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eynollah
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few minor fixes

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code-suggestions
cneud 1 week ago
parent b4d168cae3
commit 62e8d78e73
3 changed files with 19 additions and 41 deletions
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9
src/eynollah/sbb_binarize.py
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@ -1,27 +1,22 @@
"""
Tool to load model and binarize a given image.
Load model and binarize a given image.
"""
import os
import sys
from glob import glob
from os import environ, devnull
from os.path import join
from warnings import catch_warnings, simplefilter
import os
import numpy as np
from PIL import Image
import cv2
environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
stderr = sys.stderr
sys.stderr = open(devnull, 'w')
import tensorflow as tf
from tensorflow.keras.models import load_model
from tensorflow.python.keras import backend as tensorflow_backend
sys.stderr = stderr
import logging

45
src/eynollah/utils/drop_capitals.py
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@ -112,15 +112,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
# print(arg_min)
cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
1] # +all_box_coord[int(region_final)][0]
cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
@ -180,15 +177,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
# print(arg_min)
cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
1] # +all_box_coord[int(region_final)][0]
cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
@ -238,15 +232,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
# print(arg_min)
cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0,
1] # +all_box_coord[int(region_final)][0]
cnt_nearest[:, 0, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 0] # +all_box_coord[int(region_final)][2]
cnt_nearest[:, 0, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 0, 1] # +all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
contours_combined = return_contours_of_interested_region(img_textlines, 255, 0)
@ -356,15 +347,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
# print(arg_min)
cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + \
all_box_coord[int(region_final)][2]
cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + \
all_box_coord[int(region_final)][0]
cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
contours_combined = return_contours_of_interested_region(img_textlines, 255, 0)
@ -424,15 +412,12 @@ def adhere_drop_capital_region_into_corresponding_textline(
# print(arg_min)
cnt_nearest = np.copy(all_found_textline_polygons[int(region_final)][arg_min])
cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + \
all_box_coord[int(region_final)][2]
cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + \
all_box_coord[int(region_final)][0]
cnt_nearest[:, 0] = all_found_textline_polygons[int(region_final)][arg_min][:, 0] + all_box_coord[int(region_final)][2]
cnt_nearest[:, 1] = all_found_textline_polygons[int(region_final)][arg_min][:, 1] + all_box_coord[int(region_final)][0]
img_textlines = np.zeros((text_regions_p.shape[0], text_regions_p.shape[1], 3))
img_textlines = cv2.fillPoly(img_textlines, pts=[cnt_nearest], color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]],
color=(255, 255, 255))
img_textlines = cv2.fillPoly(img_textlines, pts=[polygons_of_drop_capitals[i_drop]], color=(255, 255, 255))
img_textlines = img_textlines.astype(np.uint8)
contours_combined = return_contours_of_interested_region(img_textlines, 255, 0)

6
src/eynollah/utils/marginals.py
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@ -126,8 +126,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contours(
polygons_of_marginals)
(cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main) = (find_new_features_of_contours(polygons_of_marginals))
text_regions[(text_regions[:, :] == 4)] = 1
@ -155,8 +154,7 @@ def get_marginals(text_with_lines, text_regions, num_col, slope_deskew, light_ve
polygons_of_marginals = return_contours_of_interested_region(text_regions, pixel_img, min_area_text)
cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main = find_new_features_of_contours(
polygons_of_marginals)
(cx_text_only, cy_text_only, x_min_text_only, x_max_text_only, y_min_text_only, y_max_text_only, y_cor_x_min_main) = (find_new_features_of_contours(polygons_of_marginals))
text_regions[(text_regions[:, :] == 4)] = 1

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