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opencv/modules/dnn/test/test_darknet_importer.cpp
Alexander Alekhin 4a297a2443 ts: refactor OpenCV tests 
- removed tr1 usage (dropped in C++17)
- moved includes of vector/map/iostream/limits into ts.hpp
- require opencv_test + anonymous namespace (added compile check)
- fixed norm() usage (must be from cvtest::norm for checks) and other conflict functions
- added missing license headers
2018-02-03 19:39:47 +00:00

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/*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.
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//M*/
#include "test_precomp.hpp"
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/core/ocl.hpp>
#include <opencv2/ts/ocl_test.hpp>
namespace opencv_test { namespace {
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());
}
OCL_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());
}
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
// 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_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);
}
OCL_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());
}
net.setPreferableBackend(DNN_BACKEND_DEFAULT);
net.setPreferableTarget(DNN_TARGET_OPENCL);
// 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);
}
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);
}
}} // namespace
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