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changing cnn ocr model name

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vahidrezanezhad 2024-12-26 22:45:40 +01:00
parent 92bfac4b41
commit 33fda2f8be
1 changed files with 11 additions and 11 deletions
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src/eynollah/eynollah.py
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@ -2191,18 +2191,18 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w)) img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
if not self.dir_in: if not self.dir_in:
###prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version) prediction_textline = self.do_prediction(patches, img, model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3, thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, model_textline, n_batch_inference=3) ###prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, model_textline, n_batch_inference=3)
#if not thresholding_for_artificial_class_in_light_version: #if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1: #if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, model_textline) #prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0 #prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
else: else:
##prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version) prediction_textline = self.do_prediction(patches, img, self.model_textline, marginal_of_patch_percent=0.15, n_batch_inference=3,thresholding_for_artificial_class_in_light_version=thresholding_for_artificial_class_in_light_version)
prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, self.model_textline, n_batch_inference=3) ##prediction_textline = self.do_prediction_new_concept_scatter_nd(patches, img, self.model_textline, n_batch_inference=3)
#if not thresholding_for_artificial_class_in_light_version: #if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1: #if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, model_textline) #prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
@ -2482,17 +2482,17 @@ class Eynollah:
if num_col_classifier == 1 or num_col_classifier == 2: if num_col_classifier == 1 or num_col_classifier == 2:
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)
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5: if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True) ##prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
###prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True) prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region, n_batch_inference=1, thresholding_for_some_classes_in_light_version = True)
else: else:
prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3)) prediction_regions_org = np.zeros((self.image_org.shape[0], self.image_org.shape[1], 3))
prediction_regions_page = self.do_prediction_new_concept_scatter_nd(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True) ###prediction_regions_page = self.do_prediction_new_concept_scatter_nd(False, self.image_page_org_size, model_region, n_batch_inference=1, thresholding_for_artificial_class_in_light_version = True)
##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 = True) 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 = True)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page 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_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(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True) prediction_regions_org = self.do_prediction_new_concept(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True) ###prediction_regions_org = self.do_prediction_new_concept_scatter_nd(True, resize_image(img_bin, int( (900+ (num_col_classifier-3)*100) *(img_bin.shape[0]/img_bin.shape[1]) ), 900+ (num_col_classifier-3)*100), model_region, n_batch_inference=2, thresholding_for_some_classes_in_light_version=True)
##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:
@ -5638,7 +5638,7 @@ class Eynollah_ocr:
self.model_ocr.to(self.device) self.model_ocr.to(self.device)
else: else:
self.model_ocr_dir = dir_models + "/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn" self.model_ocr_dir = dir_models + "/model_1_ocrcnn"#"/model_0_ocr_cnnrnn"#"/model_23_ocr_cnnrnn"
model_ocr = load_model(self.model_ocr_dir , compile=False) model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model( self.prediction_model = tf.keras.models.Model(