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Update separate_lines.py from local

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pull/12/head
vahidrezanezhad 4 years ago committed by GitHub
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commit 85d631aadf
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sbb_newspapers_org_image/utils/separate_lines.py
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@ -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 (
@ -150,27 +151,149 @@ 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)
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)
x_min_cont = 0
x_max_cont = img_patch.shape[1]
y_min_cont = 0
y_max_cont = img_patch.shape[0]
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[:]
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:
neg_peaks_max=np.max(y_padded_smoothed[peaks])
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 ]
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])
@ -180,9 +303,12 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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=[]
for m in range(len(clusters_to_be_deleted)):
@ -220,6 +346,7 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
peaks=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
else:
peaks_new_tot=peaks[:]
peaks=peaks_new_tot[:]
@ -227,10 +354,12 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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]
peaks_neg = peaks_neg - 20 - 20
peaks = peaks - 20
@ -242,6 +371,8 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
if peaks[jj] > len(x) - 1:
peaks[jj] = len(x) - 1
textline_boxes = []
textline_boxes_rot = []
@ -252,31 +383,36 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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)
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_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]
@ -307,6 +443,8 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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
@ -317,16 +455,26 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, 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)]]))
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]
@ -353,6 +501,7 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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:
@ -362,6 +511,7 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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
@ -372,9 +522,20 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, 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)]]))
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])
@ -383,14 +544,18 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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]
@ -412,6 +577,8 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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:
@ -431,36 +598,46 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, 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)]]))
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]
@ -491,6 +668,7 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, y_help):
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
@ -501,9 +679,19 @@ def seperate_lines(img_patch, contour_text_interest, thetha, x_help, 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)]]))
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)

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