From 4cb4414740a89741c6ff25a33932ffc16ce201f8 Mon Sep 17 00:00:00 2001 From: vahidrezanezhad Date: Wed, 30 Apr 2025 16:01:52 +0200 Subject: [PATCH] Resolve remaining issue with #158 and resolving #124 --- src/eynollah/utils/separate_lines.py | 263 ++++++++++----------------- 1 file changed, 95 insertions(+), 168 deletions(-) diff --git a/src/eynollah/utils/separate_lines.py b/src/eynollah/utils/separate_lines.py index 6602574..0322579 100644 --- a/src/eynollah/utils/separate_lines.py +++ b/src/eynollah/utils/separate_lines.py @@ -102,14 +102,15 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierarchy, max_area=1, min_area=0.0008) - y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) - sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) - # print(sigma_gaus,'sigma_gaus') + if len(np.diff(peaks_new_tot))>1: + y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) + sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) + else: + sigma_gaus = 12 except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 - # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) @@ -137,7 +138,6 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): M = cv2.getRotationMatrix2D(center, -thetha, 1.0) x_d = M[0, 2] y_d = M[1, 2] - thetha = thetha / 180. * np.pi rotation_matrix = np.array([[np.cos(thetha), -np.sin(thetha)], [np.sin(thetha), np.cos(thetha)]]) contour_text_interest_copy = contour_text_interest.copy() @@ -162,77 +162,73 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): x = np.array(range(len(y))) peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0) - if 1>0: - try: - y_padded_smoothed_e= gaussian_filter1d(y_padded, 2) - y_padded_up_to_down_e=-y_padded+np.max(y_padded) - y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40) - y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e - y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2) - + + try: + y_padded_smoothed_e= gaussian_filter1d(y_padded, 2) + y_padded_up_to_down_e=-y_padded+np.max(y_padded) + y_padded_up_to_down_padded_e=np.zeros(len(y_padded_up_to_down_e)+40) + y_padded_up_to_down_padded_e[20:len(y_padded_up_to_down_e)+20]=y_padded_up_to_down_e + y_padded_up_to_down_padded_e= gaussian_filter1d(y_padded_up_to_down_padded_e, 2) + + peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) + peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) + neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) - peaks_e, _ = find_peaks(y_padded_smoothed_e, height=0) - peaks_neg_e, _ = find_peaks(y_padded_up_to_down_padded_e, height=0) - neg_peaks_max=np.max(y_padded_up_to_down_padded_e[peaks_neg_e]) + arg_neg_must_be_deleted= np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3] + diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted) + + arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted))) + arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1] + peaks_new=peaks_e[:] + peaks_neg_new=peaks_neg_e[:] - arg_neg_must_be_deleted= np.arange(len(peaks_neg_e))[y_padded_up_to_down_padded_e[peaks_neg_e]/float(neg_peaks_max)<0.3] - diff_arg_neg_must_be_deleted=np.diff(arg_neg_must_be_deleted) - - arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted))) - arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1] - - peaks_new=peaks_e[:] - peaks_neg_new=peaks_neg_e[:] - - clusters_to_be_deleted=[] - if len(arg_diff_cluster)>0: - clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1]) - for i in range(len(arg_diff_cluster)-1): - clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1: - arg_diff_cluster[i+1]+1]) - clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:]) - if len(clusters_to_be_deleted)>0: - peaks_new_extra=[] - for m in range(len(clusters_to_be_deleted)): - min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]]) - max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]]) - peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) ) - for m1 in range(len(clusters_to_be_deleted[m])): - peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]] - peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]] - peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]] - peaks_new_tot=[] - for i1 in peaks_new: - peaks_new_tot.append(i1) - for i1 in peaks_new_extra: - peaks_new_tot.append(i1) - peaks_new_tot=np.sort(peaks_new_tot) - else: - peaks_new_tot=peaks_e[:] + clusters_to_be_deleted=[] + if len(arg_diff_cluster)>0: + clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1]) + for i in range(len(arg_diff_cluster)-1): + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i]+1: + arg_diff_cluster[i+1]+1]) + clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster)-1]+1:]) + if len(clusters_to_be_deleted)>0: + peaks_new_extra=[] + for m in range(len(clusters_to_be_deleted)): + min_cluster=np.min(peaks_e[clusters_to_be_deleted[m]]) + max_cluster=np.max(peaks_e[clusters_to_be_deleted[m]]) + peaks_new_extra.append( int( (min_cluster+max_cluster)/2.0) ) + for m1 in range(len(clusters_to_be_deleted[m])): + peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]-1]] + peaks_new=peaks_new[peaks_new!=peaks_e[clusters_to_be_deleted[m][m1]]] + peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg_e[clusters_to_be_deleted[m][m1]]] + peaks_new_tot=[] + for i1 in peaks_new: + peaks_new_tot.append(i1) + for i1 in peaks_new_extra: + peaks_new_tot.append(i1) + peaks_new_tot=np.sort(peaks_new_tot) + else: + peaks_new_tot=peaks_e[:] - textline_con,hierarchy=return_contours_of_image(img_patch) - textline_con_fil=filter_contours_area_of_image(img_patch, - textline_con, hierarchy, - max_area=1, min_area=0.0008) + textline_con,hierarchy=return_contours_of_image(img_patch) + textline_con_fil=filter_contours_area_of_image(img_patch, + textline_con, hierarchy, + max_area=1, min_area=0.0008) - if len(np.diff(peaks_new_tot))>0: - y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil) - sigma_gaus=int( y_diff_mean * (7./40.0) ) - else: - sigma_gaus=12 - - except: + if len(np.diff(peaks_new_tot))>0: + y_diff_mean=np.mean(np.diff(peaks_new_tot))#self.find_contours_mean_y_diff(textline_con_fil) + sigma_gaus=int( y_diff_mean * (7./40.0) ) + else: sigma_gaus=12 - if sigma_gaus<3: - sigma_gaus=3 - #print(sigma_gaus,'sigma') + + except: + sigma_gaus=12 + if sigma_gaus<3: + sigma_gaus=3 y_padded_smoothed= gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down=-y_padded+np.max(y_padded) y_padded_up_to_down_padded=np.zeros(len(y_padded_up_to_down)+40) y_padded_up_to_down_padded[20:len(y_padded_up_to_down)+20]=y_padded_up_to_down y_padded_up_to_down_padded= gaussian_filter1d(y_padded_up_to_down_padded, sigma_gaus) - peaks, _ = find_peaks(y_padded_smoothed, height=0) peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0) @@ -243,6 +239,7 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): arg_diff=np.array(range(len(diff_arg_neg_must_be_deleted))) arg_diff_cluster=arg_diff[diff_arg_neg_must_be_deleted>1] + except: arg_neg_must_be_deleted=[] arg_diff_cluster=[] @@ -250,7 +247,6 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): peaks_new=peaks[:] peaks_neg_new=peaks_neg[:] clusters_to_be_deleted=[] - if len(arg_diff_cluster)>=2 and len(arg_diff_cluster)>0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0:arg_diff_cluster[0]+1]) for i in range(len(arg_diff_cluster)-1): @@ -279,21 +275,6 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): peaks_new_tot.append(i1) peaks_new_tot=np.sort(peaks_new_tot) - ##plt.plot(y_padded_up_to_down_padded) - ##plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*') - ##plt.show() - - ##plt.plot(y_padded_up_to_down_padded) - ##plt.plot(peaks_neg_new,y_padded_up_to_down_padded[peaks_neg_new],'*') - ##plt.show() - - ##plt.plot(y_padded_smoothed) - ##plt.plot(peaks,y_padded_smoothed[peaks],'*') - ##plt.show() - - ##plt.plot(y_padded_smoothed) - ##plt.plot(peaks_new_tot,y_padded_smoothed[peaks_new_tot],'*') - ##plt.show() peaks=peaks_new_tot[:] peaks_neg=peaks_neg_new[:] else: @@ -302,11 +283,13 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): peaks_neg=peaks_neg_new[:] except: pass - - mean_value_of_peaks=np.mean(y_padded_smoothed[peaks]) - std_value_of_peaks=np.std(y_padded_smoothed[peaks]) + if len(y_padded_smoothed[peaks]) > 1: + mean_value_of_peaks=np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks=np.std(y_padded_smoothed[peaks]) + else: + mean_value_of_peaks = np.nan + std_value_of_peaks = np.nan peaks_values=y_padded_smoothed[peaks] - peaks_neg = peaks_neg - 20 - 20 peaks = peaks - 20 for jj in range(len(peaks_neg)): @@ -349,7 +332,6 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): point_down_narrow = peaks[jj] + first_nonzero + int( 1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2) - if point_down_narrow >= img_patch.shape[0]: point_down_narrow = img_patch.shape[0] - 2 @@ -605,7 +587,6 @@ def separate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) - return peaks, textline_boxes_rot def separate_lines_vertical(img_patch, contour_text_interest, thetha): @@ -637,7 +618,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): peaks_neg_new = peaks_neg[:] clusters_to_be_deleted = [] - if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: + if len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) >= 2: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : @@ -645,7 +626,7 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) - if len(arg_neg_must_be_deleted) == 1: + else: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] @@ -671,9 +652,14 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] - - mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) - std_value_of_peaks = np.std(y_padded_smoothed[peaks]) + + if len(y_padded_smoothed[peaks])>1: + mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks = np.std(y_padded_smoothed[peaks]) + else: + mean_value_of_peaks = np.nan + std_value_of_peaks = np.nan + peaks_values = y_padded_smoothed[peaks] peaks_neg = peaks_neg - 20 - 20 @@ -691,7 +677,6 @@ def separate_lines_vertical(img_patch, contour_text_interest, thetha): textline_boxes_rot = [] if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3: - # print('11') for jj in range(len(peaks)): if jj == (len(peaks) - 1): @@ -998,15 +983,16 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): textline_con_fil = filter_contours_area_of_image(img_patch, textline_con, hierarchy, max_area=1, min_area=0.0008) - y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) + if len(np.diff(peaks_new_tot)): + y_diff_mean = np.mean(np.diff(peaks_new_tot)) # self.find_contours_mean_y_diff(textline_con_fil) + sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) + else: + sigma_gaus = 12 - sigma_gaus = int(y_diff_mean * (7.0 / 40.0)) - # print(sigma_gaus,'sigma_gaus') except: sigma_gaus = 12 if sigma_gaus < 3: sigma_gaus = 3 - # print(sigma_gaus,'sigma') y_padded_smoothed = gaussian_filter1d(y_padded, sigma_gaus) y_padded_up_to_down = -y_padded + np.max(y_padded) @@ -1030,7 +1016,7 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): arg_diff_cluster = arg_diff[diff_arg_neg_must_be_deleted > 1] clusters_to_be_deleted = [] - if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: + if len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) >= 2: clusters_to_be_deleted.append(arg_neg_must_be_deleted[0 : arg_diff_cluster[0] + 1]) for i in range(len(arg_diff_cluster) - 1): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[i] + 1 : @@ -1038,7 +1024,7 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): clusters_to_be_deleted.append(arg_neg_must_be_deleted[arg_diff_cluster[len(arg_diff_cluster) - 1] + 1 :]) elif len(arg_neg_must_be_deleted) >= 2 and len(arg_diff_cluster) == 0: clusters_to_be_deleted.append(arg_neg_must_be_deleted[:]) - if len(arg_neg_must_be_deleted) == 1: + else: clusters_to_be_deleted.append(arg_neg_must_be_deleted) if len(clusters_to_be_deleted) > 0: peaks_new_extra = [] @@ -1081,9 +1067,14 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): peaks_new_tot = peaks[:] peaks = peaks_new_tot[:] peaks_neg = peaks_neg_new[:] - - mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) - std_value_of_peaks = np.std(y_padded_smoothed[peaks]) + + if len(y_padded_smoothed[peaks]) > 1: + mean_value_of_peaks = np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks = np.std(y_padded_smoothed[peaks]) + else: + mean_value_of_peaks = np.nan + std_value_of_peaks = np.nan + peaks_values = y_padded_smoothed[peaks] ###peaks_neg = peaks_neg - 20 - 20 @@ -1093,10 +1084,8 @@ def separate_lines_new_inside_tiles2(img_patch, thetha): if len(peaks_neg_true) > 0: peaks_neg_true = np.array(peaks_neg_true) - peaks_neg_true = peaks_neg_true - 20 - 20 - # print(peaks_neg_true) for i in range(len(peaks_neg_true)): img_patch[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0 else: @@ -1181,13 +1170,11 @@ def separate_lines_new_inside_tiles(img_path, thetha): if diff_peaks[i] <= cut_off: forest.append(peaks_neg[i + 1]) if diff_peaks[i] > cut_off: - # print(forest[np.argmin(z[forest]) ] ) if not np.isnan(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) forest = [] forest.append(peaks_neg[i + 1]) if i == (len(peaks_neg) - 1): - # print(print(forest[np.argmin(z[forest]) ] )) if not np.isnan(forest[np.argmin(z[forest])]): peaks_neg_true.append(forest[np.argmin(z[forest])]) @@ -1204,17 +1191,14 @@ def separate_lines_new_inside_tiles(img_path, thetha): if diff_peaks_pos[i] <= cut_off: forest.append(peaks[i + 1]) if diff_peaks_pos[i] > cut_off: - # print(forest[np.argmin(z[forest]) ] ) if not np.isnan(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) forest = [] forest.append(peaks[i + 1]) if i == (len(peaks) - 1): - # print(print(forest[np.argmin(z[forest]) ] )) if not np.isnan(forest[np.argmax(z[forest])]): peaks_pos_true.append(forest[np.argmax(z[forest])]) - # print(len(peaks_neg_true) ,len(peaks_pos_true) ,'lensss') if len(peaks_neg_true) > 0: peaks_neg_true = np.array(peaks_neg_true) @@ -1240,7 +1224,6 @@ def separate_lines_new_inside_tiles(img_path, thetha): """ peaks_neg_true = peaks_neg_true - 20 - 20 - # print(peaks_neg_true) for i in range(len(peaks_neg_true)): img_path[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0 @@ -1282,7 +1265,6 @@ def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i contours_imgs, hierarchy, max_area=max_area, min_area=min_area) cont_final = [] - ###print(add_boxes_coor_into_textlines,'ikki') for i in range(len(contours_imgs)): img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3)) img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255)) @@ -1297,12 +1279,10 @@ def separate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_i ##0] ##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1] ##if add_boxes_coor_into_textlines: - ##print(np.shape(contours_text_rot[0]),'sjppo') ##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0] ##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1] cont_final.append(contours_text_rot[0]) - ##print(cont_final,'nadizzzz') return None, cont_final def textline_contours_postprocessing(textline_mask, slope, contour_text_interest, box_ind, add_boxes_coor_into_textlines=False): @@ -1313,20 +1293,7 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel) textline_mask = cv2.erode(textline_mask, kernel, iterations=2) # textline_mask = cv2.erode(textline_mask, kernel, iterations=1) - - # print(textline_mask.shape[0]/float(textline_mask.shape[1]),'miz') try: - # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: - # plt.imshow(textline_mask) - # plt.show() - - # if abs(slope)>1: - # x_help=30 - # y_help=2 - # else: - # x_help=2 - # y_help=2 - x_help = 30 y_help = 2 @@ -1350,28 +1317,12 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest img_contour = np.zeros((box_ind[3], box_ind[2], 3)) img_contour = cv2.fillPoly(img_contour, pts=[contour_text_copy], color=(255, 255, 255)) - # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: - # plt.imshow(img_contour) - # plt.show() - img_contour_help = np.zeros((img_contour.shape[0] + int(2 * y_help), img_contour.shape[1] + int(2 * x_help), 3)) img_contour_help[y_help : y_help + img_contour.shape[0], x_help : x_help + img_contour.shape[1], :] = np.copy(img_contour[:, :, :]) img_contour_rot = rotate_image(img_contour_help, slope) - # plt.imshow(img_contour_rot_help) - # plt.show() - - # plt.imshow(dst_help) - # plt.show() - - # if np.abs(slope)>.5 and textline_mask.shape[0]/float(textline_mask.shape[1])>3: - # plt.imshow(img_contour_rot_help) - # plt.show() - - # plt.imshow(dst_help) - # plt.show() img_contour_rot = img_contour_rot.astype(np.uint8) # dst_help = dst_help.astype(np.uint8) @@ -1382,9 +1333,7 @@ def textline_contours_postprocessing(textline_mask, slope, contour_text_interest len_con_text_rot = [len(contours_text_rot[ib]) for ib in range(len(contours_text_rot))] ind_big_con = np.argmax(len_con_text_rot) - # print('juzaa') if abs(slope) > 45: - # print(add_boxes_coor_into_textlines,'avval') _, contours_rotated_clean = separate_lines_vertical_cont( textline_mask, contours_text_rot[ind_big_con], box_ind, slope, add_boxes_coor_into_textlines=add_boxes_coor_into_textlines) @@ -1416,7 +1365,6 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl length_x = int(img_path.shape[1] / float(num_patches)) # margin = int(0.04 * length_x) just recently this was changed because it break lines into 2 margin = int(0.04 * length_x) - # print(margin,'margin') # if margin<=4: # margin = int(0.08 * length_x) # margin=0 @@ -1456,11 +1404,9 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl # if abs(slope_region)>70 and abs(slope_xline)<25: # slope_xline=[slope_region][0] slopes_tile_wise.append(slope_xline) - # print(slope_xline,'xlineeee') img_line_rotated = rotate_image(img_xline, slope_xline) img_line_rotated[:, :][img_line_rotated[:, :] != 0] = 1 - - # print(slopes_tile_wise,'slopes_tile_wise') + img_patch_ineterst = img_path[:, :] # [peaks_neg_true[14]-dis_up:peaks_neg_true[14]+dis_down ,:] img_patch_ineterst_revised = np.zeros(img_patch_ineterst.shape) @@ -1502,8 +1448,6 @@ def separate_lines_new2(img_path, thetha, num_col, slope_region, logger=None, pl img_patch_separated_returned_true_size = img_patch_separated_returned_true_size[:, margin : length_x - margin] img_patch_ineterst_revised[:, index_x_d + margin : index_x_u - margin] = img_patch_separated_returned_true_size - # plt.imshow(img_patch_ineterst_revised) - # plt.show() return img_patch_ineterst_revised def do_image_rotation(angle, img, sigma_des, logger=None): @@ -1536,20 +1480,13 @@ def return_deskew_slop(img_patch_org, sigma_des,n_tot_angles=100, #img_resized[ int( img_int.shape[0]*(.4)):int( img_int.shape[0]*(.4))+img_int.shape[0] , int( img_int.shape[1]*(.8)):int( img_int.shape[1]*(.8))+img_int.shape[1] ]=img_int[:,:] img_resized[ onset_y:onset_y+img_int.shape[0] , onset_x:onset_x+img_int.shape[1] ]=img_int[:,:] - #print(img_resized.shape,'img_resizedshape') - #plt.imshow(img_resized) - #plt.show() if main_page and img_patch_org.shape[1] > img_patch_org.shape[0]: - #plt.imshow(img_resized) - #plt.show() angles = np.array([-45, 0, 45, 90,]) angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) angles = np.linspace(angle - 22.5, angle + 22.5, n_tot_angles) angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) elif main_page: - #plt.imshow(img_resized) - #plt.show() angles = np.linspace(-12, 12, n_tot_angles)#np.array([0 , 45 , 90 , -45]) angle = get_smallest_skew(img_resized, sigma_des, angles, map=map, logger=logger, plotter=plotter) @@ -1620,7 +1557,6 @@ def do_work_of_slopes_new( textline_con_fil = filter_contours_area_of_image(img_int_p, textline_con, hierarchy, max_area=1, min_area=0.00008) - y_diff_mean = find_contours_mean_y_diff(textline_con_fil) if len(textline_con_fil) > 1 else np.NaN if np.isnan(y_diff_mean): slope_for_all = MAX_SLOPE @@ -1637,12 +1573,9 @@ def do_work_of_slopes_new( if slope_for_all == MAX_SLOPE: slope_for_all = slope_deskew slope = slope_for_all - mask_only_con_region = np.zeros(textline_mask_tot_ea.shape) mask_only_con_region = cv2.fillPoly(mask_only_con_region, pts=[contour_par], color=(1, 1, 1)) - # plt.imshow(mask_only_con_region) - # plt.show() all_text_region_raw = textline_mask_tot_ea[y: y + h, x: x + w].copy() mask_only_con_region = mask_only_con_region[y: y + h, x: x + w] @@ -1706,20 +1639,15 @@ def do_work_of_slopes_new_curved( mask_region_in_patch_region = mask_biggest[y : y + h, x : x + w] textline_biggest_region = mask_biggest * textline_mask_tot_ea - # print(slope_for_all,'slope_for_all') textline_rotated_separated = separate_lines_new2(textline_biggest_region[y: y+h, x: x+w], 0, num_col, slope_for_all, logger=logger, plotter=plotter) - # new line added - ##print(np.shape(textline_rotated_separated),np.shape(mask_biggest)) + textline_rotated_separated[mask_region_in_patch_region[:, :] != 1] = 0 - # till here textline_region_in_image[y : y + h, x : x + w] = textline_rotated_separated - # plt.imshow(textline_region_in_image) - # plt.show() pixel_img = 1 cnt_textlines_in_image = return_contours_of_interested_textline(textline_region_in_image, pixel_img) @@ -1742,7 +1670,6 @@ def do_work_of_slopes_new_curved( logger.error(why) else: textlines_cnt_per_region = textline_contours_postprocessing(all_text_region_raw, slope_for_all, contour_par, box_text, True) - # print(np.shape(textlines_cnt_per_region),'textlines_cnt_per_region') return textlines_cnt_per_region[::-1], box_text, contour, contour_par, crop_coor, index_r_con, slope