#include <iostream>
#include <queue>
#include <iterator>
#include <sstream>
#include <fstream>
#include <iomanip>
#include <chrono>
#include<opencv2//opencv.hpp>
#include <opencv2/core.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/dnn/all_layers.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
constexpr float CONFIDENCE_THRESHOLD = 0;
constexpr float NMS_THRESHOLD = 0.4;
constexpr int NUM_CLASSES = 80;
// colors for bounding boxes
const cv::Scalar colors[] = {
{0, 255, 255},
{255, 255, 0},
{0, 255, 0},
{255, 0, 0}
};
const auto NUM_COLORS = sizeof(colors) / sizeof(colors[0]);
int main()
{
std::vector<std::string> class_names;
{
std::ifstream class_file("coco.names");
if (!class_file)
{
std::cerr << "failed to open classes.txt\n";
return 0;
}
std::string line;
while (std::getline(class_file, line))
class_names.push_back(line);
}
cv::VideoCapture source("person.mp4");
auto net = cv::dnn::readNetFromDarknet("yolov4-tiny.cfg", "yolov4-tiny.weights");
//net.setPreferableBackend(cv::dnn::DNN_BACKEND_CUDA);
//net.setPreferableTarget(cv::dnn::DNN_TARGET_CUDA);
net.setPreferableBackend(cv::dnn::DNN_BACKEND_OPENCV);
net.setPreferableTarget(cv::dnn::DNN_TARGET_CPU);
auto output_names = net.getUnconnectedOutLayersNames();
cv::Mat frame, blob;
std::vector<cv::Mat> detections;
while (cv::waitKey(1) < 1)
{
source >> frame;
if (frame.empty())
{
cv::waitKey(12);
break;
}
auto total_start = std::chrono::steady_clock::now();
cv::dnn::blobFromImage(frame, blob, 0.00392, cv::Size(416, 416), cv::Scalar(), true, false, CV_32F);
net.setInput(blob);
auto dnn_start = std::chrono::steady_clock::now();
net.forward(detections, output_names);
auto dnn_end = std::chrono::steady_clock::now();
std::vector<int> indices[NUM_CLASSES];
std::vector<cv::Rect> boxes[NUM_CLASSES];
std::vector<float> scores[NUM_CLASSES];
for (auto& output : detections)
{
const auto num_boxes = output.rows;
for (int i = 0; i < num_boxes; i++)
{
auto x = output.at<float>(i, 0) * frame.cols;
auto y = output.at<float>(i, 1) * frame.rows;
auto width = output.at<float>(i, 2) * frame.cols;
auto height = output.at<float>(i, 3) * frame.rows;
cv::Rect rect(x - width / 2, y - height / 2, width, height);
for (int c = 0; c < NUM_CLASSES; c++)
{
auto confidence = *output.ptr<float>(i, 5 + c);
if (confidence >= CONFIDENCE_THRESHOLD)
{
boxes[c].push_back(rect);
scores[c].push_back(confidence);
}
}
}
}
for (int c = 0; c < NUM_CLASSES; c++)
cv::dnn::NMSBoxes(boxes[c], scores[c], 0.0, NMS_THRESHOLD, indices[c]);
for (int c = 0; c < NUM_CLASSES; c++)
{
for (size_t i = 0; i < indices[c].size(); ++i)
{
const auto color = colors[c % NUM_COLORS];
auto idx = indices[c][i];
const auto& rect = boxes[c][idx];
cv::rectangle(frame, cv::Point(rect.x, rect.y), cv::Point(rect.x + rect.width, rect.y + rect.height), color, 3);
std::ostringstream label_ss;
label_ss << class_names[c] << ": " << std::fixed << std::setprecision(2) << scores[c][idx];
auto label = label_ss.str();
int baseline;
auto label_bg_sz = cv::getTextSize(label.c_str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, 1, &baseline);
cv::rectangle(frame, cv::Point(rect.x, rect.y - label_bg_sz.height - baseline - 10), cv::Point(rect.x + label_bg_sz.width, rect.y), color, cv::FILLED);
cv::putText(frame, label.c_str(), cv::Point(rect.x, rect.y - baseline - 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(0, 0, 0));
}
}
auto total_end = std::chrono::steady_clock::now();
float inference_fps = 1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(dnn_end - dnn_start).count();
float total_fps = 1000.0 / std::chrono::duration_cast<std::chrono::milliseconds>(total_end - total_start).count();
std::ostringstream stats_ss;
stats_ss << std::fixed << std::setprecision(2);
stats_ss << "Inference FPS: " << inference_fps << ", Total FPS: " << total_fps;
auto stats = stats_ss.str();
int baseline;
auto stats_bg_sz = cv::getTextSize(stats.c_str(), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, 1, &baseline);
cv::rectangle(frame, cv::Point(0, 0), cv::Point(stats_bg_sz.width, stats_bg_sz.height + 10), cv::Scalar(0, 0, 0), cv::FILLED);
cv::putText(frame, stats.c_str(), cv::Point(0, stats_bg_sz.height + 5), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 255, 255));
cv::namedWindow("output");
cv::imshow("output", frame);
cv::imwrite("E:\\VSprojeect\\yolov4-tiny\\yolov4-tiny\\output", blob);
cv::waitKey(1);
//destroyAllWindows();
//cv::imwrite("E:\\VSprojeect\\yolov4-tiny\\yolov4-tiny\\output", blob);
}
return 0;
}
yolov4-tiny.zip （29个子文件） 
.vs yolov4-tiny v15 
Browse.VC.db 22.34MB .suo 84KB ipch AutoPCH d78c0bad315b6040 YOLOV4-TINY.ipch 29.19MB d72f94756be35e80 EXE_COMMON.ipch 3.81MB a9b4c9ea3efcff2b YOLOV4-TINY.ipch 99.25MB yolov4-tiny yolov4-tiny.vcxproj.user 165B coco.names 625B yolov4-tiny.vcxproj.filters 956B output 
person.mp4 2.86MB yolov4-tiny.cpp 5KB yolov4-tiny.cfg 3KB x64 Release yolov4-tiny.log 1KB yolov4-tiny.obj 3.71MB yolov4-tiny.tlog CL.write.1.tlog 370B CL.read.1.tlog 34KB CL.command.1.tlog 722B yolov4-tiny.lastbuildstate 201B yolov4-tiny.write.1u.tlog 5KB link.write.1.tlog 358B link.command.1.tlog 1KB link.read.1.tlog 4KB vc141.pdb 1.88MB yolov4-tiny.vcxproj 8KB yolov4-tiny.weights 23.13MB yolov4-tiny.sln 1KB x64 Release yolov4-tiny.pdb 2.09MB
yolov4-tiny.exe 47KB yolov4-tiny.iobj 818KB yolov4-tiny.ipdb 269KB
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