Alexey Shevlyakov
5 years ago
parent
2953720169
commit
8526d5414d
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/**
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* Copyright 2020 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 "minddata/dataset/kernels/image/random_color_op.h"
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#include "minddata/dataset/core/cv_tensor.h"
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namespace mindspore {
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namespace dataset {
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RandomColorOp::RandomColorOp(float t_lb, float t_ub) : rnd_(GetSeed()), dist_(t_lb, t_ub), t_lb_(t_lb), t_ub_(t_ub) {}
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Status RandomColorOp::Compute(const std::shared_ptr<Tensor> &in, std::shared_ptr<Tensor> *out) {
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IO_CHECK(in, out);
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if (in->Rank() != 3) {
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RETURN_STATUS_UNEXPECTED("image must have 3 channels");
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}
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// 0.5 pixel precision assuming an 8 bit image
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const auto eps = 0.00195;
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const auto t = dist_(rnd_);
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if (abs(t - 1.0) < eps) {
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// Just return input? Can we do it given that input would otherwise get consumed in CVTensor constructor anyway?
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*out = in;
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return Status::OK();
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}
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auto cvt_in = CVTensor::AsCVTensor(in);
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auto m1 = cvt_in->mat();
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cv::Mat gray;
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// gray is allocated without using the allocator
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cv::cvtColor(m1, gray, cv::COLOR_RGB2GRAY);
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// luminosity is not preserved, consider using weights.
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cv::Mat temp[3] = {gray, gray, gray};
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cv::Mat cv_out;
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cv::merge(temp, 3, cv_out);
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std::shared_ptr<CVTensor> cvt_out;
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CVTensor::CreateFromMat(cv_out, &cvt_out);
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if (abs(t - 0.0) < eps) {
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// return grayscale
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*out = std::static_pointer_cast<Tensor>(cvt_out);
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return Status::OK();
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}
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// return blended image. addWeighted takes care of overflow for uint8_t
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cv::addWeighted(m1, t, cvt_out->mat(), 1 - t, 0, cvt_out->mat());
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*out = std::static_pointer_cast<Tensor>(cvt_out);
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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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@ -0,0 +1,62 @@
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/**
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* Copyright 2020 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 MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_RANDOM_COLOR_OP_H
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#define MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_RANDOM_COLOR_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 <string>
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#include <opencv2/imgproc/imgproc.hpp>
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#include "minddata/dataset/core/tensor.h"
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#include "minddata/dataset/core/cv_tensor.h"
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#include "minddata/dataset/kernels/tensor_op.h"
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#include "minddata/dataset/util/status.h"
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#include "minddata/dataset/util/random.h"
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namespace mindspore {
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namespace dataset {
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/// \class RandomColorOp random_color_op.h
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/// \brief Blends an image with its grayscale version with random weights
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/// t and 1 - t generated from a given range.
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/// If the range is trivial then the weights are determinate and
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/// t equals the bound of the interval
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class RandomColorOp : public TensorOp {
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public:
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RandomColorOp() = default;
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/// \brief Constructor
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/// \param[in] t_lb lower bound for the random weights
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/// \param[in] t_ub upper bound for the random weights
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RandomColorOp(float t_lb, float t_ub);
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/// \brief the main function performing computations
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/// \param[in] in 2- or 3- dimensional tensor representing an image
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/// \param[out] out 2- or 3- dimensional tensor representing an image
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/// with the same dimensions as in
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Status Compute(const std::shared_ptr<Tensor> &in, std::shared_ptr<Tensor> *out) override;
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/// \brief returns the name of the op
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std::string Name() const override { return kRandomColorOp; }
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private:
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std::mt19937 rnd_;
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std::uniform_real_distribution<float> dist_;
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float t_lb_;
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float t_ub_;
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};
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} // namespace dataset
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} // namespace mindspore
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#endif // MINDSPORE_CCSRC_MINDDATA_DATASET_KERNELS_RANDOM_COLOR_OP_H
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@ -0,0 +1,99 @@
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/**
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* Copyright 2020 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 "common/common.h"
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#include "common/cvop_common.h"
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#include "minddata/dataset/kernels/image/random_color_op.h"
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#include "minddata/dataset/core/cv_tensor.h"
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#include "utils/log_adapter.h"
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using namespace mindspore::dataset;
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using mindspore::LogStream;
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using mindspore::ExceptionType::NoExceptionType;
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using mindspore::MsLogLevel::INFO;
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class MindDataTestRandomColorOp : public UT::CVOP::CVOpCommon {
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public:
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MindDataTestRandomColorOp() : CVOpCommon(), shape({3, 3, 3}) {
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std::shared_ptr<Tensor> in;
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std::shared_ptr<Tensor> gray;
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(void)Tensor::CreateEmpty(shape, DataType(DataType::DE_UINT8), &in);
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(void)Tensor::CreateEmpty(shape, DataType(DataType::DE_UINT8), &input_tensor);
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Status s = in->Fill<uint8_t>(42);
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s = input_tensor->Fill<uint8_t>(42);
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cvt_in = CVTensor::AsCVTensor(in);
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cv::Mat m2;
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auto m1 = cvt_in->mat();
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cv::cvtColor(m1, m2, cv::COLOR_RGB2GRAY);
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cv::Mat temp[3] = {m2 , m2 , m2 };
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cv::Mat cv_out;
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cv::merge(temp, 3, cv_out);
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std::shared_ptr<CVTensor> cvt_out;
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CVTensor::CreateFromMat(cv_out, &cvt_out);
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gray_tensor = std::static_pointer_cast<Tensor>(cvt_out);
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}
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TensorShape shape;
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std::shared_ptr<Tensor> input_tensor;
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std::shared_ptr<CVTensor> cvt_in;
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std::shared_ptr<Tensor> gray_tensor;
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};
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int64_t Compare(std::shared_ptr<Tensor> t1, std::shared_ptr<Tensor> t2) {
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auto shape = t1->shape();
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int64_t sum = 0;
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for (auto i = 0; i < shape[0]; i++) {
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for (auto j = 0; j < shape[1]; j++) {
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for (auto k = 0; k < shape[2]; k++) {
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uint8_t value1;
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uint8_t value2;
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(void)t1->GetItemAt<uint8_t>(&value1, {i, j, k});
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(void)t2->GetItemAt<uint8_t>(&value2, {i, j, k});
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sum += abs(static_cast<int>(value1) - static_cast<int>(value2));
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}
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}
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}
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return sum;
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}
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// these tests are tautological, write better tests when the requirements for the output are determined
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// e. g. how do we want to convert to gray and what does it mean to blend with a gray image (pre- post- gamma corrected,
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// what weights).
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TEST_F(MindDataTestRandomColorOp, TestOp1) {
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std::shared_ptr<Tensor> output_tensor;
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auto op = RandomColorOp(1, 1);
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auto s = op.Compute(input_tensor, &output_tensor);
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auto res = Compare(input_tensor, output_tensor);
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EXPECT_EQ(0, res);
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}
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TEST_F(MindDataTestRandomColorOp, TestOp2) {
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std::shared_ptr<Tensor> output_tensor;
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auto op = RandomColorOp(0, 0);
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auto s = op.Compute(input_tensor, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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auto res = Compare(output_tensor, gray_tensor);
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EXPECT_EQ(res, 0);
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}
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TEST_F(MindDataTestRandomColorOp, TestOp3) {
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std::shared_ptr<Tensor> output_tensor;
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auto op = RandomColorOp(0.0, 1.0);
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for (auto i = 0; i < 1; i++) {
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auto s = op.Compute(input_tensor, &output_tensor);
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EXPECT_TRUE(s.IsOk());
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}
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}
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