def find_vertical_lines(gray_image):
# gray_image = cv2.GaussianBlur(gray_image,ksize=(5,5), sigmaX=0)
# let the horizen lines dispear
# flag_test()
# Display.image(gray_image)
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)
# Display.polar_lines(cannied_image, lines)
# tansfer negative angle to positive
lines = map(PolarLines.adjust_to_positive_rho_line, lines)
# lines.ppl()
# Display.polar_lines(cannied_image, lines)
lines = map(PolarLines.adjust_theta_when_rho_0_and_near_180, lines)
# lines.ppl()
# Display.polar_lines(cannied_image, lines)
# lines = map(to_positive_rho, lines)
# flag_test()
# Display.polar_lines(cannied_image, lines)
def filter_line(line):
rho, theta = line
# larger than 170, 10 degree equal 0.174444, 180 degree equal 3.14
if theta > 3.14 - 0.174:
theta = 3.14 - theta
return abs(theta) < 0.174
# theta_degree = (theta * 180/ numpy.pi)
# # (rho, theta_degree).pl()
# # 180 and 0 are same to the line's theta.
# if theta_degree > 170:
# theta_degree = 180 - theta_degree
# return abs(theta_degree) < 10
# remove the lines which have large angle
# Display.polar_lines(cannied_image, lines)
lines = filter(filter_line, lines)
# flag_test()
# Display.polar_lines(cannied_image, lines)
accuracy_pixs = gray_image.shape[1] / SUDOKU_SIZE *0.3 # 9
catalogued_lines = PolarLines.catalogue_lines(lines, accuracy_pixs)
# catalogued_lines.ppl()
mean_lines = PolarLines.cal_mean_lines(catalogued_lines)
# flag_test()
# Display.polar_lines(cannied_image, mean_lines)
return mean_lines
def find_sudoku_horizontal_lines(gray_image):
gray_image = cv2.GaussianBlur(gray_image,ksize=(5,5), sigmaX=0)
mean_lines = find_horizontal_lines(gray_image)
all_lines = PolarLines.fill_lost_lines(mean_lines, SUDOKU_SIZE+1)
# Display.polar_lines(cannied_image, all_lines)
return all_lines
def find_sudoku_vertical_lines(gray_image):
gray_image = cv2.GaussianBlur(gray_image,ksize=(5,5), sigmaX=0)
mean_lines = find_vertical_lines(gray_image)
all_lines = PolarLines.fill_lost_lines(mean_lines, SUDOKU_SIZE+1)
# show_lines(cannied_image, all_lines)
return all_lines
def find_intersections(vertical_lines, horizontal_lines):
'''sometimes, the point may less than 0'''
intersections = tuple(PolarLines.cal_intersection(v_line, h_line)
for h_line in horizontal_lines for v_line in vertical_lines)
def adjust_negative(point):
x, y = point
return (max(0,x), max(0,y))
intersections = map(adjust_negative, intersections)
return numpy.array(intersections, dtype=numpy.int32)
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
