qurator-spk
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sbb_textline_detection
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gettin robust and doing sth for verticals

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pull/25/head
b-vr103 5 years ago
parent f90b3cfa86
commit 3941f2f17d
1 changed files with 731 additions and 51 deletions
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qurator/sbb_textline_detector/main.py
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@ -51,9 +51,9 @@ class textline_detector:
self.f_name = self.f_name.split('.')[0]
self.dir_models = dir_models
self.kernel = np.ones((5, 5), np.uint8)
self.model_page_dir = dir_models + '/model_page_new.h5'
self.model_page_dir = dir_models + '/model_page_mixed_best.h5'
self.model_region_dir = dir_models + '/model_strukturerkennung.h5'
self.model_textline_dir = dir_models + '/model_textline.h5'
self.model_textline_dir = dir_models + '/model_textline_new.h5'
def find_polygons_size_filter(self, contours, median_area, scaler_up=1.2, scaler_down=0.8):
found_polygons_early = list()
@ -70,7 +70,7 @@ class textline_detector:
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
return found_polygons_early
def filter_contours_area_of_image(self, image, contours, hierarchy, max_area, min_area):
def filter_contours_area_of_image(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
@ -81,13 +81,13 @@ class textline_detector:
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(
image.shape[:2]) and hierarchy[0][jv][3] == -1 : # and hierarchy[0][jv][3]==-1 :
image.shape[:2]) and hirarchy[0][jv][3] == -1 : # and hirarchy[0][jv][3]==-1 :
found_polygons_early.append(
np.array([ [point] for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_interiors(self, image, contours, hierarchy, max_area, min_area):
def filter_contours_area_of_image_interiors(self, image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
@ -98,7 +98,7 @@ class textline_detector:
polygon = geometry.Polygon([point[0] for point in c])
area = polygon.area
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]) and \
hierarchy[0][jv][3] != -1:
hirarchy[0][jv][3] != -1:
# print(c[0][0][1])
found_polygons_early.append(
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
@ -198,30 +198,8 @@ class textline_detector:
self.img_hight_int = 2800
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
elif self.image.shape[0] < 2000 and self.image.shape[0] >= 1000:
self.img_hight_int = int(self.image.shape[0]*1.1)
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
elif self.image.shape[0] < 3300 and self.image.shape[0] >= 2000:
self.img_hight_int = int(self.image.shape[0]*1.1)
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
elif self.image.shape[0] < 4000 and self.image.shape[0] >= 3300 and self.image.shape[1]<2400 :
self.img_hight_int = int(self.image.shape[0]*1.1)# 6500
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
elif self.image.shape[0] < 4000 and self.image.shape[0] >= 3300 and self.image.shape[1]>=2400 :
self.img_hight_int = 6500
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
elif self.image.shape[0] < 5400 and self.image.shape[0] > 4000 and self.image.shape[1]>3300 :
self.img_hight_int = int(self.image.shape[0]*1.6)# 6500
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
elif self.image.shape[0] < 11000 and self.image.shape[0] >= 7000 :
self.img_hight_int = int(self.image.shape[0]*1.6)# 6500
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
else:
self.img_hight_int = int(self.image.shape[0]*1.1)# 6500
self.img_hight_int = int(self.image.shape[0]*1.2)# 6500
self.img_width_int = int(self.img_hight_int * self.image.shape[1] / float(self.image.shape[0]))
#self.img_hight_int = self.image.shape[0]
#self.img_width_int = self.image.shape[1]
@ -402,7 +380,7 @@ class textline_detector:
def extract_page(self):
patches=False
model_page, session_page = self.start_new_session_and_model(self.model_page_dir)
img = self.otsu_copy(self.image)
img = self.image#self.otsu_copy(self.image)
#for ii in range(1):
# img = cv2.GaussianBlur(img, (15, 15), 0)
@ -486,9 +464,9 @@ class textline_detector:
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
main_contours = self.filter_contours_area_of_image(thresh, contours, hierarchy, max_area=1, min_area=0.00001)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
self.boxes = []
for jj in range(len(main_contours)):
@ -508,7 +486,7 @@ class textline_detector:
def textline_contours(self, img):
patches=True
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
img = self.otsu_copy(img)
#####img = self.otsu_copy(img)
img = img.astype(np.uint8)
prediction_textline=self.do_prediction(patches,img,model_textline)
@ -521,7 +499,7 @@ class textline_detector:
return prediction_textline[:,:,0]
def get_textlines_for_each_textregions(self, textline_mask_tot, boxes):
textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1)
########textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1) ####should be changed
self.area_of_cropped = []
self.all_text_region_raw = []
for jk in range(len(boxes)):
@ -530,7 +508,7 @@ class textline_detector:
crop_img=crop_img.astype(np.uint8)
self.all_text_region_raw.append(crop_img[:, :, 0])
self.area_of_cropped.append(crop_img.shape[0] * crop_img.shape[1])
def seperate_lines(self, img_patch, contour_text_interest, thetha):
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
@ -564,10 +542,76 @@ class textline_detector:
x = np.array(range(len(y)))
peaks_real, _ = find_peaks(gaussian_filter1d(y, 3), height=0)
if len(peaks_real)<=2 and len(peaks_real)>1:
sigma_gaus=10
else:
sigma_gaus=8
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=self.return_contours_of_image(img_patch)
textline_con_fil=self.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)
@ -580,6 +624,98 @@ class textline_detector:
peaks, _ = find_peaks(y_padded_smoothed, height=0)
peaks_neg, _ = find_peaks(y_padded_up_to_down_padded, height=0)
#plt.plot(y_padded_up_to_down_padded)
#plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
#plt.title('negs')
#plt.show()
#plt.plot(y_padded_smoothed)
#plt.plot(peaks,y_padded_smoothed[peaks],'*')
#plt.title('poss')
#plt.show()
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]
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=[]
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) )
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=[]
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)
#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:
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])
peaks_values=y_padded_smoothed[peaks]
@ -595,6 +731,8 @@ class textline_detector:
for jj in range(len(peaks)):
if peaks[jj] > len(x) - 1:
peaks[jj] = len(x) - 1
textline_boxes = []
textline_boxes_rot = []
@ -846,6 +984,472 @@ class textline_detector:
[int(x_min), int(point_down)]]))
return peaks, textline_boxes_rot
def seperate_lines_vertical(self, img_patch, contour_text_interest, thetha):
thetha=thetha+90
(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_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=0)
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=self.return_contours_of_image(img_patch)
textline_con_fil=self.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)
#plt.plot(y_padded_up_to_down_padded)
#plt.plot(peaks_neg,y_padded_up_to_down_padded[peaks_neg],'*')
#plt.title('negs')
#plt.show()
#plt.plot(y_padded_smoothed)
#plt.plot(peaks,y_padded_smoothed[peaks],'*')
#plt.title('poss')
#plt.show()
neg_peaks_max=np.max(y_padded_up_to_down_padded[peaks_neg])
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]
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=[]
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) )
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=[]
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=peaks_new_tot[:]
peaks_neg=peaks_neg_new[:]
else:
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])
peaks_values=y_padded_smoothed[peaks]
peaks_neg = peaks_neg - 20 - 20
peaks = peaks - 20
for jj in range(len(peaks_neg)):
if peaks_neg[jj] > len(x) - 1:
peaks_neg[jj] = len(x) - 1
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:
#print('11')
for jj in range(len(peaks)):
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.:
point_up = peaks[jj] + first_nonzero - int(1.3 * dis_to_next_up) ##+int(dis_to_next_up*1./4.0)
point_down =x_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 =x_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)
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.:
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)
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 = 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)
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
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
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:
x_min = x_min_cont
x_max = x_max_cont
y_min = y_min_cont
y_max = y_max_cont
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
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
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)
if point_up < 0:
point_up = 1
point_down = peaks[jj] + first_nonzero + int(1. / 1.8 * dis_to_next)
elif jj == 1:
point_down = 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. / 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 = 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)
x_max = np.max(xvinside)
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
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
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. / 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. / 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. / 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. / 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. / 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 = 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)
p1 = np.dot(rotation_matrix, [int(point_up), int(y_min_cont)])
p2 = np.dot(rotation_matrix, [int(point_down), int(y_min_cont)])
p3 = np.dot(rotation_matrix, [int(point_down), int(y_max_cont)])
p4 = np.dot(rotation_matrix, [int(point_up), int(y_max_cont)])
x_min_rot1, point_up_rot1 = p1[0] + x_d, p1[1] + y_d
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
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
def return_rotated_contours(self,slope,img_patch):
@ -869,13 +1473,17 @@ class textline_detector:
kernel = np.ones((5, 5), np.uint8)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel)
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=2)##should be changed
try:
dst = self.rotate_image(textline_mask, slope)
dst = dst[:, :, 0]
dst[dst != 0] = 1
#plt.imshow(dst)
#plt.show()
contour_text_copy = contour_text_interest.copy()
@ -899,8 +1507,10 @@ class textline_detector:
ind_big_con = np.argmax(len_con_text_rot)
_, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope)
if abs(slope)>45:
_, contours_rotated_clean = self.seperate_lines_vertical(dst, contours_text_rot[ind_big_con], slope)
else:
_, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope)
except:
@ -916,8 +1526,8 @@ class textline_detector:
image_box_tabels=image_box_tabels.astype(np.uint8)
imgray = cv2.cvtColor(image_box_tabels, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours,hierarchy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
return contours,hierarchy
contours,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
return contours,hierachy
def find_contours_mean_y_diff(self,contours_main):
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
@ -985,23 +1595,42 @@ class textline_detector:
return interest_neg_fin,np.std(z)
def return_deskew_slope(self,img_patch,sigma_des):
max_x_y=max(img_patch.shape[0],img_patch.shape[1])
##img_patch=self.resize_image(img_patch,max_x_y,max_x_y)
img_patch_copy=np.zeros((img_patch.shape[0],img_patch.shape[1]))
img_patch_copy[:,:]=img_patch[:,:]#img_patch_org[:,:,0]
img_patch_padded=np.zeros((int( img_patch_copy.shape[0]*(1.2) ) , int( img_patch_copy.shape[1]*(2.6) ) ))
img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
angles=np.linspace(-12,12,40)
img_patch_padded=np.zeros((int( max_x_y*(1.4) ) , int( max_x_y*(1.4) ) ))
img_patch_padded_center_p=int(img_patch_padded.shape[0]/2.)
len_x_org_patch_half=int(img_patch_copy.shape[1]/2.)
len_y_org_patch_half=int(img_patch_copy.shape[0]/2.)
img_patch_padded[img_patch_padded_center_p-len_y_org_patch_half:img_patch_padded_center_p-len_y_org_patch_half+img_patch_copy.shape[0],img_patch_padded_center_p-len_x_org_patch_half:img_patch_padded_center_p-len_x_org_patch_half+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
#img_patch_padded[ int( img_patch_copy.shape[0]*(.1)):int( img_patch_copy.shape[0]*(.1))+img_patch_copy.shape[0] , int( img_patch_copy.shape[1]*(.8)):int( img_patch_copy.shape[1]*(.8))+img_patch_copy.shape[1] ]=img_patch_copy[:,:]
angles=np.linspace(-25,25,80)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
#plt.imshow(img_patch)
#plt.show()
indexer=0
for rot in angles:
#print(rot,'rot')
img_rotated=self.rotate_image(img_patch_padded,rot)
img_rotated[img_rotated!=0]=1
#plt.imshow(img_rotated)
#plt.show()
try:
neg_peaks,var_spectrum=self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated,sigma_des,20.3 )
res_me=np.mean(neg_peaks)
@ -1027,10 +1656,60 @@ class textline_detector:
try:
var_res=np.array(var_res)
#print(var_res)
ang_int=angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
if abs(ang_int)>15:
angles=np.linspace(-90,-50,30)
res=[]
num_of_peaks=[]
index_cor=[]
var_res=[]
#plt.imshow(img_patch)
#plt.show()
indexer=0
for rot in angles:
#print(rot,'rot')
img_rotated=self.rotate_image(img_patch_padded,rot)
img_rotated[img_rotated!=0]=1
#plt.imshow(img_rotated)
#plt.show()
try:
neg_peaks,var_spectrum=self.get_standard_deviation_of_summed_textline_patch_along_width(img_rotated,sigma_des,20.3 )
res_me=np.mean(neg_peaks)
if res_me==0:
res_me=1000000000000000000000
else:
pass
res_num=len(neg_peaks)
except:
res_me=1000000000000000000000
res_num=0
var_spectrum=0
if self.isNaN(res_me):
pass
else:
res.append( res_me )
var_res.append(var_spectrum)
num_of_peaks.append( res_num )
index_cor.append(indexer)
indexer=indexer+1
try:
var_res=np.array(var_res)
#print(var_res)
ang_int=angles[np.argmax(var_res)]#angels_sorted[arg_final]#angels[arg_sort_early[arg_sort[arg_final]]]#angels[arg_fin]
except:
ang_int=0
return ang_int
@ -1056,9 +1735,10 @@ class textline_detector:
slope_corresponding_textregion=self.return_deskew_slope(crop_img,sigma_des)
except:
slope_corresponding_textregion=999
#print(slope_corresponding_textregion,'slope_corresponding_textregion')
if np.abs(slope_corresponding_textregion)>12.5 and slope_corresponding_textregion!=999:
if np.abs(slope_corresponding_textregion)>120.5 and slope_corresponding_textregion!=999:
slope_corresponding_textregion=0
elif slope_corresponding_textregion==999:
slope_corresponding_textregion=0
@ -1079,7 +1759,7 @@ class textline_detector:
queue_of_quntours_of_textregion.put(contours_textregion_per_each_subprocess)
def get_slopes_and_deskew(self, contours,textline_mask_tot):
num_cores = cpu_count()
num_cores =cpu_count()
queue_of_slopes_per_textregion = Queue()
queue_of_textlines_rectangle_per_textregion=Queue()
@ -1118,7 +1798,7 @@ class textline_detector:
for i in range(num_cores):
processes[i].join()
return all_found_text_regions

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