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cavis/libnd4j/include/ops/declarable/headers/images.h

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/*******************************************************************************
* Copyright (c) 2019 Konduit K.K.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Oleh Semeniv (oleg.semeniv@gmail.com)
//
//
// @author AbdelRauf (rauf@konduit.ai)
//
#ifndef LIBND4J_HEADERS_IMAGES_H
#define LIBND4J_HEADERS_IMAGES_H
#include <ops/declarable/headers/common.h>
#include <ops/declarable/CustomOperations.h>
#include <helpers/ConstantTadHelper.h>
#include <execution/Threads.h>
#include <ops/declarable/helpers/imagesHelpers.h>
namespace sd {
namespace ops {
/**
* Rgb To Hsv
* Input arrays:
* 0 - input array with rank >= 1, must have at least one dimension equal 3, that is dimension containing channels.
* Int arguments:
* 0 - optional argument, corresponds to dimension with 3 channels
*/
#if NOT_EXCLUDED(OP_rgb_to_hsv)
DECLARE_CONFIGURABLE_OP(rgb_to_hsv, 1, 1, true, 0, 0);
#endif
/**
* Hsv To Rgb
* Input arrays:
* 0 - input array with rank >= 1, must have at least one dimension equal 3, that is dimension containing channels.
* Int arguments:
* 0 - optional argument, corresponds to dimension with 3 channels
*/
#if NOT_EXCLUDED(OP_hsv_to_rgb)
DECLARE_CONFIGURABLE_OP(hsv_to_rgb, 1, 1, true, 0, 0);
#endif
/**
* Rgb To GrayScale
* Input arrays:
* 0 - input array with rank >= 1, the RGB tensor to convert. Last dimension must have size 3 and should contain RGB values.
*/
#if NOT_EXCLUDED(OP_rgb_to_grs)
DECLARE_CUSTOM_OP(rgb_to_grs, 1, 1, false, 0, 0);
#endif
/**
* Rgb To Yuv
* Input arrays:
* 0 - input array with rank >= 1, must have at least one dimension equal 3, that is dimension containing channels.
* Int arguments:
* 0 - optional argument, corresponds to dimension with 3 channels
*/
#if NOT_EXCLUDED(OP_rgb_to_yuv)
DECLARE_CONFIGURABLE_OP(rgb_to_yuv, 1, 1, true, 0, 0);
#endif
/**
* Yuv To Rgb
* Input arrays:
* 0 - input array with rank >= 1, must have at least one dimension equal 3, that is dimension containing channels.
* Int arguments:
* 0 - optional argument, corresponds to dimension with 3 channels
*/
#if NOT_EXCLUDED(OP_rgb_to_yuv)
DECLARE_CONFIGURABLE_OP(yuv_to_rgb, 1, 1, true, 0, 0);
#endif
/**
* Rgb To Yiq
* Input arrays:
* 0 - input array with rank >= 1, must have at least one dimension equal 3, that is dimension containing channels.
* Int arguments:
* 0 - optional argument, corresponds to dimension with 3 channels
*/
#if NOT_EXCLUDED(OP_rgb_to_yiq)
DECLARE_CONFIGURABLE_OP(rgb_to_yiq, 1, 1, true, 0, 0);
#endif
/**
* Yiq To Rgb
* Input arrays:
* 0 - input array with rank >= 1, must have at least one dimension equal 3, that is dimension containing channels.
* Int arguments:
* 0 - optional argument, corresponds to dimension with 3 channels
*/
#if NOT_EXCLUDED(OP_yiq_to_rgb)
DECLARE_CONFIGURABLE_OP(yiq_to_rgb, 1, 1, true, 0, 0);
#endif
/**
* resize_images - resize image with given size and method
* there are 4 methods allowed: RESIZE_BILINEAR(0), RESIZE_NEIGHBOR(1), RESIZE_AREA(2) and RESIZE_BICUBIC(3)
* inputs:
* 0 - 4D tensor with shape {batch, height, width, channels}
* 1 - 1D integer tensor with {new_height, new_width} (optional)
* 2 - 0D integer tensor with method (0 to 3) (optional)
*
* int args:
* 0 - new_height
* 1 - new_width
* 2 - method
*
* bool args:
* 0 - align corners (default false) - optional
* 1 - preserve_aspect_ratio (default false) - optional
*
* CAUTION: one of methods can be used to give size and method - as tensors or as int args only
*
* output:
* 0 - 4D float32 tensor with shape {batch, new_height, new_width, channels}
*
*/
#if NOT_EXCLUDED(OP_resize_images)
DECLARE_CUSTOM_OP(resize_images, 1,1,false, 0, 0);
#endif
/**
* This op make bilinear or nearest neighbor interpolated resize for given tensor
*
* input array:
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels) numeric type
* 1 - 2D-Tensor with shape (num_boxes, 4) float type
* 2 - 1D-Tensor with shape (num_boxes) int type
* 3 - 1D-Tensor with 2 values (newWidth, newHeight) (optional) int type
*
* float arguments (optional)
* 0 - exprapolation_value (optional) default 0.f
*
* int arguments: (optional)
* 0 - mode (default 0 - bilinear interpolation)
*
* output array:
* the 4D-Tensor with resized to crop_size images given - float type
*/
#if NOT_EXCLUDED(OP_crop_and_resize)
DECLARE_CUSTOM_OP(crop_and_resize, 4, 1, false, -1, -1);
#endif
/**
* This op make bilinear interpolated resize for given tensor
*
* input array:
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
* 1 - 1D-Tensor with 2 values (newWidth, newHeight) (optional)
*
* int arguments: (optional)
* 0 - new width
* 1 - new height
*
* output array:
* the 4D-Tensor with calculated backproped dots
*
* CAUTION: either size tensor or a pair of int params should be provided.
*/
#if NOT_EXCLUDED(OP_resize_bilinear)
DECLARE_CUSTOM_OP(resize_bilinear, 1, 1, false, 0, -2);
#endif
/**
* This op make nearest neighbor interpolated resize for given tensor
*
* input array:
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
* 1 - 1D-Tensor with 2 values (newWidth, newHeight) (optional)
*
* int arguments: (optional)
* 0 - new width
* 1 - new height
*
* output array:
* the 4D-Tensor with resized image (shape is {batch, newWidth, newHeight, channels})
*
* CAUTION: either size tensor or a pair of int params should be provided.
*/
#if NOT_EXCLUDED(OP_resize_nearest_neighbor)
DECLARE_CUSTOM_OP(resize_nearest_neighbor, 1, 1, false, 0, -2);
#endif
/**
* This op make bicubic interpolated resize for given tensor
*
* input array:
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
* 1 - 1D-Tensor with 2 values (newWidth, newHeight)
*
* output array:
* the 4D-Tensor with resized image (shape is {batch, newWidth, newHeight, channels})
*
*/
#if NOT_EXCLUDED(OP_resize_bicubic)
DECLARE_CUSTOM_OP(resize_bicubic, 1, 1, false, 0, -2);
#endif
/**
* This op make area interpolated resize (as OpenCV INTER_AREA algorithm) for given tensor
*
* input array:
* 0 - images - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
* 1 - size - 1D-Tensor with 2 values (newWidth, newHeight) (if missing a pair of integer args should be provided).
*
* int args: - proveded only when size tensor is missing
* 0 - new height
* 1 - new width
* boolean args:
* 0 - align_corners - optional (default is false)
*
* output array:
* the 4D-Tensor with resized image (shape is {batch, newWidth, newHeight, channels})
*
*/
#if NOT_EXCLUDED(OP_resize_area)
DECLARE_CUSTOM_OP(resize_area, 1, 1, false, 0, -2);
#endif
/**
* This op make interpolated resize for given tensor with given algorithm.
* Supported algorithms are bilinear, bicubic, nearest_neighbor, lanczos5, gaussian, area and mitchellcubic.
*
* input array:
* 0 - 4D-Tensor with shape (batch, sizeX, sizeY, channels)
* 1 - 1D-Tensor with 2 values (newWidth, newHeight)
*
* optional int args:
* 0 - algorithm - bilinear by default
* optional bool args:
* 0 - preserve_aspect_ratio - default False
* 1 - antialias - default False
*
* output array:
* the 4D-Tensor with resized by given algorithm image (shape is {batch, newWidth, newHeight, channels})
*
*/
#if NOT_EXCLUDED(OP_image_resize)
DECLARE_CUSTOM_OP(image_resize, 2, 1, false, 0, 0);
#endif
}
}
#endif
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