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Diffstat (limited to 'tensorflow/core/ops/image_ops.cc')
| -rw-r--r-- | tensorflow/core/ops/image_ops.cc | 273 |
1 files changed, 273 insertions, 0 deletions
diff --git a/tensorflow/core/ops/image_ops.cc b/tensorflow/core/ops/image_ops.cc new file mode 100644 index 0000000000..88af081893 --- /dev/null +++ b/tensorflow/core/ops/image_ops.cc @@ -0,0 +1,273 @@ +#include "tensorflow/core/framework/op.h" + +namespace tensorflow { + +// -------------------------------------------------------------------------- +REGISTER_OP("ResizeArea") + .Input("images: T") + .Input("size: int32") + .Output("resized_images: float") + .Attr("T: {uint8, int8, int32, float, double}") + .Doc(R"doc( +Resize `images` to `size` using area interpolation. + +Input images can be of different types but output images are always float. + +images: 4-D with shape `[batch, height, width, channels]`. +size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +resized_images: 4-D with shape + `[batch, new_height, new_width, channels]`. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("ResizeBicubic") + .Input("images: T") + .Input("size: int32") + .Output("resized_images: float") + .Attr("T: {uint8, int8, int32, float, double}") + .Doc(R"doc( +Resize `images` to `size` using bicubic interpolation. + +Input images can be of different types but output images are always float. + +images: 4-D with shape `[batch, height, width, channels]`. +size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +resized_images: 4-D with shape + `[batch, new_height, new_width, channels]`. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("ResizeBilinear") + .Input("images: T") + .Input("size: int32") + .Output("resized_images: float") + .Attr("T: {uint8, int8, int32, float, double}") + .Doc(R"doc( +Resize `images` to `size` using bilinear interpolation. + +Input images can be of different types but output images are always float. + +images: 4-D with shape `[batch, height, width, channels]`. +size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +resized_images: 4-D with shape + `[batch, new_height, new_width, channels]`. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("ResizeNearestNeighbor") + .Input("images: T") + .Input("size: int32") + .Output("resized_images: T") + .Attr("T: {uint8, int8, int32, float, double}") + .Doc(R"doc( +Resize `images` to `size` using nearest neighbor interpolation. + +Input images can be of different types but output images are always float. + +images: 4-D with shape `[batch, height, width, channels]`. +size:= A 1-D int32 Tensor of 2 elements: `new_height, new_width`. The + new size for the images. +resized_images: 4-D with shape + `[batch, new_height, new_width, channels]`. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("RandomCrop") + .Input("image: T") + .Input("size: int64") + .Output("output: T") + .Attr("T: {uint8, int8, int16, int32, int64, float, double}") + .Attr("seed: int = 0") + .Attr("seed2: int = 0") + .SetIsStateful() + .Doc(R"doc( +Randomly crop `image`. + +`size` is a 1-D int64 tensor with 2 elements representing the crop height and +width. The values must be non negative. + +This Op picks a random location in `image` and crops a `height` by `width` +rectangle from that location. The random location is picked so the cropped +area will fit inside the original image. + +image: 3-D of shape `[height, width, channels]`. +size: 1-D of length 2 containing: `crop_height`, `crop_width`.. +seed: If either seed or seed2 are set to be non-zero, the random number + generator is seeded by the given seed. Otherwise, it is seeded by a + random seed. +seed2: An second seed to avoid seed collision. +output: 3-D of shape `[crop_height, crop_width, channels].` +)doc"); +// TODO(shlens): Support variable rank in RandomCrop. + +// -------------------------------------------------------------------------- +REGISTER_OP("DecodeJpeg") + .Input("contents: string") + .Attr("channels: int = 0") + .Attr("ratio: int = 1") + .Attr("fancy_upscaling: bool = true") + .Attr("try_recover_truncated: bool = false") + .Attr("acceptable_fraction: float = 1.0") + .Output("image: uint8") + .Doc(R"doc( +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. + +contents: 0-D. The JPEG-encoded image. +channels: Number of color channels for the decoded image. +ratio: Downscaling ratio. +fancy_upscaling: If true use a slower but nicer upscaling of the + chroma planes (yuv420/422 only). +try_recover_truncated: If true try to recover an image from truncated input. +acceptable_fraction: The minimum required fraction of lines before a truncated + input is accepted. +image: 3-D with shape `[height, width, channels]`.. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("EncodeJpeg") + .Input("image: uint8") + .Attr("format: {'', 'grayscale', 'rgb'} = ''") + .Attr("quality: int = 95") + .Attr("progressive: bool = false") + .Attr("optimize_size: bool = false") + .Attr("chroma_downsampling: bool = true") + .Attr("density_unit: {'in', 'cm'} = 'in'") + .Attr("x_density: int = 300") + .Attr("y_density: int = 300") + .Attr("xmp_metadata: string = ''") + .Output("contents: string") + .Doc(R"doc( +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. + +image: 3-D with shape `[height, width, channels]`. +format: Per pixel image format. +quality: Quality of the compression from 0 to 100 (higher is better and slower). +progressive: If True, create a JPEG that loads progressively (coarse to fine). +optimize_size: If True, spend CPU/RAM to reduce size with no quality change. +chroma_downsampling: See http://en.wikipedia.org/wiki/Chroma_subsampling. +density_unit: Unit used to specify `x_density` and `y_density`: + pixels per inch (`'in'`) or centimeter (`'cm'`). +x_density: Horizontal pixels per density unit. +y_density: Vertical pixels per density unit. +xmp_metadata: If not empty, embed this XMP metadata in the image header. +contents: 0-D. JPEG-encoded image. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("AdjustContrast") + .Input("images: T") + .Input("contrast_factor: float") + .Input("min_value: float") + .Input("max_value: float") + .Output("output: float") + .Attr("T: {uint8, int8, int16, int32, int64, float, double}") + .Doc(R"Doc( +Adjust the contrast of one or more 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, the Op first computes the mean of the image pixels in the +channel and then adjusts each component of each pixel to +`(x - mean) * contrast_factor + mean`. + +These adjusted values are then clipped to fit in the `[min_value, max_value]` +interval. + +`images: Images to adjust. At least 3-D. +contrast_factor: A float multiplier for adjusting contrast. +min_value: Minimum value for clipping the adjusted pixels. +max_value: Maximum value for clipping the adjusted pixels. +output: The constrast-adjusted image or images. +)Doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("DecodePng") + .Input("contents: string") + .Attr("channels: int = 0") + .Output("image: uint8") + .Doc(R"doc( +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. + +contents: 0-D. The PNG-encoded image. +channels: Number of color channels for the decoded image. +image: 3-D with shape `[height, width, channels]`. +)doc"); + +// -------------------------------------------------------------------------- +REGISTER_OP("EncodePng") + .Input("image: uint8") + .Attr("compression: int = -1") + .Output("contents: string") + .Doc(R"doc( +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. + +image: 3-D with shape `[height, width, channels]`. +compression: Compression level. +contents: 0-D. PNG-encoded image. +)doc"); + +} // namespace tensorflow |