dnn: move module from opencv_contrib

https://github.com/opencv/opencv_contrib/tree/e6f63c7a38ca40c5dc33e38736e3027e3528d6cb/modules/dnn
2026-07-30 07:43:03 +04:00 · 2017-06-26 13:35:51 +03:00
parent ef692350e0
commit 93729784bb
143 changed files with 135205 additions and 0 deletions
@@ -0,0 +1,160 @@
+/*M///////////////////////////////////////////////////////////////////////////////////////
+//
+//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+//
+//  By downloading, copying, installing or using the software you agree to this license.
+//  If you do not agree to this license, do not download, install,
+//  copy or use the software.
+//
+//
+//                           License Agreement
+//                For Open Source Computer Vision Library
+//
+// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Third party copyrights are property of their respective owners.
+//
+// Redistribution and use in source and binary forms, with or without modification,
+// are permitted provided that the following conditions are met:
+//
+//   * Redistribution's of source code must retain the above copyright notice,
+//     this list of conditions and the following disclaimer.
+//
+//   * Redistribution's in binary form must reproduce the above copyright notice,
+//     this list of conditions and the following disclaimer in the documentation
+//     and/or other materials provided with the distribution.
+//
+//   * The name of the copyright holders may not be used to endorse or promote products
+//     derived from this software without specific prior written permission.
+//
+// This software is provided by the copyright holders and contributors "as is" and
+// any express or implied warranties, including, but not limited to, the implied
+// warranties of merchantability and fitness for a particular purpose are disclaimed.
+// In no event shall the Intel Corporation or contributors be liable for any direct,
+// indirect, incidental, special, exemplary, or consequential damages
+// (including, but not limited to, procurement of substitute goods or services;
+// loss of use, data, or profits; or business interruption) however caused
+// and on any theory of liability, whether in contract, strict liability,
+// or tort (including negligence or otherwise) arising in any way out of
+// the use of this software, even if advised of the possibility of such damage.
+//
+//M*/
+
+#include "test_precomp.hpp"
+#include "npy_blob.hpp"
+#include <opencv2/dnn/shape_utils.hpp>
+
+namespace cvtest
+{
+
+using namespace cv;
+using namespace cv::dnn;
+
+template<typename TString>
+static std::string _tf(TString filename)
+{
+    return (getOpenCVExtraDir() + "/dnn/") + filename;
+}
+
+TEST(Test_Caffe, read_gtsrb)
+{
+    Net net;
+    {
+        Ptr<Importer> importer = createCaffeImporter(_tf("gtsrb.prototxt"), "");
+        ASSERT_TRUE(importer != NULL);
+        importer->populateNet(net);
+    }
+}
+
+TEST(Test_Caffe, read_googlenet)
+{
+    Net net;
+    {
+        Ptr<Importer> importer = createCaffeImporter(_tf("bvlc_googlenet.prototxt"), "");
+        ASSERT_TRUE(importer != NULL);
+        importer->populateNet(net);
+    }
+}
+
+TEST(Reproducibility_AlexNet, Accuracy)
+{
+    Net net;
+    {
+        const string proto = findDataFile("dnn/bvlc_alexnet.prototxt", false);
+        const string model = findDataFile("dnn/bvlc_alexnet.caffemodel", false);
+        Ptr<Importer> importer = createCaffeImporter(proto, model);
+        ASSERT_TRUE(importer != NULL);
+        importer->populateNet(net);
+    }
+
+    Mat sample = imread(_tf("grace_hopper_227.png"));
+    ASSERT_TRUE(!sample.empty());
+
+    Size inputSize(227, 227);
+
+    if (sample.size() != inputSize)
+        resize(sample, sample, inputSize);
+
+    net.setInput(blobFromImage(sample), "data");
+    Mat out = net.forward("prob");
+    Mat ref = blobFromNPY(_tf("caffe_alexnet_prob.npy"));
+    normAssert(ref, out);
+}
+
+#if !defined(_WIN32) || defined(_WIN64)
+TEST(Reproducibility_FCN, Accuracy)
+{
+    Net net;
+    {
+        const string proto = findDataFile("dnn/fcn8s-heavy-pascal.prototxt", false);
+        const string model = findDataFile("dnn/fcn8s-heavy-pascal.caffemodel", false);
+        Ptr<Importer> importer = createCaffeImporter(proto, model);
+        ASSERT_TRUE(importer != NULL);
+        importer->populateNet(net);
+    }
+
+    Mat sample = imread(_tf("street.png"));
+    ASSERT_TRUE(!sample.empty());
+
+    Size inputSize(500, 500);
+    if (sample.size() != inputSize)
+        resize(sample, sample, inputSize);
+
+    std::vector<int> layerIds;
+    std::vector<size_t> weights, blobs;
+    net.getMemoryConsumption(shape(1,3,227,227), layerIds, weights, blobs);
+
+    net.setInput(blobFromImage(sample), "data");
+    Mat out = net.forward("score");
+    Mat ref = blobFromNPY(_tf("caffe_fcn8s_prob.npy"));
+    normAssert(ref, out);
+}
+#endif
+
+TEST(Reproducibility_SSD, Accuracy)
+{
+    Net net;
+    {
+        const string proto = findDataFile("dnn/ssd_vgg16.prototxt", false);
+        const string model = findDataFile("dnn/VGG_ILSVRC2016_SSD_300x300_iter_440000.caffemodel", false);
+        Ptr<Importer> importer = createCaffeImporter(proto, model);
+        ASSERT_TRUE(importer != NULL);
+        importer->populateNet(net);
+    }
+
+    Mat sample = imread(_tf("street.png"));
+    ASSERT_TRUE(!sample.empty());
+
+    if (sample.channels() == 4)
+        cvtColor(sample, sample, COLOR_BGRA2BGR);
+
+    sample.convertTo(sample, CV_32F);
+    resize(sample, sample, Size(300, 300));
+
+    Mat in_blob = blobFromImage(sample);
+    net.setInput(in_blob, "data");
+    Mat out = net.forward("detection_out");
+
+    Mat ref = blobFromNPY(_tf("ssd_out.npy"));
+    normAssert(ref, out);
+}
+}