A simple implementation of image devignetting.

image_devignetting.py

def devignetting(image, radius_sigma=0.3, amount=0.7, power=1.1):
    """
    :param image: input image
    :param radius_sigma: vignetting radius = radius_sigma * image width
    :param amount: the amount of exposure increment for devignetting
    :param power: increase this will add extra exposure increment on image border
    :return:
    """
    tic = time.time()
    height, width = image.shape[:2]
    # generating vignette mask using Gaussian kernels
    #  这里由于镜头是圆形 所以宽高一致
    kernel_x = cv2.getGaussianKernel(width, width * radius_sigma).astype(np.float32)
    kernel_y = cv2.getGaussianKernel(height, width * radius_sigma).astype(np.float32)
    kernel = kernel_y * kernel_x.T
    mask_not_norm = 255 * kernel / np.linalg.norm(kernel)
    mask_norm = mask_not_norm / mask_not_norm.max()

    # mask_scale = 1 / np.minimum(np.power(1.4 * mask_norm, 1.1), 1)
    # mask_scale = np.maximum(amount * 1 / np.power(mask_norm, power), 1)
    mask_scale = np.maximum(amount * 1 / np.power(mask_norm, power), 0.7)
    # mask_scale = cv2.GaussianBlur(mask_scale, ksize=(55, 55), sigmaX=1)

    # image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    # rst = np.minimum((image * mask_scale), 255) / 255  ## scale pixel values up or down for channel 1(Lightness)
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    # hsv = np.array(hsv, dtype=np.float64)
    hsv[:, :, 1] = np.minimum(hsv[:, :, 1] * mask_scale, 255)  ## scale pixel values up or down for channel 1(Lightness)
    hsv[:, :, 2] = np.minimum(hsv[:, :, 2] * mask_scale, 255)  ## scale pixel values up or down for channel 1(Lightness)
    hsv = np.array(hsv, dtype=np.uint8)
    rst = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

    toc = time.time()
    print(toc - tic)
    return rst, mask_scale