*_jpeg_quality funcs only accept single image
The docs incorrectly stated that you could pass in "images".
diff --git a/tensorflow/python/ops/image_ops_impl.py b/tensorflow/python/ops/image_ops_impl.py
index 89abd31..2b843aa 100644
--- a/tensorflow/python/ops/image_ops_impl.py
+++ b/tensorflow/python/ops/image_ops_impl.py
@@ -1636,7 +1636,7 @@
Returns:
A brightness-adjusted tensor of the same shape and type as `image`.
-
+
Usage Example:
```python
import tensorflow as tf
@@ -1685,7 +1685,7 @@
Returns:
The contrast-adjusted image or images.
-
+
Usage Example:
```python
import tensorflow as tf
@@ -2003,7 +2003,7 @@
`max_jpeg_quality` must be in the interval `[0, 100]`.
Args:
- image: RGB image or images. Size of the last dimension must be 3.
+ image: 3D image. Size of the last dimension must be 1 or 3.
min_jpeg_quality: Minimum jpeg encoding quality to use.
max_jpeg_quality: Maximum jpeg encoding quality to use.
seed: An operation-specific seed. It will be used in conjunction with the
@@ -2012,7 +2012,7 @@
interaction with the graph-level random seed.
Returns:
- Adjusted image(s), same shape and DType as `image`.
+ Adjusted image, same shape and DType as `image`.
Raises:
ValueError: if `min_jpeg_quality` or `max_jpeg_quality` is invalid.
@@ -2040,19 +2040,19 @@
def adjust_jpeg_quality(image, jpeg_quality, name=None):
"""Adjust jpeg encoding quality of an image.
- This is a convenience method that converts images to uint8 representation,
- encodes them to jpeg with `jpeg_quality`, decodes them, and then converts back
+ This is a convenience method that converts an image to uint8 representation,
+ encodes it to jpeg with `jpeg_quality`, decodes it, and then converts back
to the original data type.
`jpeg_quality` must be in the interval `[0, 100]`.
Args:
- image: image or images. Size of the last dimension must be None, 1 or 3.
+ image: 3D image. Size of the last dimension must be None, 1 or 3.
jpeg_quality: Python int or Tensor of type int32. jpeg encoding quality.
name: A name for this operation (optional).
Returns:
- Adjusted image(s), same shape and DType as `image`.
+ Adjusted image, same shape and DType as `image`.
Usage Example:
```python
@@ -2136,14 +2136,14 @@
Returns:
Adjusted image(s), same shape and DType as `image`.
-
+
Usage Example:
```python
>> import tensorflow as tf
>> x = tf.random.normal(shape=(256, 256, 3))
>> tf.image.adjust_saturation(x, 0.5)
```
-
+
Raises:
InvalidArgumentError: input must have 3 channels
"""
@@ -3469,15 +3469,15 @@
Returns:
Pair of tensors (dy, dx) holding the vertical and horizontal image
gradients (1-step finite difference).
-
+
Usage Example:
```python
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),
+ 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])
@@ -3493,7 +3493,7 @@
[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)
+ [0. 0. 0. 0. 0.]], shape=(5, 5), dtype=float32)
print(dy[0, :,:,0])
tf.Tensor(
[[1. 1. 1. 1. 0.]
@@ -3502,7 +3502,7 @@
[1. 1. 1. 1. 0.]
[1. 1. 1. 1. 0.]], shape=(5, 5), dtype=float32)
```
-
+
Raises:
ValueError: If `image` is not a 4D tensor.
"""
