/**
 * Copyright 2020-2021 Huawei Technologies Co., Ltd
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://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.
 */

#ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_
#define MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_

#include <map>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include "include/api/status.h"
#include "minddata/dataset/include/constants.h"
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/include/vision_lite.h"

namespace mindspore {
namespace dataset {

// Transform operations for performing computer vision.
namespace vision {

// Transform Op classes (in alphabetical order)
class AutoContrastOperation;
class BoundingBoxAugmentOperation;
class CutMixBatchOperation;
class CutOutOperation;
class EqualizeOperation;
class HwcToChwOperation;
class InvertOperation;
class MixUpBatchOperation;
class NormalizePadOperation;
class PadOperation;
class RandomAffineOperation;
class RandomColorOperation;
class RandomColorAdjustOperation;
class RandomCropOperation;
class RandomCropDecodeResizeOperation;
class RandomCropWithBBoxOperation;
class RandomHorizontalFlipOperation;
class RandomHorizontalFlipWithBBoxOperation;
class RandomPosterizeOperation;
class RandomResizeOperation;
class RandomResizeWithBBoxOperation;
class RandomResizedCropOperation;
class RandomResizedCropWithBBoxOperation;
class RandomRotationOperation;
class RandomSelectSubpolicyOperation;
class RandomSharpnessOperation;
class RandomSolarizeOperation;
class RandomVerticalFlipOperation;
class RandomVerticalFlipWithBBoxOperation;
class RescaleOperation;
class ResizeWithBBoxOperation;
class RgbaToBgrOperation;
class RgbaToRgbOperation;
class SoftDvppDecodeRandomCropResizeJpegOperation;
class SoftDvppDecodeResizeJpegOperation;
class SwapRedBlueOperation;
class UniformAugOperation;

/// \brief Function to create a AutoContrast TensorOperation.
/// \notes Apply automatic contrast on input image.
/// \param[in] cutoff Percent of pixels to cut off from the histogram, the valid range of cutoff value is 0 to 100.
/// \param[in] ignore Pixel values to ignore.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<AutoContrastOperation> AutoContrast(float cutoff = 0.0, std::vector<uint32_t> ignore = {});

/// \brief Function to create a BoundingBoxAugment TensorOperation.
/// \notes  Apply a given image transform on a random selection of bounding box regions of a given image.
/// \param[in] transform A TensorOperation transform.
/// \param[in] ratio Ratio of bounding boxes to apply augmentation on. Range: [0, 1] (default=0.3).
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<BoundingBoxAugmentOperation> BoundingBoxAugment(std::shared_ptr<TensorOperation> transform,
                                                                float ratio = 0.3);

/// \brief Function to apply CutMix on a batch of images
/// \notes Masks a random section of each image with the corresponding part of another randomly
///     selected image in that batch
/// \param[in] image_batch_format The format of the batch
/// \param[in] alpha The hyperparameter of beta distribution (default = 1.0)
/// \param[in] prob The probability by which CutMix is applied to each image (default = 1.0)
/// \return Shared pointer to the current TensorOp
std::shared_ptr<CutMixBatchOperation> CutMixBatch(ImageBatchFormat image_batch_format, float alpha = 1.0,
                                                  float prob = 1.0);

/// \brief Function to create a CutOut TensorOp
/// \notes Randomly cut (mask) out a given number of square patches from the input image
/// \param[in] length Integer representing the side length of each square patch
/// \param[in] num_patches Integer representing the number of patches to be cut out of an image
/// \return Shared pointer to the current TensorOp
std::shared_ptr<CutOutOperation> CutOut(int32_t length, int32_t num_patches = 1);

/// \brief Function to create a Equalize TensorOperation.
/// \notes Apply histogram equalization on input image.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<EqualizeOperation> Equalize();

/// \brief Function to create a HwcToChw TensorOperation.
/// \notes Transpose the input image; shape (H, W, C) to shape (C, H, W).
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<HwcToChwOperation> HWC2CHW();

/// \brief Function to create a Invert TensorOperation.
/// \notes Apply invert on input image in RGB mode.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<InvertOperation> Invert();

/// \brief Function to create a MixUpBatch TensorOperation.
/// \notes Apply MixUp transformation on an input batch of images and labels. The labels must be in
///     one-hot format and Batch must be called before calling this function.
/// \param[in] alpha hyperparameter of beta distribution (default = 1.0)
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<MixUpBatchOperation> MixUpBatch(float alpha = 1);

/// \brief Function to create a NormalizePad TensorOperation.
/// \notes Normalize the input image with respect to mean and standard deviation and pad an extra
///     channel with value zero.
/// \param[in] mean A vector of mean values for each channel, w.r.t channel order.
///     The mean values must be in range [0.0, 255.0].
/// \param[in] std A vector of standard deviations for each channel, w.r.t. channel order.
///     The standard deviation values must be in range (0.0, 255.0]
/// \param[in] dtype The output datatype of Tensor.
///     The standard deviation values must be "float32" or "float16"（default = "float32"）
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<NormalizePadOperation> NormalizePad(const std::vector<float> &mean, const std::vector<float> &std,
                                                    const std::string &dtype = "float32");

/// \brief Function to create a Pad TensorOp
/// \notes Pads the image according to padding parameters
/// \param[in] padding A vector representing the number of pixels to pad the image
///    If vector has one value, it pads all sides of the image with that value.
///    If vector has two values, it pads left and top with the first and
///    right and bottom with the second value.
///    If vector has four values, it pads left, top, right, and bottom with
///    those values respectively.
/// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
///    BorderType.kConstant. If 1 value is provided, it is used for all RGB channels. If 3 values are provided,
///    it is used to fill R, G, B channels respectively.
/// \param[in] padding_mode The method of padding (default=BorderType.kConstant)
///    Can be any of
///    [BorderType.kConstant, BorderType.kEdge, BorderType.kReflect, BorderType.kSymmetric]
///    - BorderType.kConstant, means it fills the border with constant values
///    - BorderType.kEdge, means it pads with the last value on the edge
///    - BorderType.kReflect, means it reflects the values on the edge omitting the last value of edge
///    - BorderType.kSymmetric, means it reflects the values on the edge repeating the last value of edge
/// \return Shared pointer to the current TensorOp
std::shared_ptr<PadOperation> Pad(std::vector<int32_t> padding, std::vector<uint8_t> fill_value = {0},
                                  BorderType padding_mode = BorderType::kConstant);

/// \brief Function to create a RandomAffine TensorOperation.
/// \notes Applies a Random Affine transformation on input image in RGB or Greyscale mode.
/// \param[in] degrees A float vector of size 2, representing the starting and ending degree
/// \param[in] translate_range A float vector of size 2 or 4, representing percentages of translation on x and y axes.
///    if size is 2, (min_dx, max_dx, 0, 0)
///    if size is 4, (min_dx, max_dx, min_dy, max_dy)
///    all values are in range [-1, 1]
/// \param[in] scale_range A float vector of size 2, representing the starting and ending scales in the range.
/// \param[in] shear_ranges A float vector of size 2 or 4, representing the starting and ending shear degrees
///    vertically and horizontally.
///    if size is 2, (min_shear_x, max_shear_x, 0, 0)
///    if size is 4, (min_shear_x, max_shear_x, min_shear_y, max_shear_y)
/// \param[in] interpolation An enum for the mode of interpolation
/// \param[in] fill_value A vector representing the value to fill the area outside the transform
///    in the output image. If 1 value is provided, it is used for all RGB channels.
///    If 3 values are provided, it is used to fill R, G, B channels respectively.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomAffineOperation> RandomAffine(
  const std::vector<float_t> &degrees, const std::vector<float_t> &translate_range = {0.0, 0.0, 0.0, 0.0},
  const std::vector<float_t> &scale_range = {1.0, 1.0}, const std::vector<float_t> &shear_ranges = {0.0, 0.0, 0.0, 0.0},
  InterpolationMode interpolation = InterpolationMode::kNearestNeighbour,
  const std::vector<uint8_t> &fill_value = {0, 0, 0});

/// \brief Blends an image with its grayscale version with random weights
///        t and 1 - t generated from a given range. If the range is trivial
///        then the weights are determinate and t equals the bound of the interval
/// \param[in] t_lb Lower bound on the range of random weights
/// \param[in] t_lb Upper bound on the range of random weights
/// \return Shared pointer to the current TensorOp
std::shared_ptr<RandomColorOperation> RandomColor(float t_lb, float t_ub);

/// \brief Randomly adjust the brightness, contrast, saturation, and hue of the input image
/// \param[in] brightness Brightness adjustment factor. Must be a vector of one or two values
///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
/// \param[in] contrast Contrast adjustment factor. Must be a vector of one or two values
///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
/// \param[in] saturation Saturation adjustment factor. Must be a vector of one or two values
///     if it's a vector of two values it needs to be in the form of [min, max]. Default value is {1, 1}
/// \param[in] hue Brightness adjustment factor. Must be a vector of one or two values
///     if it's a vector of two values it must be in the form of [min, max] where -0.5 <= min <= max <= 0.5
///     Default value is {0, 0}
/// \return Shared pointer to the current TensorOp
std::shared_ptr<RandomColorAdjustOperation> RandomColorAdjust(std::vector<float> brightness = {1.0, 1.0},
                                                              std::vector<float> contrast = {1.0, 1.0},
                                                              std::vector<float> saturation = {1.0, 1.0},
                                                              std::vector<float> hue = {0.0, 0.0});

/// \brief Function to create a RandomCrop TensorOperation.
/// \notes Crop the input image at a random location.
/// \param[in] size A vector representing the output size of the cropped image.
///     If size is a single value, a square crop of size (size, size) is returned.
///     If size has 2 values, it should be (height, width).
/// \param[in] padding A vector representing the number of pixels to pad the image
///    If vector has one value, it pads all sides of the image with that value.
///    If vector has two values, it pads left and top with the first and
///    right and bottom with the second value.
///    If vector has four values, it pads left, top, right, and bottom with
///    those values respectively.
/// \param[in] pad_if_needed A boolean whether to pad the image if either side is smaller than
///     the given output size.
/// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
///     BorderType.kConstant. If 1 value is provided, it is used for all RGB channels.
///     If 3 values are provided, it is used to fill R, G, B channels respectively.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomCropOperation> RandomCrop(std::vector<int32_t> size, std::vector<int32_t> padding = {0, 0, 0, 0},
                                                bool pad_if_needed = false, std::vector<uint8_t> fill_value = {0, 0, 0},
                                                BorderType padding_mode = BorderType::kConstant);

/// \brief Function to create a RandomCropDecodeResize TensorOperation.
/// \notes Equivalent to RandomResizedCrop, but crops before decodes.
/// \param[in] size A vector representing the output size of the cropped image.
///               If size is a single value, a square crop of size (size, size) is returned.
///               If size has 2 values, it should be (height, width).
/// \param[in] scale Range [min, max) of respective size of the
///               original size to be cropped (default=(0.08, 1.0))
/// \param[in] ratio Range [min, max) of aspect ratio to be
///               cropped (default=(3. / 4., 4. / 3.))
/// \param[in] interpolation An enum for the mode of interpolation
/// \param[in] The maximum number of attempts to propose a valid crop_area (default=10).
///               If exceeded, fall back to use center_crop instead.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomCropDecodeResizeOperation> RandomCropDecodeResize(
  std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4, 4. / 3},
  InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);

/// \brief Function to create a RandomCropWithBBox TensorOperation.
/// \Crop the input image at a random location and adjust bounding boxes accordingly.
/// \param[in] size A vector representing the output size of the cropped image.
///     If size is a single value, a square crop of size (size, size) is returned.
///     If size has 2 values, it should be (height, width).
/// \param[in] padding A vector representing the number of pixels to pad the image
///    If vector has one value, it pads all sides of the image with that value.
///    If vector has two values, it pads left and top with the first and
///    right and bottom with the second value.
///    If vector has four values, it pads left, top, right, and bottom with
///    those values respectively.
/// \param[in] pad_if_needed A boolean whether to pad the image if either side is smaller than
///     the given output size.
/// \param[in] fill_value A vector representing the pixel intensity of the borders if the padding_mode is
///     BorderType.kConstant. If 1 value is provided, it is used for all RGB channels.
///     If 3 values are provided, it is used to fill R, G, B channels respectively.
/// \param[in] padding_mode The method of padding (default=BorderType::kConstant).It can be any of
///     [BorderType::kConstant, BorderType::kEdge, BorderType::kReflect, BorderType::kSymmetric].
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomCropWithBBoxOperation> RandomCropWithBBox(std::vector<int32_t> size,
                                                                std::vector<int32_t> padding = {0, 0, 0, 0},
                                                                bool pad_if_needed = false,
                                                                std::vector<uint8_t> fill_value = {0, 0, 0},
                                                                BorderType padding_mode = BorderType::kConstant);

/// \brief Function to create a RandomHorizontalFlip TensorOperation.
/// \notes Tensor operation to perform random horizontal flip.
/// \param[in] prob A float representing the probability of flip.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomHorizontalFlipOperation> RandomHorizontalFlip(float prob = 0.5);

/// \brief Function to create a RandomHorizontalFlipWithBBox TensorOperation.
/// \notes Flip the input image horizontally, randomly with a given probability and adjust bounding boxes accordingly.
/// \param[in] prob A float representing the probability of flip.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomHorizontalFlipWithBBoxOperation> RandomHorizontalFlipWithBBox(float prob = 0.5);

/// \brief Function to create a RandomPosterize TensorOperation.
/// \notes Tensor operation to perform random posterize.
/// \param[in] bit_range - uint8_t vector representing the minimum and maximum bit in range. (Default={4, 8})
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomPosterizeOperation> RandomPosterize(const std::vector<uint8_t> &bit_range = {4, 8});

/// \brief Function to create a RandomResize TensorOperation.
/// \notes Resize the input image using a randomly selected interpolation mode.
/// \param[in] size A vector representing the output size of the resized image.
///     If size is a single value, the smaller edge of the image will be resized to this value with
//      the same image aspect ratio. If size has 2 values, it should be (height, width).
std::shared_ptr<RandomResizeOperation> RandomResize(std::vector<int32_t> size);

/// \brief Function to create a RandomResizeWithBBox TensorOperation.
/// \notes Resize the input image using a randomly selected interpolation mode and adjust
///     bounding boxes accordingly.
/// \param[in] size A vector representing the output size of the resized image.
///     If size is a single value, the smaller edge of the image will be resized to this value with
//      the same image aspect ratio. If size has 2 values, it should be (height, width).
std::shared_ptr<RandomResizeWithBBoxOperation> RandomResizeWithBBox(std::vector<int32_t> size);

/// \brief Function to create a RandomResizedCrop TensorOperation.
/// \notes Crop the input image to a random size and aspect ratio.
/// \param[in] size A vector representing the output size of the cropped image.
///     If size is a single value, a square crop of size (size, size) is returned.
///     If size has 2 values, it should be (height, width).
/// \param[in] scale Range [min, max) of respective size of the original
///     size to be cropped (default=(0.08, 1.0))
/// \param[in] ratio Range [min, max) of aspect ratio to be cropped
///     (default=(3. / 4., 4. / 3.)).
/// \param[in] interpolation Image interpolation mode (default=InterpolationMode::kLinear)
/// \param[in] max_attempts The maximum number of attempts to propose a valid
///     crop_area (default=10). If exceeded, fall back to use center_crop instead.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomResizedCropOperation> RandomResizedCrop(
  std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
  InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);

/// \brief Function to create a RandomResizedCropWithBBox TensorOperation.
/// \notes Crop the input image to a random size and aspect ratio.
/// \param[in] size A vector representing the output size of the cropped image.
///     If size is a single value, a square crop of size (size, size) is returned.
///     If size has 2 values, it should be (height, width).
/// \param[in] scale Range [min, max) of respective size of the original
///     size to be cropped (default=(0.08, 1.0))
/// \param[in] ratio Range [min, max) of aspect ratio to be cropped
///     (default=(3. / 4., 4. / 3.)).
/// \param[in] interpolation Image interpolation mode (default=InterpolationMode::kLinear)
/// \param[in] max_attempts The maximum number of attempts to propose a valid
///     crop_area (default=10). If exceeded, fall back to use center_crop instead.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomResizedCropWithBBoxOperation> RandomResizedCropWithBBox(
  std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
  InterpolationMode interpolation = InterpolationMode::kLinear, int32_t max_attempts = 10);

/// \brief Function to create a RandomRotation TensorOp
/// \notes Rotates the image according to parameters
/// \param[in] degrees A float vector of size, representing the starting and ending degree
/// \param[in] resample An enum for the mode of interpolation
/// \param[in] expand A boolean representing whether the image is expanded after rotation
/// \param[in] center A float vector of size 2, representing the x and y center of rotation.
/// \param[in] fill_value A vector representing the value to fill the area outside the transform
///    in the output image. If 1 value is provided, it is used for all RGB channels.
///    If 3 values are provided, it is used to fill R, G, B channels respectively.
/// \return Shared pointer to the current TensorOp
std::shared_ptr<RandomRotationOperation> RandomRotation(
  std::vector<float> degrees, InterpolationMode resample = InterpolationMode::kNearestNeighbour, bool expand = false,
  std::vector<float> center = {-1, -1}, std::vector<uint8_t> fill_value = {0, 0, 0});

/// \brief Function to create a RandomSelectSubpolicy TensorOperation.
/// \notes Choose a random sub-policy from a list to be applied on the input image. A sub-policy is a list of tuples
///     (op, prob), where op is a TensorOp operation and prob is the probability that this op will be applied. Once
///     a sub-policy is selected, each op within the subpolicy with be applied in sequence according to its probability.
/// \param[in] policy Vector of sub-policies to choose from.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomSelectSubpolicyOperation> RandomSelectSubpolicy(
  std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> policy);

/// \brief Function to create a RandomSharpness TensorOperation.
/// \notes Tensor operation to perform random sharpness.
/// \param[in] degrees A float vector of size 2, representing the starting and ending degree to uniformly
///     sample from, to select a degree to adjust sharpness.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomSharpnessOperation> RandomSharpness(std::vector<float> degrees = {0.1, 1.9});

/// \brief Function to create a RandomSolarize TensorOperation.
/// \notes Invert pixels randomly within specified range. If min=max, it is a single fixed magnitude operation
///     to inverts all pixel above that threshold
/// \param[in] threshold A vector with two elements specifying the pixel range to invert.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomSolarizeOperation> RandomSolarize(std::vector<uint8_t> threshold = {0, 255});

/// \brief Function to create a RandomVerticalFlip TensorOperation.
/// \notes Tensor operation to perform random vertical flip.
/// \param[in] prob A float representing the probability of flip.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomVerticalFlipOperation> RandomVerticalFlip(float prob = 0.5);

/// \brief Function to create a RandomVerticalFlipWithBBox TensorOperation.
/// \notes Flip the input image vertically, randomly with a given probability and adjust bounding boxes accordingly.
/// \param[in] prob A float representing the probability of flip.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RandomVerticalFlipWithBBoxOperation> RandomVerticalFlipWithBBox(float prob = 0.5);

/// \brief Function to create a RescaleOperation TensorOperation.
/// \notes Tensor operation to rescale the input image.
/// \param[in] rescale Rescale factor.
/// \param[in] shift Shift factor.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RescaleOperation> Rescale(float rescale, float shift);

/// \brief Function to create a ResizeWithBBox TensorOperation.
/// \notes Resize the input image to the given size and adjust bounding boxes accordingly.
/// \param[in] size The output size of the resized image.
///     If size is an integer, smaller edge of the image will be resized to this value with the same image aspect ratio.
///     If size is a sequence of length 2, it should be (height, width).
/// \param[in] interpolation An enum for the mode of interpolation (default=InterpolationMode::kLinear).
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<ResizeWithBBoxOperation> ResizeWithBBox(std::vector<int32_t> size,
                                                        InterpolationMode interpolation = InterpolationMode::kLinear);

/// \brief Function to create a RgbaToBgr TensorOperation.
/// \notes Changes the input 4 channel RGBA tensor to 3 channel BGR.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RgbaToBgrOperation> RGBA2BGR();

/// \brief Function to create a RgbaToRgb TensorOperation.
/// \notes Changes the input 4 channel RGBA tensor to 3 channel RGB.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<RgbaToRgbOperation> RGBA2RGB();

/// \brief Function to create a SoftDvppDecodeRandomCropResizeJpeg TensorOperation.
/// \notes Tensor operation to decode, random crop and resize JPEG image using the simulation algorithm of
///     Ascend series chip DVPP module. The usage scenario is consistent with SoftDvppDecodeResizeJpeg.
///     The input image size should be in range [32*32, 8192*8192].
///     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
///     Only images with an even resolution can be output. The output of odd resolution is not supported.
/// \param[in] size A vector representing the output size of the resized image.
///     If size is a single value, smaller edge of the image will be resized to this value with
///     the same image aspect ratio. If size has 2 values, it should be (height, width).
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<SoftDvppDecodeRandomCropResizeJpegOperation> SoftDvppDecodeRandomCropResizeJpeg(
  std::vector<int32_t> size, std::vector<float> scale = {0.08, 1.0}, std::vector<float> ratio = {3. / 4., 4. / 3.},
  int32_t max_attempts = 10);

/// \brief Function to create a SoftDvppDecodeResizeJpeg TensorOperation.
/// \notes Tensor operation to decode and resize JPEG image using the simulation algorithm of Ascend series
///     chip DVPP module. It is recommended to use this algorithm in the following scenarios:
///     When training, the DVPP of the Ascend chip is not used,
///     and the DVPP of the Ascend chip is used during inference,
///     and the accuracy of inference is lower than the accuracy of training;
///     and the input image size should be in range [32*32, 8192*8192].
///     The zoom-out and zoom-in multiples of the image length and width should in the range [1/32, 16].
///     Only images with an even resolution can be output. The output of odd resolution is not supported.
/// \param[in] size A vector representing the output size of the resized image.
///     If size is a single value, smaller edge of the image will be resized to this value with
///     the same image aspect ratio. If size has 2 values, it should be (height, width).
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<SoftDvppDecodeResizeJpegOperation> SoftDvppDecodeResizeJpeg(std::vector<int32_t> size);

/// \brief Function to create a SwapRedBlue TensorOp
/// \notes Swaps the red and blue channels in image
/// \return Shared pointer to the current TensorOp
std::shared_ptr<SwapRedBlueOperation> SwapRedBlue();

/// \brief Function to create a UniformAugment TensorOperation.
/// \notes Tensor operation to perform randomly selected augmentation.
/// \param[in] transforms A vector of TensorOperation transforms.
/// \param[in] num_ops An integer representing the number of OPs to be selected and applied.
/// \return Shared pointer to the current TensorOperation.
std::shared_ptr<UniformAugOperation> UniformAugment(std::vector<std::shared_ptr<TensorOperation>> transforms,
                                                    int32_t num_ops = 2);

}  // namespace vision
}  // namespace dataset
}  // namespace mindspore
#endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_INCLUDE_VISION_H_