使用 python 模糊图像部分的最优雅方法是什么？

Question

我发现以下使用 PIL 在本地模糊图像的答案： Filter part of image using PIL, python。建议的答案裁剪图像的一部分，将其模糊并将其复制回原始图像。这会在模糊部分和原始图像之间创建清晰的边缘（请参见下面的示例）。

Answer 1

为了避免这个问题，可以使用以下程序：给定一个图像和一个掩码（0 到 1 之间的值）模糊完整的输入图像和蒙版使用模糊蒙版对原始图像进行加权使用倒置的模糊蒙版对模糊图像进行加权添加加权图像下面是一些使用 scipy 的示例代码：import numpy as npimport matplotlib.pyplot as pltfrom scipy import miscimport scipy.ndimagedef gaussian_blur(sharp_image, sigma):    # Filter channels individually to avoid gray scale images    blurred_image_r = scipy.ndimage.filters.gaussian_filter(sharp_image[:, :, 0], sigma=sigma)    blurred_image_g = scipy.ndimage.filters.gaussian_filter(sharp_image[:, :, 1], sigma=sigma)    blurred_image_b = scipy.ndimage.filters.gaussian_filter(sharp_image[:, :, 2], sigma=sigma)    blurred_image = np.dstack((blurred_image_r, blurred_image_g, blurred_image_b))    return blurred_imagedef uniform_blur(sharp_image, uniform_filter_size):    # The multidimensional filter is required to avoid gray scale images    multidim_filter_size = (uniform_filter_size, uniform_filter_size, 1)    blurred_image = scipy.ndimage.filters.uniform_filter(sharp_image, size=multidim_filter_size)    return blurred_imagedef blur_image_locally(sharp_image, mask, use_gaussian_blur, gaussian_sigma, uniform_filter_size):    one_values_f32 = np.full(sharp_image.shape, fill_value=1.0, dtype=np.float32)    sharp_image_f32 = sharp_image.astype(dtype=np.float32)    sharp_mask_f32 = mask.astype(dtype=np.float32)    if use_gaussian_blur:        blurred_image_f32 = gaussian_blur(sharp_image_f32, sigma=gaussian_sigma)        blurred_mask_f32 = gaussian_blur(sharp_mask_f32, sigma=gaussian_sigma)    else:        blurred_image_f32 = uniform_blur(sharp_image_f32, uniform_filter_size)        blurred_mask_f32 = uniform_blur(sharp_mask_f32, uniform_filter_size)    blurred_mask_inverted_f32 = one_values_f32 - blurred_mask_f32    weighted_sharp_image = np.multiply(sharp_image_f32, blurred_mask_f32)    weighted_blurred_image = np.multiply(blurred_image_f32, blurred_mask_inverted_f32)    locally_blurred_image_f32 = weighted_sharp_image + weighted_blurred_image    locally_blurred_image = locally_blurred_image_f32.astype(dtype=np.uint8)    return locally_blurred_imageif __name__ == '__main__':    sharp_image = misc.face()    height, width, channels = sharp_image.shape    sharp_mask = np.full((height, width, channels), fill_value=1)    sharp_mask[int(height / 4): int(3 * height / 4), int(width / 4): int(3 * width / 4), :] = 0    result = blur_image_locally(        sharp_image,        sharp_mask,        use_gaussian_blur=True,        gaussian_sigma=31,        uniform_filter_size=201)    plt.imshow(result)    plt.show()结果：