From 85d631aadf47b7f6c6a88288b73b931efcb561a4 Mon Sep 17 00:00:00 2001 From: vahidrezanezhad Date: Tue, 12 Jan 2021 10:56:53 +0100 Subject: [PATCH] Update separate_lines.py from local --- .../utils/separate_lines.py | 520 ++++++++++++------ 1 file changed, 354 insertions(+), 166 deletions(-) diff --git a/sbb_newspapers_org_image/utils/separate_lines.py b/sbb_newspapers_org_image/utils/separate_lines.py index 740495a..d339707 100644 --- a/sbb_newspapers_org_image/utils/separate_lines.py +++ b/sbb_newspapers_org_image/utils/separate_lines.py @@ -3,6 +3,7 @@ import numpy as np import cv2 from scipy.signal import find_peaks from scipy.ndimage import gaussian_filter1d +import os from .rotate import rotate_image from .contour import ( @@ -150,86 +151,214 @@ def dedup_separate_lines(img_patch, contour_text_interest, thetha, axis): def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): + (h, w) = img_patch.shape[:2] + center = (w // 2, h // 2) + 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() - x, y, x_d, y_d, xv, x_min_cont, y_min_cont, x_max_cont, y_max_cont, first_nonzero, y_padded_up_to_down_padded, y_padded_smoothed, peaks, peaks_neg, rotation_matrix = dedup_separate_lines(img_patch, contour_text_interest, thetha, 1) - try: - neg_peaks_max = np.max(y_padded_smoothed[peaks]) + x_cont = contour_text_interest[:, 0, 0] + y_cont = contour_text_interest[:, 0, 1] + x_cont = x_cont - np.min(x_cont) + y_cont = y_cont - np.min(y_cont) - arg_neg_must_be_deleted = np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg] / float(neg_peaks_max) < 0.42] + x_min_cont = 0 + x_max_cont = img_patch.shape[1] + y_min_cont = 0 + y_max_cont = img_patch.shape[0] - diff_arg_neg_must_be_deleted = np.diff(arg_neg_must_be_deleted) + xv = np.linspace(x_min_cont, x_max_cont, 1000) + + textline_patch_sum_along_width = img_patch.sum(axis=1) + + first_nonzero = 0 # (next((i for i, x in enumerate(mada_n) if x), None)) + + y = textline_patch_sum_along_width[:] # [first_nonzero:last_nonzero] + y_padded = np.zeros(len(y) + 40) + y_padded[20:len(y) + 20] = y + 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) + + + 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.array(range(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[:] - 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 = [] + textline_con,hierachy=return_contours_of_image(img_patch) + textline_con_fil=filter_contours_area_of_image(img_patch,textline_con,hierachy,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./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) + 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) + + + + + try: - peaks_new = peaks[:] - peaks_neg_new = peaks_neg[:] - clusters_to_be_deleted = [] + neg_peaks_max=np.max(y_padded_smoothed[peaks]) + - if len(arg_diff_cluster) >= 2 and len(arg_diff_cluster) > 0: + arg_neg_must_be_deleted= np.array(range(len(peaks_neg)))[y_padded_up_to_down_padded[peaks_neg]/float(neg_peaks_max)<0.42 ] - 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 :]) - 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: + 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] + except: + arg_neg_must_be_deleted=[] + arg_diff_cluster=[] + + + try: + 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): + 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:]) + 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: clusters_to_be_deleted.append(arg_neg_must_be_deleted) + - if len(clusters_to_be_deleted) > 0: - peaks_new_extra = [] + if len(clusters_to_be_deleted)>0: + peaks_new_extra=[] for m in range(len(clusters_to_be_deleted)): - min_cluster = np.min(peaks[clusters_to_be_deleted[m]]) - max_cluster = np.max(peaks[clusters_to_be_deleted[m]]) - peaks_new_extra.append(int((min_cluster + max_cluster) / 2.0)) + min_cluster=np.min(peaks[clusters_to_be_deleted[m]]) + max_cluster=np.max(peaks[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[clusters_to_be_deleted[m][m1] - 1]] - peaks_new = peaks_new[peaks_new != peaks[clusters_to_be_deleted[m][m1]]] - - peaks_neg_new = peaks_neg_new[peaks_neg_new != peaks_neg[clusters_to_be_deleted[m][m1]]] - peaks_new_tot = [] + peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]-1]] + peaks_new=peaks_new[peaks_new!=peaks[clusters_to_be_deleted[m][m1]]] + + peaks_neg_new=peaks_neg_new[peaks_neg_new!=peaks_neg[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) - + 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[:] - + + peaks=peaks_new_tot[:] + peaks_neg=peaks_neg_new[:] + + else: - peaks_new_tot = peaks[:] - peaks = peaks_new_tot[:] - peaks_neg = peaks_neg_new[:] + peaks_new_tot=peaks[:] + peaks=peaks_new_tot[:] + 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]) - peaks_values = y_padded_smoothed[peaks] + + + mean_value_of_peaks=np.mean(y_padded_smoothed[peaks]) + std_value_of_peaks=np.std(y_padded_smoothed[peaks]) + peaks_values=y_padded_smoothed[peaks] + peaks_neg = peaks_neg - 20 - 20 peaks = peaks - 20 @@ -241,42 +370,49 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): for jj in range(len(peaks)): if peaks[jj] > len(x) - 1: peaks[jj] = len(x) - 1 + + textline_boxes = [] textline_boxes_rot = [] - + if len(peaks_neg) == len(peaks) + 1 and len(peaks) >= 3: for jj in range(len(peaks)): - - if jj == (len(peaks) - 1): + + if jj==(len(peaks)-1): dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) - - if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: + + if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.: point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) - point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) + point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.3 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.4 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) - point_down = y_max_cont - 1 ##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) + point_down =y_max_cont-1##peaks[jj] + first_nonzero + int(1.6 * dis_to_next_down) #point_up# np.max(y_cont)#peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) - point_down_narrow = peaks[jj] + first_nonzero + int(1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2) + point_down_narrow = peaks[jj] + first_nonzero + int( + 1.4 * dis_to_next_down) ###-int(dis_to_next_down*1./2) else: dis_to_next_up = abs(peaks[jj] - peaks_neg[jj]) dis_to_next_down = abs(peaks[jj] - peaks_neg[jj + 1]) - - if peaks_values[jj] > mean_value_of_peaks - std_value_of_peaks / 2.0: + + if peaks_values[jj]>mean_value_of_peaks-std_value_of_peaks/2.: point_up = peaks[jj] + first_nonzero - int(1.1 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) else: point_up = peaks[jj] + first_nonzero - int(1.23 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0) point_down = peaks[jj] + first_nonzero + int(1.33 * dis_to_next_down) ###-int(dis_to_next_down*1./4.0) - point_down_narrow = peaks[jj] + first_nonzero + int(1.1 * dis_to_next_down) ###-int(dis_to_next_down*1./2) + 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 - distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] + distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) + for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] @@ -297,48 +433,60 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d - - if x_min_rot1 < 0: - x_min_rot1 = 0 - if x_min_rot4 < 0: - x_min_rot4 = 0 - if point_up_rot1 < 0: - point_up_rot1 = 0 - if point_up_rot2 < 0: - point_up_rot2 = 0 - - x_min_rot1 = x_min_rot1 - x_help - x_max_rot2 = x_max_rot2 - x_help - x_max_rot3 = x_max_rot3 - x_help - x_min_rot4 = x_min_rot4 - x_help - - point_up_rot1 = point_up_rot1 - y_help - point_up_rot2 = point_up_rot2 - y_help - point_down_rot3 = point_down_rot3 - y_help - point_down_rot4 = point_down_rot4 - y_help - - textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) - - textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) + + if x_min_rot1<0: + x_min_rot1=0 + if x_min_rot4<0: + x_min_rot4=0 + if point_up_rot1<0: + point_up_rot1=0 + if point_up_rot2<0: + point_up_rot2=0 + + + + x_min_rot1=x_min_rot1-x_help + x_max_rot2=x_max_rot2-x_help + x_max_rot3=x_max_rot3-x_help + x_min_rot4=x_min_rot4-x_help + + point_up_rot1=point_up_rot1-y_help + point_up_rot2=point_up_rot2-y_help + point_down_rot3=point_down_rot3-y_help + point_down_rot4=point_down_rot4-y_help + + + + + textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], + [int(x_max_rot2), int(point_up_rot2)], + [int(x_max_rot3), int(point_down_rot3)], + [int(x_min_rot4), int(point_down_rot4)]])) + + textline_boxes.append(np.array([[int(x_min), int(point_up)], + [int(x_max), int(point_up)], + [int(x_max), int(point_down)], + [int(x_min), int(point_down)]])) elif len(peaks) < 1: pass elif len(peaks) == 1: - - distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) for mj in range(len(xv))] + + distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) + for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] - + if len(xvinside) == 0: x_min = x_min_cont x_max = x_max_cont else: x_min = np.min(xvinside) # max(x_min_interest,x_min_cont) x_max = np.max(xvinside) # min(x_max_interest,x_max_cont) - # x_min = x_min_cont - # x_max = x_max_cont + #x_min = x_min_cont + #x_max = x_max_cont y_min = y_min_cont y_max = y_max_cont @@ -352,45 +500,62 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d + + + if x_min_rot1<0: + x_min_rot1=0 + if x_min_rot4<0: + x_min_rot4=0 + if point_up_rot1<0: + point_up_rot1=0 + if point_up_rot2<0: + point_up_rot2=0 + + + x_min_rot1=x_min_rot1-x_help + x_max_rot2=x_max_rot2-x_help + x_max_rot3=x_max_rot3-x_help + x_min_rot4=x_min_rot4-x_help + + point_up_rot1=point_up_rot1-y_help + point_up_rot2=point_up_rot2-y_help + point_down_rot3=point_down_rot3-y_help + point_down_rot4=point_down_rot4-y_help + + + + + textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], + [int(x_max_rot2), int(point_up_rot2)], + [int(x_max_rot3), int(point_down_rot3)], + [int(x_min_rot4), int(point_down_rot4)]])) + + textline_boxes.append(np.array([[int(x_min), int(y_min)], + [int(x_max), int(y_min)], + [int(x_max), int(y_max)], + [int(x_min), int(y_max)]])) - if x_min_rot1 < 0: - x_min_rot1 = 0 - if x_min_rot4 < 0: - x_min_rot4 = 0 - if point_up_rot1 < 0: - point_up_rot1 = 0 - if point_up_rot2 < 0: - point_up_rot2 = 0 - - x_min_rot1 = x_min_rot1 - x_help - x_max_rot2 = x_max_rot2 - x_help - x_max_rot3 = x_max_rot3 - x_help - x_min_rot4 = x_min_rot4 - x_help - - point_up_rot1 = point_up_rot1 - y_help - point_up_rot2 = point_up_rot2 - y_help - point_down_rot3 = point_down_rot3 - y_help - point_down_rot4 = point_down_rot4 - y_help - - textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) - textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]])) elif len(peaks) == 2: dis_to_next = np.abs(peaks[1] - peaks[0]) for jj in range(len(peaks)): if jj == 0: - point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next) + point_up = 0#peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next) if point_up < 0: point_up = 1 - point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next) + point_down = peaks_neg[1] + first_nonzero# peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next) elif jj == 1: - point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next) + point_down =peaks_neg[1] + first_nonzero# peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next) if point_down >= img_patch.shape[0]: point_down = img_patch.shape[0] - 2 - point_up = peaks[jj] + first_nonzero - int(1.0 / 1.8 * dis_to_next) + try: + point_up = peaks_neg[2] + first_nonzero#peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next) + except: + point_up =peaks[jj] + first_nonzero - int(1. / 1.8 * dis_to_next) - distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] + distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) + for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] @@ -411,56 +576,68 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d - - if x_min_rot1 < 0: - x_min_rot1 = 0 - if x_min_rot4 < 0: - x_min_rot4 = 0 - if point_up_rot1 < 0: - point_up_rot1 = 0 - if point_up_rot2 < 0: - point_up_rot2 = 0 - - x_min_rot1 = x_min_rot1 - x_help - x_max_rot2 = x_max_rot2 - x_help - x_max_rot3 = x_max_rot3 - x_help - x_min_rot4 = x_min_rot4 - x_help - - point_up_rot1 = point_up_rot1 - y_help - point_up_rot2 = point_up_rot2 - y_help - point_down_rot3 = point_down_rot3 - y_help - point_down_rot4 = point_down_rot4 - y_help - - textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) - - textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) + + + + if x_min_rot1<0: + x_min_rot1=0 + if x_min_rot4<0: + x_min_rot4=0 + if point_up_rot1<0: + point_up_rot1=0 + if point_up_rot2<0: + point_up_rot2=0 + + x_min_rot1=x_min_rot1-x_help + x_max_rot2=x_max_rot2-x_help + x_max_rot3=x_max_rot3-x_help + x_min_rot4=x_min_rot4-x_help + + point_up_rot1=point_up_rot1-y_help + point_up_rot2=point_up_rot2-y_help + point_down_rot3=point_down_rot3-y_help + point_down_rot4=point_down_rot4-y_help + + + + + textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], + [int(x_max_rot2), int(point_up_rot2)], + [int(x_max_rot3), int(point_down_rot3)], + [int(x_min_rot4), int(point_down_rot4)]])) + + textline_boxes.append(np.array([[int(x_min), int(point_up)], + [int(x_max), int(point_up)], + [int(x_max), int(point_down)], + [int(x_min), int(point_down)]])) else: for jj in range(len(peaks)): if jj == 0: dis_to_next = peaks[jj + 1] - peaks[jj] # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next) - point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next) + point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next) if point_up < 0: point_up = 1 # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next) - point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next) + point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next) elif jj == len(peaks) - 1: dis_to_next = peaks[jj] - peaks[jj - 1] # point_down=peaks[jj]+first_nonzero+int(1./3*dis_to_next) - point_down = peaks[jj] + first_nonzero + int(1.0 / 1.7 * dis_to_next) + point_down = peaks[jj] + first_nonzero + int(1. / 1.7 * dis_to_next) if point_down >= img_patch.shape[0]: point_down = img_patch.shape[0] - 2 # point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next) - point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next) + point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next) else: dis_to_next_down = peaks[jj + 1] - peaks[jj] dis_to_next_up = peaks[jj] - peaks[jj - 1] - point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next_up) - point_down = peaks[jj] + first_nonzero + int(1.0 / 1.9 * dis_to_next_down) + point_up = peaks[jj] + first_nonzero - int(1. / 1.9 * dis_to_next_up) + point_down = peaks[jj] + first_nonzero + int(1. / 1.9 * dis_to_next_down) - distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) for mj in range(len(xv))] + distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[jj] + first_nonzero), True) + for mj in range(len(xv))] distances = np.array(distances) xvinside = xv[distances >= 0] @@ -481,29 +658,40 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help): x_max_rot2, point_up_rot2 = p2[0] + x_d, p2[1] + y_d x_max_rot3, point_down_rot3 = p3[0] + x_d, p3[1] + y_d x_min_rot4, point_down_rot4 = p4[0] + x_d, p4[1] + y_d + + if x_min_rot1<0: + x_min_rot1=0 + if x_min_rot4<0: + x_min_rot4=0 + if point_up_rot1<0: + point_up_rot1=0 + if point_up_rot2<0: + point_up_rot2=0 + + + x_min_rot1=x_min_rot1-x_help + x_max_rot2=x_max_rot2-x_help + x_max_rot3=x_max_rot3-x_help + x_min_rot4=x_min_rot4-x_help + + point_up_rot1=point_up_rot1-y_help + point_up_rot2=point_up_rot2-y_help + point_down_rot3=point_down_rot3-y_help + point_down_rot4=point_down_rot4-y_help + + + + + textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], + [int(x_max_rot2), int(point_up_rot2)], + [int(x_max_rot3), int(point_down_rot3)], + [int(x_min_rot4), int(point_down_rot4)]])) + + textline_boxes.append(np.array([[int(x_min), int(point_up)], + [int(x_max), int(point_up)], + [int(x_max), int(point_down)], + [int(x_min), int(point_down)]])) - if x_min_rot1 < 0: - x_min_rot1 = 0 - if x_min_rot4 < 0: - x_min_rot4 = 0 - if point_up_rot1 < 0: - point_up_rot1 = 0 - if point_up_rot2 < 0: - point_up_rot2 = 0 - - x_min_rot1 = x_min_rot1 - x_help - x_max_rot2 = x_max_rot2 - x_help - x_max_rot3 = x_max_rot3 - x_help - x_min_rot4 = x_min_rot4 - x_help - - point_up_rot1 = point_up_rot1 - y_help - point_up_rot2 = point_up_rot2 - y_help - point_down_rot3 = point_down_rot3 - y_help - point_down_rot4 = point_down_rot4 - y_help - - textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]])) - - textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]])) return peaks, textline_boxes_rot @@ -1411,7 +1599,7 @@ def return_deskew_slop(img_patch_org, sigma_des, main_page=False, dir_of_all=Non if main_page and dir_of_all is not None: - plt.figure(figsize=(70,40)) + plt.figure(figsize=(80,40)) plt.rcParams['font.size']='50' plt.subplot(1,2,1) plt.imshow(img_patch_org)