC)
print(cyan + "createMnh() : " + endC + "image_mnt_input : " + str(image_mnt_input) + endC)
print(cyan + "createMnh() : " + endC + "image_threshold_input : " + str(image_threshold_input) + endC)
print(cyan + "createMnh() : " + endC + "vector_emprise_input : " + str(vector_emprise_input) + endC)
print(cyan + "createMnh() : " + endC + "image_mnh_output : " + str(image_mnh_output) + endC)
print(cyan + "createMnh() : " + endC + "automatic : " + str(automatic) + endC)
print(cyan + "createMnh() : " + endC + "bd_road_vector_input_list : " + str(bd_road_vector_input_list) + endC)
print(cyan + "createMnh() : " + endC + "bd_road_buff_list : " + str(bd_road_buff_list) + endC)
print(cyan + "createMnh() : " + endC + "sql_road_expression_list : " + str(sql_road_expression_list) + endC)
print(cyan + "createMnh() : " + endC + "bd_build_vector_input_list : " + str(bd_build_vector_input_list) + endC)
print(cyan + "createMnh() : " + endC + "height_bias : " + str(height_bias) + endC)
print(cyan + "createMnh() : " + endC + "threshold_bd_value : " + str(threshold_bd_value) + endC)
print(cyan + "createMnh() : " + endC + "threshold_delta_h : " + str(threshold_delta_h) + endC)
print(cyan + "createMnh() : " + endC + "mode_interpolation : " + str(mode_interpolation) + endC)
print(cyan + "createMnh() : " + endC + "method_interpolation : " + str(method_interpolation) + endC)
print(cyan + "createMnh() : " + endC + "interpolation_bco_radius : " + str(interpolation_bco_radius) + endC)
print(cyan + "createMnh() : " + endC + "simplify_vector_param : " + str(simplify_vector_param) + endC)
print(cyan + "createMnh() : " + endC + "epsg : " + str(epsg) + endC)
print(cyan + "createMnh() : " + endC + "no_data_value : " + str(no_data_value) + endC)
print(cyan + "createMnh() : " + endC + "ram_otb : " + str(ram_otb) + endC)
print(cyan + "createMnh() : " + endC + "path_time_log : " + str(path_time_log) + endC)
print(cyan + "createMnh() : " + endC + "format_raster : " + str(format_raster) + endC)
print(cyan + "createMnh() : " + endC + "format_vector : " + str(format_vector) + endC)
print(cyan + "createMnh() : " + endC + "extension_raster : " + str(extension_raster) + endC)
print(cyan + "createMnh() : " + endC + "extension_vector : " + str(extension_vector) + endC)
print(cyan + "createMnh() : " + endC + "save_results_intermediate : " + str(save_results_intermediate) + endC)
print(cyan + "createMnh() : " + endC + "overwrite : " + str(overwrite) + endC)
# LES CONSTANTES
PRECISION = 0.0000001
CODAGE_8B = "uint8"
CODAGE_F = "float"
SUFFIX_CUT = "_cut"
SUFFIX_CLEAN = "_clean"
SUFFIX_SAMPLE = "_sample"
SUFFIX_MASK = "_mask"
SUFFIX_TMP = "_tmp"
SUFFIX_MNS = "_mns"
SUFFIX_MNT = "_mnt"
SUFFIX_ROAD = "_road"
SUFFIX_BUILD = "_build"
SUFFIX_RASTER = "_raster"
SUFFIX_VECTOR = "_vector"
# DEFINIR LES REPERTOIRES ET FICHIERS TEMPORAIRES
repertory_output = os.path.dirname(image_mnh_output)
basename_mnh = os.path.splitext(os.path.basename(image_mnh_output))[0]
sub_repertory_raster_temp = repertory_output + os.sep + basename_mnh + SUFFIX_RASTER + SUFFIX_TMP
sub_repertory_vector_temp = repertory_output + os.sep + basename_mnh + SUFFIX_VECTOR + SUFFIX_TMP
cleanTempData(sub_repertory_raster_temp)
cleanTempData(sub_repertory_vector_temp)
basename_vector_emprise = os.path.splitext(os.path.basename(vector_emprise_input))[0]
basename_mns_input = os.path.splitext(os.path.basename(image_mns_input))[0]
basename_mnt_input = os.path.splitext(os.path.basename(image_mnt_input))[0]
image_mnh_tmp = sub_repertory_raster_temp + os.sep + basename_mnh + SUFFIX_TMP + extension_raster
image_mnh_road = sub_repertory_raster_temp + os.sep + basename_mnh + SUFFIX_ROAD + extension_raster
vector_bd_bati_temp = sub_repertory_vector_temp + os.sep + basename_mnh + SUFFIX_BUILD + SUFFIX_TMP + extension_vector
vector_bd_bati = repertory_output + os.sep + basename_mnh + SUFFIX_BUILD + extension_vector
raster_bd_bati = sub_repertory_vector_temp + os.sep + basename_mnh + SUFFIX_BUILD + extension_raster
removeVectorFile(vector_bd_bati)
image_emprise_mnt_mask = sub_repertory_raster_temp + os.sep + basename_vector_emprise + SUFFIX_MNT + extension_raster
image_mnt_cut = sub_repertory_raster_temp + os.sep + basename_mnt_input + SUFFIX_CUT + extension_raster
image_mnt_clean = sub_repertory_raster_temp + os.sep + basename_mnt_input + SUFFIX_CLEAN + extension_raster
image_mnt_clean_sample = sub_repertory_raster_temp + os.sep + basename_mnt_input + SUFFIX_CLEAN + SUFFIX_SAMPLE + extension_raster
image_emprise_mns_mask = sub_repertory_raster_temp + os.sep + basename_vector_emprise + SUFFIX_MNS + extension_raster
image_mns_cut = sub_repertory_raster_temp + os.sep + basename_mns_input + SUFFIX_CUT + extension_raster
image_mns_clean = sub_repertory_raster_temp + os.sep + basename_mns_input + SUFFIX_CLEAN + extension_raster
vector_bd_road_temp = sub_repertory_vector_temp + os.sep + basename_mnh + SUFFIX_ROAD + SUFFIX_TMP + extension_vector
raster_bd_road_mask = sub_repertory_raster_temp + os.sep + basename_mnh + SUFFIX_ROAD + SUFFIX_MASK + extension_raster
if image_threshold_input != "" :
basename_threshold_input = os.path.splitext(os.path.basename(image_threshold_input))[0]
image_threshold_cut = sub_repertory_raster_temp + os.sep + basename_threshold_input + SUFFIX_CUT + extension_raster
image_threshold_mask = sub_repertory_raster_temp + os.sep + basename_threshold_input + SUFFIX_MASK + extension_raster
# VERIFICATION SI LE FICHIER DE SORTIE EXISTE DEJA
# Si un fichier de sortie avec le même nom existe déjà, et si l'option ecrasement est à false, alors on ne fait rien
check = os.path.isfile(image_mnh_output)
if check and not overwrite:
print(bold + yellow + "createMnh() : " + endC + "Create mnh %s from %s and %s already done : no actualisation" % (image_mnh_output, image_mns_input, image_mnt_input) + endC)
# Si non, ou si la fonction ecrasement est désative, alors on le calcule
else:
if check:
try: # Suppression de l'éventuel fichier existant
removeFile(image_mnh_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
# DECOUPAGE DES FICHIERS MS ET MNT D'ENTREE PAR LE FICHIER D'EMPRISE
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Decoupage selon l'emprise des fichiers %s et %s " %(image_mns_input, image_mnt_input) + endC)
# Fonction de découpe du mns
if not cutImageByVector(vector_emprise_input, image_mns_input, image_mns_cut, None, None, no_data_value, epsg, format_raster, format_vector) :
raise NameError (cyan + "createMnh() : " + bold + red + "!!! Une erreur c'est produite au cours du decoupage de l'image : " + image_mns_input + ". Voir message d'erreur." + endC)
# Fonction de découpe du mnt
if not cutImageByVector(vector_emprise_input, image_mnt_input, image_mnt_cut, None, None, no_data_value, epsg, format_raster, format_vector) :
raise NameError (cyan + "createMnh() : " + bold + red + "!!! Une erreur c'est produite au cours du decoupage de l'image : " + image_mnt_input + ". Voir message d'erreur." + endC)
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Decoupage des fichiers %s et %s complet" %(image_mns_cut, image_mnt_cut) + endC)
# REBOUCHAGE DES TROUS DANS LE MNT D'ENTREE SI NECESSAIRE
nodata_mnt = getNodataValueImage(image_mnt_cut)
pixelNodataCount = countPixelsOfValue(image_mnt_cut, nodata_mnt)
if pixelNodataCount > 0 :
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Fill the holes MNT for %s" %(image_mnt_cut) + endC)
# Rasterisation du vecteur d'emprise pour creer un masque pour boucher les trous du MNT
rasterizeBinaryVector(vector_emprise_input, image_mnt_cut, image_emprise_mnt_mask, 1, CODAGE_8B)
# Utilisation de SAGA pour boucher les trous
fillNodata(image_mnt_cut, image_emprise_mnt_mask, image_mnt_clean, save_results_intermediate)
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Fill the holes MNT to %s completed" %(image_mnt_clean) + endC)
else :
image_mnt_clean = image_mnt_cut
if debug >= 3:
print(bold + green + "\ncreateMnh() : " + endC + "Fill the holes not necessary MNT for %s" %(image_mnt_cut) + endC)
# REBOUCHAGE DES TROUS DANS LE MNS D'ENTREE SI NECESSAIRE
nodata_mns = getNodataValueImage(image_mns_cut)
pixelNodataCount = countPixelsOfValue(image_mns_cut, nodata_mns)
if pixelNodataCount > 0 :
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Fill the holes MNS for %s" %(image_mns_cut) + endC)
# Rasterisation du vecteur d'emprise pour creer un masque pour boucher les trous du MNS
rasterizeBinaryVector(vector_emprise_input, image_mns_cut, image_emprise_mns_mask, 1, CODAGE_8B)
# Utilisation de SAGA pour boucher les trous
fillNodata(image_mns_cut, image_emprise_mns_mask, image_mns_clean, save_results_intermediate)
if debug >= 3:
print(bold + green + "\ncreateMnh() : " + endC + "Fill the holes MNS to %s completed" %(image_mns_clean) + endC)
else :
image_mns_clean = image_mns_cut
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Fill the holes not necessary MNS for %s" %(image_mns_cut) + endC)
# CALLER LE FICHIER MNT AU FORMAT DU FICHIER MNS
# Commande de mise en place de la geométrie re-echantionage
command = "otbcli_Superimpose -inr " + image_mns_clean + " -inm " + image_mnt_clean + " -mode " + mode_interpolation + " -interpolator " + method_interpolation + " -out " + image_mnt_clean_sample
if method_interpolation.lower() == 'bco' :
command += " -interpolator.bco.radius " + str(interpolation_bco_radius)
if ram_otb > 0:
command += " -ram %d" %(ram_otb)
if debug >= 3:
print(cyan + "createMnh() : " + bold + green + "Réechantillonage du fichier %s par rapport à la reference %s" %(image_mnt_clean, image_mns_clean) + endC)
print(command)
exit_code = os.system(command)
if exit_code != 0:
print(command)
raise NameError (cyan + "createMnh() : " + bold + red + "!!! Une erreur c'est produite au cours du superimpose de l'image : " + image_mnt_input + ". Voir message d'erreur." + endC)
# INCRUSTATION DANS LE MNH DES DONNEES VECTEURS ROUTES
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Use BD road to clean MNH" + endC)
# Creation d'un masque de filtrage des donnes routes (exemple : le NDVI)
if image_threshold_input != "" :
if not cutImageByVector(vector_emprise_input, image_threshold_input, image_threshold_cut, None, None, no_data_value, epsg, format_raster, format_vector) :
raise NameError (cyan + "createMnh() : " + bold + red + "!!! Une erreur c'est produite au cours du decoupage de l'image : " + image_threshold_input + ". Voir message d'erreur." + endC)
createBinaryMask(image_threshold_cut, image_threshold_mask, threshold_bd_value, False, CODAGE_8B)
# Execution de la fonction createMacroSamples pour une image correspondant au données routes
if bd_road_vector_input_list != [] :
createMacroSamples(image_mns_clean, vector_emprise_input, vector_bd_road_temp, raster_bd_road_mask, bd_road_vector_input_list, bd_road_buff_list, sql_road_expression_list, path_time_log, basename_mnh, simplify_vector_param, format_vector, extension_vector, save_results_intermediate, overwrite)
if debug >= 3:
print(bold + green + "\ncreateMnh() : " + endC + "File raster from BD road is create %s" %(raster_bd_road_mask) + endC)
# CALCUL DU MNH
# Calcul par bandMath du MNH definir l'expression qui soustrait le MNT au MNS en introduisant le biais et en mettant les valeurs à 0 à une valeur approcher de 0.0000001
delta = ""
if height_bias > 0 :
delta = "+%s" %(str(height_bias))
elif height_bias < 0 :
delta = "-%s" %(str(abs(height_bias)))
else :
delta = ""
# Definition de l'expression
if bd_road_vector_input_list != [] :
if image_threshold_input != "" :
expression = "\"im3b1 > 0 and im4b1 > 0?%s:(im1b1-im2b1%s) > 0.0?im1b1-im2b1%s:%s\"" %(str(PRECISION), delta, delta, str(PRECISION))
command = "otbcli_BandMath -il %s %s %s %s -out %s %s -exp %s" %(image_mns_clean, image_mnt_clean_sample, raster_bd_road_mask, image_threshold_mask, image_mnh_tmp, CODAGE_F, expression)
else :
expression = "\"im3b1 > 0?%s:(im1b1-im2b1%s) > 0.0?im1b1-im2b1%s:%s\"" %(str(PRECISION), delta, delta, str(PRECISION))
command = "otbcli_BandMath -il %s %s %s -out %s %s -exp %s" %(image_mns_clean, image_mnt_clean_sample, raster_bd_road_mask, image_mnh_tmp, CODAGE_F, expression)
else :
expression = "\"(im1b1-im2b1%s) > 0.0?im1b1-im2b1%s:%s\"" %(delta, delta, str(PRECISION))
command = "otbcli_BandMath -il %s %s -out %s %s -exp %s" %(image_mns_clean, image_mnt_clean_sample, image_mnh_tmp, CODAGE_F, expression)
if ram_otb > 0:
command += " -ram %d" %(ram_otb)
if debug >= 3:
print(cyan + "createMnh() : " + bold + green + "Calcul du MNH %s difference du MNS : %s par le MNT :%s" %(image_mnh_tmp, image_mns_clean, image_mnt_clean_sample) + endC)
print(command)
exitCode = os.system(command)
if exitCode != 0:
print(command)
raise NameError(cyan + "createMnh() : " + bold + red + "An error occured during otbcli_BandMath command to compute MNH " + image_mnh_tmp + ". See error message above." + endC)
# DECOUPAGE DU MNH
if bd_build_vector_input_list == []:
image_mnh_road = image_mnh_output
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Decoupage selon l'emprise du fichier mnh %s " %(image_mnh_tmp) + endC)
# Fonction de découpe du mnh
if not cutImageByVector(vector_emprise_input, image_mnh_tmp, image_mnh_road, None, None, no_data_value, epsg, format_raster, format_vector) :
raise NameError (cyan + "createMnh() : " + bold + red + "!!! Une erreur c'est produite au cours du decoupage de l'image : " + image_mns_input + ". Voir message d'erreur." + endC)
if debug >= 3:
print(bold + green + "createMnh() : " + endC + "Decoupage du fichier mnh %s complet" %(image_mnh_road) + endC)
# INCRUSTATION DANS LE MNH DES DONNEES VECTEURS BATIS
# Si demander => liste de fichier vecteur bati passé en donnée d'entrée
if bd_build_vector_input_list != []:
# Découpage des vecteurs de bd bati exogenes avec l'emprise
vectors_build_cut_list = []
for vector_build_input in bd_build_vector_input_list :
vector_name = os.path.splitext(os.path.basename(vector_build_input))[0]
vector_build_cut = sub_repertory_vector_temp + os.sep + vector_name + SUFFIX_CUT + extension_vector
vectors_build_cut_list.append(vector_build_cut)
cutoutVectors(vector_emprise_input, bd_build_vector_input_list, vectors_build_cut_list, format_vector)
# Fusion des vecteurs batis découpés
fusionVectors (vectors_build_cut_list, vector_bd_bati_temp)
# Croisement vecteur rasteur entre le vecteur fusion des batis et le MNH créé precedement
statisticsVectorRaster(image_mnh_road, vector_bd_bati_temp, "", 1, False, False, True, ['PREC_PLANI','PREC_ALTI','ORIGIN_BAT','median','sum','std','unique','range'], [], {}, path_time_log, True, format_vector, save_results_intermediate, overwrite)
# Calcul de la colonne delta_H entre les hauteurs des batis et la hauteur moyenne du MNH sous le bati
COLUMN_ID = "ID"
COLUMN_H_BUILD = "HAUTEUR"
COLUMN_H_BUILD_MIN = "Z_MIN"
COLUMN_H_BUILD_MAX = "Z_MAX"
COLUMN_H_MNH = "mean"
COLUMN_H_MNH_MIN = "min"
COLUMN_H_MNH_MAX = "max"
COLUMN_H_DIFF = "H_diff"
field_type = ogr.OFTReal
field_value = 0.0
field_width = 20
field_precision = 2
attribute_name_dico = {}
attribute_name_dico[COLUMN_ID] = ogr.OFTString
attribute_name_dico[COLUMN_H_BUILD] = ogr.OFTReal
attribute_name_dico[COLUMN_H_MNH] = ogr.OFTReal
# Ajouter la nouvelle colonne H_diff
addNewFieldVector(vector_bd_bati_temp, COLUMN_H_DIFF, field_type, field_value, field_width, field_precision, format_vector)
# Recuperer les valeur de hauteur du bati et du mnt dans le vecteur
data_z_dico = getAttributeValues(vector_bd_bati_temp, None, None, attribute_name_dico, format_vector)
# Calculer la difference des Hauteur bati et mnt
field_new_values_dico = {}
for index in range(len(data_z_dico[COLUMN_ID])) :
index_polygon = data_z_dico[COLUMN_ID][index]
delta_h = abs(data_z_dico[COLUMN_H_BUILD][index] - data_z_dico[COLUMN_H_MNH][index])
field_new_values_dico[index_polygon] = {COLUMN_H_DIFF:delta_h}
# Mettre à jour la colonne H_diff dans le vecteur
setAttributeIndexValuesList(vector_bd_bati_temp, COLUMN_ID, field_new_values_dico, format_vector)
# Suppression de tous les polygones bati dons la valeur du delat H est inferieur à threshold_delta_h
column = "'%s, %s, %s, %s, %s, %s, %s, %s'"% (COLUMN_ID, COLUMN_H_BUILD, COLUMN_H_BUILD_MIN, COLUMN_H_BUILD_MAX, COLUMN_H_MNH, COLUMN_H_MNH_MIN, COLUMN_H_MNH_MAX, COLUMN_H_DIFF)
expression = "%s > %s" % (COLUMN_H_DIFF, threshold_delta_h)
filterSelectDataVector(vector_bd_bati_temp, vector_bd_bati, column, expression, overwrite, format_vector)
# Attention!!!! PAUSE pour trie et verification des polygones bati nom deja present dans le MNH ou non
if not automatic :
print(bold + blue + "Application MnhCreation => " + endC + "Vérification manuelle du vecteur bati %s pour ne concerver que les batis non présent dans le MNH courant %s" %(vector_bd_bati_temp, image_mnh_road) + endC)
input(bold + red + "Appuyez sur entree pour continuer le programme..." + endC)
# Creation du masque bati avec pour H la hauteur des batiments
rasterizeVector(vector_bd_bati, raster_bd_bati, image_mnh_road, COLUMN_H_BUILD)
# Fusion du mask des batis et du MNH temporaire
expression = "\"im1b1 > 0.0?im1b1:im2b1\""
command = "otbcli_BandMath -il %s %s -out %s %s -exp %s" %(raster_bd_bati, image_mnh_road, image_mnh_output, CODAGE_F, expression)
if ram_otb > 0:
command += " -ram %d" %(ram_otb)
if debug >= 3:
print(cyan + "createMnh() : " + bold + green + "Amelioration du MNH %s ajout des hauteurs des batis %s" %(image_mnh_road, raster_bd_bati) + endC)
print(command)
exitCode = os.system(command)
if exitCode != 0:
print(command)
raise NameError(cyan + "createMnh() : " + bold + red + "An error occured during otbcli_BandMath command to compute MNH Final" + image_mnh_output + ". See error message above." + endC)
# SUPPRESIONS FICHIERS INTERMEDIAIRES INUTILES
# Suppression des fichiers intermédiaires
if not save_results_intermediate :
if bd_build_vector_input_list != []:
removeFile(image_mnh_road)
removeFile(image_threshold_cut)
removeFile(image_threshold_mask)
removeFile(raster_bd_bati)
removeVectorFile(vector_bd_road_temp)
removeVectorFile(vector_bd_bati_temp)
removeVectorFile(vector_bd_bati) # A confirmer!!!
removeFile(raster_bd_road_mask)
removeFile(image_mnh_tmp)
deleteDir(sub_repertory_raster_temp)
deleteDir(sub_repertory_vector_temp)
print(endC)
print(bold + green + "## END : MNH CREATION" + endC)
print(endC)
# Mise à jour du Log
ending_event = "createMnh() : MNH creation ending : "
timeLine(path_time_log,ending_event)
return
def prepareData(input_buffer_tdc,
input_paysage,
output_dir,
input_repertories_list,
id_paysage,
id_name_sub_rep,
epsg,
optimization_zone,
no_cover,
zone_date,
separ_name,
pos_date,
nb_char_date,
separ_date,
path_time_log,
format_raster='GTiff',
format_vector="ESRI Shapefile",
extension_raster=".tif",
extension_vector=".shp",
save_results_intermediate=True,
overwrite=True):
# Mise à jour du Log
starting_event = "prepareData() : Select prepare data starting : "
timeLine(path_time_log, starting_event)
# Affichage des paramètres
if debug >= 3:
print(bold + green +
"Variables dans le prepareData - Variables générales" + endC)
print(cyan + "PrepareData() : " + endC + "input_buffer_tdc : " +
str(input_buffer_tdc) + endC)
print(cyan + "PrepareData() : " + endC + "input_paysage : " +
str(input_paysage) + endC)
print(cyan + "PrepareData() : " + endC + "output_dir : " +
str(output_dir) + endC)
print(cyan + "PrepareData() : " + endC + "input_repertories_list : " +
str(input_repertories_list) + endC)
print(cyan + "PrepareData() : " + endC + "id_paysage : " +
str(id_paysage) + endC)
print(cyan + "PrepareData() : " + endC + "id_name_sub_rep : " +
str(id_name_sub_rep) + endC)
print(cyan + "PrepareData() : " + endC + "epsg : " + str(epsg) + endC)
print(cyan + "PrepareData() : " + endC + "optimization_zone : " +
str(optimization_zone) + endC)
print(cyan + "PrepareData() : " + endC + "no_cover : " +
str(no_cover) + endC)
print(cyan + "PrepareData() : " + endC + "zone_date : " +
str(zone_date) + endC)
print(cyan + "PrepareData() : " + endC + "separ_name : " +
str(separ_name) + endC)
print(cyan + "PrepareData() : " + endC + "pos_date : " +
str(pos_date) + endC)
print(cyan + "PrepareData() : " + endC + "nb_char_date : " +
str(nb_char_date) + endC)
print(cyan + "PrepareData() : " + endC + "separ_date : " +
str(separ_date) + endC)
print(cyan + "PrepareData() : " + endC + "path_time_log : " +
str(path_time_log) + endC)
print(cyan + "PrepareData() : " + endC + "format_raster : " +
str(format_raster) + endC)
print(cyan + "PrepareData() : " + endC + "format_vector : " +
str(format_vector) + endC)
print(cyan + "PrepareData() : " + endC + "extension_raster : " +
str(extension_raster) + endC)
print(cyan + "PrepareData() : " + endC + "extension_vector : " +
str(extension_vector) + endC)
print(cyan + "PrepareData() : " + endC + "save_results_inter : " +
str(save_results_intermediate) + endC)
print(cyan + "PrepareData() : " + endC + "overwrite : " +
str(overwrite) + endC)
REPERTORY_PAYSAGES = "Paysages"
REPERTORY_IMAGES = "Images"
ID_P = "id_p"
SUFFIX_OPTI = "_opti"
SUFFIX_ASS = "_ass"
SUFFIX_CUT = "_cut"
SUFFIX_ERROR = "_error"
SUFFIX_MERGE = "_merge"
SUFFIX_CLEAN = "_clean"
SUFFIX_STACK = "_stack"
output_dir_paysages = output_dir + os.sep + REPERTORY_PAYSAGES
output_dir_images = output_dir + os.sep + REPERTORY_IMAGES
# Création du répertoire de sortie s'il n'existe pas déjà
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Création du répertoire de sortie pour les paysages s'il n'existe pas
if not os.path.exists(output_dir_paysages):
os.makedirs(output_dir_paysages)
# Création du répertoire de sortie pour les images s'il n'existe pas
if not os.path.exists(output_dir_images):
os.makedirs(output_dir_images)
# Recuperer l'epsg du fichier d'emprise
if epsg == 0:
epsg = getProjection(input_paysage, format_vector)
# Création du paysage optimal
optimPaysage(input_buffer_tdc, input_paysage, optimization_zone,
SUFFIX_OPTI, output_dir_paysages, id_paysage, format_vector)
# Création un shapefile par polygone
paysage_opti = output_dir_paysages + os.sep + os.path.splitext(
os.path.basename(input_paysage))[0] + SUFFIX_OPTI + os.path.splitext(
input_paysage)[1]
if id_paysage != "":
paysages_list = splitVector(paysage_opti, str(output_dir_paysages),
str(id_paysage), epsg, format_vector,
extension_vector)
else:
paysages_list = splitVector(paysage_opti, str(output_dir_paysages),
ID_P, epsg, format_vector,
extension_vector)
if debug >= 3:
print(cyan + "PrepareData() : " + endC +
"Liste des fichiers en entrée de imagesAssembly() : " +
str(paysages_list))
# Création du fichier de sortie des images s'il n'existe pas dejà
if not os.path.exists(output_dir_images):
os.makedirs(output_dir_images)
# Assemblage des images dans les paysages optimisés
# Si on choisit pas de recouvrement entre les images
if no_cover:
sub_repertory_images_paysages_list = []
repertory_images_sources_list = []
repertory_input_image_dir = input_repertories_list[
0] # ATTENTION !!!! GFT c'est louche comme methode de prendre uniquement le premier repertoire d'image????
# On cherche la colonne contenant le nom de l'image
for shape in paysages_list:
attribute_name_dico = {}
attribute_name_dico[id_name_sub_rep] = ogr.OFTString
res_values_dico = getAttributeValues(shape, None, None,
attribute_name_dico,
format_vector)
sub_repertory_images_paysage = res_values_dico[id_name_sub_rep][0]
sub_repertory_images_paysages_list.append(
sub_repertory_images_paysage)
# Si la colonne existe
if len(sub_repertory_images_paysages_list) != 0:
for sub_repertory_images_paysage in sub_repertory_images_paysages_list:
repertory_images_sources_list.append(
repertory_input_image_dir + os.sep +
sub_repertory_images_paysage)
# Sinon on demande d'entrer les noms des images
else:
print("Liste des paysages optimisés : " + str(paysages_list))
repertory_images_sources_list = input(
"Rentrez la liste des images associées à chaque paysage dans l'ordre sous forme de liste [..., ..., ...] \n"
)
while len(repertory_images_sources_list) != len(paysages_list):
print(
"Longueur de la liste images différente de celle des paysages \n"
)
repertory_images_sources_list = input(
"Rentrez la liste des images associées à chaque paysage dans l'ordre \n"
)
if debug >= 2:
print("repertory_images_sources_list " +
str(repertory_images_sources_list))
# Commande ImagesAssembly sur les éléments des 2 listes
for i in range(len(paysages_list)):
image_output = output_dir_images + os.sep + os.path.splitext(
os.path.basename(
paysages_list[i]))[0] + SUFFIX_ASS + extension_raster
try:
selectAssembyImagesByHold(
paysages_list[i], [repertory_images_sources_list[i]],
image_output, False, True, epsg, False, False, False,
False, 0, 0, 0, 0, separ_name, pos_date, nb_char_date,
separ_date, path_time_log, SUFFIX_ERROR, SUFFIX_MERGE,
SUFFIX_CLEAN, SUFFIX_STACK, format_raster, format_vector,
extension_raster, extension_vector,
save_results_intermediate, overwrite)
except Exception:
pass
else:
for shape in paysages_list:
image_output = output_dir_images + os.sep + os.path.splitext(
os.path.basename(shape))[0] + SUFFIX_ASS + extension_raster
if optimization_zone:
shape_cut = os.path.splitext(
shape)[0] + SUFFIX_CUT + os.path.splitext(shape)[1]
cutVector(input_buffer_tdc, shape, shape_cut, overwrite,
format_vector)
else:
shape_cut = shape
selectAssembyImagesByHold(shape_cut, input_repertories_list,
image_output, False, zone_date, epsg,
False, False, False, False, 0, 0, 0, 0,
separ_name, pos_date, nb_char_date,
separ_date, path_time_log, SUFFIX_ERROR,
SUFFIX_MERGE, SUFFIX_CLEAN, SUFFIX_STACK,
format_raster, format_vector,
save_results_intermediate, overwrite)
if optimization_zone and os.path.exists(shape_cut):
removeVectorFile(shape_cut, format_vector)
# Mise à jour du Log
ending_event = "prepareData() : Select prepare data ending : "
timeLine(path_time_log, ending_event)
return
def computeRoughnessByOcsMnh( grid_input, grid_output, mnh_input, classif_input, class_build_list, epsg, no_data_value, path_time_log, format_raster='GTiff', format_vector='ESRI Shapefile', extension_raster=".tif", extension_vector=".shp", save_results_intermediate=False, overwrite=True):
# Constante
FIELD_H_TYPE = ogr.OFTReal
FIELD_ID_TYPE = ogr.OFTInteger
FIELD_NAME_HSUM = "sum_h"
FIELD_NAME_HRE = "mean_h"
FIELD_NAME_AREA = "nb_area"
FIELD_NAME_ID = "id"
SUFFIX_HEIGHT = '_hauteur'
SUFFIX_BUILT = '_bati'
SUFFIX_TEMP = '_temp'
SUFFIX_MASK = '_mask'
# Mise à jour du Log
timeLine(path_time_log, "Début du calcul de l'indicateur Height of Roughness Elements par OCS et MNT starting : ")
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "Début du calcul de l'indicateur Height of Roughness Elements par OCS et MNT." + endC + "\n")
if debug >= 3:
print(bold + green + "computeRoughnessByOcsMnh() : Variables dans la fonction" + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "grid_input : " + str(grid_input) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "grid_output : " + str(grid_output) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "mnh_input : " + str(mnh_input) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "classif_input : " + str(classif_input) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "class_build_list : " + str(class_build_list) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "epsg : " + str(epsg) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "no_data_value : " + str(no_data_value) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "path_time_log : " + str(path_time_log) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "format_vector : " + str(format_vector) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "save_results_intermediate : " + str(save_results_intermediate) + endC)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "overwrite : " + str(overwrite) + endC)
# Test si le vecteur de sortie existe déjà et si il doit être écrasés
check = os.path.isfile(grid_output)
if check and not overwrite: # Si le fichier de sortie existent deja et que overwrite n'est pas activé
print(cyan + "computeRoughnessByOcsMnh() : " + bold + yellow + "Le calcul de Roughness par OCS et MNT a déjà eu lieu." + endC + "\n")
print(cyan + "computeRoughnessByOcsMnh() : " + bold + yellow + "Grid vector output : " + grid_output + " already exists and will not be created again." + endC)
else :
if check:
try:
removeVectorFile(grid_output)
except Exception:
pass # si le fichier n'existe pas, il ne peut pas être supprimé : cette étape est ignorée
############################################
### Préparation générale des traitements ###
############################################
# Récuperation de la projection de l'image
epsg_proj = getProjectionImage(classif_input)
if epsg_proj == 0:
epsg_proj = epsg
# Préparation des fichiers temporaires
temp_path = os.path.dirname(grid_output) + os.sep + "RoughnessByOcsAndMnh"
# Nettoyage du repertoire temporaire si il existe
if os.path.exists(temp_path):
shutil.rmtree(temp_path)
os.makedirs(temp_path)
basename = os.path.splitext(os.path.basename(grid_output))[0]
built_height = temp_path + os.sep + basename + SUFFIX_HEIGHT + SUFFIX_BUILT + extension_raster
built_height_temp = temp_path + os.sep + basename + SUFFIX_HEIGHT + SUFFIX_BUILT + SUFFIX_TEMP + extension_raster
classif_built_mask = temp_path + os.sep + basename + SUFFIX_BUILT + SUFFIX_MASK + extension_raster
grid_output_temp = temp_path + os.sep + basename + SUFFIX_TEMP + extension_vector
##############################
### Calcul de l'indicateur ###
##############################
# liste des classes de bati a prendre en compte dans l'expression du BandMath
expression_bati = ""
for id_class in class_build_list :
expression_bati += "im1b1==%s or " %(str(id_class))
expression_bati = expression_bati[:-4]
expression = "(%s) and (im2b1!=%s) and (im2b1>0)" %(expression_bati, str(no_data_value))
# Creation d'un masque vecteur des batis pour la surface des zones baties
command = "otbcli_BandMath -il %s %s -out %s uint8 -exp '%s ? 1 : 0'" %(classif_input, mnh_input, classif_built_mask, expression)
if debug >= 3:
print(command)
exit_code = os.system(command)
if exit_code != 0:
print(command)
print(cyan + "computeRoughnessByOcsMnh() : " + bold + red + "!!! Une erreur c'est produite au cours de la commande otbcli_BandMath : " + command + ". Voir message d'erreur." + endC, file=sys.stderr)
raise
# Récupération de la hauteur du bati
command = "otbcli_BandMath -il %s %s -out %s float -exp '%s ? im2b1 : 0'" %(classif_input, mnh_input, built_height_temp, expression)
if debug >= 3:
print(command)
exit_code = os.system(command)
if exit_code != 0:
print(command)
print(cyan + "computeRoughnessByOcsMnh() : " + bold + red + "!!! Une erreur c'est produite au cours de la commande otbcli_BandMath : " + command + ". Voir message d'erreur." + endC, file=sys.stderr)
raise
command = "gdal_translate -a_srs EPSG:%s -a_nodata %s -of %s %s %s" %(str(epsg_proj), str(no_data_value), format_raster, built_height_temp, built_height)
if debug >= 3:
print(command)
exit_code = os.system(command)
if exit_code != 0:
print(command)
print(cyan + "computeRoughnessByOcsMnh() : " + bold + red + "!!! Une erreur c'est produite au cours de la comande : gdal_translate : " + command + ". Voir message d'erreur." + endC, file=sys.stderr)
raise
# Récupération du nombre de pixel bati de chaque maille pour definir la surface
statisticsVectorRaster(classif_built_mask, grid_input, grid_output_temp, 1, False, False, True, ["min", "max", "median", "mean", "std", "unique", "range"], [], {}, path_time_log, True, format_vector, save_results_intermediate, overwrite)
# Renomer le champ 'sum' en FIELD_NAME_AREA
renameFieldsVector(grid_output_temp, ['sum'], [FIELD_NAME_AREA], format_vector)
# Récupération de la hauteur moyenne du bati de chaque maille
statisticsVectorRaster(built_height, grid_output_temp, grid_output, 1, False, False, True, ["min", "max", "median", 'mean', "std", "unique", "range"], [], {}, path_time_log, True, format_vector, save_results_intermediate, overwrite)
# Renomer le champ 'mean' en FIELD_NAME_HRE
renameFieldsVector(grid_output, ['sum'], [FIELD_NAME_HSUM], format_vector)
# Calcul de la colonne FIELD_NAME_HRE division de FIELD_NAME_HSUM par FIELD_NAME_AREA
field_values_list = getAttributeValues(grid_output, None, None, {FIELD_NAME_ID:FIELD_ID_TYPE, FIELD_NAME_HSUM:FIELD_H_TYPE, FIELD_NAME_AREA:FIELD_H_TYPE}, format_vector)
field_new_values_list = []
for index in range(0, len(field_values_list[FIELD_NAME_ID])) :
value_h = 0.0
if field_values_list[FIELD_NAME_AREA][index] > 0 :
value_h = field_values_list[FIELD_NAME_HSUM][index] / field_values_list[FIELD_NAME_AREA][index]
field_new_values_list.append({FIELD_NAME_HRE:value_h})
# Ajour de la nouvelle colonne calculé FIELD_NAME_HRE
addNewFieldVector(grid_output, FIELD_NAME_HRE, FIELD_H_TYPE, 0, None, None, format_vector)
setAttributeValuesList(grid_output, field_new_values_list, format_vector)
##########################################
### Nettoyage des fichiers temporaires ###
##########################################
if not save_results_intermediate:
if os.path.exists(temp_path):
shutil.rmtree(temp_path)
print(cyan + "computeRoughnessByOcsMnh() : " + endC + "Fin du calcul de l'indicateur Height of Roughness Elements par OCS et MNT." + endC + "\n")
timeLine(path_time_log, "Fin du calcul de l'indicateur Height of Roughness Elements par OCS et MNT ending : ")
return
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
