Alexey Shevlyakov
5 years ago
parent
e4f4b9f4cd
commit
6acae622dc
@ -1,117 +0,0 @@
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/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "dataset/kernels/image/distort_bounding_box_crop_op.h"
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#include <random>
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#include "dataset/core/cv_tensor.h"
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#include "dataset/kernels/image/image_utils.h"
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#include "dataset/util/random.h"
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#include "dataset/util/status.h"
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namespace mindspore {
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namespace dataset {
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const int32_t DistortBoundingBoxCropOp::kDefMaxAttempts = 100;
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const int32_t DistortBoundingBoxCropOp::kDefBoxGenAttempts = 10;
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DistortBoundingBoxCropOp::DistortBoundingBoxCropOp(float aspect_ratio, float intersect_ratio, float crop_ratio_lb,
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float crop_ratio_ub, int32_t max_attempts, int32_t box_gen_attempts)
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: max_attempts_(max_attempts),
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box_gen_attempts_(box_gen_attempts),
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aspect_ratio_(aspect_ratio),
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intersect_ratio_(intersect_ratio),
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crop_ratio_lb_(crop_ratio_lb),
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crop_ratio_ub_(crop_ratio_ub) {
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seed_ = GetSeed();
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rnd_.seed(seed_);
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}
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Status DistortBoundingBoxCropOp::Compute(const std::vector<std::shared_ptr<Tensor>> &input,
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std::vector<std::shared_ptr<Tensor>> *output) {
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IO_CHECK_VECTOR(input, output);
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if (input.size() != NumInput())
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return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Number of inputs is not 5");
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CHECK_FAIL_RETURN_UNEXPECTED(input[1]->shape().Size() >= 1, "The shape of the second tensor is abnormal");
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int64_t num_boxes = 0;
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for (uint64_t i = 1; i < input.size(); i++) {
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if (i == 1) num_boxes = input[i]->shape()[0];
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if (num_boxes != input[i]->shape()[0])
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return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "Numbers of boxes do not match");
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if (input[i]->type() != DataType::DE_FLOAT32)
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return Status(StatusCode::kUnexpectedError, __LINE__, __FILE__, "boxes' type is not DE_FLOAT21");
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}
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// assume input Tensor vector in the order of [img, bbox_y_min, bbox_y_max, bbox_x_min, bbox_x_max]
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CHECK_FAIL_RETURN_UNEXPECTED(input[0]->shape().Size() >= 2, "The shape of the first tensor is abnormal");
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int h_in = input[0]->shape()[0];
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int w_in = input[0]->shape()[1];
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std::vector<cv::Rect> bounding_boxes;
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for (int64_t i = 0; i < num_boxes; ++i) {
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// bbox coordinates are floats relative to the image width and height
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float y_min, y_max, x_min, x_max;
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RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&y_min, {i}));
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RETURN_IF_NOT_OK(input[2]->GetItemAt<float>(&y_max, {i}));
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RETURN_IF_NOT_OK(input[3]->GetItemAt<float>(&x_min, {i}));
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RETURN_IF_NOT_OK(input[4]->GetItemAt<float>(&x_max, {i}));
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bounding_boxes.emplace_back(static_cast<int>(x_min * w_in), static_cast<int>(y_min * h_in),
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static_cast<int>((x_max - x_min) * w_in), static_cast<int>((y_max - y_min) * h_in));
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}
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cv::Rect output_box;
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bool should_crop = false;
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// go over iterations, if no satisfying box found we return the original image
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for (int32_t t = 0; t < max_attempts_; ++t) {
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// try to generate random box
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RETURN_IF_NOT_OK(GenerateRandomCropBox(h_in, w_in, aspect_ratio_, crop_ratio_lb_, crop_ratio_ub_,
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box_gen_attempts_, // int maxIter, should not be needed here
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&output_box, seed_));
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RETURN_IF_NOT_OK(CheckOverlapConstraint(output_box,
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bounding_boxes, // have to change, should take tensor or add bbox logic
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intersect_ratio_, &should_crop));
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if (should_crop) {
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// found a box to crop
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break;
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}
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}
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// essentially we have to check this again at the end to return original tensor
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if (should_crop) {
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std::shared_ptr<Tensor> out;
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RETURN_IF_NOT_OK(Crop(input[0], &out, output_box.x, output_box.y, output_box.width, output_box.height));
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output->push_back(out);
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} else {
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output->push_back(input[0]);
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}
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return Status::OK();
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}
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Status DistortBoundingBoxCropOp::OutputShape(const std::vector<TensorShape> &inputs,
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std::vector<TensorShape> &outputs) {
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RETURN_IF_NOT_OK(TensorOp::OutputShape(inputs, outputs));
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outputs.clear();
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TensorShape out = TensorShape{-1, -1};
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if (inputs[0].Rank() == 2) outputs.emplace_back(out);
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if (inputs[0].Rank() == 3) outputs.emplace_back(out.AppendDim(inputs[0][2]));
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if (!outputs.empty()) return Status::OK();
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return Status(StatusCode::kUnexpectedError, "Input has a wrong shape");
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}
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Status DistortBoundingBoxCropOp::OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) {
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RETURN_IF_NOT_OK(TensorOp::OutputType(inputs, outputs));
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outputs[0] = inputs[0];
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return Status::OK();
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}
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} // namespace dataset
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} // namespace mindspore
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@ -1,72 +0,0 @@
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/**
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* Copyright 2019 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#ifndef DATASET_KERNELS_IMAGE_DISTORT_BOUNDING_BOX_CROP_OP_H_
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#define DATASET_KERNELS_IMAGE_DISTORT_BOUNDING_BOX_CROP_OP_H_
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#include <memory>
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#include <random>
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#include <vector>
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#include "dataset/core/tensor.h"
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#include "dataset/kernels/tensor_op.h"
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#include "dataset/util/status.h"
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namespace mindspore {
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namespace dataset {
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class DistortBoundingBoxCropOp : public TensorOp {
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public:
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// Default values, also used by python_bindings.cc
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static const int32_t kDefMaxAttempts;
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static const int32_t kDefBoxGenAttempts;
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// Constructor for DistortBoundingBoxCropOp
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// @param max_attempts tries before the crop happens
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// @param box_gen_attempts crop box generation attempts
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// @param aspect_ratio aspect ratio of the generated crop box
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// @param intersect_ratio area overlap ratio, condition for crop only if area over lap between the generated
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// crop box has sufficient overlap with any 1 bounding box
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// @param crop_ratio_lb the crop ratio lower bound
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// @param crop_ratio_ub the crop ratio upper bound
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// @param seed
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DistortBoundingBoxCropOp(float aspect_ratio, float intersect_ratio, float crop_ratio_lb, float crop_ratio_ub,
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int32_t max_attempts = kDefMaxAttempts, int32_t box_gen_attempts = kDefBoxGenAttempts);
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~DistortBoundingBoxCropOp() override = default;
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void Print(std::ostream &out) const override {
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out << "DistortBoundingBoxCropOp: " << max_attempts_ << " " << intersect_ratio_;
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}
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Status Compute(const std::vector<std::shared_ptr<Tensor>> &input,
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std::vector<std::shared_ptr<Tensor>> *output) override;
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uint32_t NumInput() override { return 5; }
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Status OutputShape(const std::vector<TensorShape> &inputs, std::vector<TensorShape> &outputs) override;
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Status OutputType(const std::vector<DataType> &inputs, std::vector<DataType> &outputs) override;
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private:
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int32_t max_attempts_;
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int32_t box_gen_attempts_;
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float aspect_ratio_;
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float intersect_ratio_;
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float crop_ratio_lb_;
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float crop_ratio_ub_;
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std::mt19937 rnd_;
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uint32_t seed_;
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};
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} // namespace dataset
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} // namespace mindspore
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#endif // DATASET_KERNELS_IMAGE_DISTORT_BOUNDING_BOX_CROP_OP_H_
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