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eynollah
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updating light version

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pull/138/head^2
vahidrezanezhad 3 months ago
parent 133091137d
commit ad32316217
1 changed files with 30 additions and 16 deletions
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qurator/eynollah/eynollah.py
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@ -252,7 +252,7 @@ class Eynollah:
self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425" self.model_region_dir_p_ens = dir_models + "/eynollah-main-regions-ensembled_20210425"
self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314" self.model_region_dir_p_ens_light = dir_models + "/eynollah-main-regions_20220314"
self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based" self.model_reading_order_machine_dir = dir_models + "/model_ens_reading_order_machine_based"
self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige" self.model_region_dir_p_1_2_sp_np = dir_models + "/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_earlylayout_12spaltige_2_3_5_6_7_8"#"/modelens_early12_sp_2_3_5_6_7_8_9_10_12_14_15_16_18"#"/modelens_1_2_4_5_early_lay_1_2_spaltige"#"/model_3_eraly_layout_no_patches_1_2_spaltige"
##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans" ##self.model_region_dir_fully_new = dir_models + "/model_2_full_layout_new_trans"
self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans" self.model_region_dir_fully = dir_models + "/modelens_full_layout_24_till_28"#"/model_2_full_layout_new_trans"
if self.textline_light: if self.textline_light:
@ -541,6 +541,7 @@ class Eynollah:
img = self.imread() img = self.imread()
_, page_coord = self.early_page_for_num_of_column_classification(img) _, page_coord = self.early_page_for_num_of_column_classification(img)
if not self.dir_in: if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier) model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
if self.input_binary: if self.input_binary:
@ -611,6 +612,10 @@ class Eynollah:
width_early = img.shape[1] width_early = img.shape[1]
t1 = time.time() t1 = time.time()
_, page_coord = self.early_page_for_num_of_column_classification(img_bin) _, page_coord = self.early_page_for_num_of_column_classification(img_bin)
self.image_page_org_size = img[page_coord[0] : page_coord[1], page_coord[2] : page_coord[3], :]
self.page_coord = page_coord
if not self.dir_in: if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier) model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
@ -737,7 +742,7 @@ class Eynollah:
def get_image_and_scales_after_enhancing(self, img_org, img_res): def get_image_and_scales_after_enhancing(self, img_org, img_res):
self.logger.debug("enter get_image_and_scales_after_enhancing") self.logger.debug("enter get_image_and_scales_after_enhancing")
self.image = np.copy(img_res) self.image = np.copy(img_res)
self.image = self.image.astype(np.uint8) #self.image = self.image.astype(np.uint8)
self.image_org = np.copy(img_org) self.image_org = np.copy(img_org)
self.height_org = self.image_org.shape[0] self.height_org = self.image_org.shape[0]
self.width_org = self.image_org.shape[1] self.width_org = self.image_org.shape[1]
@ -1059,19 +1064,18 @@ class Eynollah:
if not patches: if not patches:
img_h_page = img.shape[0] img_h_page = img.shape[0]
img_w_page = img.shape[1] img_w_page = img.shape[1]
img = img / float(255.0) img = img / 255.0
img = resize_image(img, img_height_model, img_width_model) img = resize_image(img, img_height_model, img_width_model)
label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0) label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
#seg_not_base = label_p_pred[0,:,:,4]
#seg_not_base[seg_not_base>0.4] =1
#seg_not_base[seg_not_base<1] =0
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
#seg[seg_not_base==1]=4 if thresholding_for_artificial_class_in_light_version:
seg_art = label_p_pred[0,:,:,4]
seg_art[seg_art<0.1] =0
seg_art[seg_art>0] =1
seg[seg_art==1]=4
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
prediction_true = resize_image(seg_color, img_h_page, img_w_page) prediction_true = resize_image(seg_color, img_h_page, img_w_page)
@ -2151,7 +2155,7 @@ class Eynollah:
#print(num_col_classifier,'num_col_classifier') #print(num_col_classifier,'num_col_classifier')
if num_col_classifier == 1: if num_col_classifier == 1:
img_w_new = 1000 img_w_new = 800
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new) img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
elif num_col_classifier == 2: elif num_col_classifier == 2:
@ -2206,30 +2210,40 @@ class Eynollah:
textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h ) textline_mask_tot_ea = resize_image(textline_mask_tot_ea,img_height_h, img_width_h )
#print(self.image_org.shape)
#plt.imshwo(self.image_page_org_size)
#plt.show()
if not skip_layout_and_reading_order: if not skip_layout_and_reading_order:
#print("inside 2 ", time.time()-t_in) #print("inside 2 ", time.time()-t_in)
#print(img_resized.shape, num_col_classifier, "num_col_classifier")
if not self.dir_in: if not self.dir_in:
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
prediction_regions_org = self.do_prediction_new_concept(False, img_resized, model_region, n_batch_inference=1) prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = False)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else: else:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light, n_batch_inference=3) model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region) prediction_regions_org = self.do_prediction_new_concept(True, img_bin, model_region)
##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light) ##model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) ##prediction_regions_org = self.do_prediction(True, img_bin, model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
else: else:
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
prediction_regions_org = self.do_prediction_new_concept(False, img_resized, self.model_region_1_2, n_batch_inference=1) prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
prediction_regions_page = self.do_prediction_new_concept(False, self.image_page_org_size, self.model_region_1_2, n_batch_inference=1, thresholding_for_artificial_class_in_light_version=False)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else: else:
prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region, n_batch_inference=3) prediction_regions_org = self.do_prediction_new_concept(True, img_bin, self.model_region, n_batch_inference=3)
###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True) ###prediction_regions_org = self.do_prediction(True, img_bin, self.model_region, n_batch_inference=3, thresholding_for_some_classes_in_light_version=True)
#print("inside 3 ", time.time()-t_in) #print("inside 3 ", time.time()-t_in)
#plt.imshow(prediction_regions_org[:,:,0]) #plt.imshow(prediction_regions_org[:,:,0])
#plt.show() #plt.show()
prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h ) prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
img_bin = resize_image(img_bin,img_height_h, img_width_h ) img_bin = resize_image(img_bin,img_height_h, img_width_h )
@ -3195,7 +3209,7 @@ class Eynollah:
scale = 1 scale = 1
if is_image_enhanced: if is_image_enhanced:
if self.allow_enhancement: if self.allow_enhancement:
img_res = img_res.astype(np.uint8) #img_res = img_res.astype(np.uint8)
self.get_image_and_scales(img_org, img_res, scale) self.get_image_and_scales(img_org, img_res, scale)
if self.plotter: if self.plotter:
self.plotter.save_enhanced_image(img_res) self.plotter.save_enhanced_image(img_res)

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