qurator-spk
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sbb_textline_detection
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Update main.py

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Rezanezhad, Vahid 5 years ago
parent 8c954a6c7a
commit 9f97f34255
1 changed files with 27 additions and 131 deletions
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qurator/sbb_textline_detector/main.py
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@ -264,37 +264,7 @@ class textlineerkenner:
iou = tf.gather(iou, indices=tf.where(legal_labels)) iou = tf.gather(iou, indices=tf.where(legal_labels))
return K.mean(iou) return K.mean(iou)
def IoU(self, Yi, y_predi):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
IoUs = []
Nclass = np.unique(Yi)
for c in Nclass:
TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum((Yi == c) & (y_predi != c))
IoU = TP / float(TP + FP + FN)
print("class {:02.0f}: #TP={:6.0f}, #FP={:6.0f}, #FN={:5.0f}, IoU={:4.3f}".format(c, TP, FP, FN, IoU))
IoUs.append(IoU)
mIoU = np.mean(IoUs)
print("_________________")
print("Mean IoU: {:4.3f}".format(mIoU))
return mIoU
def IoU_case(self, Yi, y_predi, n_classes):
## mean Intersection over Union
## Mean IoU = TP/(FN + TP + FP)
IoUs = []
Nclass = n_classes
for c in range(Nclass):
TP = np.sum((Yi == c) & (y_predi == c))
FP = np.sum((Yi != c) & (y_predi == c))
FN = np.sum((Yi == c) & (y_predi != c))
IoUs.append(np.array([TP, FP, FN]))
return IoUs
def color_images(self, seg, n_classes): def color_images(self, seg, n_classes):
ann_u = range(n_classes) ann_u = range(n_classes)
@ -421,7 +391,7 @@ class textlineerkenner:
img = cv2.GaussianBlur(img, (15, 15), 0) img = cv2.GaussianBlur(img, (15, 15), 0)
img = img / 255.0 img = img /float( 255.0)
img = self.resize_image(img, img_height_page, img_width_page) img = self.resize_image(img, img_height_page, img_width_page)
label_p_pred = model_page.predict( label_p_pred = model_page.predict(
@ -488,14 +458,14 @@ class textlineerkenner:
img = self.otsu_copy(img) img = self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = cv2.medianBlur(img,5) ##img = cv2.medianBlur(img,5)
# img = cv2.medianBlur(img,5) # img = cv2.medianBlur(img,5)
# img=cv2.bilateralFilter(img,9,75,75) # img=cv2.bilateralFilter(img,9,75,75)
# img=cv2.bilateralFilter(img,9,75,75) # img=cv2.bilateralFilter(img,9,75,75)
img = img / 255.0 img = img / float(255.0)
img_h = img.shape[0] img_h = img.shape[0]
img_w = img.shape[1] img_w = img.shape[1]
@ -648,6 +618,8 @@ class textlineerkenner:
_, thresh = cv2.threshold(imgray, 0, 255, 0) _, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hirarchy = 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)
# commenst_contours=self.filter_contours_area_of_image(thresh,contours,hirarchy,max_area=0.0002,min_area=0.0001) # commenst_contours=self.filter_contours_area_of_image(thresh,contours,hirarchy,max_area=0.0002,min_area=0.0001)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, 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)
@ -697,7 +669,7 @@ class textlineerkenner:
if img.shape[1] < img_width_textline: if img.shape[1] < img_width_textline:
img = cv2.resize(img, (img_height_textline, img.shape[0]), interpolation=cv2.INTER_NEAREST) img = cv2.resize(img, (img_height_textline, img.shape[0]), interpolation=cv2.INTER_NEAREST)
margin = False margin = True
if not margin: if not margin:
width = img_width_textline width = img_width_textline
@ -706,8 +678,8 @@ class textlineerkenner:
img = self.otsu_copy(img) img = self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
# for _ in range(4): # for _ in range(4):
img = cv2.medianBlur(img,5) #img = cv2.medianBlur(img,5)
img = img / 255.0 img = img / float(255.0)
img_h = img.shape[0] img_h = img.shape[0]
img_w = img.shape[1] img_w = img.shape[1]
@ -770,7 +742,7 @@ class textlineerkenner:
img = self.otsu_copy(img) img = self.otsu_copy(img)
img = img.astype(np.uint8) img = img.astype(np.uint8)
img = img / 255.0 img = img /float( 255.0)
img_h = img.shape[0] img_h = img.shape[0]
img_w = img.shape[1] img_w = img.shape[1]
@ -1205,11 +1177,8 @@ class textlineerkenner:
[int(x_max), int(point_down)], [int(x_max), int(point_down)],
[int(x_min), int(point_down)]])) [int(x_min), int(point_down)]]))
mada_new = np.zeros((img_path.shape[0], img_path.shape[1], 3))
mada_new = cv2.fillPoly(mada_new, pts=textline_boxes, color=(255, 255, 255))
mada_new = mada_new.astype(np.uint8) return peaks, textline_boxes_rot
return mada_new, peaks, textline_boxes_rot
def ruturn_rotated_contours(self,slope,img_patch): def ruturn_rotated_contours(self,slope,img_patch):
dst = self.rotate_image(img_patch, slope) dst = self.rotate_image(img_patch, slope)
@ -1226,6 +1195,7 @@ class textlineerkenner:
return contours return contours
def textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind): def textline_contours_postprocessing(self, textline_mask, slope, contour_text_interest, box_ind):
textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255 textline_mask = np.repeat(textline_mask[:, :, np.newaxis], 3, axis=2) * 255
@ -1234,33 +1204,9 @@ class textlineerkenner:
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel) textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_OPEN, kernel)
textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel) textline_mask = cv2.morphologyEx(textline_mask, cv2.MORPH_CLOSE, kernel)
textline_mask = cv2.erode(textline_mask, kernel, iterations=1) textline_mask = cv2.erode(textline_mask, kernel, iterations=1)
imgray = cv2.cvtColor(textline_mask, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
commenst_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=0.01,
min_area=0.003)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.0003)
img_comm = np.zeros(thresh.shape)
img_comm_in = cv2.fillPoly(img_comm, pts=main_contours, color=(255, 255, 255))
img_comm_in = np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
img_comm_in = img_comm_in.astype(np.uint8)
imgray = cv2.cvtColor(img_comm_in, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
contours, hirarchy = cv2.findContours(thresh.copy(), cv2.cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0: try:
dst = self.rotate_image(textline_mask, slope) dst = self.rotate_image(textline_mask, slope)
dst = dst[:, :, 0] dst = dst[:, :, 0]
@ -1289,31 +1235,14 @@ class textlineerkenner:
sep_img, _, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope) _, contours_rotated_clean = self.seperate_lines(dst, contours_text_rot[ind_big_con], slope)
dst = self.rotate_image(sep_img, -slope)
imgray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
found_polygons, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
img_in = np.zeros(textline_mask.shape) except:
img_p_in = cv2.fillPoly(img_in, pts=found_polygons, color=(255, 255, 255))
else:
img_in = np.zeros(textline_mask.shape)
img_p_in = cv2.fillPoly(img_in, pts=commenst_contours, color=(255, 255, 255))
img_p_in = cv2.dilate(img_p_in, kernel, iterations=1)
contours_rotated_clean = [] contours_rotated_clean = []
return img_p_in, contours_rotated_clean return contours_rotated_clean
def textline_contours_to_get_slope_correctly(self, textline_mask, img_patch, contour_interest): def textline_contours_to_get_slope_correctly(self, textline_mask, img_patch, contour_interest):
@ -1338,7 +1267,7 @@ class textlineerkenner:
textline_maskt = textline_mask[:, :, 0] textline_maskt = textline_mask[:, :, 0]
textline_maskt[textline_maskt != 0] = 1 textline_maskt[textline_maskt != 0] = 1
_, peaks_point, _ = self.seperate_lines(textline_maskt, contour_interest, slope_new) peaks_point, _ = self.seperate_lines(textline_maskt, contour_interest, slope_new)
mean_dis = np.mean(np.diff(peaks_point)) mean_dis = np.mean(np.diff(peaks_point))
@ -1514,47 +1443,23 @@ class textlineerkenner:
def get_slopes_for_each_text_region(self, contours,textline_mask_tot): def get_slopes_for_each_text_region(self, contours,textline_mask_tot):
# first lets find slope for biggest patch of text region (slope of deskewing)
#index_max_area = np.argmax(self.area_of_cropped)
#img_int_p=self.all_text_region_raw[index_max_area]
#textline_con=self.return_contours_of_image(img_int_p)
#textline_con_fil=self.filter_contours_area_of_image(img_int_p,textline_con,denoised,max_area=1,min_area=0.0008)
#y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)
#sigma_des=int( y_diff_mean * (4./40.0) )
#refrence : sigma =4 for diff=40
#if sigma_des<1:
# sigma_des=1
#img_int_p[img_int_p>0]=1
slope_biggest=0#self.return_deskew_slop(img_int_p,sigma_des) slope_biggest=0#self.return_deskew_slop(img_int_p,sigma_des)
# this was the old method. By now it seems the new one works better. By the way more tests are required.
#slope_biggest = self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[index_max_area],
# denoised, contours[index_max_area])
#if np.abs(slope_biggest) > 20:
# slope_biggest = 0
self.slopes = [] self.slopes = []
for mv in range(len(self.boxes)): for mv in range(len(self.boxes)):
textline_mask_tot = cv2.erode(textline_mask_tot, self.kernel, iterations=1)
crop_img, _ = self.crop_image_inside_box(self.boxes[mv], crop_img, _ = self.crop_image_inside_box(self.boxes[mv],
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2)) np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
crop_img=crop_img[:,:,0]
crop_img=cv2.erode(crop_img,self.kernel,iterations = 1)
try: try:
denoised=None hierachy=None
textline_con=self.return_contours_of_image(crop_img) textline_con=self.return_contours_of_image(crop_img)
textline_con_fil=self.filter_contours_area_of_image(crop_img,textline_con,denoised,max_area=1,min_area=0.0008) textline_con_fil=self.filter_contours_area_of_image(crop_img,textline_con,hierachy,max_area=1,min_area=0.0008)
y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil) y_diff_mean=self.find_contours_mean_y_diff(textline_con_fil)
sigma_des=int( y_diff_mean * (4./40.0) ) sigma_des=int( y_diff_mean * (4./40.0) )
if sigma_des<1: if sigma_des<1:
@ -1562,10 +1467,7 @@ class textlineerkenner:
crop_img[crop_img>0]=1 crop_img[crop_img>0]=1
slope_corresponding_textregion=self.return_deskew_slop(crop_img,sigma_des) slope_corresponding_textregion=self.return_deskew_slop(crop_img,sigma_des)
#old method
#slope_corresponding_textregion=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
#text_patch_processed=textline_contours_postprocessing(gada)
except: except:
slope_corresponding_textregion=999 slope_corresponding_textregion=999
@ -1703,19 +1605,12 @@ class textlineerkenner:
crop_img, _ = self.crop_image_inside_box(self.boxes[jj], crop_img, _ = self.crop_image_inside_box(self.boxes[jj],
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2)) np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
text_patch_processed, cnt_clean_rot = self.textline_contours_postprocessing(crop_img[:,:,0] cnt_clean_rot = self.textline_contours_postprocessing(crop_img[:,:,0]
, self.slopes[jj], , self.slopes[jj],
contours[jj], self.boxes[jj]) contours[jj], self.boxes[jj])
self.all_text_region_processed.append(text_patch_processed)
text_patch_processed = text_patch_processed.astype(np.uint8)
imgray = cv2.cvtColor(text_patch_processed, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
self.found_polygons, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
self.all_found_texline_polygons.append(cnt_clean_rot) self.all_found_texline_polygons.append(cnt_clean_rot)
def write_into_page_xml(self,contours,page_coord,dir_of_image,order_of_texts , id_of_texts): def write_into_page_xml(self,contours,page_coord,dir_of_image,order_of_texts , id_of_texts):
@ -1973,6 +1868,7 @@ class textlineerkenner:
print( "time needed to get slopes of regions (deskewing) = "+"{0:.2f}".format(t6-t5) ) print( "time needed to get slopes of regions (deskewing) = "+"{0:.2f}".format(t6-t5) )
print( "time needed to implement deskewing = "+"{0:.2f}".format(t7-t6) ) print( "time needed to implement deskewing = "+"{0:.2f}".format(t7-t6) )
@click.command() @click.command()

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