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eynollah
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simplify loading models w/o dir_in mode

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pull/142/head
Robert Sachunsky 3 weeks ago
parent 329fac23f6
commit 14beb46224
1 changed files with 75 additions and 128 deletions
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203
src/eynollah/eynollah.py
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@ -567,7 +567,8 @@ class Eynollah:
_, page_coord = self.early_page_for_num_of_column_classification(img)
if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
self.model_classifier, _ = self.start_new_session_and_model(self.model_dir_of_col_classifier)
if self.input_binary:
img_in = np.copy(img)
img_in = img_in / 255.0
@ -590,10 +591,7 @@ class Eynollah:
img_in[0, :, :, 1] = img_1ch[:, :]
img_in[0, :, :, 2] = img_1ch[:, :]
if not self.dir_in:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
else:
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
@ -613,12 +611,10 @@ class Eynollah:
self.logger.info("Detected %s DPI", dpi)
if self.input_binary:
img = self.imread()
if self.dir_in:
prediction_bin = self.do_prediction(True, img, self.model_bin, n_batch_inference=5)
else:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img, model_bin, n_batch_inference=5)
if not self.dir_in:
self.model_bin, _ = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img, self.model_bin, n_batch_inference=5)
prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1
@ -641,7 +637,7 @@ class Eynollah:
self.page_coord = page_coord
if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
self.model_classifier, _ = self.start_new_session_and_model(self.model_dir_of_col_classifier)
if self.num_col_upper and not self.num_col_lower:
num_col = self.num_col_upper
@ -669,10 +665,7 @@ class Eynollah:
img_in[0, :, :, 2] = img_1ch[:, :]
if self.dir_in:
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
else:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
elif (self.num_col_upper and self.num_col_lower) and (self.num_col_upper!=self.num_col_lower):
if self.input_binary:
@ -693,10 +686,7 @@ class Eynollah:
img_in[0, :, :, 2] = img_1ch[:, :]
if self.dir_in:
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
else:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
label_p_pred = self.model_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
if num_col > self.num_col_upper:
@ -1381,12 +1371,9 @@ class Eynollah:
img = cv2.GaussianBlur(self.image, (5, 5), 0)
if not self.dir_in:
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
self.model_page, _ = self.start_new_session_and_model(self.model_page_dir)
if not self.dir_in:
img_page_prediction = self.do_prediction(False, img, model_page)
else:
img_page_prediction = self.do_prediction(False, img, self.model_page)
img_page_prediction = self.do_prediction(False, img, self.model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, KERNEL, iterations=3)
@ -1429,13 +1416,10 @@ class Eynollah:
else:
img = self.imread()
if not self.dir_in:
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
self.model_page, _ = self.start_new_session_and_model(self.model_page_dir)
img = cv2.GaussianBlur(img, (5, 5), 0)
if self.dir_in:
img_page_prediction = self.do_prediction(False, img, self.model_page)
else:
img_page_prediction = self.do_prediction(False, img, model_page)
img_page_prediction = self.do_prediction(False, img, self.model_page)
imgray = cv2.cvtColor(img_page_prediction, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
@ -1462,9 +1446,12 @@ class Eynollah:
img_height_h = img.shape[0]
img_width_h = img.shape[1]
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
else:
model_region = self.model_region_fl if patches else self.model_region_fl_np
if patches:
self.model_region_fl, _ = self.start_new_session_and_model(self.model_region_dir_fully)
else:
self.model_region_fl_np, _ = self.start_new_session_and_model(self.model_region_dir_fully_np)
model_region = self.model_region_fl if patches else self.model_region_fl_np
if not patches:
if self.light_version:
@ -1546,9 +1533,12 @@ class Eynollah:
img_height_h = img.shape[0]
img_width_h = img.shape[1]
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_fully if patches else self.model_region_dir_fully_np)
else:
model_region = self.model_region_fl if patches else self.model_region_fl_np
if patches:
self.model_region_fl, _ = self.start_new_session_and_model(self.model_region_dir_fully)
else:
self.model_region_fl_np, _ = self.start_new_session_and_model(self.model_region_dir_fully_np)
model_region = self.model_region_fl if patches else self.model_region_fl_np
if not patches:
img = otsu_copy_binary(img)
@ -2049,26 +2039,18 @@ class Eynollah:
else:
thresholding_for_artificial_class_in_light_version = False
if not self.dir_in:
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
self.model_textline, _ = self.start_new_session_and_model(self.model_textline_dir)
#img = img.astype(np.uint8)
img_org = np.copy(img)
img_h = img_org.shape[0]
img_w = img_org.shape[1]
img = resize_image(img_org, int(img_org.shape[0] * scaler_h), int(img_org.shape[1] * scaler_w))
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)
#if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
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)
#if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, model_textline)
#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
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)
#if not thresholding_for_artificial_class_in_light_version:
#if num_col_classifier==1:
#prediction_textline_nopatch = self.do_prediction(False, img, self.model_textline)
#prediction_textline[:,:][prediction_textline_nopatch[:,:]==0] = 0
prediction_textline = resize_image(prediction_textline, img_h, img_w)
textline_mask_tot_ea_art = (prediction_textline[:,:]==2)*1
@ -2092,10 +2074,7 @@ class Eynollah:
if not thresholding_for_artificial_class_in_light_version:
prediction_textline[:,:][old_art[:,:]==1]=2
if not self.dir_in:
prediction_textline_longshot = self.do_prediction(False, img, model_textline)
else:
prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
prediction_textline_longshot = self.do_prediction(False, img, self.model_textline)
prediction_textline_longshot_true_size = resize_image(prediction_textline_longshot, img_h, img_w)
return ((prediction_textline[:, :, 0]==1)*1).astype('uint8'), ((prediction_textline_longshot_true_size[:, :, 0]==1)*1).astype('uint8')
@ -2161,10 +2140,8 @@ class Eynollah:
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens_light_only_images_extraction)
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, model_region)
else:
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region)
self.model_region, _ = self.start_new_session_and_model(self.model_region_dir_p_ens_light_only_images_extraction)
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region)
prediction_regions_org = resize_image(prediction_regions_org,img_height_h, img_width_h )
@ -2256,7 +2233,7 @@ class Eynollah:
img_height_h = img_org.shape[0]
img_width_h = img_org.shape[1]
#model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
#model_region, _ = self.start_new_session_and_model(self.model_region_dir_p_ens)
#print(num_col_classifier,'num_col_classifier')
@ -2290,10 +2267,8 @@ class Eynollah:
#img_bin = np.copy(img_resized)
###if (not self.input_binary and self.full_layout) or (not self.input_binary and num_col_classifier >= 30):
###if not self.dir_in:
###model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
###prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
###else:
###prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
###self.model_bin, _ = self.start_new_session_and_model(self.model_dir_of_binarization)
###prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
####print("inside bin ", time.time()-t_bin)
###prediction_bin=prediction_bin[:,:,0]
@ -2309,10 +2284,8 @@ class Eynollah:
###img_bin = np.copy(img_resized)
if self.ocr and not self.input_binary:
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, model_bin, n_batch_inference=5)
else:
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
self.model_bin, _ = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_resized, self.model_bin, n_batch_inference=5)
prediction_bin=prediction_bin[:,:,0]
prediction_bin = (prediction_bin[:,:]==0)*1
prediction_bin = prediction_bin*255
@ -2341,30 +2314,27 @@ class Eynollah:
if not skip_layout_and_reading_order:
#print("inside 2 ", time.time()-t_in)
if not self.dir_in:
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)
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
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:
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, 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
self.model_region_1_2, _ = self.start_new_session_and_model(self.model_region_dir_p_1_2_sp_np)
##self.model_region, _ = self.start_new_session_and_model(self.model_region_dir_p_ens_light)
if num_col_classifier == 1 or num_col_classifier == 2:
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
prediction_regions_org = self.do_prediction_new_concept(
True, img_resized, self.model_region_1_2, n_batch_inference=1,
thresholding_for_some_classes_in_light_version=True)
else:
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)
##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 = 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=True)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else:
if num_col_classifier == 1 or num_col_classifier == 2:
if self.image_org.shape[0]/self.image_org.shape[1] > 2.5:
prediction_regions_org = self.do_prediction_new_concept(True, img_resized, self.model_region_1_2, n_batch_inference=1, thresholding_for_some_classes_in_light_version=True)
else:
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=True)
prediction_regions_org[self.page_coord[0] : self.page_coord[1], self.page_coord[2] : self.page_coord[3],:] = prediction_regions_page
else:
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), self.model_region_1_2, n_batch_inference=2, 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)
new_h = (900+ (num_col_classifier-3)*100)
img_resized = resize_image(img_bin, int(new_h * img_bin.shape[0] /img_bin.shape[1]), new_h)
prediction_regions_org = self.do_prediction_new_concept(
True, img_resized, self.model_region_1_2, n_batch_inference=2,
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)
@ -2466,16 +2436,13 @@ class Eynollah:
img_width_h = img_org.shape[1]
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
self.model_region, _ = self.start_new_session_and_model(self.model_region_dir_p_ens)
ratio_y=1.3
ratio_x=1
img = resize_image(img_org, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if not self.dir_in:
prediction_regions_org_y = self.do_prediction(True, img, model_region)
else:
prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
prediction_regions_org_y = self.do_prediction(True, img, self.model_region)
prediction_regions_org_y = resize_image(prediction_regions_org_y, img_height_h, img_width_h )
#plt.imshow(prediction_regions_org_y[:,:,0])
@ -2494,10 +2461,7 @@ class Eynollah:
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]*(1.2 if is_image_enhanced else 1)))
if self.dir_in:
prediction_regions_org = self.do_prediction(True, img, self.model_region)
else:
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
prediction_regions_org=prediction_regions_org[:,:,0]
@ -2505,14 +2469,11 @@ class Eynollah:
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
self.model_region_p2, _ = self.start_new_session_and_model(self.model_region_dir_p2)
img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1]))
if self.dir_in:
prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, marginal_of_patch_percent=0.2)
else:
prediction_regions_org2 = self.do_prediction(True, img, model_region, marginal_of_patch_percent=0.2)
prediction_regions_org2 = self.do_prediction(True, img, self.model_region_p2, marginal_of_patch_percent=0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
@ -2544,10 +2505,8 @@ class Eynollah:
prediction_bin = np.copy(img_org)
else:
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin, n_batch_inference=5)
else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5)
self.model_bin, _ = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
prediction_bin=prediction_bin[:,:,0]
@ -2557,17 +2516,14 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
self.model_region, _ = self.start_new_session_and_model(self.model_region_dir_p_ens)
ratio_y=1
ratio_x=1
img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if not self.dir_in:
prediction_regions_org = self.do_prediction(True, img, model_region)
else:
prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
prediction_regions_org=prediction_regions_org[:,:,0]
@ -2597,10 +2553,8 @@ class Eynollah:
prediction_bin = np.copy(img_org)
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, model_bin, n_batch_inference=5)
else:
prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5)
self.model_bin, _ = self.start_new_session_and_model(self.model_dir_of_binarization)
prediction_bin = self.do_prediction(True, img_org, self.model_bin, n_batch_inference=5)
prediction_bin = resize_image(prediction_bin, img_height_h, img_width_h )
prediction_bin=prediction_bin[:,:,0]
@ -2612,7 +2566,7 @@ class Eynollah:
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p_ens)
self.model_region, _ = self.start_new_session_and_model(self.model_region_dir_p_ens)
else:
prediction_bin = np.copy(img_org)
@ -2621,17 +2575,14 @@ class Eynollah:
img = resize_image(prediction_bin, int(img_org.shape[0]*ratio_y), int(img_org.shape[1]*ratio_x))
if not self.dir_in:
prediction_regions_org = self.do_prediction(True, img, model_region)
else:
prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = self.do_prediction(True, img, self.model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
prediction_regions_org=prediction_regions_org[:,:,0]
#mask_lines_only=(prediction_regions_org[:,:]==3)*1
#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
#prediction_regions_org = self.do_prediction(True, img, model_region)
#prediction_regions_org = self.do_prediction(True, img, self.model_region)
#prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
@ -3173,9 +3124,7 @@ class Eynollah:
if self.dir_in:
pass
else:
if not self.dir_in:
self.model_table, _ = self.start_new_session_and_model(self.model_table_dir)
patches = False
@ -3937,9 +3886,7 @@ class Eynollah:
img_poly[text_regions_p[:,:]==3] = 4
img_poly[text_regions_p[:,:]==6] = 5
if self.dir_in:
pass
else:
if not self.dir_in:
self.model_reading_order, _ = self.start_new_session_and_model(self.model_reading_order_dir)
height1 =672#448

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