def i_images_processing(self, vs):
"""
Interface function to launch processing VHRS images :func:`i_vhrs` and satellite images :func:`i_img_sat` in multi-processing.
:param vs: Boolean variable to launch processing because of interface checkbox -> 0 or 1.
- 0 means, not texture processing
- 1 means, launch texture processing
:type vs: int
"""
# Multiprocessing
mgr = BaseManager()
mgr.register('defaultdict', defaultdict, DictProxy)
mgr.start()
self.out_ndvistats_folder_tab = mgr.defaultdict(list)
p_img_sat = Process(target=self.i_img_sat)
p_img_sat.start()
if self.mp == 0:
p_img_sat.join()
if vs == 1:
p_vhrs = Process(target=self.i_vhrs)#, args=(vs, ))
p_vhrs.start()
p_vhrs.join()
if self.mp == 1:
p_img_sat.join()
# List of output raster path
self.raster_path.append(self.out_ndvistats_folder_tab[0])
# List of output raster band
self.list_band_outraster.append(1)
if vs == 1:
self.raster_path.append(self.out_ndvistats_folder_tab['sfs'])
self.list_band_outraster.append(4)
self.raster_path.append(self.out_ndvistats_folder_tab['haralick'])
self.list_band_outraster.append(2)
# To slope, to extract scree
if self.path_mnt != '':
self.raster_path.append(self.path_mnt)
self.list_band_outraster.append(1)
self.raster_path.append(self.out_ndvistats_folder_tab[1])
# example raster path tab :
# [path_folder_dpt + '/' + folder_processing + '/' + classif_year + '/Min_2014.TIF',\
# os.path.dirname(path_ortho) + '/Clip_buffer_surface_dep_18_IRCOrtho65_2m_sfs.TIF',\
# os.path.dirname(path_ortho) + '/Clip_buffer_surface_dep_18_IRCOrtho65_2m_haralick.TIF',\
# path_folder_dpt + '/' + folder_processing + '/' + classif_year + '/Max_2014.TIF']
# List of output raster band
self.list_band_outraster.append(1) #[1, 4, 2, 1]
print("End of images processing !")
def i_classifier(self):
"""
Interface function to launch decision tree classification with a input segmentation :func:`Segmentation.Segmentation`.
This function store optimal threshold by class **Segmentation.out_threshold**. Then compute zonal statistics by polygons
for every images in multi-processing (if **mp** = 1).
"""
# Multiprocessing
mgr = BaseManager()
mgr.register('defaultdict', defaultdict, DictProxy)
mgr.start()
multi_process_var = [] # Multi processing variable
# Extract final cartography
out_carto = Segmentation(self.path_segm, self.path_area)
out_carto.output_file = self.output_name_moba
out_carto.out_class_name = self.in_class_name
out_carto.out_threshold = []
for ind_th in range(len(self.sample_name)):
out_carto.out_threshold.append(self.decis[ind_th].threshold[0])
if '>' in self.decis[ind_th].threshold[0]:
out_carto.out_threshold.append(self.decis[ind_th].threshold[0].replace('>', '<='))
elif '<' in self.decis[ind_th].threshold[0]:
out_carto.out_threshold.append(self.decis[ind_th].threshold[0].replace('<', '>='))
# out_carto.zonal_stats((raster_path[ind_th], list_band_outraster[ind_th]))
multi_process_var.append([self.raster_path[ind_th], self.list_band_outraster[ind_th]])
# Compute zonal stats on slope raster
multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
out_carto.out_threshold.append('<'+str(self.slope_degree)) # To agriculture
out_carto.out_threshold.append('>='+str(self.slope_degree)) # To scree
if self.path_mnt != '':
# Add class indexes
self.tree_direction[0].append(6)
self.tree_direction[0].append(7)
# Compute zonal stats on Max NDVI raster
try:
# out_carto.zonal_stats((raster_path[ind_th+1], list_band_outraster[ind_th+1]))
multi_process_var.append([self.raster_path[ind_th+2], self.list_band_outraster[ind_th+2]])
# Compute stats twice, because there is 3 classes and not 2
# out_carto.zonal_stats((raster_path[ind_th+1], list_band_outraster[ind_th+1]))
multi_process_var.append([self.raster_path[ind_th+2], self.list_band_outraster[ind_th+2]])
except:
print('Not MNT on the 3rd step')
multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
multi_process_var.append([self.raster_path[ind_th+1], self.list_band_outraster[ind_th+1]])
# Compute zonal stats with multi processing
out_carto.stats_dict = mgr.defaultdict(list)
p = []
kwargs = {}
for i in range(len(multi_process_var)):
kwargs['rank'] = i
kwargs['nb_img'] = len(multi_process_var)
p.append(Process(target=out_carto.zonal_stats, args=(multi_process_var[i], ), kwargs=kwargs))
p[i].start()
if self.mp == 0:
p[i].join()
if self.mp == 1:
for i in range(len(multi_process_var)):
p[i].join()
# If there is more one fieldnames line edit fulled in classification tab
if len(self.sample_name) > 2:
# Compute the biomass and density distribution
out_carto.compute_biomass_density()
out_carto.class_tab_final = defaultdict(list)
self.i_tree_direction()
out_carto.decision_tree(self.tree_direction)
# If there is more one fieldnames line edit fulled in classification tab
if len(self.sample_name) > 2:
# Compute biomass and density scale
out_carto.append_scale(self.in_class_name[2], 'self.stats_dict[ind_stats][3]/self.max_bio')
out_carto.append_scale(self.in_class_name[3], 'self.stats_dict[ind_stats][2]/self.max_wood_idm')
# Final cartography
out_carto.create_cartography(self.out_fieldname_carto, self.out_fieldtype_carto)
