tf.keras.ops.image.sobel_edges

Computes Sobel edge detection on images.

tf.keras.ops.image.sobel_edges(
    images, data_format=None
)

The Sobel operator computes the gradient of the image intensity at each pixel, giving the direction of the largest increase from light to dark and the rate of change in that direction.

images Input tensor of shape (batch, height, width, channels) if data_format="channels_last", or (batch, channels, height, width) if data_format="channels_first".
data_format A string specifying the data format of the input tensor. It can be either "channels_last" or "channels_first". If not specified, the value will default to keras.config.image_data_format.

A tensor containing the Sobel edges. For data_format="channels_last", the output shape is (batch, height, width, channels, 2) where the last dimension contains [dy, dx] representing the vertical and horizontal gradients. For data_format="channels_first", the output shape is (batch, channels, height, width, 2).

Example:

import numpy as np
from keras import ops
# Create image with vertical edge
image = np.zeros((1, 8, 8, 1), dtype="float32")
image[0, :, 4:, 0] = 1.0
edges = ops.image.sobel_edges(image)
edges.shape
(1, 8, 8, 1, 2)