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https://github.com/qurator-spk/eynollah.git
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do_work_of_slopes_new*, do_back_rotation_and_get_cnt_back, do_work_of_contours_in_image: use mp.Pool, simplify
This commit is contained in:
Robert Sachunsky
parent
25e967397d
commit
68456ea002
3 changed files with 324 additions and 513 deletions
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@ -1,22 +1,23 @@
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import os
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from functools import partial
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from multiprocessing import Pool, cpu_count
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import numpy as np
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import cv2
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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import os
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from multiprocessing import Process, Queue, cpu_count
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from multiprocessing import Pool
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from .rotate import rotate_image
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from .resize import resize_image
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from .contour import (
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return_parent_contours,
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filter_contours_area_of_image_tables,
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return_contours_of_image,
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filter_contours_area_of_image
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filter_contours_area_of_image,
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return_contours_of_interested_textline,
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find_contours_mean_y_diff,
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)
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from .is_nan import isNaN
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from . import (
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find_num_col_deskew,
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isNaN,
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crop_image_inside_box,
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)
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def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis):
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@ -1249,13 +1250,13 @@ def separate_lines_new_inside_tiles(img_path, thetha):
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forest.append(peaks_neg[i + 1])
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if diff_peaks[i] > cut_off:
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# print(forest[np.argmin(z[forest]) ] )
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if not isNaN(forest[np.argmin(z[forest])]):
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if not np.isnan(forest[np.argmin(z[forest])]):
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peaks_neg_true.append(forest[np.argmin(z[forest])])
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forest = []
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forest.append(peaks_neg[i + 1])
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if i == (len(peaks_neg) - 1):
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# print(print(forest[np.argmin(z[forest]) ] ))
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if not isNaN(forest[np.argmin(z[forest])]):
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if not np.isnan(forest[np.argmin(z[forest])]):
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peaks_neg_true.append(forest[np.argmin(z[forest])])
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diff_peaks_pos = np.abs(np.diff(peaks))
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@ -1272,13 +1273,13 @@ def separate_lines_new_inside_tiles(img_path, thetha):
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forest.append(peaks[i + 1])
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if diff_peaks_pos[i] > cut_off:
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# print(forest[np.argmin(z[forest]) ] )
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if not isNaN(forest[np.argmax(z[forest])]):
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if not np.isnan(forest[np.argmax(z[forest])]):
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peaks_pos_true.append(forest[np.argmax(z[forest])])
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forest = []
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forest.append(peaks[i + 1])
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if i == (len(peaks) - 1):
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# print(print(forest[np.argmin(z[forest]) ] ))
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if not isNaN(forest[np.argmax(z[forest])]):
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if not np.isnan(forest[np.argmax(z[forest])]):
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peaks_pos_true.append(forest[np.argmax(z[forest])])
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# print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss')
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@ -1658,3 +1659,189 @@ def get_smallest_skew(img, sigma_des, angles, num_cores=1, plotter=None):
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except:
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angle = 0
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return angle
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def do_work_of_slopes_new(
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box_text, contour, contour_par, index_r_con,
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textline_mask_tot_ea, image_page_rotated, slope_deskew,
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logger, MAX_SLOPE=999, KERNEL=None, plotter=None
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):
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logger.debug('enter do_work_of_slopes_new')
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if KERNEL is None:
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KERNEL = np.ones((5, 5), np.uint8)
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x, y, w, h = box_text
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_, crop_coor = crop_image_inside_box(box_text, image_page_rotated)
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mask_textline = np.zeros(textline_mask_tot_ea.shape)
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mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1))
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all_text_region_raw = textline_mask_tot_ea * mask_textline
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all_text_region_raw = all_text_region_raw[y: y + h, x: x + w].astype(np.uint8)
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img_int_p = all_text_region_raw[:,:]
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img_int_p = cv2.erode(img_int_p, KERNEL, iterations=2)
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if img_int_p.shape[0] /img_int_p.shape[1] < 0.1:
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slope = 0
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slope_for_all = slope_deskew
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all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w]
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cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, 0)
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else:
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try:
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textline_con, hierarchy = return_contours_of_image(img_int_p)
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textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008)
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y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
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if np.isnan(y_diff_mean):
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slope_for_all = MAX_SLOPE
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else:
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sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
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img_int_p[img_int_p > 0] = 1
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slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=plotter)
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if abs(slope_for_all) <= 0.5:
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slope_for_all = slope_deskew
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except Exception as why:
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logger.error(why)
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slope_for_all = MAX_SLOPE
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if slope_for_all == MAX_SLOPE:
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slope_for_all = slope_deskew
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slope = slope_for_all
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mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
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mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1))
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# plt.imshow(mask_only_con_region)
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# plt.show()
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all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].copy()
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mask_only_con_region = mask_only_con_region[y: y + h, x: x + w]
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##plt.imshow(textline_mask_tot_ea)
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##plt.show()
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##plt.imshow(all_text_region_raw)
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##plt.show()
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##plt.imshow(mask_only_con_region)
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##plt.show()
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all_text_region_raw[mask_only_con_region == 0] = 0
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cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text)
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return cnt_clean_rot, box_text, contour, contour_par, crop_coor, index_r_con, slope
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def do_work_of_slopes_new_curved(
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box_text, contour, contour_par, index_r_con,
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textline_mask_tot_ea, image_page_rotated, mask_texts_only, num_col, scale_par, slope_deskew,
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logger, MAX_SLOPE=999, KERNEL=None, plotter=None
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):
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logger.debug("enter do_work_of_slopes_new_curved")
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if KERNEL is None:
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KERNEL = np.ones((5, 5), np.uint8)
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x, y, w, h = box_text
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all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].astype(np.uint8)
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img_int_p = all_text_region_raw[:, :]
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# img_int_p=cv2.erode(img_int_p,KERNEL,iterations = 2)
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# plt.imshow(img_int_p)
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# plt.show()
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if img_int_p.shape[0] / img_int_p.shape[1] < 0.1:
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slope = 0
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slope_for_all = slope_deskew
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else:
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try:
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textline_con, hierarchy = return_contours_of_image(img_int_p)
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textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.0008)
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y_diff_mean = find_contours_mean_y_diff(textline_con_fil)
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if np.isnan(y_diff_mean):
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slope_for_all = MAX_SLOPE
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else:
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sigma_des = max(1, int(y_diff_mean * (4.0 / 40.0)))
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img_int_p[img_int_p > 0] = 1
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slope_for_all = return_deskew_slop(img_int_p, sigma_des, plotter=plotter)
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if abs(slope_for_all) < 0.5:
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slope_for_all = slope_deskew
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except Exception as why:
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logger.error(why)
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slope_for_all = MAX_SLOPE
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if slope_for_all == MAX_SLOPE:
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slope_for_all = slope_deskew
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slope = slope_for_all
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_, crop_coor = crop_image_inside_box(box_text, image_page_rotated)
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if abs(slope_for_all) < 45:
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textline_region_in_image = np.zeros(textline_mask_tot_ea.shape)
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x, y, w, h = cv2.boundingRect(contour_par)
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mask_biggest = np.zeros(mask_texts_only.shape)
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mask_biggest = cv2.fillPoly(mask_biggest, pts=[contour_par], color=(1, 1, 1))
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mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w]
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textline_biggest_region = mask_biggest * textline_mask_tot_ea
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# print(slope_for_all,'slope_for_all')
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textline_rotated_separated = separate_lines_new2(textline_biggest_region[y: y+h, x: x+w], 0, num_col, slope_for_all,
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plotter=plotter)
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# new line added
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##print(np.shape(textline_rotated_separated),np.shape(mask_biggest))
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textline_rotated_separated[mask_region_in_patch_region[:, :] != 1] = 0
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# till here
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textline_region_in_image[y : y + h, x : x + w] = textline_rotated_separated
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# plt.imshow(textline_region_in_image)
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# plt.show()
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pixel_img = 1
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cnt_textlines_in_image = return_contours_of_interested_textline(textline_region_in_image, pixel_img)
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textlines_cnt_per_region = []
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for jjjj in range(len(cnt_textlines_in_image)):
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mask_biggest2 = np.zeros(mask_texts_only.shape)
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mask_biggest2 = cv2.fillPoly(mask_biggest2, pts=[cnt_textlines_in_image[jjjj]], color=(1, 1, 1))
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if num_col + 1 == 1:
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mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=5)
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else:
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mask_biggest2 = cv2.dilate(mask_biggest2, KERNEL, iterations=4)
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pixel_img = 1
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mask_biggest2 = resize_image(mask_biggest2, int(mask_biggest2.shape[0] * scale_par), int(mask_biggest2.shape[1] * scale_par))
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cnt_textlines_in_image_ind = return_contours_of_interested_textline(mask_biggest2, pixel_img)
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try:
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textlines_cnt_per_region.append(cnt_textlines_in_image_ind[0])
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except Exception as why:
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logger.error(why)
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else:
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textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, True)
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# print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region')
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return textlines_cnt_per_region[::-1], box_text, contour, contour_par, crop_coor, index_r_con, slope
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def do_work_of_slopes_new_light(
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box_text, contour, contour_par, index_r_con,
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textline_mask_tot_ea, image_page_rotated, slope_deskew,
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logger
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):
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logger.debug('enter do_work_of_slopes_new_light')
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x, y, w, h = box_text
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_, crop_coor = crop_image_inside_box(box_text, image_page_rotated)
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mask_textline = np.zeros(textline_mask_tot_ea.shape)
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mask_textline = cv2.fillPoly(mask_textline, pts=[contour], color=(1,1,1))
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all_text_region_raw = textline_mask_tot_ea * mask_textline
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all_text_region_raw = all_text_region_raw[y: y + h, x: x + w].astype(np.uint8)
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mask_only_con_region = np.zeros(textline_mask_tot_ea.shape)
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mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1))
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if self.textline_light:
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all_text_region_raw = np.copy(textline_mask_tot_ea)
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all_text_region_raw[mask_only_con_region == 0] = 0
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cnt_clean_rot_raw, hir_on_cnt_clean_rot = return_contours_of_image(all_text_region_raw)
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cnt_clean_rot = filter_contours_area_of_image(all_text_region_raw, cnt_clean_rot_raw, hir_on_cnt_clean_rot,
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max_area=1, min_area=0.00001)
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else:
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all_text_region_raw = np.copy(textline_mask_tot_ea[y: y + h, x: x + w])
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mask_only_con_region = mask_only_con_region[y: y + h, x: x + w]
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all_text_region_raw[mask_only_con_region == 0] = 0
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cnt_clean_rot = textline_contours_postprocessing(all_text_region_raw, slope_deskew, contour_par, box_text)
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return cnt_clean_rot, box_text, contour, contour_par, crop_coor, index_r_con, slope
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