tf.image.image_gradients

Returns image gradients (dy, dx) for each color channel.

tf.image.image_gradients( image )

Both output tensors have the same shape as the input: [batch_size, h, w, d]. The gradient values are organized so that [I(x+1, y) - I(x, y)] is in location (x, y). That means that dy will always have zeros in the last row, and dx will always have zeros in the last column.

BATCH_SIZE = 1 IMAGE_HEIGHT = 5 IMAGE_WIDTH = 5 CHANNELS = 1 image = tf.reshape(tf.range(IMAGE_HEIGHT * IMAGE_WIDTH * CHANNELS, delta=1, dtype=tf.float32), shape=(BATCH_SIZE, IMAGE_HEIGHT, IMAGE_WIDTH, CHANNELS)) dy, dx = tf.image.image_gradients(image) print(image[0, :,:,0]) tf.Tensor( [[ 0. 1. 2. 3. 4.] [ 5. 6. 7. 8. 9.] [10. 11. 12. 13. 14.] [15. 16. 17. 18. 19.] [20. 21. 22. 23. 24.]], shape=(5, 5), dtype=float32) print(dy[0, :,:,0]) tf.Tensor( [[5. 5. 5. 5. 5.] [5. 5. 5. 5. 5.] [5. 5. 5. 5. 5.] [5. 5. 5. 5. 5.] [0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32) print(dx[0, :,:,0]) tf.Tensor( [[1. 1. 1. 1. 0.] [1. 1. 1. 1. 0.] [1. 1. 1. 1. 0.] [1. 1. 1. 1. 0.] [1. 1. 1. 1. 0.]], shape=(5, 5), dtype=float32)

image Tensor with shape [batch_size, h, w, d].

Pair of tensors (dy, dx) holding the vertical and horizontal image gradients (1-step finite difference).

ValueError If image is not a 4D tensor.