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"""
@file hough_lines.py
@brief This program demonstrates line finding with the Hough transform
"""
import sys
import math
import cv2 as cv
import numpy as np
def main(argv):
## [load]
default_file = 'sudoku.png'
filename = argv[0] if len(argv) > 0 else default_file
# Loads an image
src = cv.imread(cv.samples.findFile(filename), cv.IMREAD_GRAYSCALE)
# Check if image is loaded fine
if src is None:
print ('Error opening image!')
print ('Usage: hough_lines.py [image_name -- default ' + default_file + '] \n')
return -1
## [load]
## [edge_detection]
# Edge detection
dst = cv.Canny(src, 50, 200, None, 3)
## [edge_detection]
# Copy edges to the images that will display the results in BGR
cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
cdstP = np.copy(cdst)
## [hough_lines]
# Standard Hough Line Transform
lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
## [hough_lines]
## [draw_lines]
# Draw the lines
if lines is not None:
for i in range(0, len(lines)):
rho = lines[i][0][0]
theta = lines[i][0][1]
a = math.cos(theta)
b = math.sin(theta)
x0 = a * rho
y0 = b * rho
pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))
cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)
## [draw_lines]
## [hough_lines_p]
# Probabilistic Line Transform
linesP = cv.HoughLinesP(dst, 1, np.pi / 180, 50, None, 50, 10)
## [hough_lines_p]
## [draw_lines_p]
# Draw the lines
if linesP is not None:
for i in range(0, len(linesP)):
l = linesP[i][0]
cv.line(cdstP, (l[0], l[1]), (l[2], l[3]), (0,0,255), 3, cv.LINE_AA)
## [draw_lines_p]
## [imshow]
# Show results
cv.imshow("Source", src)
cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
cv.imshow("Detected Lines (in red) - Probabilistic Line Transform", cdstP)
## [imshow]
## [exit]
# Wait and Exit
cv.waitKey()
return 0
## [exit]
if __name__ == "__main__":
main(sys.argv[1:])
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