Gitea Mirror Bot
115 lines
3.5 KiB
C++
115 lines
3.5 KiB
C++
// This file is part of OpenCV project.
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// It is subject to the license terms in the LICENSE file found in the top-level directory
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// of this distribution and at http://opencv.org/license.html.
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#include "test_precomp.hpp"
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namespace opencv_test { namespace {
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const std::string DNN_SUPERRES_DIR = "dnn_superres";
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const std::string IMAGE_FILENAME = "butterfly.png";
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/****************************************************************************************\
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* Test single output models *
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\****************************************************************************************/
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void runSingleModel(std::string algorithm, int scale, std::string model_filename)
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{
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SCOPED_TRACE(algorithm);
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Ptr <DnnSuperResImpl> dnn_sr = makePtr<DnnSuperResImpl>();
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std::string path = cvtest::findDataFile(DNN_SUPERRES_DIR + "/" + IMAGE_FILENAME);
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Mat img = imread(path);
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ASSERT_FALSE(img.empty()) << "Test image can't be loaded: " << path;
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std::string pb_path = cvtest::findDataFile(DNN_SUPERRES_DIR + "/" + model_filename);
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dnn_sr->readModel(pb_path);
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dnn_sr->setModel(algorithm, scale);
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ASSERT_EQ(scale, dnn_sr->getScale());
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ASSERT_EQ(algorithm, dnn_sr->getAlgorithm());
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Mat result;
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dnn_sr->upsample(img, result);
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ASSERT_FALSE(result.empty()) << "Could not perform upsampling for scale algorithm " << algorithm << " and scale factor " << scale;
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int new_cols = img.cols * scale;
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int new_rows = img.rows * scale;
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ASSERT_EQ(new_cols, result.cols);
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ASSERT_EQ(new_rows, result.rows);
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}
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TEST(CV_DnnSuperResSingleOutputTest, accuracy_espcn_2)
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{
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runSingleModel("espcn", 2, "ESPCN_x2.pb");
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}
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TEST(CV_DnnSuperResSingleOutputTest, accuracy_fsrcnn_2)
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{
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runSingleModel("fsrcnn", 2, "FSRCNN_x2.pb");
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}
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TEST(CV_DnnSuperResSingleOutputTest, accuracy_fsrcnn_3)
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{
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runSingleModel("fsrcnn", 3, "FSRCNN_x3.pb");
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}
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/****************************************************************************************\
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* Test multi output models *
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\****************************************************************************************/
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void runMultiModel(std::string algorithm, int scale, std::string model_filename,
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std::vector<int> scales, std::vector<String> node_names)
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{
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SCOPED_TRACE(algorithm);
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Ptr <DnnSuperResImpl> dnn_sr = makePtr<DnnSuperResImpl>();
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std::string path = cvtest::findDataFile(DNN_SUPERRES_DIR + "/" + IMAGE_FILENAME);
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Mat img = imread(path);
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ASSERT_FALSE(img.empty()) << "Test image can't be loaded: " << path;
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std::string pb_path = cvtest::findDataFile(DNN_SUPERRES_DIR + "/" + model_filename);
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dnn_sr->readModel(pb_path);
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dnn_sr->setModel(algorithm, scale);
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ASSERT_EQ(scale, dnn_sr->getScale());
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ASSERT_EQ(algorithm, dnn_sr->getAlgorithm());
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std::vector<Mat> outputs;
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dnn_sr->upsampleMultioutput(img, outputs, scales, node_names);
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for(unsigned int i = 0; i < outputs.size(); i++)
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{
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SCOPED_TRACE(cv::format("i=%d scale[i]=%d", i, scales[i]));
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ASSERT_FALSE(outputs[i].empty());
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int new_cols = img.cols * scales[i];
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int new_rows = img.rows * scales[i];
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EXPECT_EQ(new_cols, outputs[i].cols);
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EXPECT_EQ(new_rows, outputs[i].rows);
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}
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}
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TEST(CV_DnnSuperResMultiOutputTest, accuracy)
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{
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//LAPSRN
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//x4
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std::vector<String> names_4x {"NCHW_output_2x", "NCHW_output_4x"};
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std::vector<int> scales_4x {2, 4};
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runMultiModel("lapsrn", 4, "LapSRN_x4.pb", scales_4x, names_4x);
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}
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}}
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