float negative_slope_;

#include <opencv2/core/ocl.hpp>

#include <opencv2/ts/ocl_test.hpp>

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);

}