python detect lines
img = cv2.imread('src.png') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) kernel_size = 5 blur_gray = cv2.GaussianBlur(gray,(kernel_size, kernel_size),0) low_threshold = 50 high_threshold = 150 edges = cv2.Canny(blur_gray, low_threshold, high_threshold) rho = 1 # distance resolution in pixels of the Hough grid theta = np.pi / 180 # angular resolution in radians of the Hough grid threshold = 15 # minimum number of votes (intersections in Hough grid cell) min_line_length = 50 # minimum number of pixels making up a line max_line_gap = 20 # maximum gap in pixels between connectable line segments line_image = np.copy(img) * 0 # creating a blank to draw lines on # Run Hough on edge detected image # Output "lines" is an array containing endpoints of detected line segments lines = cv2.HoughLinesP(edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap) for line in lines: for x1,y1,x2,y2 in line: cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),5) lines_edges = cv2.addWeighted(img, 0.8, line_image, 1, 0)
