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142 lines
4.4 KiB
C++
142 lines
4.4 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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//
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// Copyright (C) 2017, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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#include "test_precomp.hpp"
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#include <opencv2/dnn/layer.details.hpp> // CV_DNN_REGISTER_LAYER_CLASS
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namespace opencv_test { namespace {
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TEST(blobFromImage_4ch, Regression)
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{
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Mat ch[4];
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for(int i = 0; i < 4; i++)
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ch[i] = Mat::ones(10, 10, CV_8U)*i;
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Mat img;
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merge(ch, 4, img);
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Mat blob = dnn::blobFromImage(img, 1., Size(), Scalar(), false, false);
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for(int i = 0; i < 4; i++)
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{
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ch[i] = Mat(img.rows, img.cols, CV_32F, blob.ptr(0, i));
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ASSERT_DOUBLE_EQ(cvtest::norm(ch[i], cv::NORM_INF), i);
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}
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}
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TEST(blobFromImage, allocated)
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{
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int size[] = {1, 3, 4, 5};
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Mat img(size[2], size[3], CV_32FC(size[1]));
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Mat blob(4, size, CV_32F);
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void* blobData = blob.data;
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dnn::blobFromImage(img, blob, 1.0 / 255, Size(), Scalar(), false, false);
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ASSERT_EQ(blobData, blob.data);
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}
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TEST(imagesFromBlob, Regression)
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{
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int nbOfImages = 8;
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std::vector<cv::Mat> inputImgs(nbOfImages);
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for (int i = 0; i < nbOfImages; i++)
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{
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inputImgs[i] = cv::Mat::ones(100, 100, CV_32FC3);
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cv::randu(inputImgs[i], cv::Scalar::all(0), cv::Scalar::all(1));
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}
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cv::Mat blob = cv::dnn::blobFromImages(inputImgs, 1., cv::Size(), cv::Scalar(), false, false);
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std::vector<cv::Mat> outputImgs;
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cv::dnn::imagesFromBlob(blob, outputImgs);
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for (int i = 0; i < nbOfImages; i++)
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{
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ASSERT_EQ(cv::countNonZero(inputImgs[i] != outputImgs[i]), 0);
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}
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}
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TEST(readNet, Regression)
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{
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Net net = readNet(findDataFile("dnn/squeezenet_v1.1.prototxt", false),
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findDataFile("dnn/squeezenet_v1.1.caffemodel", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/opencv_face_detector.caffemodel", false),
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findDataFile("dnn/opencv_face_detector.prototxt", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/openface_nn4.small2.v1.t7", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/tiny-yolo-voc.cfg", false),
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findDataFile("dnn/tiny-yolo-voc.weights", false));
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EXPECT_FALSE(net.empty());
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net = readNet(findDataFile("dnn/ssd_mobilenet_v1_coco.pbtxt", false),
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findDataFile("dnn/ssd_mobilenet_v1_coco.pb", false));
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EXPECT_FALSE(net.empty());
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}
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class FirstCustomLayer CV_FINAL : public Layer
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{
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public:
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FirstCustomLayer(const LayerParams ¶ms) : Layer(params) {}
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static Ptr<Layer> create(LayerParams& params)
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{
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return Ptr<Layer>(new FirstCustomLayer(params));
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}
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virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
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virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat>& internals) CV_OVERRIDE
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{
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outputs[0].setTo(1);
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}
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};
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class SecondCustomLayer CV_FINAL : public Layer
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{
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public:
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SecondCustomLayer(const LayerParams ¶ms) : Layer(params) {}
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static Ptr<Layer> create(LayerParams& params)
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{
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return Ptr<Layer>(new SecondCustomLayer(params));
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}
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virtual void forward(InputArrayOfArrays, OutputArrayOfArrays, OutputArrayOfArrays) CV_OVERRIDE {}
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virtual void forward(std::vector<Mat*> &inputs, std::vector<Mat> &outputs, std::vector<Mat>& internals) CV_OVERRIDE
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{
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outputs[0].setTo(2);
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}
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};
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TEST(LayerFactory, custom_layers)
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{
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LayerParams lp;
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lp.name = "name";
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lp.type = "CustomType";
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Mat inp(1, 1, CV_32FC1);
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for (int i = 0; i < 3; ++i)
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{
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if (i == 0) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, FirstCustomLayer); }
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else if (i == 1) { CV_DNN_REGISTER_LAYER_CLASS(CustomType, SecondCustomLayer); }
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else if (i == 2) { LayerFactory::unregisterLayer("CustomType"); }
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Net net;
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net.addLayerToPrev(lp.name, lp.type, lp);
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net.setInput(inp);
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net.setPreferableBackend(DNN_BACKEND_OPENCV);
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Mat output = net.forward();
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if (i == 0) EXPECT_EQ(output.at<float>(0), 1);
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else if (i == 1) EXPECT_EQ(output.at<float>(0), 2);
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else if (i == 2) EXPECT_EQ(output.at<float>(0), 1);
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}
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LayerFactory::unregisterLayer("CustomType");
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}
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}} // namespace
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