qurator-spk
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sbb_textline_detection
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Merge branch 'master' of code.dev.sbb.berlin:qurator/mono-repo

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pull/1/head
Gerber, Mike 5 years ago
parent 2528573b4f d8e04e3de4
commit f0dd955606
1 changed files with 528 additions and 205 deletions
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qurator/sbb_textline_detector/main.py
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@ -79,7 +79,7 @@ class textlineerkenner:
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(
image.shape[:2]): # and hirarchy[0][jv][3]==-1 :
found_polygons_early.append(
np.array([point for point in polygon.exterior.coords], dtype=np.uint))
np.array([ [point] for point in polygon.exterior.coords], dtype=np.uint))
jv += 1
return found_polygons_early
@ -414,15 +414,11 @@ class textlineerkenner:
img_width_page = model_page.layers[len(model_page.layers) - 1].output_shape[2]
n_classes_page = model_page.layers[len(model_page.layers) - 1].output_shape[3]
img_org_copy = self.image.copy()
img = self.otsu_copy(self.image)
for ii in range(60):
img = cv2.GaussianBlur(img, (15, 15), 0)
# img=self.image.astype(np.uint8)
# img = cv2.medianBlur(img,5)
img = img / 255.0
img = self.resize_image(img, img_height_page, img_width_page)
@ -432,19 +428,14 @@ class textlineerkenner:
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = self.color_images(seg, n_classes_page)
imgs = self.resize_image(seg_color, self.image.shape[0], self.image.shape[1])
imgs = seg_color # /np.max(seg_color)*255#np.repeat(seg_color[:, :, np.newaxis], 3, axis=2)
imgs = self.resize_image(imgs, img_org_copy.shape[0], img_org_copy.shape[1])
# plt.imshow(imgs*255)
# plt.show()
imgs = imgs.astype(np.uint8)
imgray = cv2.cvtColor(imgs, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(imgray, 0, 255, 0)
thresh = cv2.dilate(thresh, self.kernel, iterations=30)
thresh = cv2.dilate(thresh, self.kernel, iterations=3)
contours, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnt_size = np.array([cv2.contourArea(contours[j]) for j in range(len(contours))])
@ -455,11 +446,23 @@ class textlineerkenner:
box = [x, y, w, h]
croped_page, page_coord = self.crop_image_inside_box(box, img_org_copy)
croped_page, page_coord = self.crop_image_inside_box(box, self.image)
self.cont_page=[]
self.cont_page.append( np.array( [ [ page_coord[2] , page_coord[0] ] ,
[ page_coord[3] , page_coord[0] ] ,
[ page_coord[3] , page_coord[1] ] ,
[ page_coord[2] , page_coord[1] ]] ) )
session_page.close()
del model_page
del session_page
del self.image
del seg
del contours
del thresh
del imgs
del img
gc.collect()
return croped_page, page_coord
@ -477,7 +480,7 @@ class textlineerkenner:
height = img_height_region
# offset=int(.1*width)
offset = int(0.03 * width)
offset = int(0.1 * width)
width_mid = width - 2 * offset
height_mid = height - 2 * offset
@ -535,6 +538,8 @@ class textlineerkenner:
index_y_u = img_h
index_y_d = img_h - height
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_region.predict(
@ -544,6 +549,71 @@ class textlineerkenner:
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i==0 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - offset, 0:seg_color.shape[1] - offset, :]
seg = seg[0:seg.shape[0] - offset, 0:seg.shape[1] - offset]
mask_true[index_y_d + 0:index_y_u - offset, index_x_d + 0:index_x_u - offset] = seg
prediction_true[index_y_d + 0:index_y_u - offset, index_x_d + 0:index_x_u - offset,
:] = seg_color
elif i==nxf-1 and j==nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - 0, offset:seg_color.shape[1] - 0, :]
seg = seg[offset:seg.shape[0] - 0, offset:seg.shape[1] - 0]
mask_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - 0] = seg
prediction_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - 0,
:] = seg_color
elif i==0 and j==nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - 0, 0:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - 0, 0:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - 0, index_x_d + 0:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - 0, index_x_d + 0:index_x_u - offset,
:] = seg_color
elif i==nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - offset, offset:seg_color.shape[1] - 0, :]
seg = seg[0:seg.shape[0] - offset, offset:seg.shape[1] - 0]
mask_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - 0] = seg
prediction_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - 0,
:] = seg_color
elif i==0 and j!=0 and j!=nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - offset, 0:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - offset, 0:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - offset, index_x_d + 0:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - offset, index_x_d + 0:index_x_u - offset,
:] = seg_color
elif i==nxf-1 and j!=0 and j!=nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - offset, offset:seg_color.shape[1] - 0, :]
seg = seg[offset:seg.shape[0] - offset, offset:seg.shape[1] - 0]
mask_true[index_y_d + offset:index_y_u - offset, index_x_d + offset:index_x_u - 0] = seg
prediction_true[index_y_d + offset:index_y_u - offset, index_x_d + offset:index_x_u - 0,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - offset, offset:seg_color.shape[1] - offset, :]
seg = seg[0:seg.shape[0] - offset, offset:seg.shape[1] - offset]
mask_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - offset] = seg
prediction_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - offset,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - 0, offset:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - 0, offset:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - offset,
:] = seg_color
else:
seg_color = seg_color[offset:seg_color.shape[0] - offset, offset:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - offset, offset:seg.shape[1] - offset]
@ -568,7 +638,7 @@ class textlineerkenner:
image = cv2.morphologyEx(image, cv2.MORPH_OPEN, self.kernel)
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, self.kernel)
# image = cv2.erode(image,self.kernel,iterations = 3)
#image = cv2.erode(image,self.kernel,iterations = 2)
# image = cv2.dilate(image,self.kernel,iterations = 3)
@ -579,7 +649,7 @@ class textlineerkenner:
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)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.0001)
main_contours = self.filter_contours_area_of_image(thresh, contours, hirarchy, max_area=1, min_area=0.00001)
img_comm = np.zeros(thresh.shape)
img_comm_in = cv2.fillPoly(img_comm, pts=main_contours, color=(255, 255, 255))
@ -606,12 +676,11 @@ class textlineerkenner:
return boxes, contours_new
def get_all_image_patches_based_on_text_regions(self, boxes, image_page):
self.all_text_images = []
self.all_box_coord = []
self.all_box_coord=[]
for jk in range(len(boxes)):
crop_img, crop_coor = self.crop_image_inside_box(boxes[jk], image_page)
self.all_text_images.append(crop_img)
crop_img,crop_coor=self.crop_image_inside_box(boxes[jk],image_page)
self.all_box_coord.append(crop_coor)
del crop_img
def textline_contours(self, img):
model_textline, session_textline = self.start_new_session_and_model(self.model_textline_dir)
@ -627,7 +696,7 @@ class textlineerkenner:
if img.shape[1] < img_width_textline:
img = cv2.resize(img, (img_height_textline, img.shape[0]), interpolation=cv2.INTER_NEAREST)
margin = False
margin = True
if not margin:
width = img_width_textline
@ -684,6 +753,150 @@ class textlineerkenner:
y_predi = mask_true
y_predi = cv2.resize(y_predi, (img_org.shape[1], img_org.shape[0]), interpolation=cv2.INTER_NEAREST)
if margin:
width = img_width_textline
height = img_height_textline
# offset=int(.1*width)
offset = int(0.1 * width)
width_mid = width - 2 * offset
height_mid = height - 2 * offset
img = self.otsu_copy(img)
img = img.astype(np.uint8)
img = img / 255.0
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + width # (i+1)*width
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + width # (i+1)*width
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + height # (j+1)*height
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + height # (j+1)*height
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - width
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - height
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model_textline.predict(
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]))
seg = np.argmax(label_p_pred, axis=3)[0]
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i==0 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - offset, 0:seg_color.shape[1] - offset, :]
seg = seg[0:seg.shape[0] - offset, 0:seg.shape[1] - offset]
mask_true[index_y_d + 0:index_y_u - offset, index_x_d + 0:index_x_u - offset] = seg
prediction_true[index_y_d + 0:index_y_u - offset, index_x_d + 0:index_x_u - offset,
:] = seg_color
elif i==nxf-1 and j==nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - 0, offset:seg_color.shape[1] - 0, :]
seg = seg[offset:seg.shape[0] - 0, offset:seg.shape[1] - 0]
mask_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - 0] = seg
prediction_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - 0,
:] = seg_color
elif i==0 and j==nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - 0, 0:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - 0, 0:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - 0, index_x_d + 0:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - 0, index_x_d + 0:index_x_u - offset,
:] = seg_color
elif i==nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - offset, offset:seg_color.shape[1] - 0, :]
seg = seg[0:seg.shape[0] - offset, offset:seg.shape[1] - 0]
mask_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - 0] = seg
prediction_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - 0,
:] = seg_color
elif i==0 and j!=0 and j!=nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - offset, 0:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - offset, 0:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - offset, index_x_d + 0:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - offset, index_x_d + 0:index_x_u - offset,
:] = seg_color
elif i==nxf-1 and j!=0 and j!=nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - offset, offset:seg_color.shape[1] - 0, :]
seg = seg[offset:seg.shape[0] - offset, offset:seg.shape[1] - 0]
mask_true[index_y_d + offset:index_y_u - offset, index_x_d + offset:index_x_u - 0] = seg
prediction_true[index_y_d + offset:index_y_u - offset, index_x_d + offset:index_x_u - 0,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - offset, offset:seg_color.shape[1] - offset, :]
seg = seg[0:seg.shape[0] - offset, offset:seg.shape[1] - offset]
mask_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - offset] = seg
prediction_true[index_y_d + 0:index_y_u - offset, index_x_d + offset:index_x_u - offset,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==nyf-1:
seg_color = seg_color[offset:seg_color.shape[0] - 0, offset:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - 0, offset:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - 0, index_x_d + offset:index_x_u - offset,
:] = seg_color
else:
seg_color = seg_color[offset:seg_color.shape[0] - offset, offset:seg_color.shape[1] - offset, :]
seg = seg[offset:seg.shape[0] - offset, offset:seg.shape[1] - offset]
mask_true[index_y_d + offset:index_y_u - offset, index_x_d + offset:index_x_u - offset] = seg
prediction_true[index_y_d + offset:index_y_u - offset, index_x_d + offset:index_x_u - offset,
:] = seg_color
y_predi = mask_true.astype(np.uint8)
session_textline.close()
del model_textline
@ -698,6 +911,7 @@ class textlineerkenner:
for jk in range(len(boxes)):
crop_img, crop_coor = self.crop_image_inside_box(boxes[jk],
np.repeat(textline_mask_tot[:, :, np.newaxis], 3, axis=2))
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])
@ -803,6 +1017,15 @@ class textlineerkenner:
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)],
@ -833,6 +1056,16 @@ class textlineerkenner:
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)],
@ -882,6 +1115,15 @@ class textlineerkenner:
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)],
@ -940,6 +1182,16 @@ class textlineerkenner:
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)],
@ -976,12 +1228,11 @@ class textlineerkenner:
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=0.003)
# interior_contours=self.filter_contours_area_of_image_interiors(thresh,contours,hirarchy,max_area=1,min_area=0)
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=cv2.fillPoly(img_comm, pts =interior_contours, color=(0,0,0))
img_comm_in = np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
img_comm_in = img_comm_in.astype(np.uint8)
@ -1018,7 +1269,7 @@ class textlineerkenner:
contour_text_copy = contour_text_interest.copy()
contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
0] # np.min(contour_text_interest_copy[:,0,0])
0]
contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
img_contour = np.zeros((box_ind[3], box_ind[2], 3))
@ -1026,7 +1277,6 @@ class textlineerkenner:
img_contour_rot = self.rotate_image(img_contour, slope)
# img_comm_in=np.repeat(img_comm_in[:, :, np.newaxis], 3, axis=2)
img_contour_rot = img_contour_rot.astype(np.uint8)
imgrayrot = cv2.cvtColor(img_contour_rot, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
@ -1081,9 +1331,7 @@ class textlineerkenner:
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=0.003)
# interior_contours=self.filter_contours_area_of_image_interiors(thresh,contours,hirarchy,max_area=1,min_area=0)
textline_maskt = textline_mask[:, :, 0]
textline_maskt[textline_maskt != 0] = 1
@ -1091,10 +1339,8 @@ class textlineerkenner:
_, peaks_point, _ = self.seperate_lines(textline_maskt, contour_interest, slope_new)
mean_dis = np.mean(np.diff(peaks_point))
# mean_dis=np.median(np.diff(peaks_point))
len_x = thresh.shape[1]
# print(len_x,mean_dis,'x')
slope_lines = []
contours_slope_new = []
@ -1106,7 +1352,6 @@ class textlineerkenner:
yminh = np.min(main_contours[kk][:, 1])
ymaxh = np.max(main_contours[kk][:, 1])
# print(xminh,xmaxh ,yminh,ymaxh,ymaxh-yminh)
if ymaxh - yminh <= mean_dis and (
xmaxh - xminh) >= 0.3 * len_x: # xminh>=0.05*len_x and xminh<=0.4*len_x and xmaxh<=0.95*len_x and xmaxh>=0.6*len_x:
@ -1127,41 +1372,206 @@ class textlineerkenner:
slope = 0
return slope
def return_contours_of_image(self,image_box_tabels_1):
image_box_tabels=np.repeat(image_box_tabels_1[:, :, np.newaxis], 3, axis=2)
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,hierachy=cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
return contours
def find_contours_mean_y_diff(self,contours_main):
M_main=[cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cy_main=[(M_main[j]['m01']/(M_main[j]['m00']+1e-32)) for j in range(len(M_main))]
return np.mean( np.diff( np.sort( np.array(cy_main) ) ) )
def isNaN(self,num):
return num != num
def find_num_col(self,regions_without_seperators,sigma_,multiplier=3.8 ):
regions_without_seperators_0=regions_without_seperators[:,:].sum(axis=1)
meda_n_updown=regions_without_seperators_0[len(regions_without_seperators_0)::-1]
first_nonzero=(next((i for i, x in enumerate(regions_without_seperators_0) if x), 0))
last_nonzero=(next((i for i, x in enumerate(meda_n_updown) if x), 0))
last_nonzero=len(regions_without_seperators_0)-last_nonzero
y=regions_without_seperators_0#[first_nonzero:last_nonzero]
y_help=np.zeros(len(y)+20)
y_help[10:len(y)+10]=y
x=np.array( range(len(y)) )
zneg_rev=-y_help+np.max(y_help)
zneg=np.zeros(len(zneg_rev)+20)
zneg[10:len(zneg_rev)+10]=zneg_rev
z=gaussian_filter1d(y, sigma_)
zneg= gaussian_filter1d(zneg, sigma_)
peaks_neg, _ = find_peaks(zneg, height=0)
peaks, _ = find_peaks(z, height=0)
peaks_neg=peaks_neg-10-10
last_nonzero=last_nonzero-0#100
first_nonzero=first_nonzero+0#+100
peaks_neg=peaks_neg[(peaks_neg>first_nonzero) & (peaks_neg<last_nonzero)]
peaks=peaks[(peaks>.06*regions_without_seperators.shape[1]) & (peaks<0.94*regions_without_seperators.shape[1])]
interest_pos=z[peaks]
interest_pos=interest_pos[interest_pos>10]
interest_neg=z[peaks_neg]
if interest_neg[0]<0.1:
interest_neg=interest_neg[1:]
if interest_neg[len(interest_neg)-1]<0.1:
interest_neg=interest_neg[:len(interest_neg)-1]
min_peaks_pos=np.min(interest_pos)
min_peaks_neg=0#np.min(interest_neg)
dis_talaei=(min_peaks_pos-min_peaks_neg)/multiplier
grenze=min_peaks_pos-dis_talaei#np.mean(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])-np.std(y[peaks_neg[0]:peaks_neg[len(peaks_neg)-1]])/2.0
interest_neg_fin=interest_neg#[(interest_neg<grenze)]
peaks_neg_fin=peaks_neg#[(interest_neg<grenze)]
interest_neg_fin=interest_neg#[(interest_neg<grenze)]
num_col=(len(interest_neg_fin))+1
p_l=0
p_u=len(y)-1
p_m=int(len(y)/2.)
p_g_l=int(len(y)/3.)
p_g_u=len(y)-int(len(y)/3.)
diff_peaks=np.abs( np.diff(peaks_neg_fin) )
diff_peaks_annormal=diff_peaks[diff_peaks<30]
return interest_neg_fin
def return_deskew_slop(self,img_patch_org,sigma_des):
img_int=np.zeros((img_patch_org.shape[0],img_patch_org.shape[1]))
img_int[:,:]=img_patch_org[:,:]#img_patch_org[:,:,0]
img_resized=np.zeros((int( img_int.shape[0]*(1.2) ) , int( img_int.shape[1]*(1.2) ) ))
img_resized[ int( img_int.shape[0]*(.1)):int( img_int.shape[0]*(.1))+img_int.shape[0] , int( img_int.shape[1]*(.1)):int( img_int.shape[1]*(.1))+img_int.shape[1] ]=img_int[:,:]
angels=np.linspace(-4,4,60)
res=[]
index_cor=[]
indexer=0
for rot in angels:
img_rot=self.rotate_image(img_resized,rot)
img_rot[img_rot!=0]=1
res_me=np.mean(self.find_num_col(img_rot,sigma_des,200.3 ))
if self.isNaN(res_me):
pass
else:
res.append( res_me )
index_cor.append(indexer)
indexer=indexer+1
res=np.array(res)
arg_int=np.argmin(res)
arg_fin=index_cor[arg_int]
ang_int=angels[arg_fin]
img_rot=self.rotate_image(img_resized,ang_int)
img_rot[img_rot!=0]=1
return ang_int
def get_slopes_for_each_text_region(self, contours):
# first let find the slop for biggest patch of text region
# first lets find slope for biggest patch of text region (slope of deskewing)
denoised=None
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)
denoised = cv2.blur(self.all_text_images[index_max_area], (5, 5)) # otsu_copy(crop_img)#
denoised = cv2.medianBlur(denoised, 5) # cv2.GaussianBlur(crop_img, (5, 5), 0)
denoised = cv2.GaussianBlur(denoised, (5, 5), 0)
denoised = self.otsu_copy(denoised)
denoised = denoised.astype(np.uint8)
slope_biggest = self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[index_max_area],
denoised, contours[index_max_area])
sigma_des=int( y_diff_mean * (4./40.0) )
#refrence : sigma =4 for diff=40
if sigma_des<1:
sigma_des=1
if np.abs(slope_biggest) > 2.5:
img_int_p[img_int_p>0]=1
slope_biggest=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 = []
for mv in range(len(self.all_text_images)):
denoised = cv2.blur(self.all_text_images[mv], (5, 5)) # otsu_copy(crop_img)#
denoised = cv2.medianBlur(denoised, 5) # cv2.GaussianBlur(crop_img, (5, 5), 0)
denoised = cv2.GaussianBlur(denoised, (5, 5), 0)
denoised = self.otsu_copy(denoised)
denoised = denoised.astype(np.uint8)
slope_for_all = self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv], denoised,
contours[mv])
# text_patch_processed=textline_contours_postprocessing(gada)
if np.abs(slope_for_all) > 2.5 and slope_for_all != 999:
slope_for_all = 0
elif slope_for_all == 999:
slope_for_all = slope_biggest
for mv in range(len(self.all_text_region_raw)):
img_int_p=self.all_text_region_raw[mv]
try:
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) )
if sigma_des<1:
sigma_des=1
img_int_p[img_int_p>0]=1
slope_for_all=self.return_deskew_slop(img_int_p,sigma_des)
#old method
#slope_for_all=self.textline_contours_to_get_slope_correctly(self.all_text_region_raw[mv],denoised,contours[mv])
#text_patch_processed=textline_contours_postprocessing(gada)
except:
slope_for_all=999
if np.abs(slope_for_all)>12.5 and slope_for_all!=999:
slope_for_all=slope_biggest
elif slope_for_all==999:
slope_for_all=slope_biggest
self.slopes.append(slope_for_all)
def order_of_regions(self, textline_mask,contours_main):
mada_n=textline_mask.sum(axis=1)
y=mada_n[:]
@ -1202,7 +1612,6 @@ class textlineerkenner:
y_min_main=np.array([np.min(contours_main[j][:,0,1]) for j in range(len(contours_main))])
y_max_main=np.array([np.max(contours_main[j][:,0,1]) for j in range(len(contours_main))])
#print(contours_main[0],np.shape(contours_main[0]),contours_main[0][:,0,0])
@ -1232,9 +1641,7 @@ class textlineerkenner:
matrix_of_orders[:len_main,4]=np.array( range( len_main ) )
#matrix_of_orders[len_main:,4]=np.array( range( len_head ) )
#print(matrix_of_orders)
peaks_neg_new=[]
@ -1284,21 +1691,13 @@ class textlineerkenner:
self.all_text_region_processed = []
self.all_found_texline_polygons = []
for jj in range(len(self.all_text_images)):
# print(all_text_images[jj][0,0,0],np.unique(all_text_images[jj][:,:,0]))
###gada=self.all_text_images[jj][:,:,0]
###gada=(gada[:,:]==0)*1
# print(gada[0,0])
denoised = cv2.blur(self.all_text_images[jj], (5, 5)) # otsu_copy(crop_img)#
denoised = cv2.medianBlur(denoised, 5) # cv2.GaussianBlur(crop_img, (5, 5), 0)
denoised = cv2.GaussianBlur(denoised, (5, 5), 0)
denoised = self.otsu_copy(denoised)
denoised = denoised.astype(np.uint8)
denoised=None
for jj in range(len(self.all_text_region_raw)):
text_patch_processed, cnt_clean_rot = self.textline_contours_postprocessing(self.all_text_region_raw[jj]
, denoised, self.slopes[jj],
contours[jj], boxes[jj])
# text_patch_processed=textline_contours_postprocessing(gada)
self.all_text_region_processed.append(text_patch_processed)
text_patch_processed = text_patch_processed.astype(np.uint8)
@ -1307,12 +1706,8 @@ class textlineerkenner:
_, thresh = cv2.threshold(imgray, 0, 255, 0)
self.found_polygons, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
####all_found_texline_polygons.append(found_polygons)cnt_clean_rot
self.all_found_texline_polygons.append(cnt_clean_rot)
# img_v=np.zeros(text_patch_processed.shape)
# img_v=cv2.fillPoly(img_v, pts =found_polygons, color=(255,255,255))
# sumi=np.sum(np.sum(self.all_text_images[jj],axis=2),axis=1)
def write_into_page_xml(self,contours,page_coord,dir_of_image,order_of_texts , id_of_texts):
@ -1325,14 +1720,13 @@ class textlineerkenner:
data.set('xmlns',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xmlns:xsi',"http://www.w3.org/2001/XMLSchema-instance")
data.set('xsi:schemaLocation',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
#data.set('http',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2018-07-15/pagecontent.xsd")
metadata=ET.SubElement(data,'Metadata')
author=ET.SubElement(metadata, 'Creator')
author.text = 'Vahid'
author.text = 'SBB_QURATOR'
created=ET.SubElement(metadata, 'Created')
@ -1353,26 +1747,25 @@ class textlineerkenner:
page.set('textLineOrder',"top-to-bottom" )
"""
page_print_sub=ET.SubElement(page, 'PrintSpace')
coord_page = ET.SubElement(page_print_sub, 'Coords')
points_page_print=''
for lmm in range(len(cont_page[0])):
if len(cont_page[0][lmm])==2:
points_page_print=points_page_print+str( int( (cont_page[0][lmm][0])/self.scale_x ) )
for lmm in range(len(self.cont_page[0])):
if len(self.cont_page[0][lmm])==2:
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][0])/self.scale_x ) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int( (cont_page[0][lmm][1])/self.scale_y ) )
points_page_print=points_page_print+str( int( (self.cont_page[0][lmm][1])/self.scale_y ) )
else:
points_page_print=points_page_print+str( int((cont_page[0][lmm][0][0])/self.scale_x) )
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][0])/self.scale_x) )
points_page_print=points_page_print+','
points_page_print=points_page_print+str( int((cont_page[0][lmm][0][1])/self.scale_y) )
points_page_print=points_page_print+str( int((self.cont_page[0][lmm][0][1])/self.scale_y) )
if lmm<(len(cont_page[0])-1):
if lmm<(len(self.cont_page[0])-1):
points_page_print=points_page_print+' '
#print(points_co)
coord_page.set('points',points_page_print)
"""
if len(contours)>0:
region_order=ET.SubElement(page, 'ReadingOrder')
@ -1477,135 +1870,65 @@ class textlineerkenner:
tree = ET.ElementTree(data)
tree.write(os.path.join(self.dir_out, self.f_name) + ".xml")
"""
def write_into_page_xml(self, contours, page_coord):
found_polygons_text_region = contours
data = ET.Element('PcGts')
data.set('xmlns', "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
data.set('xmlns:xsi', "http://www.w3.org/2001/XMLSchema-instance")
data.set('xsi:schemaLocation', "http://schema.primaresearch.org/PAGE/gts/pagecontent/2017-07-15")
# data.set('http',"http://schema.primaresearch.org/PAGE/gts/pagecontent/2018-07-15/pagecontent.xsd")
metadata = ET.SubElement(data, 'Metadata')
author = ET.SubElement(metadata, 'Creator')
author.text = 'Vahid'
created = ET.SubElement(metadata, 'Created')
created.text = '2019-06-17T18:15:12'
changetime = ET.SubElement(metadata, 'LastChange')
changetime.text = '2019-06-17T18:15:12'
page = ET.SubElement(data, 'Page')
page.set('imageFilename', self.image_dir)
page.set('imageHeight', str(self.height_org))
page.set('imageWidth', str(self.width_org))
id_indexer = 0
for mm in range(len(found_polygons_text_region)):
textregion = ET.SubElement(page, 'TextRegion')
textregion.set('id', 'r' + str(id_indexer))
id_indexer += 1
if mm == 0:
textregion.set('type', 'heading')
else:
textregion.set('type', 'paragraph')
coord_text = ET.SubElement(textregion, 'Coords')
points_co = ''
for lmm in range(len(found_polygons_text_region[mm])):
if len(found_polygons_text_region[mm][lmm]) == 2:
points_co = points_co + str(
int((found_polygons_text_region[mm][lmm][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ','
points_co = points_co + str(
int((found_polygons_text_region[mm][lmm][1] + page_coord[0]) / self.scale_y))
else:
points_co = points_co + str(
int((found_polygons_text_region[mm][lmm][0][0] + page_coord[2]) / self.scale_x))
points_co = points_co + ','
points_co = points_co + str(
int((found_polygons_text_region[mm][lmm][0][1] + page_coord[0]) / self.scale_y))
if lmm < (len(found_polygons_text_region[mm]) - 1):
points_co = points_co + ' '
# print(points_co)
coord_text.set('points', points_co)
def run(self):
for j in range(len(self.all_found_texline_polygons[mm])):
#get image and sclaes, then extract the page of scanned image
self.get_image_and_scales()
image_page,page_coord=self.extract_page()
textline = ET.SubElement(textregion, 'TextLine')
##########
K.clear_session()
gc.collect()
textline.set('id', 'l' + str(id_indexer))
# extract text regions and corresponding contours and surrounding box
text_regions=self.extract_text_regions(image_page)
boxes,contours=self.get_text_region_contours_and_boxes(text_regions)
id_indexer += 1
##########
K.clear_session()
gc.collect()
coord = ET.SubElement(textline, 'Coords')
if len(contours)>0:
texteq = ET.SubElement(textline, 'TextEquiv')
self.get_all_image_patches_based_on_text_regions(boxes,image_page)
uni = ET.SubElement(texteq, 'Unicode')
uni.text = ' '
##########
gc.collect()
# points = ET.SubElement(coord, 'Points')
# extracting textlines using segmentation
textline_mask_tot=self.textline_contours(image_page)
points_co = ''
for l in range(len(self.all_found_texline_polygons[mm][j])):
# point = ET.SubElement(coord, 'Point')
# point.set('x',str(found_polygons[j][l][0]))
# point.set('y',str(found_polygons[j][l][1]))
if len(self.all_found_texline_polygons[mm][j][l]) == 2:
points_co = points_co + str(int((self.all_found_texline_polygons[mm][j][l][0] + page_coord[2]
+ self.all_box_coord[mm][2]) / self.scale_x))
points_co = points_co + ','
points_co = points_co + str(int((self.all_found_texline_polygons[mm][j][l][1] + page_coord[0]
+ self.all_box_coord[mm][0]) / self.scale_y))
else:
points_co = points_co + str(int((self.all_found_texline_polygons[mm][j][l][0][0] + page_coord[2]
+ self.all_box_coord[mm][2]) / self.scale_x))
points_co = points_co + ','
points_co = points_co + str(int((self.all_found_texline_polygons[mm][j][l][0][1] + page_coord[0]
+ self.all_box_coord[mm][0]) / self.scale_y))
##########
K.clear_session()
gc.collect()
if l < (len(self.all_found_texline_polygons[mm][j]) - 1):
points_co = points_co + ' '
# print(points_co)
coord.set('points', points_co)
# get orders of each textregion. This method by now only works for one column documents.
indexes_sorted, matrix_of_orders=self.order_of_regions(textline_mask_tot,contours)
order_of_texts, id_of_texts=self.order_and_id_of_texts(contours ,matrix_of_orders ,indexes_sorted )
texteqreg = ET.SubElement(textregion, 'TextEquiv')
##########
gc.collect()
unireg = ET.SubElement(texteqreg, 'Unicode')
unireg.text = ' '
tree = ET.ElementTree(data)
tree.write(os.path.join(self.dir_out, self.f_name) + ".xml")
"""
# just get the textline result for each box of text regions
self.get_textlines_for_each_textregions(textline_mask_tot,boxes)
def run(self):
self.get_image_and_scales()
image_page,page_coord=self.extract_page()
text_regions=self.extract_text_regions(image_page)
boxes,contours=self.get_text_region_contours_and_boxes(text_regions)
##########
gc.collect()
if len(contours)>0:
self.get_all_image_patches_based_on_text_regions(boxes,image_page)
textline_mask_tot=self.textline_contours(image_page)
# calculate the slope for deskewing for each box of text region.
self.get_slopes_for_each_text_region(contours)
indexes_sorted, matrix_of_orders=self.order_of_regions(textline_mask_tot,contours)
order_of_texts, id_of_texts=self.order_and_id_of_texts(contours ,matrix_of_orders ,indexes_sorted )
##########
gc.collect()
self.get_textlines_for_each_textregions(textline_mask_tot,boxes)
self.get_slopes_for_each_text_region(contours)
# do deskewing for each box of text region.
self.deskew_textline_patches(contours, boxes)
##########
gc.collect()
else:
contours=[]
order_of_texts=None

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