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pep 8 code style

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cneud 2024-09-20 23:39:34 +02:00
parent 826d38b865
commit 593cf64693
1 changed files with 61 additions and 125 deletions
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src/eynollah/eynollah.py
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@ -383,9 +383,7 @@ class Eynollah:
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_enhancement.predict( label_p_pred = model_enhancement.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), verbose=0)
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = label_p_pred[0, :, :, :] seg = label_p_pred[0, :, :, :]
seg = seg * 255 seg = seg * 255
@ -587,8 +585,7 @@ class Eynollah:
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)
if not self.dir_in: if not self.dir_in:
model_num_classifier, session_col_classifier = self.start_new_session_and_model( model_num_classifier, session_col_classifier = self.start_new_session_and_model(self.model_dir_of_col_classifier)
self.model_dir_of_col_classifier)
if self.input_binary: if self.input_binary:
img_in = np.copy(img) img_in = np.copy(img)
@ -618,8 +615,7 @@ class Eynollah:
if not self.extract_only_images: if not self.extract_only_images:
if dpi < DPI_THRESHOLD: if dpi < DPI_THRESHOLD:
img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, img_new, num_column_is_classified = self.calculate_width_height_by_columns(img, num_col, width_early, label_p_pred)
label_p_pred)
if light_version: if light_version:
image_res = np.copy(img_new) image_res = np.copy(img_new)
else: else:
@ -719,8 +715,7 @@ class Eynollah:
model = load_model(model_dir, compile=False) model = load_model(model_dir, compile=False)
self.models[model_dir] = model self.models[model_dir] = model
except: except:
model = load_model(model_dir, compile=False, model = load_model(model_dir, compile=False, custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches})
custom_objects={"PatchEncoder": PatchEncoder, "Patches": Patches})
self.models[model_dir] = model self.models[model_dir] = model
return model, None return model, None
@ -737,8 +732,7 @@ class Eynollah:
img = img / float(255.0) img = img / float(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]), label_p_pred = model.predict(img.reshape(1, img.shape[0], img.shape[1], img.shape[2]), verbose=0)
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@ -789,9 +783,7 @@ class Eynollah:
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict( label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), verbose=0)
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2) seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
@ -918,9 +910,7 @@ class Eynollah:
index_y_d = img_h - img_height_model index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :] img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict( label_p_pred = model.predict(img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), verbose=0)
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]),
verbose=0)
seg = np.argmax(label_p_pred, axis=3)[0] seg = np.argmax(label_p_pred, axis=3)[0]
seg_not_base = label_p_pred[0, :, :, 4] seg_not_base = label_p_pred[0, :, :, 4]
@ -1017,8 +1007,7 @@ class Eynollah:
prediction_true[index_y_d + margin: index_y_u - 0, index_x_d + margin: index_x_u - margin, prediction_true[index_y_d + margin: index_y_u - 0, index_x_d + margin: index_x_u - margin,
:] = seg_color :] = seg_color
else: else:
seg_color = seg_color[margin: seg_color.shape[0] - margin, margin: seg_color.shape[1] - margin, seg_color = seg_color[margin: seg_color.shape[0] - margin, margin: seg_color.shape[1] - margin, :]
:]
seg = seg[margin: seg.shape[0] - margin, margin: seg.shape[1] - margin] seg = seg[margin: seg.shape[0] - margin, margin: seg.shape[1] - margin]
mask_true[index_y_d + margin: index_y_u - margin, index_x_d + margin: index_x_u - margin] = seg mask_true[index_y_d + margin: index_y_u - margin, index_x_d + margin: index_x_u - margin] = seg
prediction_true[index_y_d + margin: index_y_u - margin, index_x_d + margin: index_x_u - margin, prediction_true[index_y_d + margin: index_y_u - margin, index_x_d + margin: index_x_u - margin,
@ -1398,8 +1387,7 @@ class Eynollah:
else: else:
try: try:
textline_con, hierarchy = return_contours_of_image(img_int_p) textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008)
min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
if self.isNaN(y_diff_mean): if self.isNaN(y_diff_mean):
slope_for_all = MAX_SLOPE slope_for_all = MAX_SLOPE
@ -1433,8 +1421,7 @@ class Eynollah:
textline_biggest_region = mask_biggest * textline_mask_tot_ea textline_biggest_region = mask_biggest * textline_mask_tot_ea
# print(slope_for_all,'slope_for_all') # print(slope_for_all,'slope_for_all')
textline_rotated_separated = separate_lines_new2(textline_biggest_region[y: y + h, x: x + w], 0, textline_rotated_separated = separate_lines_new2(textline_biggest_region[y: y + h, x: x + w], 0, num_col, slope_for_all, plotter=self.plotter)
num_col, slope_for_all, plotter=self.plotter)
# new line added # new line added
# print(np.shape(textline_rotated_separated),np.shape(mask_biggest)) # print(np.shape(textline_rotated_separated),np.shape(mask_biggest))
@ -1566,18 +1553,15 @@ class Eynollah:
if img_int_p.shape[0] / img_int_p.shape[1] < 0.1: if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
slopes_per_each_subprocess.append(0) slopes_per_each_subprocess.append(0)
slope_for_all = [slope_deskew][0] slope_for_all = [slope_deskew][0]
all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1]: boxes_text[mv][1] + boxes_text[mv][3], all_text_region_raw = textline_mask_tot_ea[boxes_text[mv][1]: boxes_text[mv][1] + boxes_text[mv][3], boxes_text[mv][0]: boxes_text[mv][0] + boxes_text[mv][2]]
boxes_text[mv][0]: boxes_text[mv][0] + boxes_text[mv][2]] cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv], 0)
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all,
contours_par_per_process[mv], boxes_text[mv], 0)
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot) textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv]) index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv]) bounding_box_of_textregion_per_each_subprocess.append(boxes_text[mv])
else: else:
try: try:
textline_con, hierarchy = return_contours_of_image(img_int_p) textline_con, hierarchy = return_contours_of_image(img_int_p)
textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008)
min_area=0.00008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
if self.isNaN(y_diff_mean): if self.isNaN(y_diff_mean):
slope_for_all = MAX_SLOPE slope_for_all = MAX_SLOPE
@ -1596,8 +1580,7 @@ class Eynollah:
slope_for_all = [slope_deskew][0] slope_for_all = [slope_deskew][0]
slopes_per_each_subprocess.append(slope_for_all) slopes_per_each_subprocess.append(slope_for_all)
mask_only_con_region = np.zeros(textline_mask_tot_ea.shape) mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contours_par_per_process[mv]], color=(1, 1, 1))
color=(1, 1, 1))
# plt.imshow(mask_only_con_region) # plt.imshow(mask_only_con_region)
# plt.show() # plt.show()
@ -1615,8 +1598,7 @@ class Eynollah:
# plt.show() # plt.show()
all_text_region_raw[mask_only_con_region == 0] = 0 all_text_region_raw[mask_only_con_region == 0] = 0
cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contours_par_per_process[mv], boxes_text[mv])
contours_par_per_process[mv], boxes_text[mv])
textlines_rectangles_per_each_subprocess.append(cnt_clean_rot) textlines_rectangles_per_each_subprocess.append(cnt_clean_rot)
index_by_text_region_contours.append(indexes_r_con_per_pro[mv]) index_by_text_region_contours.append(indexes_r_con_per_pro[mv])
@ -1663,14 +1645,12 @@ class Eynollah:
boxes_sub_new = [] boxes_sub_new = []
poly_sub = [] poly_sub = []
for mv in range(len(boxes_per_process)): for mv in range(len(boxes_per_process)):
crop_img, _ = crop_image_inside_box(boxes_per_process[mv], crop_img, _ = crop_image_inside_box(boxes_per_process[mv], np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
crop_img = crop_img[:, :, 0] crop_img = crop_img[:, :, 0]
crop_img = cv2.erode(crop_img, KERNEL, iterations=2) crop_img = cv2.erode(crop_img, KERNEL, iterations=2)
try: try:
textline_con, hierarchy = return_contours_of_image(crop_img) textline_con, hierarchy = return_contours_of_image(crop_img)
textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierarchy, max_area=1, textline_con_fil = filter_contours_area_of_image(crop_img, textline_con, hierarchy, max_area=1, min_area=0.0008)
min_area=0.0008)
y_diff_mean = find_contours_mean_y_diff(textline_con_fil) y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0))) sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
crop_img[crop_img > 0] = 1 crop_img[crop_img > 0] = 1
@ -1683,8 +1663,7 @@ class Eynollah:
slope_corresponding_textregion = slope_biggest slope_corresponding_textregion = slope_biggest
slopes_sub.append(slope_corresponding_textregion) slopes_sub.append(slope_corresponding_textregion)
cnt_clean_rot = textline_contours_postprocessing(crop_img, slope_corresponding_textregion, cnt_clean_rot = textline_contours_postprocessing(crop_img, slope_corresponding_textregion, contours_per_process[mv], boxes_per_process[mv])
contours_per_process[mv], boxes_per_process[mv])
poly_sub.append(cnt_clean_rot) poly_sub.append(cnt_clean_rot)
boxes_sub_new.append(boxes_per_process[mv]) boxes_sub_new.append(boxes_per_process[mv])
@ -1931,8 +1910,7 @@ class Eynollah:
_, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0) _, _ = find_num_col(img_only_regions, num_col_classifier, self.tables, multiplier=6.0)
img = resize_image(img_org, int(img_org.shape[0]), img = resize_image(img_org, int(img_org.shape[0]), int(img_org.shape[1] * (1.2 if is_image_enhanced else 1)))
int(img_org.shape[1] * (1.2 if is_image_enhanced else 1)))
if self.dir_in: if self.dir_in:
prediction_regions_org = self.do_prediction(True, img, self.model_region) prediction_regions_org = self.do_prediction(True, img, self.model_region)
@ -2108,16 +2086,14 @@ class Eynollah:
arg_text_con = [] arg_text_con = []
for ii in range(len(cx_text_only)): for ii in range(len(cx_text_only)):
for jj in range(len(boxes)): for jj in range(len(boxes)):
if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and \ if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
arg_text_con.append(jj) arg_text_con.append(jj)
break break
args_contours = np.array(range(len(arg_text_con))) args_contours = np.array(range(len(arg_text_con)))
arg_text_con_h = [] arg_text_con_h = []
for ii in range(len(cx_text_only_h)): for ii in range(len(cx_text_only_h)):
for jj in range(len(boxes)): for jj in range(len(boxes)):
if (x_min_text_only_h[ii] + 80) >= boxes[jj][0] and (x_min_text_only_h[ii] + 80) < boxes[jj][1] and \ if (x_min_text_only_h[ii] + 80) >= boxes[jj][0] and (x_min_text_only_h[ii] + 80) < boxes[jj][1] and y_cor_x_min_main_h[ii] >= boxes[jj][2] and y_cor_x_min_main_h[ii] < boxes[jj][3]:
y_cor_x_min_main_h[ii] >= boxes[jj][2] and y_cor_x_min_main_h[ii] < boxes[jj][3]:
arg_text_con_h.append(jj) arg_text_con_h.append(jj)
break break
args_contours_h = np.array(range(len(arg_text_con_h))) args_contours_h = np.array(range(len(arg_text_con_h)))
@ -2156,13 +2132,11 @@ class Eynollah:
for zahler, _ in enumerate(args_contours_box): for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler] arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for zahler, _ in enumerate(args_contours_box_h): for zahler, _ in enumerate(args_contours_box_h):
arg_order_v = indexes_sorted_head[zahler] arg_order_v = indexes_sorted_head[zahler]
order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = \ order_by_con_head[args_contours_box_h[indexes_by_type_head[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji in range(len(id_of_texts)): for jji in range(len(id_of_texts)):
order_of_texts_tot.append(order_of_texts[jji] + ref_point) order_of_texts_tot.append(order_of_texts[jji] + ref_point)
@ -2185,8 +2159,7 @@ class Eynollah:
arg_text_con = [] arg_text_con = []
for ii in range(len(cx_text_only)): for ii in range(len(cx_text_only)):
for jj in range(len(boxes)): for jj in range(len(boxes)):
if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= \ if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
arg_text_con.append(jj) arg_text_con.append(jj)
break break
args_contours = np.array(range(len(arg_text_con))) args_contours = np.array(range(len(arg_text_con)))
@ -2260,8 +2233,7 @@ class Eynollah:
order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0]) order_text_new.append(np.where(np.array(order_of_texts_tot) == iii)[0][0])
return order_text_new, id_of_texts_tot return order_text_new, id_of_texts_tot
def do_order_of_regions_no_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, def do_order_of_regions_no_full_layout(self, contours_only_text_parent, contours_only_text_parent_h, boxes, textline_mask_tot):
textline_mask_tot):
self.logger.debug("enter do_order_of_regions_no_full_layout") self.logger.debug("enter do_order_of_regions_no_full_layout")
cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours( cx_text_only, cy_text_only, x_min_text_only, _, _, _, y_cor_x_min_main = find_new_features_of_contours(
contours_only_text_parent) contours_only_text_parent)
@ -2270,8 +2242,7 @@ class Eynollah:
arg_text_con = [] arg_text_con = []
for ii in range(len(cx_text_only)): for ii in range(len(cx_text_only)):
for jj in range(len(boxes)): for jj in range(len(boxes)):
if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and \ if (x_min_text_only[ii] + 80) >= boxes[jj][0] and (x_min_text_only[ii] + 80) < boxes[jj][1] and y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
y_cor_x_min_main[ii] >= boxes[jj][2] and y_cor_x_min_main[ii] < boxes[jj][3]:
arg_text_con.append(jj) arg_text_con.append(jj)
break break
args_contours = np.array(range(len(arg_text_con))) args_contours = np.array(range(len(arg_text_con)))
@ -2300,8 +2271,7 @@ class Eynollah:
for zahler, _ in enumerate(args_contours_box): for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler] arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji, _ in enumerate(id_of_texts): for jji, _ in enumerate(id_of_texts):
order_of_texts_tot.append(order_of_texts[jji] + ref_point) order_of_texts_tot.append(order_of_texts[jji] + ref_point)
@ -2321,8 +2291,7 @@ class Eynollah:
arg_text_con = [] arg_text_con = []
for ii in range(len(cx_text_only)): for ii in range(len(cx_text_only)):
for jj in range(len(boxes)): for jj in range(len(boxes)):
if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= \ if cx_text_only[ii] >= boxes[jj][0] and cx_text_only[ii] < boxes[jj][1] and cy_text_only[ii] >= boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
boxes[jj][2] and cy_text_only[ii] < boxes[jj][3]: # this is valid if the center of region identify in which box it is located
arg_text_con.append(jj) arg_text_con.append(jj)
break break
args_contours = np.array(range(len(arg_text_con))) args_contours = np.array(range(len(arg_text_con)))
@ -2353,8 +2322,7 @@ class Eynollah:
for zahler, _ in enumerate(args_contours_box): for zahler, _ in enumerate(args_contours_box):
arg_order_v = indexes_sorted_main[zahler] arg_order_v = indexes_sorted_main[zahler]
order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = \ order_by_con_main[args_contours_box[indexes_by_type_main[zahler]]] = np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
np.where(indexes_sorted == arg_order_v)[0][0] + ref_point
for jji, _ in enumerate(id_of_texts): for jji, _ in enumerate(id_of_texts):
order_of_texts_tot.append(order_of_texts[jji] + ref_point) order_of_texts_tot.append(order_of_texts[jji] + ref_point)
@ -2372,8 +2340,7 @@ class Eynollah:
return order_text_new, id_of_texts_tot return order_text_new, id_of_texts_tot
def check_iou_of_bounding_box_and_contour_for_tables(self, layout, table_prediction_early, pixel_tabel, def check_iou_of_bounding_box_and_contour_for_tables(self, layout, table_prediction_early, pixel_tabel, num_col_classifier):
num_col_classifier):
layout_org = np.copy(layout) layout_org = np.copy(layout)
layout_org[:, :, 0][layout_org[:, :, 0] == pixel_tabel] = 0 layout_org[:, :, 0][layout_org[:, :, 0] == pixel_tabel] = 0
layout = (layout[:, :, 0] == pixel_tabel) * 1 layout = (layout[:, :, 0] == pixel_tabel) * 1
@ -2421,40 +2388,34 @@ class Eynollah:
contours_new.append(contours_sep[ji]) contours_new.append(contours_sep[ji])
if num_col_classifier >= 2: if num_col_classifier >= 2:
only_recent_contour_image = np.zeros((layout.shape[0], layout.shape[1])) only_recent_contour_image = np.zeros((layout.shape[0], layout.shape[1]))
only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, pts=[contours_sep[ji]], only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, pts=[contours_sep[ji]], color=(1, 1, 1))
color=(1, 1, 1))
table_pixels_masked_from_early_pre = only_recent_contour_image[:, :] * table_prediction_early[:, :] table_pixels_masked_from_early_pre = only_recent_contour_image[:, :] * table_prediction_early[:, :]
iou_in = table_pixels_masked_from_early_pre.sum() / float(only_recent_contour_image.sum()) * 100 iou_in = table_pixels_masked_from_early_pre.sum() / float(only_recent_contour_image.sum()) * 100
# print(iou_in,'iou_in_in1') # print(iou_in,'iou_in_in1')
if iou_in > 30: if iou_in > 30:
layout_org = cv2.fillPoly(layout_org, pts=[contours_sep[ji]], layout_org = cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=(pixel_tabel, pixel_tabel, pixel_tabel))
color=(pixel_tabel, pixel_tabel, pixel_tabel))
else: else:
pass pass
else: else:
layout_org = cv2.fillPoly(layout_org, pts=[contours_sep[ji]], layout_org = cv2.fillPoly(layout_org, pts=[contours_sep[ji]], color=(pixel_tabel, pixel_tabel, pixel_tabel))
color=(pixel_tabel, pixel_tabel, pixel_tabel))
else: else:
contours_new.append(contours[i]) contours_new.append(contours[i])
if num_col_classifier >= 2: if num_col_classifier >= 2:
only_recent_contour_image = np.zeros((layout.shape[0], layout.shape[1])) only_recent_contour_image = np.zeros((layout.shape[0], layout.shape[1]))
only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, pts=[contours[i]], only_recent_contour_image = cv2.fillPoly(only_recent_contour_image, pts=[contours[i]], color=(1, 1, 1))
color=(1, 1, 1))
table_pixels_masked_from_early_pre = only_recent_contour_image[:, :] * table_prediction_early[:, :] table_pixels_masked_from_early_pre = only_recent_contour_image[:, :] * table_prediction_early[:, :]
iou_in = table_pixels_masked_from_early_pre.sum() / float(only_recent_contour_image.sum()) * 100 iou_in = table_pixels_masked_from_early_pre.sum() / float(only_recent_contour_image.sum()) * 100
# print(iou_in,'iou_in') # print(iou_in,'iou_in')
if iou_in > 30: if iou_in > 30:
layout_org = cv2.fillPoly(layout_org, pts=[contours[i]], layout_org = cv2.fillPoly(layout_org, pts=[contours[i]], color=(pixel_tabel, pixel_tabel, pixel_tabel))
color=(pixel_tabel, pixel_tabel, pixel_tabel))
else: else:
pass pass
else: else:
layout_org = cv2.fillPoly(layout_org, pts=[contours[i]], layout_org = cv2.fillPoly(layout_org, pts=[contours[i]], color=(pixel_tabel, pixel_tabel, pixel_tabel))
color=(pixel_tabel, pixel_tabel, pixel_tabel))
return layout_org, contours_new return layout_org, contours_new
@ -2526,11 +2487,9 @@ class Eynollah:
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if indiv == pixel_table: if indiv == pixel_table:
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=0.001)
min_area=0.001)
else: else:
main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, main_contours = filter_contours_area_of_image_tables(thresh, contours, hirarchy, max_area=1, min_area=min_area)
min_area=min_area)
img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv)) img_comm = cv2.fillPoly(img_comm, pts=main_contours, color=(indiv, indiv, indiv))
img_comm = img_comm.astype(np.uint8) img_comm = img_comm.astype(np.uint8)
@ -2563,10 +2522,8 @@ class Eynollah:
y_min_main_line, y_max_main_line = find_features_of_contours(contours_line) y_min_main_line, y_max_main_line = find_features_of_contours(contours_line)
y_min_main_tab, y_max_main_tab = find_features_of_contours(contours_tab) y_min_main_tab, y_max_main_tab = find_features_of_contours(contours_tab)
cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text, _ = find_new_features_of_contours( cx_tab_m_text, cy_tab_m_text, x_min_tab_m_text, x_max_tab_m_text, y_min_tab_m_text, y_max_tab_m_text, _ = find_new_features_of_contours(contours_table_m_text)
contours_table_m_text) cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contours(contours_tab)
cx_tabl, cy_tabl, x_min_tabl, x_max_tabl, y_min_tabl, y_max_tabl, _ = find_new_features_of_contours(
contours_tab)
if len(y_min_main_tab) > 0: if len(y_min_main_tab) > 0:
y_down_tabs = [] y_down_tabs = []
@ -2607,13 +2564,11 @@ class Eynollah:
for ii in range(len(y_up_tabs)): for ii in range(len(y_up_tabs)):
image_box[y_up_tabs[ii]:y_down_tabs[ii], :, 0] = pixel_table image_box[y_up_tabs[ii]:y_down_tabs[ii], :, 0] = pixel_table
image_revised_last[int(boxes[i][2]):int(boxes[i][3]), int(boxes[i][0]):int(boxes[i][1]), :] = image_box[ image_revised_last[int(boxes[i][2]):int(boxes[i][3]), int(boxes[i][0]):int(boxes[i][1]), :] = image_box[:, :, :]
:, :, :]
else: else:
for i in range(len(boxes)): for i in range(len(boxes)):
image_box = img_comm[int(boxes[i][2]):int(boxes[i][3]), int(boxes[i][0]):int(boxes[i][1]), :] image_box = img_comm[int(boxes[i][2]):int(boxes[i][3]), int(boxes[i][0]):int(boxes[i][1]), :]
image_revised_last[int(boxes[i][2]):int(boxes[i][3]), int(boxes[i][0]):int(boxes[i][1]), :] = image_box[ image_revised_last[int(boxes[i][2]):int(boxes[i][3]), int(boxes[i][0]):int(boxes[i][1]), :] = image_box[:, :, :]
:, :, :]
if num_col_classifier == 1: if num_col_classifier == 1:
img_tables_col_1 = (image_revised_last[:, :, 0] == pixel_table) * 1 img_tables_col_1 = (image_revised_last[:, :, 0] == pixel_table) * 1
@ -2724,8 +2679,7 @@ class Eynollah:
prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20) prediction_table_erode = cv2.dilate(prediction_table_erode, KERNEL, iterations=20)
return prediction_table_erode.astype(np.int16) return prediction_table_erode.astype(np.int16)
def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, def run_graphics_and_columns_light(self, text_regions_p_1, textline_mask_tot_ea, num_col_classifier, num_column_is_classified, erosion_hurts):
num_column_is_classified, erosion_hurts):
img_g = self.imread(grayscale=True, uint8=True) img_g = self.imread(grayscale=True, uint8=True)
img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3)) img_g3 = np.zeros((img_g.shape[0], img_g.shape[1], 3))
@ -2818,8 +2772,7 @@ class Eynollah:
def run_enhancement(self, light_version): def run_enhancement(self, light_version):
self.logger.info("Resizing and enhancing image...") 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( 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)
light_version)
self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ') self.logger.info("Image was %senhanced.", '' if is_image_enhanced else 'not ')
scale = 1 scale = 1
@ -2837,8 +2790,7 @@ class Eynollah:
else: else:
self.get_image_and_scales(img_org, img_res, scale) self.get_image_and_scales(img_org, img_res, scale)
if self.allow_scaling: if self.allow_scaling:
img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced, img_org, img_res, is_image_enhanced = self.resize_image_with_column_classifier(is_image_enhanced, img_bin)
img_bin)
self.get_image_and_scales_after_enhancing(img_org, img_res) self.get_image_and_scales_after_enhancing(img_org, img_res)
return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified return img_res, is_image_enhanced, num_col_classifier, num_column_is_classified
@ -2856,8 +2808,7 @@ class Eynollah:
def run_deskew(self, textline_mask_tot_ea): def run_deskew(self, textline_mask_tot_ea):
sigma = 2 sigma = 2
main_page_deskew = True main_page_deskew = True
slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, slope_deskew = return_deskew_slop(cv2.erode(textline_mask_tot_ea, KERNEL, iterations=2), sigma, main_page_deskew, plotter=self.plotter)
main_page_deskew, plotter=self.plotter)
slope_first = 0 slope_first = 0
if self.plotter: if self.plotter:
@ -2865,8 +2816,7 @@ class Eynollah:
self.logger.info("slope_deskew: %.2f°", slope_deskew) self.logger.info("slope_deskew: %.2f°", slope_deskew)
return slope_deskew, slope_first return slope_deskew, slope_first
def run_marginals(self, image_page, textline_mask_tot_ea, mask_images, mask_lines, num_col_classifier, slope_deskew, 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):
text_regions_p_1, table_prediction):
image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :] image_page_rotated, textline_mask_tot = image_page[:, :], textline_mask_tot_ea[:, :]
textline_mask_tot[mask_images[:, :] == 1] = 0 textline_mask_tot[mask_images[:, :] == 1] = 0
@ -2880,8 +2830,7 @@ class Eynollah:
if self.tables: if self.tables:
regions_without_separators[table_prediction == 1] = 1 regions_without_separators[table_prediction == 1] = 1
regions_without_separators = regions_without_separators.astype(np.uint8) regions_without_separators = regions_without_separators.astype(np.uint8)
text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, text_regions_p = get_marginals(rotate_image(regions_without_separators, slope_deskew), text_regions_p, num_col_classifier, slope_deskew, kernel=KERNEL)
num_col_classifier, slope_deskew, kernel=KERNEL)
except Exception as e: except Exception as e:
self.logger.error("exception %s", e) self.logger.error("exception %s", e)
@ -2890,8 +2839,7 @@ class Eynollah:
self.plotter.save_plot_of_layout_main(text_regions_p, image_page) self.plotter.save_plot_of_layout_main(text_regions_p, image_page)
return textline_mask_tot, text_regions_p, image_page_rotated return textline_mask_tot, text_regions_p, image_page_rotated
def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, def run_boxes_no_full_layout(self, image_page, textline_mask_tot, text_regions_p, slope_deskew, num_col_classifier, table_prediction, erosion_hurts):
table_prediction, erosion_hurts):
self.logger.debug('enter run_boxes_no_full_layout') self.logger.debug('enter run_boxes_no_full_layout')
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: if np.abs(slope_deskew) >= SLOPE_THRESHOLD:
_, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = rotation_not_90_func(image_page, _, textline_mask_tot_d, text_regions_p_1_n, table_prediction_n = rotation_not_90_func(image_page,
@ -2978,8 +2926,7 @@ class Eynollah:
img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew) img_revised_tab2_d_rotated = rotate_image(img_revised_tab2_d, -slope_deskew)
img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated) img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated)
img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8) img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8)
img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1])
text_regions_p.shape[1])
self.logger.info("detecting boxes took %.1fs", time.time() - t1) self.logger.info("detecting boxes took %.1fs", time.time() - t1)
@ -3019,8 +2966,7 @@ class Eynollah:
image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew) image_page, textline_mask_tot, text_regions_p, table_prediction, slope_deskew)
text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1]) text_regions_p_1_n = resize_image(text_regions_p_1_n, text_regions_p.shape[0], text_regions_p.shape[1])
textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], textline_mask_tot_d = resize_image(textline_mask_tot_d, text_regions_p.shape[0], text_regions_p.shape[1])
text_regions_p.shape[1])
table_prediction_n = resize_image(table_prediction_n, text_regions_p.shape[0], text_regions_p.shape[1]) table_prediction_n = resize_image(table_prediction_n, text_regions_p.shape[0], text_regions_p.shape[1])
regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1 regions_without_separators_d = (text_regions_p_1_n[:, :] == 1) * 1
@ -3030,20 +2976,17 @@ class Eynollah:
textline_mask_tot_d = None textline_mask_tot_d = None
regions_without_separators_d = None regions_without_separators_d = None
regions_without_separators = (text_regions_p[:, regions_without_separators = (text_regions_p[:, :] == 1) * 1 # ((text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
:] == 1) * 1 # ((text_regions_p[:,:]==1) | (text_regions_p[:,:]==2) )*1 #self.return_regions_without_seperators_new(text_regions_p[:,:,0],img_only_regions)
regions_without_separators[table_prediction == 1] = 1 regions_without_separators[table_prediction == 1] = 1
pixel_lines = 3 pixel_lines = 3
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
num_col, peaks_neg_fin, matrix_of_lines_ch, splitter_y_new, seperators_closeup_n = find_number_of_columns_in_document( num_col, peaks_neg_fin, matrix_of_lines_ch, splitter_y_new, seperators_closeup_n = find_number_of_columns_in_document(
np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, np.repeat(text_regions_p[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
pixel_lines)
if np.abs(slope_deskew) >= SLOPE_THRESHOLD: 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( 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, np.repeat(text_regions_p_1_n[:, :, np.newaxis], 3, axis=2), num_col_classifier, self.tables, pixel_lines)
pixel_lines)
if num_col_classifier >= 3: if num_col_classifier >= 3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
@ -3099,8 +3042,7 @@ class Eynollah:
img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated) img_revised_tab2_d_rotated = np.round(img_revised_tab2_d_rotated)
img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8) img_revised_tab2_d_rotated = img_revised_tab2_d_rotated.astype(np.int8)
img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], img_revised_tab2_d_rotated = resize_image(img_revised_tab2_d_rotated, text_regions_p.shape[0], text_regions_p.shape[1])
text_regions_p.shape[1])
if np.abs(slope_deskew) < 0.13: if np.abs(slope_deskew) < 0.13:
img_revised_tab = np.copy(img_revised_tab2[:, :, 0]) img_revised_tab = np.copy(img_revised_tab2[:, :, 0])
@ -3141,8 +3083,7 @@ class Eynollah:
else: else:
regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p) regions_fully_np = filter_small_drop_capitals_from_no_patch_layout(regions_fully_np, text_regions_p)
regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, regions_fully = boosting_headers_by_longshot_region_segmentation(regions_fully, regions_fully_np, img_only_regions)
img_only_regions)
# plt.imshow(regions_fully[:,:,0]) # plt.imshow(regions_fully[:,:,0])
# plt.show() # plt.show()
text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4 text_regions_p[:, :][regions_fully[:, :, 0] == 4] = 4
@ -3200,8 +3141,7 @@ class Eynollah:
self.reset_file_name_dir(os.path.join(self.dir_in, img_name)) self.reset_file_name_dir(os.path.join(self.dir_in, img_name))
if self.extract_only_images: if self.extract_only_images:
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement( img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
self.light_version)
self.logger.info("Enhancing took %.1fs ", time.time() - t0) self.logger.info("Enhancing took %.1fs ", time.time() - t0)
text_regions_p_1, erosion_hurts, polygons_lines_xml, polygons_of_images, image_page, page_coord, cont_page = self.get_regions_light_v_extract_only_images( text_regions_p_1, erosion_hurts, polygons_lines_xml, polygons_of_images, image_page, page_coord, cont_page = self.get_regions_light_v_extract_only_images(
@ -3219,8 +3159,7 @@ class Eynollah:
return pcgts return pcgts
else: else:
img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement( img_res, is_image_enhanced, num_col_classifier, num_column_is_classified = self.run_enhancement(self.light_version)
self.light_version)
self.logger.info("Enhancing took %.1fs ", time.time() - t0) self.logger.info("Enhancing took %.1fs ", time.time() - t0)
t1 = time.time() t1 = time.time()
@ -3242,8 +3181,7 @@ class Eynollah:
t1 = time.time() t1 = time.time()
num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction = \ num_col, num_col_classifier, img_only_regions, page_coord, image_page, mask_images, mask_lines, text_regions_p_1, cont_page, table_prediction = \
self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, self.run_graphics_and_columns(text_regions_p_1, num_col_classifier, num_column_is_classified, erosion_hurts)
erosion_hurts)
self.logger.info("Graphics detection took %.1fs ", time.time() - t1) self.logger.info("Graphics detection took %.1fs ", time.time() - t1)
# self.logger.info('cont_page %s', cont_page) # self.logger.info('cont_page %s', cont_page)
@ -3521,13 +3459,11 @@ class Eynollah:
if num_col_classifier >= 3: if num_col_classifier >= 3:
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
regions_without_separators = regions_without_separators.astype(np.uint8) regions_without_separators = regions_without_separators.astype(np.uint8)
regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, regions_without_separators = cv2.erode(regions_without_separators[:, :], KERNEL, iterations=6)
iterations=6)
else: else:
regions_without_separators_d = regions_without_separators_d.astype(np.uint8) regions_without_separators_d = regions_without_separators_d.astype(np.uint8)
regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, regions_without_separators_d = cv2.erode(regions_without_separators_d[:, :], KERNEL, iterations=6)
iterations=6)
if np.abs(slope_deskew) < SLOPE_THRESHOLD: if np.abs(slope_deskew) < SLOPE_THRESHOLD:
boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new, boxes, peaks_neg_tot_tables = return_boxes_of_images_by_order_of_reading_new(splitter_y_new,