tf.image.image_gradients

TensorFlow 1 version View source on GitHub

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.

Usage Example:

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))
dx, dy = 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(dx[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(dy[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.