vision_processing.py 文件源码

def get_contours(orig_image):
    """
    Get edge points (hopefully corners) from the given opencv image (called
    contours in opencv)

    Parameters:
        :param: `orig_image` - the thresholded image from which to find contours
    """
    new_image = numpy.copy(orig_image)
    # cv2.imshow("Vision", new_image)
    # cv2.waitKey(1000)
    new_image, contours, hierarchy = cv2.findContours(new_image,
                                                      cv2.RETR_EXTERNAL,
                                                      cv2.CHAIN_APPROX_SIMPLE)
    # print(len(contours))
    # print(len(contours[0]))
    # print(len(contours[0][0]))
    # print(len(contours[0][0][0]))
    largest_contour = 0
    most_matching = 0
    min_score = 0
    max_area = 0
    if len(contours) > 1:
        print("Length of contours:", len(contours))
        max_area = cv2.contourArea(contours[0])
        min_score = average_goal_matching(contours[0])
        for i in range(1, len(contours)):
            # print(contours[i])
            current_score = average_goal_matching(contours[i])
            current_area = cv2.contourArea(contours[i])
            if current_area > max_area:
                max_area = current_area
                largest_contour = i
            if current_score < min_score and current_score != 0 and current_area > 300 and current_area < 1500:
                min_score = current_score
                most_matching = i
    elif len(contours) == 0:
        raise GoalNotFoundException("Goal not found!")
    if min_score >= 9999999999999999:
        raise GoalNotFoundException("Goal not found!")
    print("largest_contour:", largest_contour)
    print("Area:", max_area)
    # print("largest_contour:", largest_contour)
    print("Most matching:", most_matching)
    print("Score:", min_score)
    print("Area of most matching:", cv2.contourArea(contours[most_matching]))

    rect = cv2.minAreaRect(contours[most_matching])
    box = cv2.boxPoints(rect)
    box = numpy.int0(box)
    # print(box)
    return numpy.array(contours[most_matching]), box