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