Changed example and output
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 21668c5..b73bfa4 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -3252,24 +3252,19 @@
Outputs a tensor of the same shape as the `images` tensor, containing the YUV
value of the pixels.
The output is only well defined if the value in images are in [0,1].
- You need to scale your RGB images if their pixel values are not in the
- required range. Below given example illustrates preprocessing of each channel
- of images before feeding them to `rgb_to_yuv`.
Usage Example:
- >>> rgb_images = tf.random.uniform(shape=[100,64,64,3], maxval=255)
- >>> preprocessed_rgb_images = tf.truediv(
- ... tf.subtract(
- ... rgb_images,
- ... tf.reduce_min(rgb_images)
- ... ),
- ... tf.subtract(
- ... tf.reduce_max(rgb_images),
- ... tf.reduce_min(rgb_images)
- ... )
- ... )
- >>> yub_tensor_images = tf.image.rgb_to_yuv(preprocessed_rgb_images)
+ >>> x = [[[0.1, 0.2, 0.3],
+ ... [0.4, 0.5, 0.6]],
+ ... [[0.7, 0.8, 0.9],
+ ... [0.10, 0.11, 0.12]]]
+ >>> tf.image.rgb_to_yuv(x)
+ <tf.Tensor: shape=(2, 2, 3), dtype=float32, numpy=
+ array([[[ 0.1815 , 0.05831515, -0.07149857],
+ [ 0.4815 , 0.05831517, -0.07149856]],
+ [[ 0.7815 , 0.05831515, -0.07149857],
+ [ 0.10815 , 0.00583152, -0.00714985]]], dtype=float32)>
Args:
images: 2-D or higher rank. Image data to convert. Last dimension must be
