def find_vertical_lines(gray_image):
# gray_image = cv2.GaussianBlur(gray_image,ksize=(5,5), sigmaX=0)
# let the horizen lines dispear
dx_image = cv2.Sobel(gray_image, cv2.CV_16S, 2, 0)
# convert from dtype=int16 to dtype=uint8
dx_image = cv2.convertScaleAbs(dx_image)
# detect the edges
low_threshold, high_threshold = 40, 80
cannied_image = cv2.Canny(dx_image, low_threshold, high_threshold)
lines = PolarLines.find_suitable_lines(cannied_image)
# show_lines(cannied_image, lines)
def to_positive_rho(line):
if line[0] < 0:
line[0] = abs(line[0])
line[1] = line[1] - numpy.pi
return line
# tansfer negative angle to positive
lines = map(to_positive_rho, lines)
def filter_line(line):
rho, theta = line
theta_degree = theta * 180 / numpy.pi
return abs(theta_degree) < 10
# remove the lines which have large angle
lines = filter(filter_line, lines)
# show_lines(cannied_image, lines)
accuracy_pixs = gray_image.shape[1] / SUDOKU_SIZE * 0.3 # 9
catalogued_lines = PolarLines.catalogue_lines(lines, accuracy_pixs)
mean_lines = PolarLines.cal_mean_lines(catalogued_lines)
# all_lines = PolarLines.fill_lost_lines(mean_lines, SUDOKU_SIZE+1)
# show_lines(cannied_image, all_lines)
return mean_lines
def find_horizontal_lines(gray_image):
# gray_image = cv2.GaussianBlur(gray_image,ksize=(5,5), sigmaX=0)
dy_image = cv2.Sobel(gray_image, cv2.CV_16S, 0, 2)
# convert from dtype=int16 to dtype=uint8
dy_image = cv2.convertScaleAbs(dy_image)
low_threshold, high_threshold = 40, 80
cannied_image = cv2.Canny(dy_image, low_threshold, high_threshold)
lines = PolarLines.find_suitable_lines(cannied_image)
def filter_line(line):
rho, theta = line
theta_degree = (theta * 180 / numpy.pi) - 90
return abs(theta_degree) < 10
lines = filter(filter_line, lines)
accuracy_pixs = gray_image.shape[0] / SUDOKU_SIZE * 0.3 # 9
catalogued_lines = PolarLines.catalogue_lines(lines, accuracy_pixs)
mean_lines = PolarLines.cal_mean_lines(catalogued_lines)
return mean_lines
