Added DNN Darknet Yolo v2 for object detection

2026-07-31 08:13:04 +04:00 · 2017-09-24 23:34:08 +03:00
parent 39e742765a
commit ecc34dc521
11 changed files with 1764 additions and 1 deletions
@@ -0,0 +1,186 @@
+/*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
+//                        (3-clause BSD License)
+//
+// Copyright (C) 2017, Intel Corporation, 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:
+//
+// * Redistributions of source code must retain the above copyright notice,
+// this list of conditions and the following disclaimer.
+//
+// * Redistributions 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.
+//
+// * Neither the names of the copyright holders nor the names of the contributors
+// may 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 copyright holders 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 <opencv2/dnn/shape_utils.hpp>
+#include <algorithm>
+
+namespace cvtest
+{
+
+using namespace cv;
+using namespace cv::dnn;
+
+template<typename TString>
+static std::string _tf(TString filename)
+{
+    return (getOpenCVExtraDir() + "/dnn/") + filename;
+}
+
+TEST(Test_Darknet, read_tiny_yolo_voc)
+{
+    Net net = readNetFromDarknet(_tf("tiny-yolo-voc.cfg"));
+    ASSERT_FALSE(net.empty());
+}
+
+TEST(Test_Darknet, read_yolo_voc)
+{
+    Net net = readNetFromDarknet(_tf("yolo-voc.cfg"));
+    ASSERT_FALSE(net.empty());
+}
+
+TEST(Reproducibility_TinyYoloVoc, Accuracy)
+{
+    Net net;
+    {
+        const string cfg = findDataFile("dnn/tiny-yolo-voc.cfg", false);
+        const string model = findDataFile("dnn/tiny-yolo-voc.weights", false);
+        net = readNetFromDarknet(cfg, model);
+        ASSERT_FALSE(net.empty());
+    }
+
+    // dog416.png is dog.jpg that resized to 416x416 in the lossless PNG format
+    Mat sample = imread(_tf("dog416.png"));
+    ASSERT_TRUE(!sample.empty());
+
+    Size inputSize(416, 416);
+
+    if (sample.size() != inputSize)
+        resize(sample, sample, inputSize);
+
+    net.setInput(blobFromImage(sample, 1 / 255.F), "data");
+    Mat out = net.forward("detection_out");
+
+    Mat detection;
+    const float confidenceThreshold = 0.24;
+
+    for (int i = 0; i < out.rows; i++) {
+        const int probability_index = 5;
+        const int probability_size = out.cols - probability_index;
+        float *prob_array_ptr = &out.at<float>(i, probability_index);
+        size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
+        float confidence = out.at<float>(i, (int)objectClass + probability_index);
+
+        if (confidence > confidenceThreshold)
+            detection.push_back(out.row(i));
+    }
+
+    // obtained by: ./darknet detector test ./cfg/voc.data  ./cfg/tiny-yolo-voc.cfg ./tiny-yolo-voc.weights -thresh 0.24 ./dog416.png
+    // There are 2 objects (6-car, 11-dog) with 25 values for each:
+    // { relative_center_x, relative_center_y, relative_width, relative_height, unused_t0, probability_for_each_class[20] }
+    float ref_array[] = {
+        0.736762F, 0.239551F, 0.315440F, 0.160779F, 0.761977F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.761967F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+
+        0.287486F, 0.653731F, 0.315579F, 0.534527F, 0.782737F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.780595F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F
+    };
+
+    const int number_of_objects = 2;
+    Mat ref(number_of_objects, sizeof(ref_array) / (number_of_objects * sizeof(float)), CV_32FC1, &ref_array);
+
+    normAssert(ref, detection);
+}
+
+TEST(Reproducibility_YoloVoc, Accuracy)
+{
+    Net net;
+    {
+        const string cfg = findDataFile("dnn/yolo-voc.cfg", false);
+        const string model = findDataFile("dnn/yolo-voc.weights", false);
+        net = readNetFromDarknet(cfg, model);
+        ASSERT_FALSE(net.empty());
+    }
+
+    // dog416.png is dog.jpg that resized to 416x416 in the lossless PNG format
+    Mat sample = imread(_tf("dog416.png"));
+    ASSERT_TRUE(!sample.empty());
+
+    Size inputSize(416, 416);
+
+    if (sample.size() != inputSize)
+        resize(sample, sample, inputSize);
+
+    net.setInput(blobFromImage(sample, 1 / 255.F), "data");
+    Mat out = net.forward("detection_out");
+
+    Mat detection;
+    const float confidenceThreshold = 0.24;
+
+    for (int i = 0; i < out.rows; i++) {
+        const int probability_index = 5;
+        const int probability_size = out.cols - probability_index;
+        float *prob_array_ptr = &out.at<float>(i, probability_index);
+        size_t objectClass = std::max_element(prob_array_ptr, prob_array_ptr + probability_size) - prob_array_ptr;
+        float confidence = out.at<float>(i, (int)objectClass + probability_index);
+
+        if (confidence > confidenceThreshold)
+            detection.push_back(out.row(i));
+    }
+
+    // obtained by: ./darknet detector test ./cfg/voc.data  ./cfg/yolo-voc.cfg ./yolo-voc.weights -thresh 0.24 ./dog416.png
+    // There are 3 objects (6-car, 1-bicycle, 11-dog) with 25 values for each:
+    // { relative_center_x, relative_center_y, relative_width, relative_height, unused_t0, probability_for_each_class[20] }
+    float ref_array[] = {
+        0.740161F, 0.214100F, 0.325575F, 0.173418F, 0.750769F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.750469F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+
+        0.501618F, 0.504757F, 0.461713F, 0.481310F, 0.783550F, 0.000000F, 0.780879F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+
+        0.279968F, 0.638651F, 0.282737F, 0.600284F, 0.901864F, 0.000000F, 0.000000F, 0.000000F, 0.000000F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.901615F,
+        0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F, 0.000000F
+    };
+
+    const int number_of_objects = 3;
+    Mat ref(number_of_objects, sizeof(ref_array) / (number_of_objects * sizeof(float)), CV_32FC1, &ref_array);
+
+    normAssert(ref, detection);
+}
+
+}
@@ -10,7 +10,7 @@
 //                           License Agreement
 //                For Open Source Computer Vision Library
 //
-// Copyright (C) 2013, OpenCV Foundation, all rights reserved.
+// Copyright (C) 2017, Intel Corporation, all rights reserved.
 // Third party copyrights are property of their respective owners.
 //
 // Redistribution and use in source and binary forms, with or without modification,
@@ -420,4 +420,36 @@ TEST_F(Layer_RNN_Test, get_set_test)
    EXPECT_EQ(shape(outputs[1]), shape(nT, nS, nH));
 }

+void testLayerUsingDarknetModels(String basename, bool useDarknetModel = false, bool useCommonInputBlob = true)
+{
+    String cfg = _tf(basename + ".cfg");
+    String weights = _tf(basename + ".weights");
+
+    String inpfile = (useCommonInputBlob) ? _tf("blob.npy") : _tf(basename + ".input.npy");
+    String outfile = _tf(basename + ".npy");
+
+    cv::setNumThreads(cv::getNumberOfCPUs());
+
+    Net net = readNetFromDarknet(cfg, (useDarknetModel) ? weights : String());
+    ASSERT_FALSE(net.empty());
+
+    Mat inp = blobFromNPY(inpfile);
+    Mat ref = blobFromNPY(outfile);
+
+    net.setInput(inp, "data");
+    Mat out = net.forward();
+
+    normAssert(ref, out);
+}
+
+TEST(Layer_Test_Region, Accuracy)
+{
+    testLayerUsingDarknetModels("region", false, false);
+}
+
+TEST(Layer_Test_Reorg, Accuracy)
+{
+    testLayerUsingDarknetModels("reorg", false, false);
+}
+
 }