qurator-spk
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eynollah
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pushing commits 2d9ccac and 7345f6b into eynollah_light

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pull/86/head
vahid 1 year ago
parent 22a8e93031
commit d3735b12f4
1 changed files with 38 additions and 166 deletions
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qurator/eynollah/eynollah.py
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@ -229,6 +229,8 @@ class Eynollah:
self.model_textline_dir = dir_models + "/eynollah-textline_20210425.h5"
self.model_tables = dir_models + "/eynollah-tables_20210319.h5"
self.models = {}
if dir_in and light_version:
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
@ -391,10 +393,6 @@ class Eynollah:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg
prediction_true = prediction_true.astype(int)
session_enhancement.close()
del model_enhancement
del session_enhancement
gc.collect()
return prediction_true
@ -500,13 +498,6 @@ class Eynollah:
num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
if not self.dir_in:
session_col_classifier.close()
del model_num_classifier
del session_col_classifier
K.clear_session()
gc.collect()
@ -537,12 +528,6 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
if not self.dir_in:
session_bin.close()
del model_bin
del session_bin
gc.collect()
prediction_bin = prediction_bin.astype(np.uint8)
img= np.copy(prediction_bin)
img_bin = np.copy(prediction_bin)
@ -579,10 +564,7 @@ class Eynollah:
label_p_pred = model_num_classifier.predict(img_in, verbose=0)
num_col = np.argmax(label_p_pred[0]) + 1
self.logger.info("Found %s columns (%s)", num_col, label_p_pred)
if not self.dir_in:
session_col_classifier.close()
K.clear_session()
self.logger.info("Found %d columns (%s)", num_col, np.around(label_p_pred, decimals=5))
if dpi < DPI_THRESHOLD:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
@ -595,8 +577,6 @@ class Eynollah:
num_column_is_classified = True
image_res = np.copy(img)
is_image_enhanced = False
if not self.dir_in:
session_col_classifier.close()
self.logger.debug("exit resize_and_enhance_image_with_column_classifier")
@ -665,9 +645,14 @@ class Eynollah:
def start_new_session_and_model(self, model_dir):
self.logger.debug("enter start_new_session_and_model (model_dir=%s)", model_dir)
gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
#gpu_options = tf.compat.v1.GPUOptions(allow_growth=True)
#gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=7.7, allow_growth=True)
session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
#session = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options))
physical_devices = tf.config.list_physical_devices('GPU')
try:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
except:
self.logger.warning("no GPU device available")
# try:
# model = load_model(model_dir, compile=False)
@ -676,9 +661,13 @@ class Eynollah:
if model_dir.endswith('.h5') and Path(model_dir[:-3]).exists():
# prefer SavedModel over HDF5 format if it exists
model_dir = model_dir[:-3]
model = load_model(model_dir, compile=False)
if model_dir in self.models:
model = self.models[model_dir]
else:
model = load_model(model_dir, compile=False)
self.models[model_dir] = model
return model, session
return model, None
def do_prediction(self, patches, img, model, marginal_of_patch_percent=0.1):
self.logger.debug("enter do_prediction")
@ -797,8 +786,8 @@ class Eynollah:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
prediction_true = prediction_true.astype(np.uint8)
del model
gc.collect()
#del model
#gc.collect()
return prediction_true
def do_prediction_new_concept(self, patches, img, model, marginal_of_patch_percent=0.1):
self.logger.debug("enter do_prediction")
@ -963,17 +952,19 @@ class Eynollah:
prediction_true[index_y_d + margin : index_y_u - margin, index_x_d + margin : index_x_u - margin, :] = seg_color
prediction_true = prediction_true.astype(np.uint8)
del model
gc.collect()
##del model
##gc.collect()
return prediction_true
def extract_page(self):
self.logger.debug("enter extract_page")
cont_page = []
if not self.ignore_page_extraction:
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)
img = cv2.GaussianBlur(self.image, (5, 5), 0)
if not self.dir_in:
img_page_prediction = self.do_prediction(False, img, model_page)
else:
@ -1003,12 +994,7 @@ class Eynollah:
box = [0, 0, img.shape[1], img.shape[0]]
croped_page, page_coord = crop_image_inside_box(box, self.image)
cont_page.append(np.array([[page_coord[2], page_coord[0]], [page_coord[3], page_coord[0]], [page_coord[3], page_coord[1]], [page_coord[2], page_coord[1]]]))
if not self.dir_in:
session_page.close()
del model_page
del session_page
K.clear_session()
gc.collect()
self.logger.debug("exit extract_page")
else:
box = [0, 0, self.image.shape[1], self.image.shape[0]]
@ -1046,14 +1032,6 @@ class Eynollah:
box = [0, 0, img.shape[1], img.shape[0]]
croped_page, page_coord = crop_image_inside_box(box, img)
if not self.dir_in:
session_page.close()
del model_page
del session_page
K.clear_session()
gc.collect()
self.logger.debug("exit early_page_for_num_of_column_classification")
else:
img = self.imread()
@ -1156,12 +1134,6 @@ class Eynollah:
prediction_regions = self.do_prediction(patches, img, model_region, marginal_of_patch_percent)
prediction_regions = resize_image(prediction_regions, img_height_h, img_width_h)
if not self.dir_in:
session_region.close()
del model_region
del session_region
gc.collect()
self.logger.debug("exit extract_text_regions")
return prediction_regions, prediction_regions2
@ -1558,8 +1530,6 @@ class Eynollah:
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)
if not self.dir_in:
session_textline.close()
if self.textline_light:
return (prediction_textline[:, :, 0]==1)*1, (prediction_textline_longshot_true_size[:, :, 0]==1)*1
@ -1631,8 +1601,6 @@ class Eynollah:
else:
img_w_new = 4000
img_h_new = int(img_org.shape[0] / float(img_org.shape[1]) * img_w_new)
gc.collect()
##img_resized = resize_image(img_bin,img_height_h, img_width_h )
img_resized = resize_image(img,img_h_new, img_w_new )
if not self.dir_in:
@ -1645,11 +1613,6 @@ class Eynollah:
prediction_bin = prediction_bin*255
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
if not self.dir_in:
session_bin.close()
del model_bin
del session_bin
gc.collect()
prediction_bin = prediction_bin.astype(np.uint16)
#img= np.copy(prediction_bin)
@ -1695,9 +1658,6 @@ class Eynollah:
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
#erosion_hurts = True
if not self.dir_in:
K.clear_session()
return text_regions_p_true, erosion_hurts, polygons_lines_xml, textline_mask_tot_ea
def get_regions_from_xy_2models(self,img,is_image_enhanced, num_col_classifier):
@ -1742,16 +1702,9 @@ class Eynollah:
prediction_regions_org = self.do_prediction(True, img, model_region)
prediction_regions_org = resize_image(prediction_regions_org, img_height_h, img_width_h )
##plt.imshow(prediction_regions_org[:,:,0])
##plt.show()
prediction_regions_org=prediction_regions_org[:,:,0]
prediction_regions_org[(prediction_regions_org[:,:]==1) & (mask_zeros_y[:,:]==1)]=0
if not self.dir_in:
session_region.close()
del model_region
del session_region
gc.collect()
if not self.dir_in:
model_region, session_region = self.start_new_session_and_model(self.model_region_dir_p2)
@ -1763,11 +1716,6 @@ class Eynollah:
prediction_regions_org2 = self.do_prediction(True, img, model_region, 0.2)
prediction_regions_org2=resize_image(prediction_regions_org2, img_height_h, img_width_h )
if not self.dir_in:
session_region.close()
del model_region
del session_region
gc.collect()
mask_zeros2 = (prediction_regions_org2[:,:,0] == 0)
mask_lines2 = (prediction_regions_org2[:,:,0] == 3)
@ -1788,8 +1736,6 @@ class Eynollah:
mask_lines_only=(prediction_regions_org[:,:]==3)*1
prediction_regions_org = cv2.erode(prediction_regions_org[:,:], KERNEL, iterations=2)
#plt.imshow(text_region2_1st_channel)
#plt.show()
prediction_regions_org = cv2.dilate(prediction_regions_org[:,:], KERNEL, iterations=2)
@ -1811,11 +1757,6 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
if not self.dir_in:
session_bin.close()
del model_bin
del session_bin
gc.collect()
if not self.dir_in:
@ -1834,11 +1775,6 @@ class Eynollah:
prediction_regions_org=prediction_regions_org[:,:,0]
mask_lines_only=(prediction_regions_org[:,:]==3)*1
if not self.dir_in:
session_region.close()
del model_region
del session_region
gc.collect()
mask_texts_only=(prediction_regions_org[:,:]==1)*1
@ -1859,19 +1795,11 @@ class Eynollah:
text_regions_p_true=cv2.fillPoly(text_regions_p_true,pts=polygons_of_only_texts, color=(1,1,1))
if not self.dir_in:
K.clear_session()
return text_regions_p_true, erosion_hurts, polygons_lines_xml
except:
if self.input_binary:
prediction_bin = np.copy(img_org)
else:
if not self.dir_in:
session_region.close()
del model_region
del session_region
gc.collect()
if not self.dir_in:
model_bin, session_bin = self.start_new_session_and_model(self.model_dir_of_binarization)
@ -1887,12 +1815,6 @@ class Eynollah:
prediction_bin =np.repeat(prediction_bin[:, :, np.newaxis], 3, axis=2)
if not self.dir_in:
session_bin.close()
del model_bin
del session_bin
gc.collect()
if not self.dir_in:
@ -1910,11 +1832,6 @@ class Eynollah:
prediction_regions_org=prediction_regions_org[:,:,0]
#mask_lines_only=(prediction_regions_org[:,:]==3)*1
if not self.dir_in:
session_region.close()
del model_region
del session_region
gc.collect()
#img = resize_image(img_org, int(img_org.shape[0]*1), int(img_org.shape[1]*1))
@ -1925,12 +1842,6 @@ class Eynollah:
#prediction_regions_org = prediction_regions_org[:,:,0]
#prediction_regions_org[(prediction_regions_org[:,:] == 1) & (mask_zeros_y[:,:] == 1)]=0
#session_region.close()
#del model_region
#del session_region
#gc.collect()
mask_lines_only = (prediction_regions_org[:,:] ==3)*1
@ -1957,8 +1868,6 @@ class Eynollah:
text_regions_p_true = cv2.fillPoly(text_regions_p_true, pts = polygons_of_only_texts, color=(1,1,1))
erosion_hurts = True
if not self.dir_in:
K.clear_session()
return text_regions_p_true, erosion_hurts, polygons_lines_xml
def do_order_of_regions_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
@ -2515,10 +2424,6 @@ class Eynollah:
prediction_table_erode = cv2.erode(prediction_table[:,:,0], KERNEL, iterations=20)
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
del model_region
del session_region
gc.collect()
return prediction_table_erode.astype(np.int16)
@ -2619,8 +2524,7 @@ class Eynollah:
self.logger.info("Resizing and enhancing image...")
is_image_enhanced, img_org, img_res, num_col_classifier, num_column_is_classified, img_bin = self.resize_and_enhance_image_with_column_classifier(light_version)
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
if not self.dir_in:
K.clear_session()
scale = 1
if is_image_enhanced:
if self.allow_enhancement:
@ -2646,8 +2550,6 @@ class Eynollah:
textline_mask_tot_ea, _ = self.textline_contours(image_page, True, scaler_h_textline, scaler_w_textline)
if self.textline_light:
textline_mask_tot_ea = textline_mask_tot_ea.astype(np.int16)
if not self.dir_in:
K.clear_session()
if self.plotter:
self.plotter.save_plot_of_textlines(textline_mask_tot_ea, image_page)
return textline_mask_tot_ea
@ -2660,7 +2562,7 @@ class Eynollah:
if self.plotter:
self.plotter.save_deskewed_image(slope_deskew)
self.logger.info("slope_deskew: %s", slope_deskew)
self.logger.info("slope_deskew: %.2f°", slope_deskew)
return slope_deskew, slope_first
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, text_regions_p_1, table_prediction):
@ -2709,8 +2611,6 @@ class Eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, _, matrix_of_lines_ch_d, splitter_y_new_d, _ = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
if not self.dir_in:
K.clear_session()
self.logger.info("num_col_classifier: %s", num_col_classifier)
@ -2775,8 +2675,6 @@ class Eynollah:
pixel_img = 10
contours_tables = return_contours_of_interested_region(text_regions_p, pixel_img, min_area_mar)
if not self.dir_in:
K.clear_session()
self.logger.debug('exit run_boxes_no_full_layout')
return polygons_of_images, img_revised_tab, text_regions_p_1_n, textline_mask_tot_d, regions_without_separators_d, boxes, boxes_d, polygons_of_marginals, contours_tables
@ -2807,9 +2705,6 @@ class Eynollah:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
num_col_d, peaks_neg_fin_d, matrix_of_lines_ch_d, splitter_y_new_d, seperators_closeup_n_d = find_number_of_columns_in_document(np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2),num_col_classifier, self.tables, pixel_lines)
if not self.dir_in:
K.clear_session()
gc.collect()
if num_col_classifier>=3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD:
@ -2875,38 +2770,22 @@ class Eynollah:
text_regions_p[:, :][text_regions_p[:, :] == 2] = 5
text_regions_p[:, :][text_regions_p[:, :] == 3] = 6
text_regions_p[:, :][text_regions_p[:, :] == 4] = 8
if not self.dir_in:
K.clear_session()
image_page = image_page.astype(np.uint8)
regions_fully, regions_fully_only_drop = self.extract_text_regions(image_page, True, cols=num_col_classifier)
text_regions_p[:,:][regions_fully[:,:,0]==6]=6
regions_fully_only_drop = put_drop_out_from_only_drop_model(regions_fully_only_drop, text_regions_p)
regions_fully[:, :, 0][regions_fully_only_drop[:, :, 0] == 4] = 4
if not self.dir_in:
K.clear_session()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = putt_bb_of_drop_capitals_of_model_in_patches_in_layout(regions_fully)
# plt.imshow(regions_fully[:,:,0])
# plt.show()
if not self.dir_in:
K.clear_session()
regions_fully_np, _ = self.extract_text_regions(image_page, False, cols=num_col_classifier)
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
if num_col_classifier > 2:
regions_fully_np[:, :, 0][regions_fully_np[:, :, 0] == 4] = 0
else:
regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
# plt.imshow(regions_fully_np[:,:,0])
# plt.show()
if not self.dir_in:
K.clear_session()
# plt.imshow(regions_fully[:,:,0])
# plt.show()
regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
# plt.imshow(regions_fully[:,:,0])
# plt.show()
@ -2929,8 +2808,6 @@ class Eynollah:
regions_without_separators_d = None
if not self.tables:
regions_without_separators = (text_regions_p[:, :] == 1) * 1
if not self.dir_in:
K.clear_session()
img_revised_tab = np.copy(text_regions_p[:, :])
polygons_of_images = return_contours_of_interested_region(img_revised_tab, 5)
self.logger.debug('exit run_boxes_full_layout')
@ -3025,13 +2902,12 @@ class Eynollah:
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
if len(contours_only_text_parent) > 0:
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
#self.logger.info('areas_cnt_text %s', areas_cnt_text)
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
areas_cnt_text_parent = list(np.array(areas_cnt_text_parent)[index_con_parents])
@ -3042,14 +2918,14 @@ class Eynollah:
contours_only_text_d, hir_on_text_d = return_contours_of_image(text_only_d)
contours_only_text_parent_d = return_parent_contours(contours_only_text_d, hir_on_text_d)
areas_cnt_text_d = np.array([cv2.contourArea(contours_only_text_parent_d[j]) for j in range(len(contours_only_text_parent_d))])
areas_cnt_text_d = np.array([cv2.contourArea(c) for c in contours_only_text_parent_d])
areas_cnt_text_d = areas_cnt_text_d / float(text_only_d.shape[0] * text_only_d.shape[1])
if len(areas_cnt_text_d)>0:
contours_biggest_d = contours_only_text_parent_d[np.argmax(areas_cnt_text_d)]
index_con_parents_d=np.argsort(areas_cnt_text_d)
contours_only_text_parent_d=list(np.array(contours_only_text_parent_d)[index_con_parents_d] )
areas_cnt_text_d=list(np.array(areas_cnt_text_d)[index_con_parents_d] )
index_con_parents_d = np.argsort(areas_cnt_text_d)
contours_only_text_parent_d = list(np.array(contours_only_text_parent_d)[index_con_parents_d])
areas_cnt_text_d = list(np.array(areas_cnt_text_d)[index_con_parents_d])
cx_bigest_d_big, cy_biggest_d_big, _, _, _, _, _ = find_new_features_of_contours([contours_biggest_d])
cx_bigest_d, cy_biggest_d, _, _, _, _, _ = find_new_features_of_contours(contours_only_text_parent_d)
@ -3103,12 +2979,12 @@ class Eynollah:
contours_only_text_parent = return_parent_contours(contours_only_text, hir_on_text)
if len(contours_only_text_parent) > 0:
areas_cnt_text = np.array([cv2.contourArea(contours_only_text_parent[j]) for j in range(len(contours_only_text_parent))])
areas_cnt_text = np.array([cv2.contourArea(c) for c in contours_only_text_parent])
areas_cnt_text = areas_cnt_text / float(text_only.shape[0] * text_only.shape[1])
contours_biggest = contours_only_text_parent[np.argmax(areas_cnt_text)]
contours_only_text_parent = [contours_only_text_parent[jz] for jz in range(len(contours_only_text_parent)) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > min_con_area]
contours_only_text_parent = [c for jz, c in enumerate(contours_only_text_parent) if areas_cnt_text[jz] > min_con_area]
areas_cnt_text_parent = [area for area in areas_cnt_text if area > min_con_area]
index_con_parents = np.argsort(areas_cnt_text_parent)
contours_only_text_parent = list(np.array(contours_only_text_parent)[index_con_parents])
@ -3146,8 +3022,6 @@ class Eynollah:
all_found_texline_polygons = small_textlines_to_parent_adherence2(all_found_texline_polygons, textline_mask_tot_ea, num_col_classifier)
all_found_texline_polygons_marginals, boxes_marginals, _, polygons_of_marginals, all_box_coord_marginals, _, slopes_marginals = self.get_slopes_and_deskew_new_curved(polygons_of_marginals, polygons_of_marginals, cv2.erode(textline_mask_tot_ea, kernel=KERNEL, iterations=1), image_page_rotated, boxes_marginals, text_only, num_col_classifier, scale_param, slope_deskew)
all_found_texline_polygons_marginals = small_textlines_to_parent_adherence2(all_found_texline_polygons_marginals, textline_mask_tot_ea, num_col_classifier)
if not self.dir_in:
K.clear_session()
if self.full_layout:
if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
@ -3167,8 +3041,6 @@ class Eynollah:
if self.plotter:
self.plotter.save_plot_of_layout(text_regions_p, image_page)
self.plotter.save_plot_of_layout_all(text_regions_p, image_page)
if not self.dir_in:
K.clear_session()
pixel_img = 4
polygons_of_drop_capitals = return_contours_of_interested_region_by_min_size(text_regions_p, pixel_img)

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