image_analysis.py 文件源码

def process_image(image):
    """

    Args:
        image: The image to process

    Returns:
        sub_image: The rotated and extracted.

    """

    # Convert image to black and white - we cannot take the photos in black and white as we
    # must first search for the red triangle.

    if len(image.shape) == 3:
        processed_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    else:
        processed_img = image

    if config.real_hardware:
        num_iterations = 8
    else:
        num_iterations = 8

        processed_img = cv2.GaussianBlur(processed_img, (21, 21), 0)
    _, processed_img = cv2.threshold(processed_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    # Put a border around the image to stop the edges of the images creating artifacts.
    padded_image = np.zeros((processed_img.shape[0] + 10, processed_img.shape[1] + 10), np.uint8)
    padded_image[5:processed_img.shape[0]+5, 5:processed_img.shape[1]+5] = processed_img

    kernel = np.array([[0, 1, 1, 1, 0],
                       [1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1],
                       [1, 1, 1, 1, 1],
                       [0, 1, 1, 1, 0]], np.uint8)

    padded_image = cv2.erode(padded_image, kernel, iterations=num_iterations)
    processed_img = padded_image[25:padded_image.shape[0] - 25, 25:padded_image.shape[1] - 25]

    #cv2.imshow('Padded Image', padded_image)
    #cv2.imshow('Processed image', processed_img)
    #cv2.waitKey(0)



    # Debugging code - useful to show the images are being eroded correctly.
    #spacer = processed_img[:, 0:2].copy()
    #spacer.fill(100)
    #combined_image = np.concatenate((processed_img, spacer), axis=1)
    #combined_image = np.concatenate((combined_image, image), axis=1)
    #cv2.imshow('PreProcessed and Processed Image', combined_image)
    #cv2.waitKey(0)

    # Save sub_image to debug folder if required.
    if __debug__:
        iadebug.save_processed_image(processed_img)

    return processed_img