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-# Images
-
-Note: Functions taking `Tensor` arguments can also take anything accepted by
-`tf.convert_to_tensor`.
-
-[TOC]
-
-## Encoding and Decoding
-
-TensorFlow provides Ops to decode and encode JPEG and PNG formats. Encoded
-images are represented by scalar string Tensors, decoded images by 3-D uint8
-tensors of shape `[height, width, channels]`. (PNG also supports uint16.)
-
-The encode and decode Ops apply to one image at a time. Their input and output
-are all of variable size. If you need fixed size images, pass the output of
-the decode Ops to one of the cropping and resizing Ops.
-
-Note: The PNG encode and decode Ops support RGBA, but the conversions Ops
-presently only support RGB, HSV, and GrayScale. Presently, the alpha channel has
-to be stripped from the image and re-attached using slicing ops.
-
-* `tf.image.decode_bmp`
-* `tf.image.decode_gif`
-* `tf.image.decode_jpeg`
-* `tf.image.encode_jpeg`
-* `tf.image.decode_png`
-* `tf.image.encode_png`
-* `tf.image.decode_image`
-
-## Resizing
-
-The resizing Ops accept input images as tensors of several types. They always
-output resized images as float32 tensors.
-
-The convenience function `tf.image.resize_images` supports both 4-D
-and 3-D tensors as input and output. 4-D tensors are for batches of images,
-3-D tensors for individual images.
-
-Other resizing Ops only support 4-D batches of images as input:
-`tf.image.resize_area`, `tf.image.resize_bicubic`,
-`tf.image.resize_bilinear`,
-`tf.image.resize_nearest_neighbor`.
-
-Example:
-
-```python
-# Decode a JPG image and resize it to 299 by 299 using default method.
-image = tf.image.decode_jpeg(...)
-resized_image = tf.image.resize_images(image, [299, 299])
-```
-
-* `tf.image.resize_images`
-* `tf.image.resize_area`
-* `tf.image.resize_bicubic`
-* `tf.image.resize_bilinear`
-* `tf.image.resize_nearest_neighbor`
-
-## Cropping
-
-* `tf.image.resize_image_with_crop_or_pad`
-* `tf.image.central_crop`
-* `tf.image.pad_to_bounding_box`
-* `tf.image.crop_to_bounding_box`
-* `tf.image.extract_glimpse`
-* `tf.image.crop_and_resize`
-
-## Flipping, Rotating and Transposing
-
-* `tf.image.flip_up_down`
-* `tf.image.random_flip_up_down`
-* `tf.image.flip_left_right`
-* `tf.image.random_flip_left_right`
-* `tf.image.transpose_image`
-* `tf.image.rot90`
-
-## Converting Between Colorspaces
-
-Image ops work either on individual images or on batches of images, depending on
-the shape of their input Tensor.
-
-If 3-D, the shape is `[height, width, channels]`, and the Tensor represents one
-image. If 4-D, the shape is `[batch_size, height, width, channels]`, and the
-Tensor represents `batch_size` images.
-
-Currently, `channels` can usefully be 1, 2, 3, or 4. Single-channel images are
-grayscale, images with 3 channels are encoded as either RGB or HSV. Images
-with 2 or 4 channels include an alpha channel, which has to be stripped from the
-image before passing the image to most image processing functions (and can be
-re-attached later).
-
-Internally, images are either stored in as one `float32` per channel per pixel
-(implicitly, values are assumed to lie in `[0,1)`) or one `uint8` per channel
-per pixel (values are assumed to lie in `[0,255]`).
-
-TensorFlow can convert between images in RGB or HSV. The conversion functions
-work only on float images, so you need to convert images in other formats using
-`tf.image.convert_image_dtype`.
-
-Example:
-
-```python
-# Decode an image and convert it to HSV.
-rgb_image = tf.image.decode_png(..., channels=3)
-rgb_image_float = tf.image.convert_image_dtype(rgb_image, tf.float32)
-hsv_image = tf.image.rgb_to_hsv(rgb_image)
-```
-
-* `tf.image.rgb_to_grayscale`
-* `tf.image.grayscale_to_rgb`
-* `tf.image.hsv_to_rgb`
-* `tf.image.rgb_to_hsv`
-* `tf.image.convert_image_dtype`
-
-## Image Adjustments
-
-TensorFlow provides functions to adjust images in various ways: brightness,
-contrast, hue, and saturation. Each adjustment can be done with predefined
-parameters or with random parameters picked from predefined intervals. Random
-adjustments are often useful to expand a training set and reduce overfitting.
-
-If several adjustments are chained it is advisable to minimize the number of
-redundant conversions by first converting the images to the most natural data
-type and representation (RGB or HSV).
-
-* `tf.image.adjust_brightness`
-* `tf.image.random_brightness`
-* `tf.image.adjust_contrast`
-* `tf.image.random_contrast`
-* `tf.image.adjust_hue`
-* `tf.image.random_hue`
-* `tf.image.adjust_gamma`
-* `tf.image.adjust_saturation`
-* `tf.image.random_saturation`
-* `tf.image.per_image_standardization`
-
-## Working with Bounding Boxes
-
-* `tf.image.draw_bounding_boxes`
-* `tf.image.non_max_suppression`
-* `tf.image.sample_distorted_bounding_box`
-
-## Denoising
-
-* `tf.image.total_variation`