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diff --git a/tensorflow/g3doc/api_docs/python/image.md b/tensorflow/g3doc/api_docs/python/image.md new file mode 100644 index 0000000000..6b3d6c3ca7 --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/image.md @@ -0,0 +1,857 @@ +<!-- This file is machine generated: DO NOT EDIT! --> + +# Images +<!-- TOC-BEGIN This section is generated by neural network: DO NOT EDIT! --> +## Contents +* [Encoding and Decoding.](#AUTOGENERATED-encoding-and-decoding.) + * [tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, name=None)](#decode_jpeg) + * [tf.image.encode_jpeg(image, format=None, quality=None, progressive=None, optimize_size=None, chroma_downsampling=None, density_unit=None, x_density=None, y_density=None, xmp_metadata=None, name=None)](#encode_jpeg) + * [tf.image.decode_png(contents, channels=None, name=None)](#decode_png) + * [tf.image.encode_png(image, compression=None, name=None)](#encode_png) +* [Resizing.](#AUTOGENERATED-resizing.) + * [tf.image.resize_images(images, new_height, new_width, method=0)](#resize_images) + * [tf.image.resize_area(images, size, name=None)](#resize_area) + * [tf.image.resize_bicubic(images, size, name=None)](#resize_bicubic) + * [tf.image.resize_bilinear(images, size, name=None)](#resize_bilinear) + * [tf.image.resize_nearest_neighbor(images, size, name=None)](#resize_nearest_neighbor) +* [Cropping.](#AUTOGENERATED-cropping.) + * [tf.image.resize_image_with_crop_or_pad(image, target_height, target_width)](#resize_image_with_crop_or_pad) + * [tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width)](#pad_to_bounding_box) + * [tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width)](#crop_to_bounding_box) + * [tf.image.random_crop(image, size, seed=None, name=None)](#random_crop) + * [tf.image.extract_glimpse(input, size, offsets, centered=None, normalized=None, uniform_noise=None, name=None)](#extract_glimpse) +* [Flipping and Transposing.](#AUTOGENERATED-flipping-and-transposing.) + * [tf.image.flip_up_down(image)](#flip_up_down) + * [tf.image.random_flip_up_down(image, seed=None)](#random_flip_up_down) + * [tf.image.flip_left_right(image)](#flip_left_right) + * [tf.image.random_flip_left_right(image, seed=None)](#random_flip_left_right) + * [tf.image.transpose_image(image)](#transpose_image) +* [Image Adjustments.](#AUTOGENERATED-image-adjustments.) + * [tf.image.adjust_brightness(image, delta, min_value=None, max_value=None)](#adjust_brightness) + * [tf.image.random_brightness(image, max_delta, seed=None)](#random_brightness) + * [tf.image.adjust_contrast(images, contrast_factor, min_value=None, max_value=None)](#adjust_contrast) + * [tf.image.random_contrast(image, lower, upper, seed=None)](#random_contrast) + * [tf.image.per_image_whitening(image)](#per_image_whitening) + + +<!-- TOC-END This section was generated by neural network, THANKS FOR READING! --> + +## Encoding and Decoding. <div class="md-anchor" id="AUTOGENERATED-encoding-and-decoding.">{#AUTOGENERATED-encoding-and-decoding.}</div> + +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]`. + +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. + +- - - + +### tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, name=None) <div class="md-anchor" id="decode_jpeg">{#decode_jpeg}</div> + +Decode a JPEG-encoded image to a uint8 tensor. + +The attr `channels` indicates the desired number of color channels for the +decoded image. + +Accepted values are: + +* 0: Use the number of channels in the JPEG-encoded image. +* 1: output a grayscale image. +* 3: output an RGB image. + +If needed, the JPEG-encoded image is transformed to match the requested number +of color channels. + +The attr `ratio` allows downscaling the image by an integer factor during +decoding. Allowed values are: 1, 2, 4, and 8. This is much faster than +downscaling the image later. + +##### Args: + + +* <b>contents</b>: A `Tensor` of type `string`. 0-D. The JPEG-encoded image. +* <b>channels</b>: An optional `int`. Defaults to `0`. + Number of color channels for the decoded image. +* <b>ratio</b>: An optional `int`. Defaults to `1`. Downscaling ratio. +* <b>fancy_upscaling</b>: An optional `bool`. Defaults to `True`. + If true use a slower but nicer upscaling of the + chroma planes (yuv420/422 only). +* <b>try_recover_truncated</b>: An optional `bool`. Defaults to `False`. + If true try to recover an image from truncated input. +* <b>acceptable_fraction</b>: An optional `float`. Defaults to `1`. + The minimum required fraction of lines before a truncated + input is accepted. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `uint8`. 3-D with shape `[height, width, channels]`.. + + +- - - + +### tf.image.encode_jpeg(image, format=None, quality=None, progressive=None, optimize_size=None, chroma_downsampling=None, density_unit=None, x_density=None, y_density=None, xmp_metadata=None, name=None) <div class="md-anchor" id="encode_jpeg">{#encode_jpeg}</div> + +JPEG-encode an image. + +`image` is a 3-D uint8 Tensor of shape `[height, width, channels]`. + +The attr `format` can be used to override the color format of the encoded +output. Values can be: + +* `''`: Use a default format based on the number of channels in the image. +* `grayscale`: Output a grayscale JPEG image. The `channels` dimension + of `image` must be 1. +* `rgb`: Output an RGB JPEG image. The `channels` dimension + of `image` must be 3. + +If `format` is not specified or is the empty string, a default format is picked +in function of the number of channels in `image`: + +* 1: Output a grayscale image. +* 3: Output an RGB image. + +##### Args: + + +* <b>image</b>: A `Tensor` of type `uint8`. + 3-D with shape `[height, width, channels]`. +* <b>format</b>: An optional `string` from: `"", "grayscale", "rgb"`. Defaults to `""`. + Per pixel image format. +* <b>quality</b>: An optional `int`. Defaults to `95`. + Quality of the compression from 0 to 100 (higher is better and slower). +* <b>progressive</b>: An optional `bool`. Defaults to `False`. + If True, create a JPEG that loads progressively (coarse to fine). +* <b>optimize_size</b>: An optional `bool`. Defaults to `False`. + If True, spend CPU/RAM to reduce size with no quality change. +* <b>chroma_downsampling</b>: An optional `bool`. Defaults to `True`. + See http://en.wikipedia.org/wiki/Chroma_subsampling. +* <b>density_unit</b>: An optional `string` from: `"in", "cm"`. Defaults to `"in"`. + Unit used to specify `x_density` and `y_density`: + pixels per inch (`'in'`) or centimeter (`'cm'`). +* <b>x_density</b>: An optional `int`. Defaults to `300`. + Horizontal pixels per density unit. +* <b>y_density</b>: An optional `int`. Defaults to `300`. + Vertical pixels per density unit. +* <b>xmp_metadata</b>: An optional `string`. Defaults to `""`. + If not empty, embed this XMP metadata in the image header. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `string`. 0-D. JPEG-encoded image. + + + +- - - + +### tf.image.decode_png(contents, channels=None, name=None) <div class="md-anchor" id="decode_png">{#decode_png}</div> + +Decode a PNG-encoded image to a uint8 tensor. + +The attr `channels` indicates the desired number of color channels for the +decoded image. + +Accepted values are: + +* 0: Use the number of channels in the PNG-encoded image. +* 1: output a grayscale image. +* 3: output an RGB image. +* 4: output an RGBA image. + +If needed, the PNG-encoded image is transformed to match the requested number +of color channels. + +##### Args: + + +* <b>contents</b>: A `Tensor` of type `string`. 0-D. The PNG-encoded image. +* <b>channels</b>: An optional `int`. Defaults to `0`. + Number of color channels for the decoded image. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `uint8`. 3-D with shape `[height, width, channels]`. + + +- - - + +### tf.image.encode_png(image, compression=None, name=None) <div class="md-anchor" id="encode_png">{#encode_png}</div> + +PNG-encode an image. + +`image` is a 3-D uint8 Tensor of shape `[height, width, channels]` where +`channels` is: + +* 1: for grayscale. +* 3: for RGB. +* 4: for RGBA. + +The ZLIB compression level, `compression`, can be -1 for the PNG-encoder +default or a value from 0 to 9. 9 is the highest compression level, generating +the smallest output, but is slower. + +##### Args: + + +* <b>image</b>: A `Tensor` of type `uint8`. + 3-D with shape `[height, width, channels]`. +* <b>compression</b>: An optional `int`. Defaults to `-1`. Compression level. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `string`. 0-D. PNG-encoded image. + + + +## Resizing. <div class="md-anchor" id="AUTOGENERATED-resizing.">{#AUTOGENERATED-resizing.}</div> + +The resizing Ops accept input images as tensors of several types. They always +output resized images as float32 tensors. + +The convenience function [resize_images()](#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 3-D individual images as input: +[resize_area](#resize_area), [resize_bicubic](#resize_bicubic), +[resize_bilinear](#resize_bilinear), +[resize_nearest_neighbor](#resize_nearest_neighbor). + +Example: + +```python +# Decode a JPG image and resize it to 299 by 299. +image = tf.image.decode_jpeg(...) +resized_image = tf.image.resize_bilinear(image, [299, 299]) +``` + +<i>Maybe refer to the Queue examples that show how to add images to a Queue +after resizing them to a fixed size, and how to dequeue batches of resized +images from the Queue.</i> + +- - - + +### tf.image.resize_images(images, new_height, new_width, method=0) <div class="md-anchor" id="resize_images">{#resize_images}</div> + +Resize `images` to `new_width`, `new_height` using the specified `method`. + +Resized images will be distorted if their original aspect ratio is not +the same as `new_width`, `new_height`. To avoid distortions see +[resize_image_with_crop_or_pad](#resize_image_with_crop_or_pad). + +`method` can be one of: + +* <b>ResizeMethod.BILINEAR</b>: [Bilinear interpolation.] + (https://en.wikipedia.org/wiki/Bilinear_interpolation) +* <b>ResizeMethod.NEAREST_NEIGHBOR</b>: [Nearest neighbor interpolation.] + (https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation) +* <b>ResizeMethod.BICUBIC</b>: [Bicubic interpolation.] + (https://en.wikipedia.org/wiki/Bicubic_interpolation) +* <b>ResizeMethod.AREA</b>: Area interpolation. + +##### Args: + + +* <b>images</b>: 4-D Tensor of shape `[batch, height, width, channels]` or + 3-D Tensor of shape `[height, width, channels]`. +* <b>new_height</b>: integer. +* <b>new_width</b>: integer. +* <b>method</b>: ResizeMethod. Defaults to `ResizeMethod.BILINEAR`. + +##### Raises: + + +* <b>ValueError</b>: if the shape of `images` is incompatible with the + shape arguments to this function +* <b>ValueError</b>: if an unsupported resize method is specified. + +##### Returns: + + If `images` was 4-D, a 4-D float Tensor of shape + `[batch, new_height, new_width, channels]`. + If `images` was 3-D, a 3-D float Tensor of shape + `[new_height, new_width, channels]`. + + + +- - - + +### tf.image.resize_area(images, size, name=None) <div class="md-anchor" id="resize_area">{#resize_area}</div> + +Resize `images` to `size` using area interpolation. + +Input images can be of different types but output images are always float. + +##### Args: + + +* <b>images</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `float32`, `float64`. + 4-D with shape `[batch, height, width, channels]`. +* <b>size</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `float32`. 4-D with shape + `[batch, new_height, new_width, channels]`. + + +- - - + +### tf.image.resize_bicubic(images, size, name=None) <div class="md-anchor" id="resize_bicubic">{#resize_bicubic}</div> + +Resize `images` to `size` using bicubic interpolation. + +Input images can be of different types but output images are always float. + +##### Args: + + +* <b>images</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `float32`, `float64`. + 4-D with shape `[batch, height, width, channels]`. +* <b>size</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `float32`. 4-D with shape + `[batch, new_height, new_width, channels]`. + + +- - - + +### tf.image.resize_bilinear(images, size, name=None) <div class="md-anchor" id="resize_bilinear">{#resize_bilinear}</div> + +Resize `images` to `size` using bilinear interpolation. + +Input images can be of different types but output images are always float. + +##### Args: + + +* <b>images</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `float32`, `float64`. + 4-D with shape `[batch, height, width, channels]`. +* <b>size</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `float32`. 4-D with shape + `[batch, new_height, new_width, channels]`. + + +- - - + +### tf.image.resize_nearest_neighbor(images, size, name=None) <div class="md-anchor" id="resize_nearest_neighbor">{#resize_nearest_neighbor}</div> + +Resize `images` to `size` using nearest neighbor interpolation. + +Input images can be of different types but output images are always float. + +##### Args: + + +* <b>images</b>: A `Tensor`. Must be one of the following types: `uint8`, `int8`, `int32`, `float32`, `float64`. + 4-D with shape `[batch, height, width, channels]`. +* <b>size</b>: A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor`. Has the same type as `images`. 4-D with shape + `[batch, new_height, new_width, channels]`. + + + + +## Cropping. <div class="md-anchor" id="AUTOGENERATED-cropping.">{#AUTOGENERATED-cropping.}</div> + +- - - + +### tf.image.resize_image_with_crop_or_pad(image, target_height, target_width) <div class="md-anchor" id="resize_image_with_crop_or_pad">{#resize_image_with_crop_or_pad}</div> + +Crops and/or pads an image to a target width and height. + +Resizes an image to a target width and height by either centrally +cropping the image or padding it evenly with zeros. + +If `width` or `height` is greater than the specified `target_width` or +`target_height` respectively, this op centrally crops along that dimension. +If `width` or `height` is smaller than the specified `target_width` or +`target_height` respectively, this op centrally pads with 0 along that +dimension. + +##### Args: + + +* <b>image</b>: 3-D tensor of shape [height, width, channels] +* <b>target_height</b>: Target height. +* <b>target_width</b>: Target width. + +##### Raises: + + +* <b>ValueError</b>: if `target_height` or `target_width` are zero or negative. + +##### Returns: + + Cropped and/or padded image of shape + `[target_height, target_width, channels]` + + + +- - - + +### tf.image.pad_to_bounding_box(image, offset_height, offset_width, target_height, target_width) <div class="md-anchor" id="pad_to_bounding_box">{#pad_to_bounding_box}</div> + +Pad `image` with zeros to the specified `height` and `width`. + +Adds `offset_height` rows of zeros on top, `offset_width` columns of +zeros on the left, and then pads the image on the bottom and right +with zeros until it has dimensions `target_height`, `target_width`. + +This op does nothing if `offset_*` is zero and the image already has size +`target_height` by `target_width`. + +##### Args: + + +* <b>image</b>: 3-D tensor with shape `[height, width, channels]` +* <b>offset_height</b>: Number of rows of zeros to add on top. +* <b>offset_width</b>: Number of columns of zeros to add on the left. +* <b>target_height</b>: Height of output image. +* <b>target_width</b>: Width of output image. + +##### Returns: + + 3-D tensor of shape `[target_height, target_width, channels]` + +##### Raises: + + +* <b>ValueError</b>: If the shape of `image` is incompatible with the `offset_*` or + `target_*` arguments + + +- - - + +### tf.image.crop_to_bounding_box(image, offset_height, offset_width, target_height, target_width) <div class="md-anchor" id="crop_to_bounding_box">{#crop_to_bounding_box}</div> + +Crops an image to a specified bounding box. + +This op cuts a rectangular part out of `image`. The top-left corner of the +returned image is at `offset_height, offset_width` in `image`, and its +lower-right corner is at +`offset_height + target_height, offset_width + target_width'. + +##### Args: + + +* <b>image</b>: 3-D tensor with shape `[height, width, channels]` +* <b>offset_height</b>: Vertical coordinate of the top-left corner of the result in + the input. +* <b>offset_width</b>: Horizontal coordinate of the top-left corner of the result in + the input. +* <b>target_height</b>: Height of the result. +* <b>target_width</b>: Width of the result. + +##### Returns: + + 3-D tensor of image with shape `[target_height, target_width, channels]` + +##### Raises: + + +* <b>ValueError</b>: If the shape of `image` is incompatible with the `offset_*` or + `target_*` arguments + + +- - - + +### tf.image.random_crop(image, size, seed=None, name=None) <div class="md-anchor" id="random_crop">{#random_crop}</div> + +Randomly crops `image` to size `[target_height, target_width]`. + +The offset of the output within `image` is uniformly random. `image` always +fully contains the result. + +##### Args: + + +* <b>image</b>: 3-D tensor of shape `[height, width, channels]` +* <b>size</b>: 1-D tensor with two elements, specifying target `[height, width]` +* <b>seed</b>: A Python integer. Used to create a random seed. + See [`set_random_seed`](constant_op.md#set_random_seed) for behavior. +* <b>name</b>: A name for this operation (optional). + +##### Returns: + + A cropped 3-D tensor of shape `[target_height, target_width, channels]`. + + +- - - + +### tf.image.extract_glimpse(input, size, offsets, centered=None, normalized=None, uniform_noise=None, name=None) <div class="md-anchor" id="extract_glimpse">{#extract_glimpse}</div> + +Extracts a glimpse from the input tensor. + +Returns a set of windows called glimpses extracted at location `offsets` +from the input tensor. If the windows only partially overlaps the inputs, the +non overlapping areas will be filled with random noise. + +The result is a 4-D tensor of shape `[batch_size, glimpse_height, +glimpse_width, channels]`. The channels and batch dimensions are the same as that +of the input tensor. The height and width of the output windows are +specified in the `size` parameter. + +The argument `normalized` and `centered` controls how the windows are built: +* If the coordinates are normalized but not centered, 0.0 and 1.0 + correspond to the minimum and maximum of each height and width dimension. +* If the coordinates are both normalized and centered, they range from -1.0 to + 1.0. The coordinates (-1.0, -1.0) correspond to the upper left corner, the + lower right corner is located at (1.0, 1.0) and the center is at (0, 0). +* If the coordinates are not normalized they are interpreted as numbers of pixels. + +##### Args: + + +* <b>input</b>: A `Tensor` of type `float32`. + A 4-D float tensor of shape `[batch_size, height, width, channels]`. +* <b>size</b>: A `Tensor` of type `int32`. + A 1-D tensor of 2 elements containing the size of the glimpses to extract. + The glimpse height must be specified first, following by the glimpse width. +* <b>offsets</b>: A `Tensor` of type `float32`. + A 2-D integer tensor of shape `[batch_size, 2]` containing the x, y + locations of the center of each window. +* <b>centered</b>: An optional `bool`. Defaults to `True`. + indicates if the offset coordinates are centered relative to + the image, in which case the (0, 0) offset is relative to the center of the + input images. If false, the (0,0) offset corresponds to the upper left corner + of the input images. +* <b>normalized</b>: An optional `bool`. Defaults to `True`. + indicates if the offset coordinates are normalized. +* <b>uniform_noise</b>: An optional `bool`. Defaults to `True`. + indicates if the noise should be generated using a + uniform distribution or a gaussian distribution. +* <b>name</b>: A name for the operation (optional). + +##### Returns: + + A `Tensor` of type `float32`. + A tensor representing the glimpses `[batch_size, glimpse_height, + glimpse_width, channels]`. + + + +## Flipping and Transposing. <div class="md-anchor" id="AUTOGENERATED-flipping-and-transposing.">{#AUTOGENERATED-flipping-and-transposing.}</div> + +- - - + +### tf.image.flip_up_down(image) <div class="md-anchor" id="flip_up_down">{#flip_up_down}</div> + +Flip an image horizontally (upside down). + +Outputs the contents of `image` flipped along the first dimension, which is +`height`. + +See also `reverse()`. + +##### Args: + + +* <b>image</b>: A 3-D tensor of shape `[height, width, channels].` + +##### Returns: + + A 3-D tensor of the same type and shape as `image`. + +##### Raises: + + +* <b>ValueError</b>: if the shape of `image` not supported. + + +- - - + +### tf.image.random_flip_up_down(image, seed=None) <div class="md-anchor" id="random_flip_up_down">{#random_flip_up_down}</div> + +Randomly flips an image vertically (upside down). + +With a 1 in 2 chance, outputs the contents of `image` flipped along the first +dimension, which is `height`. Otherwise output the image as-is. + +##### Args: + + +* <b>image</b>: A 3-D tensor of shape `[height, width, channels].` +* <b>seed</b>: A Python integer. Used to create a random seed. + See [`set_random_seed`](constant_op.md#set_random_seed) for behavior. + +##### Returns: + + A 3-D tensor of the same type and shape as `image`. + +##### Raises: + + +* <b>ValueError</b>: if the shape of `image` not supported. + + + +- - - + +### tf.image.flip_left_right(image) <div class="md-anchor" id="flip_left_right">{#flip_left_right}</div> + +Flip an image horizontally (left to right). + +Outputs the contents of `image` flipped along the second dimension, which is +`width`. + +See also `reverse()`. + +##### Args: + + +* <b>image</b>: A 3-D tensor of shape `[height, width, channels].` + +##### Returns: + + A 3-D tensor of the same type and shape as `image`. + +##### Raises: + + +* <b>ValueError</b>: if the shape of `image` not supported. + + +- - - + +### tf.image.random_flip_left_right(image, seed=None) <div class="md-anchor" id="random_flip_left_right">{#random_flip_left_right}</div> + +Randomly flip an image horizontally (left to right). + +With a 1 in 2 chance, outputs the contents of `image` flipped along the +second dimension, which is `width`. Otherwise output the image as-is. + +##### Args: + + +* <b>image</b>: A 3-D tensor of shape `[height, width, channels].` +* <b>seed</b>: A Python integer. Used to create a random seed. + See [`set_random_seed`](constant_op.md#set_random_seed) for behavior. + +##### Returns: + + A 3-D tensor of the same type and shape as `image`. + +##### Raises: + + +* <b>ValueError</b>: if the shape of `image` not supported. + + + +- - - + +### tf.image.transpose_image(image) <div class="md-anchor" id="transpose_image">{#transpose_image}</div> + +Transpose an image by swapping the first and second dimension. + +See also `transpose()`. + +##### Args: + + +* <b>image</b>: 3-D tensor of shape `[height, width, channels]` + +##### Returns: + + A 3-D tensor of shape `[width, height, channels]` + +##### Raises: + + +* <b>ValueError</b>: if the shape of `image` not supported. + + + +## Image Adjustments. <div class="md-anchor" id="AUTOGENERATED-image-adjustments.">{#AUTOGENERATED-image-adjustments.}</div> + +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. + +- - - + +### tf.image.adjust_brightness(image, delta, min_value=None, max_value=None) <div class="md-anchor" id="adjust_brightness">{#adjust_brightness}</div> + +Adjust the brightness of RGB or Grayscale images. + +The value `delta` is added to all components of the tensor `image`. `image` +and `delta` are cast to `float` before adding, and the resulting values are +clamped to `[min_value, max_value]`. Finally, the result is cast back to +`images.dtype`. + +If `min_value` or `max_value` are not given, they are set to the minimum and +maximum allowed values for `image.dtype` respectively. + +##### Args: + + +* <b>image</b>: A tensor. +* <b>delta</b>: A scalar. Amount to add to the pixel values. +* <b>min_value</b>: Minimum value for output. +* <b>max_value</b>: Maximum value for output. + +##### Returns: + + A tensor of the same shape and type as `image`. + + +- - - + +### tf.image.random_brightness(image, max_delta, seed=None) <div class="md-anchor" id="random_brightness">{#random_brightness}</div> + +Adjust the brightness of images by a random factor. + +Equivalent to `adjust_brightness()` using a `delta` randomly picked in the +interval `[-max_delta, max_delta)`. + +Note that `delta` is picked as a float. Because for integer type images, +the brightness adjusted result is rounded before casting, integer images may +have modifications in the range `[-max_delta,max_delta]`. + +##### Args: + + +* <b>image</b>: 3-D tensor of shape `[height, width, channels]`. +* <b>max_delta</b>: float, must be non-negative. +* <b>seed</b>: A Python integer. Used to create a random seed. + See [`set_random_seed`](constant_op.md#set_random_seed) for behavior. + +##### Returns: + + 3-D tensor of images of shape `[height, width, channels]` + +##### Raises: + + +* <b>ValueError</b>: if max_delta is negative. + + + +- - - + +### tf.image.adjust_contrast(images, contrast_factor, min_value=None, max_value=None) <div class="md-anchor" id="adjust_contrast">{#adjust_contrast}</div> + +Adjust contrast of RGB or grayscale images. + +`images` is a tensor of at least 3 dimensions. The last 3 dimensions are +interpreted as `[height, width, channels]`. The other dimensions only +represent a collection of images, such as `[batch, height, width, channels].` + +Contrast is adjusted independently for each channel of each image. + +For each channel, this Op first computes the mean of the image pixels in the +channel and then adjusts each component `x` of each pixel to +`(x - mean) * contrast_factor + mean`. + +The adjusted values are then clipped to fit in the `[min_value, max_value]` +interval. If `min_value` or `max_value` is not given, it is replaced with the +minimum and maximum values for the data type of `images` respectively. + +The contrast-adjusted image is always computed as `float`, and it is +cast back to its original type after clipping. + +##### Args: + + +* <b>images</b>: Images to adjust. At least 3-D. +* <b>contrast_factor</b>: A float multiplier for adjusting contrast. +* <b>min_value</b>: Minimum value for clipping the adjusted pixels. +* <b>max_value</b>: Maximum value for clipping the adjusted pixels. + +##### Returns: + + The constrast-adjusted image or images. + +##### Raises: + + +* <b>ValueError</b>: if the arguments are invalid. + + +- - - + +### tf.image.random_contrast(image, lower, upper, seed=None) <div class="md-anchor" id="random_contrast">{#random_contrast}</div> + +Adjust the contrase of an image by a random factor. + +Equivalent to `adjust_constrast()` but uses a `contrast_factor` randomly +picked in the interval `[lower, upper]`. + +##### Args: + + +* <b>image</b>: 3-D tensor of shape `[height, width, channels]`. +* <b>lower</b>: float. Lower bound for the random contrast factor. +* <b>upper</b>: float. Upper bound for the random contrast factor. +* <b>seed</b>: A Python integer. Used to create a random seed. + See [`set_random_seed`](constant_op.md#set_random_seed) for behavior. + +##### Returns: + + 3-D tensor of shape `[height, width, channels]`. + +##### Raises: + + +* <b>ValueError</b>: if `upper <= lower` or if `lower < 0`. + + + +- - - + +### tf.image.per_image_whitening(image) <div class="md-anchor" id="per_image_whitening">{#per_image_whitening}</div> + +Linearly scales `image` to have zero mean and unit norm. + +This op computes `(x - mean) / adjusted_stddev`, where `mean` is the average +of all values in image, and +`adjusted_stddev = max(stddev, 1.0/srqt(image.NumElements()))`. + +`stddev` is the standard deviation of all values in `image`. It is capped +away from zero to protect against division by 0 when handling uniform images. + +Note that this implementation is limited: +* It only whitens based on the statistics of an individual image. +* It does not take into account the covariance structure. + +##### Args: + + +* <b>image</b>: 3-D tensor of shape `[height, width, channels]`. + +##### Returns: + + The whitened image with same shape as `image`. + +##### Raises: + + +* <b>ValueError</b>: if the shape of 'image' is incompatible with this function. + + |