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eynollah/sbb_newspapers_org_image/utils.py

2120 lines
87 KiB
Python

import numpy as np
from shapely import geometry
import cv2
import imutils
def filter_contours_area_of_image(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
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 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(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
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 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))
jv += 1
return found_polygons_early
def filter_contours_area_of_image_tables(image, contours, hirarchy, max_area, min_area):
found_polygons_early = list()
jv = 0
for c in contours:
if len(c) < 3: # A polygon cannot have less than 3 points
continue
polygon = geometry.Polygon([point[0] for point in c])
# area = cv2.contourArea(c)
area = polygon.area
##print(np.prod(thresh.shape[:2]))
# Check that polygon has area greater than minimal area
# print(hirarchy[0][jv][3],hirarchy )
if area >= min_area * np.prod(image.shape[:2]) and area <= max_area * np.prod(image.shape[:2]): # and 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.int32))
jv += 1
return found_polygons_early
def resize_image(img_in, input_height, input_width):
return cv2.resize(img_in, (input_width, input_height), interpolation=cv2.INTER_NEAREST)
def rotatedRectWithMaxArea(w, h, angle):
if w <= 0 or h <= 0:
return 0, 0
width_is_longer = w >= h
side_long, side_short = (w, h) if width_is_longer else (h, w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2.0 * sin_a * cos_a * side_long or abs(sin_a - cos_a) < 1e-10:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5 * side_short
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a * cos_a - sin_a * sin_a
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
return wr, hr
def rotate_max_area_new(image, rotated, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2]
def rotation_image_new(img, thetha):
rotated = imutils.rotate(img, thetha)
return rotate_max_area_new(img, rotated, thetha)
def rotate_image(img_patch, slope):
(h, w) = img_patch.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, slope, 1.0)
return cv2.warpAffine(img_patch, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
def rotyate_image_different( img, slope):
# img = cv2.imread('images/input.jpg')
num_rows, num_cols = img.shape[:2]
rotation_matrix = cv2.getRotationMatrix2D((num_cols / 2, num_rows / 2), slope, 1)
img_rotation = cv2.warpAffine(img, rotation_matrix, (num_cols, num_rows))
return img_rotation
def rotate_max_area(image, rotated, rotated_textline, rotated_layout, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2]
def rotation_not_90_func(img, textline, text_regions_p_1, thetha):
rotated = imutils.rotate(img, thetha)
rotated_textline = imutils.rotate(textline, thetha)
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
return rotate_max_area(img, rotated, rotated_textline, rotated_layout, thetha)
def rotation_not_90_func_full_layout(img, textline, text_regions_p_1, text_regions_p_fully, thetha):
rotated = imutils.rotate(img, thetha)
rotated_textline = imutils.rotate(textline, thetha)
rotated_layout = imutils.rotate(text_regions_p_1, thetha)
rotated_layout_full = imutils.rotate(text_regions_p_fully, thetha)
return rotate_max_area_full_layout(img, rotated, rotated_textline, rotated_layout, rotated_layout_full, thetha)
def rotate_max_area_full_layout(image, rotated, rotated_textline, rotated_layout, rotated_layout_full, angle):
wr, hr = rotatedRectWithMaxArea(image.shape[1], image.shape[0], math.radians(angle))
h, w, _ = rotated.shape
y1 = h // 2 - int(hr / 2)
y2 = y1 + int(hr)
x1 = w // 2 - int(wr / 2)
x2 = x1 + int(wr)
return rotated[y1:y2, x1:x2], rotated_textline[y1:y2, x1:x2], rotated_layout[y1:y2, x1:x2], rotated_layout_full[y1:y2, x1:x2]
def crop_image_inside_box(box, img_org_copy):
image_box = img_org_copy[box[1] : box[1] + box[3], box[0] : box[0] + box[2]]
return image_box, [box[1], box[1] + box[3], box[0], box[0] + box[2]]
def otsu_copy(img):
img_r = np.zeros(img.shape)
img1 = img[:, :, 0]
img2 = img[:, :, 1]
img3 = img[:, :, 2]
# print(img.min())
# print(img[:,:,0].min())
# blur = cv2.GaussianBlur(img,(5,5))
# ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
retval2, threshold2 = cv2.threshold(img2, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
retval3, threshold3 = cv2.threshold(img3, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_r[:, :, 0] = threshold1
img_r[:, :, 1] = threshold1
img_r[:, :, 2] = threshold1
return img_r
def otsu_copy_binary(img):
img_r = np.zeros((img.shape[0], img.shape[1], 3))
img1 = img[:, :, 0]
retval1, threshold1 = cv2.threshold(img1, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_r[:, :, 0] = threshold1
img_r[:, :, 1] = threshold1
img_r[:, :, 2] = threshold1
img_r = img_r / float(np.max(img_r)) * 255
return img_r
def return_bonding_box_of_contours(cnts):
boxes_tot = []
for i in range(len(cnts)):
x, y, w, h = cv2.boundingRect(cnts[i])
box = [x, y, w, h]
boxes_tot.append(box)
return boxes_tot
def find_features_of_lines(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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))])
slope_lines = []
for kk in range(len(contours_main)):
[vx, vy, x, y] = cv2.fitLine(contours_main[kk], cv2.DIST_L2, 0, 0.01, 0.01)
slope_lines.append(((vy / vx) / np.pi * 180)[0])
slope_lines_org = slope_lines
slope_lines = np.array(slope_lines)
slope_lines[(slope_lines < 10) & (slope_lines > -10)] = 0
slope_lines[(slope_lines < -200) | (slope_lines > 200)] = 1
slope_lines[(slope_lines != 0) & (slope_lines != 1)] = 2
dis_x = np.abs(x_max_main - x_min_main)
return slope_lines, dis_x, x_min_main, x_max_main, np.array(cy_main), np.array(slope_lines_org), y_min_main, y_max_main, np.array(cx_main)
def isNaN(num):
return num != num
def return_parent_contours(contours, hierarchy):
contours_parent = [contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1]
return contours_parent
def return_contours_of_interested_region(region_pre_p, pixel, min_area=0.0002):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_area)
return contours_imgs
def boosting_headers_by_longshot_region_segmentation(textregion_pre_p, textregion_pre_np, img_only_text):
textregion_pre_p_org = np.copy(textregion_pre_p)
# 4 is drop capitals
headers_in_longshot = (textregion_pre_np[:, :, 0] == 2) * 1
# headers_in_longshot= ( (textregion_pre_np[:,:,0]==2) | (textregion_pre_np[:,:,0]==1) )*1
textregion_pre_p[:, :, 0][(headers_in_longshot[:, :] == 1) & (textregion_pre_p[:, :, 0] != 4)] = 2
textregion_pre_p[:, :, 0][textregion_pre_p[:, :, 0] == 1] = 0
# textregion_pre_p[:,:,0][( img_only_text[:,:]==1) & (textregion_pre_p[:,:,0]!=7) & (textregion_pre_p[:,:,0]!=2)]=1 # eralier it was so, but by this manner the drop capitals are alse deleted
textregion_pre_p[:, :, 0][(img_only_text[:, :] == 1) & (textregion_pre_p[:, :, 0] != 7) & (textregion_pre_p[:, :, 0] != 4) & (textregion_pre_p[:, :, 0] != 2)] = 1
return textregion_pre_p
def return_contours_of_image(image):
if len(image.shape) == 2:
image = np.repeat(image[:, :, np.newaxis], 3, axis=2)
image = image.astype(np.uint8)
else:
image = image.astype(np.uint8)
imgray = cv2.cvtColor(image, 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, hierachy
def return_contours_of_interested_region_by_min_size(region_pre_p, pixel, min_size=0.00003):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=min_size)
return contours_imgs
def get_textregion_contours_in_org_image(cnts, img, slope_first):
cnts_org = []
# print(cnts,'cnts')
for i in range(len(cnts)):
img_copy = np.zeros(img.shape)
img_copy = cv2.fillPoly(img_copy, pts=[cnts[i]], color=(1, 1, 1))
# plt.imshow(img_copy)
# plt.show()
# print(img.shape,'img')
img_copy = rotation_image_new(img_copy, -slope_first)
##print(img_copy.shape,'img_copy')
# plt.imshow(img_copy)
# plt.show()
img_copy = img_copy.astype(np.uint8)
imgray = cv2.cvtColor(img_copy, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
cont_int, _ = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cont_int[0][:, 0, 0] = cont_int[0][:, 0, 0] + np.abs(img_copy.shape[1] - img.shape[1])
cont_int[0][:, 0, 1] = cont_int[0][:, 0, 1] + np.abs(img_copy.shape[0] - img.shape[0])
# print(np.shape(cont_int[0]))
cnts_org.append(cont_int[0])
# print(cnts_org,'cnts_org')
# sys.exit()
# self.y_shift = np.abs(img_copy.shape[0] - img.shape[0])
# self.x_shift = np.abs(img_copy.shape[1] - img.shape[1])
return cnts_org
def return_contours_of_interested_textline(region_pre_p, pixel):
# pixels of images are identified by 5
if len(region_pre_p.shape) == 3:
cnts_images = (region_pre_p[:, :, 0] == pixel) * 1
else:
cnts_images = (region_pre_p[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=1, min_area=0.000000003)
return contours_imgs
def seperate_lines_vertical_cont(img_patch, contour_text_interest, thetha, box_ind, add_boxes_coor_into_textlines):
kernel = np.ones((5, 5), np.uint8)
pixel = 255
min_area = 0
max_area = 1
if len(img_patch.shape) == 3:
cnts_images = (img_patch[:, :, 0] == pixel) * 1
else:
cnts_images = (img_patch[:, :] == pixel) * 1
cnts_images = cnts_images.astype(np.uint8)
cnts_images = np.repeat(cnts_images[:, :, np.newaxis], 3, axis=2)
imgray = cv2.cvtColor(cnts_images, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(imgray, 0, 255, 0)
contours_imgs, hiearchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours_imgs = return_parent_contours(contours_imgs, hiearchy)
contours_imgs = filter_contours_area_of_image_tables(thresh, contours_imgs, hiearchy, max_area=max_area, min_area=min_area)
cont_final = []
###print(add_boxes_coor_into_textlines,'ikki')
for i in range(len(contours_imgs)):
img_contour = np.zeros((cnts_images.shape[0], cnts_images.shape[1], 3))
img_contour = cv2.fillPoly(img_contour, pts=[contours_imgs[i]], color=(255, 255, 255))
img_contour = img_contour.astype(np.uint8)
img_contour = cv2.dilate(img_contour, kernel, iterations=4)
imgrayrot = cv2.cvtColor(img_contour, cv2.COLOR_BGR2GRAY)
_, threshrot = cv2.threshold(imgrayrot, 0, 255, 0)
contours_text_rot, _ = cv2.findContours(threshrot.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
##contour_text_copy[:, 0, 0] = contour_text_copy[:, 0, 0] - box_ind[
##0]
##contour_text_copy[:, 0, 1] = contour_text_copy[:, 0, 1] - box_ind[1]
##if add_boxes_coor_into_textlines:
##print(np.shape(contours_text_rot[0]),'sjppo')
##contours_text_rot[0][:, 0, 0]=contours_text_rot[0][:, 0, 0] + box_ind[0]
##contours_text_rot[0][:, 0, 1]=contours_text_rot[0][:, 0, 1] + box_ind[1]
cont_final.append(contours_text_rot[0])
##print(cont_final,'nadizzzz')
return None, cont_final
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 / 180.0 * 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=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 * (7.0 / 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])
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[:]
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
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:
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 / 2.0:
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)
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:
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(x_min), int(point_up)])
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
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
x_min_rot1 = x_min_rot1 - x_help
x_max_rot2 = x_max_rot2 - x_help
x_max_rot3 = x_max_rot3 - x_help
x_min_rot4 = x_min_rot4 - x_help
point_up_rot1 = point_up_rot1 - y_help
point_up_rot2 = point_up_rot2 - y_help
point_down_rot3 = point_down_rot3 - y_help
point_down_rot4 = point_down_rot4 - y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
elif len(peaks) < 1:
pass
elif len(peaks) == 1:
distances = [cv2.pointPolygonTest(contour_text_interest_copy, (xv[mj], peaks[0] + first_nonzero), True) for mj in range(len(xv))]
distances = np.array(distances)
xvinside = xv[distances >= 0]
if len(xvinside) == 0:
x_min = x_min_cont
x_max = x_max_cont
else:
x_min = np.min(xvinside) # max(x_min_interest,x_min_cont)
x_max = np.max(xvinside) # min(x_max_interest,x_max_cont)
# x_min = x_min_cont
# x_max = x_max_cont
y_min = y_min_cont
y_max = y_max_cont
p1 = np.dot(rotation_matrix, [int(x_min), int(y_min)])
p2 = np.dot(rotation_matrix, [int(x_max), int(y_min)])
p3 = np.dot(rotation_matrix, [int(x_max), int(y_max)])
p4 = np.dot(rotation_matrix, [int(x_min), int(y_max)])
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
x_min_rot1 = x_min_rot1 - x_help
x_max_rot2 = x_max_rot2 - x_help
x_max_rot3 = x_max_rot3 - x_help
x_min_rot4 = x_min_rot4 - x_help
point_up_rot1 = point_up_rot1 - y_help
point_up_rot2 = point_up_rot2 - y_help
point_down_rot3 = point_down_rot3 - y_help
point_down_rot4 = point_down_rot4 - y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(y_min)], [int(x_max), int(y_min)], [int(x_max), int(y_max)], [int(x_min), int(y_max)]]))
elif len(peaks) == 2:
dis_to_next = np.abs(peaks[1] - peaks[0])
for jj in range(len(peaks)):
if jj == 0:
point_up = 0 # peaks[jj] + first_nonzero - int(1. / 1.7 * dis_to_next)
if point_up < 0:
point_up = 1
point_down = peaks[jj] + first_nonzero + int(1.0 / 1.8 * dis_to_next)
elif jj == 1:
point_down = peaks[jj] + first_nonzero + int(1.0 / 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)
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(x_min), int(point_up)])
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
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
x_min_rot1 = x_min_rot1 - x_help
x_max_rot2 = x_max_rot2 - x_help
x_max_rot3 = x_max_rot3 - x_help
x_min_rot4 = x_min_rot4 - x_help
point_up_rot1 = point_up_rot1 - y_help
point_up_rot2 = point_up_rot2 - y_help
point_down_rot3 = point_down_rot3 - y_help
point_down_rot4 = point_down_rot4 - y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
else:
for jj in range(len(peaks)):
if jj == 0:
dis_to_next = peaks[jj + 1] - peaks[jj]
# point_up=peaks[jj]+first_nonzero-int(1./3*dis_to_next)
point_up = peaks[jj] + first_nonzero - int(1.0 / 1.9 * dis_to_next)
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)
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)
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)
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)
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(x_min), int(point_up)])
p2 = np.dot(rotation_matrix, [int(x_max), int(point_up)])
p3 = np.dot(rotation_matrix, [int(x_max), int(point_down)])
p4 = np.dot(rotation_matrix, [int(x_min), int(point_down)])
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
x_min_rot1 = x_min_rot1 - x_help
x_max_rot2 = x_max_rot2 - x_help
x_max_rot3 = x_max_rot3 - x_help
x_min_rot4 = x_min_rot4 - x_help
point_up_rot1 = point_up_rot1 - y_help
point_up_rot2 = point_up_rot2 - y_help
point_down_rot3 = point_down_rot3 - y_help
point_down_rot4 = point_down_rot4 - y_help
textline_boxes_rot.append(np.array([[int(x_min_rot1), int(point_up_rot1)], [int(x_max_rot2), int(point_up_rot2)], [int(x_max_rot3), int(point_down_rot3)], [int(x_min_rot4), int(point_down_rot4)]]))
textline_boxes.append(np.array([[int(x_min), int(point_up)], [int(x_max), int(point_up)], [int(x_max), int(point_down)], [int(x_min), int(point_down)]]))
return peaks, textline_boxes_rot
def seperate_lines_vertical(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 / 180.0 * 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 = 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 * (7.0 / 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 / 2.0:
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 / 2.0:
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(1.0 / 1.8 * dis_to_next)
elif jj == 1:
point_down = peaks[jj] + first_nonzero + int(1.0 / 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)
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(1.0 / 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)
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)
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)
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)
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 seperate_lines_new_inside_teils2(img_patch, thetha):
(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 / 180.0 * 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=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 * (7.0 / 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)
peaks_new = peaks[:]
peaks_neg_new = peaks_neg[:]
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) < 0.24]
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]
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[:]
except:
pass
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
peaks_neg_true = peaks_neg[:]
peaks_pos_true = peaks[:]
if len(peaks_neg_true) > 0:
peaks_neg_true = np.array(peaks_neg_true)
peaks_neg_true = peaks_neg_true - 20 - 20
# print(peaks_neg_true)
for i in range(len(peaks_neg_true)):
img_patch[peaks_neg_true[i] - 6 : peaks_neg_true[i] + 6, :] = 0
else:
pass
if len(peaks_pos_true) > 0:
peaks_pos_true = np.array(peaks_pos_true)
peaks_pos_true = peaks_pos_true - 20
for i in range(len(peaks_pos_true)):
##img_patch[peaks_pos_true[i]-8:peaks_pos_true[i]+8,:]=1
img_patch[peaks_pos_true[i] - 6 : peaks_pos_true[i] + 6, :] = 1
else:
pass
kernel = np.ones((5, 5), np.uint8)
# img_patch = cv2.erode(img_patch,kernel,iterations = 3)
#######################img_patch = cv2.erode(img_patch,kernel,iterations = 2)
img_patch = cv2.erode(img_patch, kernel, iterations=1)
return img_patch
def filter_small_drop_capitals_from_no_patch_layout(layout_no_patch, layout1):
drop_only = (layout_no_patch[:, :, 0] == 4) * 1
contours_drop, hir_on_drop = return_contours_of_image(drop_only)
contours_drop_parent = return_parent_contours(contours_drop, hir_on_drop)
areas_cnt_text = np.array([cv2.contourArea(contours_drop_parent[j]) for j in range(len(contours_drop_parent))])
areas_cnt_text = areas_cnt_text / float(drop_only.shape[0] * drop_only.shape[1])
contours_drop_parent = [contours_drop_parent[jz] for jz in range(len(contours_drop_parent)) if areas_cnt_text[jz] > 0.001]
areas_cnt_text = [areas_cnt_text[jz] for jz in range(len(areas_cnt_text)) if areas_cnt_text[jz] > 0.001]
contours_drop_parent_final = []
for jj in range(len(contours_drop_parent)):
x, y, w, h = cv2.boundingRect(contours_drop_parent[jj])
# boxes.append([int(x), int(y), int(w), int(h)])
iou_of_box_and_contoure = float(drop_only.shape[0] * drop_only.shape[1]) * areas_cnt_text[jj] / float(w * h) * 100
height_to_weight_ratio = h / float(w)
weigh_to_height_ratio = w / float(h)
if iou_of_box_and_contoure > 60 and weigh_to_height_ratio < 1.2 and height_to_weight_ratio < 2:
map_of_drop_contour_bb = np.zeros((layout1.shape[0], layout1.shape[1]))
map_of_drop_contour_bb[y : y + h, x : x + w] = layout1[y : y + h, x : x + w]
if (((map_of_drop_contour_bb == 1) * 1).sum() / float(((map_of_drop_contour_bb == 5) * 1).sum()) * 100) >= 15:
contours_drop_parent_final.append(contours_drop_parent[jj])
layout_no_patch[:, :, 0][layout_no_patch[:, :, 0] == 4] = 0
layout_no_patch = cv2.fillPoly(layout_no_patch, pts=contours_drop_parent_final, color=(4, 4, 4))
return layout_no_patch
def find_num_col_deskew(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
# print(np.std(z),'np.std(z)np.std(z)np.std(z)')
##plt.plot(z)
##plt.show()
##plt.imshow(regions_without_seperators)
##plt.show()
"""
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]
min_peaks_pos = np.mean(interest_pos)
min_peaks_neg = 0 # np.min(interest_neg)
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
# print(interest_pos)
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)]
"""
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) / 2.0)
p_g_l = int(len(y) / 3.0)
p_g_u = len(y) - int(len(y) / 3.0)
diff_peaks = np.abs(np.diff(peaks_neg_fin))
diff_peaks_annormal = diff_peaks[diff_peaks < 30]
# print(len(interest_neg_fin),np.mean(interest_neg_fin))
return interest_neg_fin, np.std(z)
def return_hor_spliter_by_index_for_without_verticals(peaks_neg_fin_t, x_min_hor_some, x_max_hor_some):
# print(peaks_neg_fin_t,x_min_hor_some,x_max_hor_some)
arg_min_hor_sort = np.argsort(x_min_hor_some)
x_min_hor_some_sort = np.sort(x_min_hor_some)
x_max_hor_some_sort = x_max_hor_some[arg_min_hor_sort]
arg_minmax = np.array(range(len(peaks_neg_fin_t)))
indexer_lines = []
indexes_to_delete = []
indexer_lines_deletions_len = []
indexr_uniq_ind = []
for i in range(len(x_min_hor_some_sort)):
min_h = peaks_neg_fin_t - x_min_hor_some_sort[i]
max_h = peaks_neg_fin_t - x_max_hor_some_sort[i]
min_h[0] = min_h[0] # +20
max_h[len(max_h) - 1] = max_h[len(max_h) - 1] - 20
min_h_neg = arg_minmax[(min_h < 0)]
min_h_neg_n = min_h[min_h < 0]
try:
min_h_neg = [min_h_neg[np.argmax(min_h_neg_n)]]
except:
min_h_neg = []
max_h_neg = arg_minmax[(max_h > 0)]
max_h_neg_n = max_h[max_h > 0]
if len(max_h_neg_n) > 0:
max_h_neg = [max_h_neg[np.argmin(max_h_neg_n)]]
else:
max_h_neg = []
if len(min_h_neg) > 0 and len(max_h_neg) > 0:
deletions = list(range(min_h_neg[0] + 1, max_h_neg[0]))
unique_delets_int = []
# print(deletions,len(deletions),'delii')
if len(deletions) > 0:
for j in range(len(deletions)):
indexes_to_delete.append(deletions[j])
# print(deletions,indexes_to_delete,'badiii')
unique_delets = np.unique(indexes_to_delete)
# print(min_h_neg[0],unique_delets)
unique_delets_int = unique_delets[unique_delets < min_h_neg[0]]
indexer_lines_deletions_len.append(len(deletions))
indexr_uniq_ind.append([deletions])
else:
indexer_lines_deletions_len.append(0)
indexr_uniq_ind.append(-999)
index_line_true = min_h_neg[0] - len(unique_delets_int)
# print(index_line_true)
if index_line_true > 0 and min_h_neg[0] >= 2:
index_line_true = index_line_true
else:
index_line_true = min_h_neg[0]
indexer_lines.append(index_line_true)
if len(unique_delets_int) > 0:
for dd in range(len(unique_delets_int)):
indexes_to_delete.append(unique_delets_int[dd])
else:
indexer_lines.append(-999)
indexer_lines_deletions_len.append(-999)
indexr_uniq_ind.append(-999)
peaks_true = []
for m in range(len(peaks_neg_fin_t)):
if m in indexes_to_delete:
pass
else:
peaks_true.append(peaks_neg_fin_t[m])
return indexer_lines, peaks_true, arg_min_hor_sort, indexer_lines_deletions_len, indexr_uniq_ind
def find_new_features_of_contoures(contours_main):
areas_main = np.array([cv2.contourArea(contours_main[j]) for j in range(len(contours_main))])
M_main = [cv2.moments(contours_main[j]) for j in range(len(contours_main))]
cx_main = [(M_main[j]["m10"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
cy_main = [(M_main[j]["m01"] / (M_main[j]["m00"] + 1e-32)) for j in range(len(M_main))]
try:
x_min_main = np.array([np.min(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0, 1] for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0, 0]) for j in range(len(contours_main))])
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))])
except:
x_min_main = np.array([np.min(contours_main[j][:, 0]) for j in range(len(contours_main))])
argmin_x_main = np.array([np.argmin(contours_main[j][:, 0]) for j in range(len(contours_main))])
x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 0] for j in range(len(contours_main))])
y_corr_x_min_from_argmin = np.array([contours_main[j][argmin_x_main[j], 1] for j in range(len(contours_main))])
x_max_main = np.array([np.max(contours_main[j][:, 0]) for j in range(len(contours_main))])
y_min_main = np.array([np.min(contours_main[j][:, 1]) for j in range(len(contours_main))])
y_max_main = np.array([np.max(contours_main[j][:, 1]) for j in range(len(contours_main))])
# dis_x=np.abs(x_max_main-x_min_main)
return cx_main, cy_main, x_min_main, x_max_main, y_min_main, y_max_main, y_corr_x_min_from_argmin
def find_num_col(regions_without_seperators, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
##plt.plot(regions_without_seperators_0)
##plt.show()
sigma_ = 35 # 70#35
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)
# print(last_nonzero)
# print(isNaN(last_nonzero))
# last_nonzero=0#halalikh
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 - 100
first_nonzero = first_nonzero + 200
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
peaks = peaks[(peaks > 0.06 * regions_without_seperators.shape[1]) & (peaks < 0.94 * regions_without_seperators.shape[1])]
peaks_neg = peaks_neg[(peaks_neg > 370) & (peaks_neg < (regions_without_seperators.shape[1] - 370))]
# print(peaks)
interest_pos = z[peaks]
interest_pos = interest_pos[interest_pos > 10]
# plt.plot(z)
# plt.show()
interest_neg = z[peaks_neg]
min_peaks_pos = np.min(interest_pos)
max_peaks_pos = np.max(interest_pos)
if max_peaks_pos / min_peaks_pos >= 35:
min_peaks_pos = np.mean(interest_pos)
min_peaks_neg = 0 # np.min(interest_neg)
# print(np.min(interest_pos),np.max(interest_pos),np.max(interest_pos)/np.min(interest_pos),'minmax')
# $print(min_peaks_pos)
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
# print(interest_pos)
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
# print(interest_neg,'interest_neg')
# print(grenze,'grenze')
# print(min_peaks_pos,'min_peaks_pos')
# print(dis_talaei,'dis_talaei')
# print(peaks_neg,'peaks_neg')
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
# print(peaks_neg_fin,'peaks_neg_fin')
# print(num_col,'diz')
p_l = 0
p_u = len(y) - 1
p_m = int(len(y) / 2.0)
p_g_l = int(len(y) / 4.0)
p_g_u = len(y) - int(len(y) / 4.0)
if num_col == 3:
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or ((peaks_neg_fin[0] + 200) < p_m and peaks_neg_fin[1] < p_m) or ((peaks_neg_fin[0] - 200) > p_m and peaks_neg_fin[1] > p_m):
num_col = 1
peaks_neg_fin = []
else:
pass
if num_col == 2:
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
num_col = 1
peaks_neg_fin = []
else:
pass
##print(len(peaks_neg_fin))
diff_peaks = np.abs(np.diff(peaks_neg_fin))
cut_off = 400
peaks_neg_true = []
forest = []
# print(len(peaks_neg_fin),'len_')
for i in range(len(peaks_neg_fin)):
if i == 0:
forest.append(peaks_neg_fin[i])
if i < (len(peaks_neg_fin) - 1):
if diff_peaks[i] <= cut_off:
forest.append(peaks_neg_fin[i + 1])
if diff_peaks[i] > cut_off:
# print(forest[np.argmin(z[forest]) ] )
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
forest = []
forest.append(peaks_neg_fin[i + 1])
if i == (len(peaks_neg_fin) - 1):
# print(print(forest[np.argmin(z[forest]) ] ))
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
num_col = (len(peaks_neg_true)) + 1
p_l = 0
p_u = len(y) - 1
p_m = int(len(y) / 2.0)
p_quarter = int(len(y) / 5.0)
p_g_l = int(len(y) / 4.0)
p_g_u = len(y) - int(len(y) / 4.0)
p_u_quarter = len(y) - p_quarter
##print(num_col,'early')
if num_col == 3:
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and (peaks_neg_true[1] + 200) < p_m) or ((peaks_neg_true[0] - 200) > p_m and peaks_neg_true[1] > p_m):
num_col = 1
peaks_neg_true = []
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
peaks_neg_true = [peaks_neg_true[0]]
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
peaks_neg_true = [peaks_neg_true[1]]
else:
pass
if num_col == 2:
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
num_col = 1
peaks_neg_true = []
else:
pass
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
if len(diff_peaks_annormal) > 0:
arg_help = np.array(range(len(diff_peaks)))
arg_help_ann = arg_help[diff_peaks < 360]
peaks_neg_fin_new = []
for ii in range(len(peaks_neg_fin)):
if ii in arg_help_ann:
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
if arg_min == 0:
peaks_neg_fin_new.append(peaks_neg_fin[ii])
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
elif (ii - 1) in arg_help_ann:
pass
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii])
else:
peaks_neg_fin_new = peaks_neg_fin
# plt.plot(gaussian_filter1d(y, sigma_))
# plt.plot(peaks_neg_true,z[peaks_neg_true],'*')
# plt.plot([0,len(y)], [grenze,grenze])
# plt.show()
##print(len(peaks_neg_true))
return len(peaks_neg_true), peaks_neg_true
def find_num_col_only_image(regions_without_seperators, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :].sum(axis=0)
##plt.plot(regions_without_seperators_0)
##plt.show()
sigma_ = 15
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
peaks_neg_org = np.copy(peaks_neg)
peaks_neg = peaks_neg[(peaks_neg > first_nonzero) & (peaks_neg < last_nonzero)]
peaks = peaks[(peaks > 0.09 * regions_without_seperators.shape[1]) & (peaks < 0.91 * regions_without_seperators.shape[1])]
peaks_neg = peaks_neg[(peaks_neg > 500) & (peaks_neg < (regions_without_seperators.shape[1] - 500))]
# print(peaks)
interest_pos = z[peaks]
interest_pos = interest_pos[interest_pos > 10]
interest_neg = z[peaks_neg]
min_peaks_pos = np.mean(interest_pos) # np.min(interest_pos)
min_peaks_neg = 0 # np.min(interest_neg)
# $print(min_peaks_pos)
dis_talaei = (min_peaks_pos - min_peaks_neg) / multiplier
# print(interest_pos)
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)]
num_col = (len(interest_neg_fin)) + 1
p_l = 0
p_u = len(y) - 1
p_m = int(len(y) / 2.0)
p_g_l = int(len(y) / 3.0)
p_g_u = len(y) - int(len(y) / 3.0)
if num_col == 3:
if (peaks_neg_fin[0] > p_g_u and peaks_neg_fin[1] > p_g_u) or (peaks_neg_fin[0] < p_g_l and peaks_neg_fin[1] < p_g_l) or (peaks_neg_fin[0] < p_m and peaks_neg_fin[1] < p_m) or (peaks_neg_fin[0] > p_m and peaks_neg_fin[1] > p_m):
num_col = 1
else:
pass
if num_col == 2:
if (peaks_neg_fin[0] > p_g_u) or (peaks_neg_fin[0] < p_g_l):
num_col = 1
else:
pass
diff_peaks = np.abs(np.diff(peaks_neg_fin))
cut_off = 400
peaks_neg_true = []
forest = []
for i in range(len(peaks_neg_fin)):
if i == 0:
forest.append(peaks_neg_fin[i])
if i < (len(peaks_neg_fin) - 1):
if diff_peaks[i] <= cut_off:
forest.append(peaks_neg_fin[i + 1])
if diff_peaks[i] > cut_off:
# print(forest[np.argmin(z[forest]) ] )
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
forest = []
forest.append(peaks_neg_fin[i + 1])
if i == (len(peaks_neg_fin) - 1):
# print(print(forest[np.argmin(z[forest]) ] ))
if not isNaN(forest[np.argmin(z[forest])]):
peaks_neg_true.append(forest[np.argmin(z[forest])])
num_col = (len(peaks_neg_true)) + 1
p_l = 0
p_u = len(y) - 1
p_m = int(len(y) / 2.0)
p_quarter = int(len(y) / 4.0)
p_g_l = int(len(y) / 3.0)
p_g_u = len(y) - int(len(y) / 3.0)
p_u_quarter = len(y) - p_quarter
if num_col == 3:
if (peaks_neg_true[0] > p_g_u and peaks_neg_true[1] > p_g_u) or (peaks_neg_true[0] < p_g_l and peaks_neg_true[1] < p_g_l) or (peaks_neg_true[0] < p_m and peaks_neg_true[1] < p_m) or (peaks_neg_true[0] > p_m and peaks_neg_true[1] > p_m):
num_col = 1
peaks_neg_true = []
elif (peaks_neg_true[0] < p_g_u and peaks_neg_true[0] > p_g_l) and (peaks_neg_true[1] > p_u_quarter):
peaks_neg_true = [peaks_neg_true[0]]
elif (peaks_neg_true[1] < p_g_u and peaks_neg_true[1] > p_g_l) and (peaks_neg_true[0] < p_quarter):
peaks_neg_true = [peaks_neg_true[1]]
else:
pass
if num_col == 2:
if (peaks_neg_true[0] > p_g_u) or (peaks_neg_true[0] < p_g_l):
num_col = 1
peaks_neg_true = []
if num_col == 4:
if len(np.array(peaks_neg_true)[np.array(peaks_neg_true) < p_g_l]) == 2 or len(np.array(peaks_neg_true)[np.array(peaks_neg_true) > (len(y) - p_g_l)]) == 2:
num_col = 1
peaks_neg_true = []
else:
pass
# no deeper hill around found hills
peaks_fin_true = []
for i in range(len(peaks_neg_true)):
hill_main = peaks_neg_true[i]
# deep_depth=z[peaks_neg]
hills_around = peaks_neg_org[((peaks_neg_org > hill_main) & (peaks_neg_org <= hill_main + 400)) | ((peaks_neg_org < hill_main) & (peaks_neg_org >= hill_main - 400))]
deep_depth_around = z[hills_around]
# print(hill_main,z[hill_main],hills_around,deep_depth_around,'manoooo')
try:
if np.min(deep_depth_around) < z[hill_main]:
pass
else:
peaks_fin_true.append(hill_main)
except:
pass
diff_peaks_annormal = diff_peaks[diff_peaks < 360]
if len(diff_peaks_annormal) > 0:
arg_help = np.array(range(len(diff_peaks)))
arg_help_ann = arg_help[diff_peaks < 360]
peaks_neg_fin_new = []
for ii in range(len(peaks_neg_fin)):
if ii in arg_help_ann:
arg_min = np.argmin([interest_neg_fin[ii], interest_neg_fin[ii + 1]])
if arg_min == 0:
peaks_neg_fin_new.append(peaks_neg_fin[ii])
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii + 1])
elif (ii - 1) in arg_help_ann:
pass
else:
peaks_neg_fin_new.append(peaks_neg_fin[ii])
else:
peaks_neg_fin_new = peaks_neg_fin
# sometime pages with one columns gives also some negative peaks. delete those peaks
param = z[peaks_neg_true] / float(min_peaks_pos) * 100
if len(param[param <= 41]) == 0:
peaks_neg_true = []
return len(peaks_fin_true), peaks_fin_true
def find_num_col_by_vertical_lines(regions_without_seperators, multiplier=3.8):
regions_without_seperators_0 = regions_without_seperators[:, :, 0].sum(axis=0)
##plt.plot(regions_without_seperators_0)
##plt.show()
sigma_ = 35 # 70#35
z = gaussian_filter1d(regions_without_seperators_0, sigma_)
peaks, _ = find_peaks(z, height=0)
# print(peaks,'peaksnew')
return peaks
def contours_in_same_horizon(cy_main_hor):
X1 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
X2 = np.zeros((len(cy_main_hor), len(cy_main_hor)))
X1[0::1, :] = cy_main_hor[:]
X2 = X1.T
X_dif = np.abs(X2 - X1)
args_help = np.array(range(len(cy_main_hor)))
all_args = []
for i in range(len(cy_main_hor)):
list_h = list(args_help[X_dif[i, :] <= 20])
list_h.append(i)
if len(list_h) > 1:
all_args.append(list(set(list_h)))
return np.unique(all_args)
def find_contours_mean_y_diff(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))))