vision_processing.py 文件源码

def get_warped_image_from_corners(image, corners):
    """
    Returns unwarped image of goal, using corners of goal and the original
    source image.
    Parameters:
        :param: `image` - the original source image with the goal in it
        :param: `corners` - a numpy array of the corner pixels of the goal

    """
    orig_image = numpy.copy(image)
    center = get_center(corners)
    corners = sort_corners(corners, center)

    height_right = int(math.sqrt((corners[1][0][0] - corners[2][0][0]) ** 2 +
                                 (corners[1][0][1] - corners[2][0][1]) ** 2))
    height_left = int(math.sqrt((corners[0][0][0] - corners[3][0][0]) ** 2 +
                                (corners[0][0][1] - corners[3][0][1]) ** 2))
    height = int((height_left + height_right) / 2)
    width = int(height * (300 / 210))

    quad = numpy.zeros((width, height))
    quad_pts = numpy.array([[[0, 0]],      [[width, 0]],
                            [[width, height]], [[0, height]]], numpy.float32)

    new_image_to_process = numpy.array(image, numpy.float32)
    quad_pts = cv2.getPerspectiveTransform(corners, quad_pts)
    warped_image = cv2.warpPerspective(new_image_to_process, quad_pts,
                                      (width, height))
    return warped_image

# def get_distance_to_goal(orig_image, warped_image):
#     angle_between_sides = (len(warped_image[0]) / len(orig_image[0])) * FOV_OF_CAMERA
#     print(math.degrees(angle_between_sides))
#     return ((WIDTH_OF_GOAL_IN_METERS / 2) / math.sin(angle_between_sides / 2)) * math.sin((math.pi + angle_between_sides) / 2)