houghTransform.py 文件源码

def houghTransformAndRegionSelect(image, edges):
    rho = 1
    theta = np.pi/180
    threshold = 1
    min_line_length = 5
    max_line_gap = 3


    # Next we'll create a masked edges image using cv2.fillPoly()
    mask = np.zeros_like(edges)
    ignore_mask_color = 255

    # This time we are defining a four sided polygon to mask
    imshape = image.shape
    vertices = np.array([[(0,imshape[0]),(450, 290), (490, 290), (imshape[1],imshape[0])]], dtype=np.int32)
    cv2.fillPoly(mask, vertices, ignore_mask_color)
    masked_edges = cv2.bitwise_and(edges, mask)

    line_image = np.copy(image)*0

    # Run Hough on edge detected image
    # Output "lines" is an array containing endpoints of detected line segments
    lines = cv2.HoughLinesP(masked_edges, rho, theta, threshold, np.array([]), min_line_length, max_line_gap)

    # Iterate over the output "lines" and draw lines on a blank image
    for line in lines:
        for x1,y1,x2,y2 in line:
            cv2.line(line_image,(x1,y1),(x2,y2),(255,0,0),10)

    # Create a "color" binary image to combine with line image
    color_edges = np.dstack((edges, edges, edges))

    # Draw the lines on the edge image
    lines_edges = cv2.addWeighted(color_edges, 0.8, line_image, 1, 0)

    return lines_edges