/******************************************************************************* * 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 #include #include #include #include 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