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1 /**
2  * Copyright 2019-2021 Huawei Technologies Co., Ltd
3  *
4  * Licensed under the Apache License, Version 2.0 (the "License");
5  * you may not use this file except in compliance with the License.
6  * You may obtain a copy of the License at
7  *
8  * http://www.apache.org/licenses/LICENSE-2.0
9  *
10  * Unless required by applicable law or agreed to in writing, software
11  * distributed under the License is distributed on an "AS IS" BASIS,
12  * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13  * See the License for the specific language governing permissions and
14  * limitations under the License.
15  */
16 #ifndef MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_CROP_OP_H_
17 #define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_CROP_OP_H_
18 
19 #include <memory>
20 #include <random>
21 #include <vector>
22 #include <string>
23 
24 #include "minddata/dataset/core/tensor.h"
25 #include "minddata/dataset/kernels/tensor_op.h"
26 #include "minddata/dataset/kernels/image/image_utils.h"
27 #include "minddata/dataset/util/status.h"
28 
29 namespace mindspore {
30 namespace dataset {
31 class RandomCropOp : public TensorOp {
32  public:
33   // Default values, also used by python_bindings.cc
34   static const int32_t kDefPadTop;
35   static const int32_t kDefPadBottom;
36   static const int32_t kDefPadLeft;
37   static const int32_t kDefPadRight;
38   static const BorderType kDefBorderType;
39   static const bool kDefPadIfNeeded;
40   static const uint8_t kDefFillR;
41   static const uint8_t kDefFillG;
42   static const uint8_t kDefFillB;
43 
44   RandomCropOp(int32_t crop_height, int32_t crop_width, int32_t pad_top = kDefPadTop,
45                int32_t pad_bottom = kDefPadBottom, int32_t pad_left = kDefPadLeft, int32_t pad_right = kDefPadRight,
46                bool pad_if_needed = kDefPadIfNeeded, BorderType padding_mode = kDefBorderType,
47                uint8_t fill_r = kDefFillR, uint8_t fill_g = kDefFillG, uint8_t fill_b = kDefFillB);
48 
49   RandomCropOp(const RandomCropOp &rhs) = default;
50 
51   RandomCropOp(RandomCropOp &&rhs) = default;
52 
53   ~RandomCropOp() override = default;
54 
Print(std::ostream & out)55   void Print(std::ostream &out) const override { out << Name() << ": " << crop_height_ << " " << crop_width_; }
56 
57   Status Compute(const TensorRow &input, TensorRow *output) override;
58 
59   // Function breaks out the compute function's image padding functionality and makes available to other Ops
60   // Using this class as a base - re-structured to allow for RandomCropWithBBox Augmentation Op
61   // @param input: Input is the original Image
62   // @param pad_image: Pointer to new Padded image
63   // @param t_pad_top: Total Top Padding - Based on input and value calculated in function if required
64   // @param t_pad_bottom: Total bottom Padding - Based on input and value calculated in function if required
65   // @param t_pad_left: Total left Padding - Based on input and value calculated in function if required
66   // @param t_pad_right: Total right Padding - Based on input and value calculated in function if required
67   // @param padded_image_w: Final Width of the 'pad_image'
68   // @param padded_image_h: Final Height of the 'pad_image'
69   // @param crop_further: Whether image required cropping after padding - False if new padded image matches required
70   // dimensions
71   Status ImagePadding(const std::shared_ptr<Tensor> &input, std::shared_ptr<Tensor> *pad_image, int32_t *t_pad_top,
72                       int32_t *t_pad_bottom, int32_t *t_pad_left, int32_t *t_pad_right, int32_t *padded_image_w,
73                       int32_t *padded_image_h, bool *crop_further);
74 
75   // Function breaks X,Y generation functionality out of original compute function and makes available to other Ops
76   void GenRandomXY(int *x, int *y, const int32_t &padded_image_w, const int32_t &padded_image_h);
77 
78   Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
79 
Name()80   std::string Name() const override { return kRandomCropOp; }
81 
NumInput()82   uint32_t NumInput() override { return -1; }
83 
NumOutput()84   uint32_t NumOutput() override { return -1; }
85 
86  protected:
87   int32_t crop_height_ = 0;
88   int32_t crop_width_ = 0;
89 
90  private:
91   int32_t pad_top_ = 0;
92   int32_t pad_bottom_ = 0;
93   int32_t pad_left_ = 0;
94   int32_t pad_right_ = 0;
95   bool pad_if_needed_ = false;
96   BorderType border_type_;
97   uint8_t fill_r_ = 0;
98   uint8_t fill_g_ = 0;
99   uint8_t fill_b_ = 0;
100   std::mt19937 rnd_;
101 };
102 }  // namespace dataset
103 }  // namespace mindspore
104 
105 #endif  // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_IMAGE_RANDOM_CROP_OP_H_
106