Returns a tensor holding Sobel edge maps.

Used in the notebooks

Used in the tutorials

Example usage:

For general usage, image would be loaded from a file as below:

image_bytes =
image = tf.image.decode_image(image_bytes)
image = tf.cast(image, tf.float32)
image = tf.expand_dims(image, 0)

But for demo purposes, we are using randomly generated values for image:

image = tf.random.uniform(
  maxval=255, shape=[1, 28, 28, 3], dtype=tf.float32)
sobel = tf.image.sobel_edges(image)
sobel_y = np.asarray(sobel[0, :, :, :, 0]) # sobel in y-direction
sobel_x = np.asarray(sobel[0, :, :, :, 1]) # sobel in x-direction

For displaying the sobel results, PIL's Image Module can be used:

# Display edge maps for the first channel (at index 0)
Image.fromarray(sobel_y[..., 0] / 4 + 0.5).show()
Image.fromarray(sobel_x[..., 0] / 4 + 0.5).show()

image Image tensor with shape [batch_size, h, w, d] and type float32 or float64. The image(s) must be 2x2 or larger.

Tensor holding edge maps for each channel. Returns a tensor with shape [batch_size, h, w, d, 2] where the last two dimensions hold [[dy[0], dx[0]], [dy[1], dx[1]], ..., [dy[d-1], dx[d-1]]] calculated using the Sobel filter.