检测RGB图像中的模糊性

Question

我想检测图像中的模糊度。为此，我将RGB图像转换为灰度，并用拉普拉斯内核对其进行卷积并计算方差。如果方差高，则图像将聚焦并且否则模糊。我在下面附上了ss代码片段。但是我遇到一个错误。请帮我这个代码。下面是对图像进行卷积并计算方差的函数：

def is_blur(image) :
   """
   This function convolves a grayscale image with
   laplacian kernel and calculates its variance.
   """
   #Laplacian kernel
   laplacian_kernel = np.array([[0,1,0],[1,-4,1],[0,1,0]])
   laplacian_kernel = tf.expand_dims(laplacian_kernel, 0)
   laplacian_kernel = tf.expand_dims(laplacian_kernel, 0)
   laplacian_kernel = tf.cast(laplacian_kernel, tf.float32)
   #Convolving image with laplacian kernel
   new_img = tf.nn.conv2d(image, laplacian_kernel, stride=[1, 1, 1, 1], padding="SAME")
   #Calculating variance
   img_var = tf.math.reduce_variance(new_img)
   return img_var

下面是加载图像并调用上述函数的代码：

from tqdm import tqdm
path = "/content/Data/Train/{0}/"
N_CLASSES = 43
blurness_list = []
for itr in tqdm(range(N_CLASSES)) :
   files = os.listdir(path.format(itr))
   for img in files :
      image_string = tf.io.read_file(path.format(itr) + img)
      #decoding image
      image = tf.image.decode_png(image_string, channels=3)
      #Converting image to grayscale
      image = tf.image.rgb_to_grayscale(image)
      # This will convert to float values in [0, 1]
      image = tf.image.convert_image_dtype(image, tf.float32)
      #Reshaping image since conv2d accepts a 4-d tensor.
      image = tf.reshape(image, shape=[1, image.shape[0], image.shape[1], 1])
      blurness_list.append(is_blur(image))

下面是我得到的错误：

InvalidArgumentError：输入深度必须可以被过滤器深度均匀除以：1 vs 3 [Op：Conv2D]

请帮助我提供此代码。

Answer 1

这应该很容易解决：您正在构造2d 3x3滤镜，然后通过tf.expand_dims在开头插入size-1尺寸，大概是按conv2d的要求制作了4d滤镜。但是，conv2d需要过滤器为width x height x in_channels x out_channels，即过滤器尺寸必须为最后。这应该解决它：

laplacian_kernel = np.array([[0,1,0],[1,-4,1],[0,1,0]])
laplacian_kernel = tf.expand_dims(laplacian_kernel, -1)
laplacian_kernel = tf.expand_dims(laplacian_kernel, -1)
laplacian_kernel = tf.cast(laplacian_kernel, tf.float32)

或者，请注意，您也可以执行以下操作：

laplacian_kernel = laplacian_kernel[..., None, None]

其中None用于插入轴。