OPENCV的dll封装
MyVisionDetect.h
#pragma once
/************************************************************************/
/* 以C++接口为基础，对常用函数进行二次封装，方便用户使用 */
/************************************************************************/
#ifndef _MY_VISION_DETECT_H_
#define _MY_VISION_DETECT_H_
#include <fstream>
#include <sstream>
#include <iostream>
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
//#define SHOW_DEBUG_INFO
using namespace cv;
using namespace dnn;
using namespace std;
typedef struct
{
Rect roi;
string species;
float confidence;
}boxParameters;
class CMyVisionDetect
{
public:
// CMyCamera();
CMyVisionDetect() //B函数体内初始化
{
string classesFile = "yolov3.names";
ifstream ifs(classesFile.c_str());
string line;
while (getline(ifs, line)) classes.push_back(line);
// Give the configuration and weight files for the model
String modelConfiguration = "yolov3.cfg";
String modelWeights = "yolov3.weights";
// Load the network
net = readNetFromDarknet(modelConfiguration, modelWeights);
net.setPreferableBackend(DNN_BACKEND_OPENCV);
net.setPreferableTarget(DNN_TARGET_CPU);
}
~CMyVisionDetect();
private:
// Initialize the parameters
float confThreshold = 0.5; // Confidence threshold
float nmsThreshold = 0.4; // Non-maximum suppression threshold
int inpWidth = 416; // Width of network's input image
int inpHeight = 416; // Height of network's input image
vector<string> classes;
Net net;
// Remove the bounding boxes with low confidence using non-maxima suppression
void postprocess(Mat& frame, const vector<Mat>& out, vector<boxParameters>& boxsResult);
// Draw the predicted bounding box
void drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
// Get the names of the output layers
vector<String> getOutputsNames(const Net& net);
public:
void detectPicture(Mat frame, Mat &result, vector<boxParameters>& boxsResult);
};
#endif
MyVisionDetect.cpp
#include"MyVisionDetect.h"
//#define USECOLOR 1
//
--------------------------------------------
//int iPicNum = 0;//Set channel NO.
//LONG nPort = -1;
//HWND hWnd = NULL;
//CMyCamera::CMyCamera(int weight)
//{
// m_bIsLogin = FALSE;
// // m_lLoginID = -1;
// m_bIsPlaying = FALSE;
// m_bIsRecording = FALSE;
//}
CMyVisionDetect::~CMyVisionDetect()
{
}
void CMyVisionDetect::detectPicture(Mat frame, Mat &result, vector<boxParameters>& boxsResult)
{
//string classesFile = "yolov3.names";
//ifstream ifs(classesFile.c_str());
//string line;
//while (getline(ifs, line)) classes.push_back(line);
Give the configuration and weight files for the model
//String modelConfiguration = "yolov3.cfg";
//String modelWeights = "yolov3.weights";
Load the network
//Net net = readNetFromDarknet(modelConfiguration, modelWeights);
//net.setPreferableBackend(DNN_BACKEND_OPENCV);
//net.setPreferableTarget(DNN_TARGET_CPU);
// Open a video file or an image file or a camera stream.
string str, outputFile;
//VideoCapture cap("run.mp4");
//VideoWriter video;
Mat blob;
/*frame = imread("1.jpg");*/
Process frames.
//while (waitKey(1) != 27)
//{
// // get frame from the video
// cap >> frame;
// Stop the program if reached end of video
//if (frame.empty()) {
// //waitKey(3000);
// return 0;
//}
// Create a 4D blob from a frame.
blobFromImage(frame, blob, 1 / 255.0, cv::Size(inpWidth, inpHeight), Scalar(0, 0, 0), true, false);
//Sets the input to the network
net.setInput(blob);
/*std::vector<String> outNames = net.getUnconnectedOutLayersNames();*/
// Runs the forward pass to get output of the output layers
vector<Mat> outs;
vector<String> names00 = getOutputsNames(net);
net.forward(outs, names00);
//保存输出结果
//vector<boxParameters> boxsResult;
// Remove the bounding boxes with low confidence
postprocess(frame, outs, boxsResult);
// Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
vector<double> layersTimes;
double freq = getTickFrequency() / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
string label = format("Inference time for a frame : %.2f ms", t);
#ifdef SHOW_DEBUG_INFO
std::cout << "检测时间：" << label << std::endl;
#endif
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 1.5, Scalar(0, 0, 255));
// Write the frame with the detection boxes
Mat detectedFrame;
frame.convertTo(detectedFrame, CV_8U);
#ifdef SHOW_DEBUG_INFO
// Create a window
static const string kWinName = "Deep learning object detection in OpenCV";
namedWindow(kWinName, WINDOW_NORMAL);
imshow(kWinName, frame);
#endif // SHOW_DEBUG_INFO
result = frame;
//return frame;
}
// Remove the bounding boxes with low confidence using non-maxima suppression
void CMyVisionDetect::postprocess(Mat& frame, const vector<Mat>& outs, vector<boxParameters>& boxsResult)
{
vector<int> classIds;
vector<float> confidences;
vector<Rect> boxes;
#ifdef SHOW_DEBUG_INFO
std::cout << "检测到的box数：" << outs.size() << std::endl;
#endif
for (size_t i = 0; i < outs.size(); ++i)
{
// Scan through all the bounding boxes output from the network and keep only the
// ones with high confidence scores. Assign the box's class label as the class
// with the highest score for the box.
float* data = (float*)outs[i].data;
for (int j = 0; j < outs[i].rows; ++j, data += outs[i].cols)
{
Mat scores = outs[i].row(j).colRange(5, outs[i].cols);
Point classIdPoint;
double confidence;
// Get the value and location of the maximum score
minMaxLoc(scores, 0, &confidence, 0, &classIdPoint);
if (confidence > confThreshold)
{
int centerX = (int)(data[0] * frame.cols);
int centerY = (int)(data[1] * frame.rows);
int width = (int)(data[2] * frame.cols);
int height = (int)(data[3] * frame.rows);
int left = centerX - width / 2;
int top = centerY - height / 2;
classIds.push_back(classIdPoint.x);
confidences.push_back((float)confidence);
boxes.push_back(Rect(left, top, width, height));
}
}
}
// Perform non maximum suppression to eliminate redundant overlapping boxes with
// lower confidences
vector<int> indices;
NMSBoxes(boxes, confidences, confThreshold, nmsThreshold, indices);
#ifdef SHOW_DEBUG_INFO
std::cout << "符合要求的box数：" << indices.size() << std::endl;
#endif
for (size_t i = 0; i < indices.size(); ++i)
{
int idx = indices[i];
Rect box = boxes[idx];
drawPred(classIds[idx], confidences[idx], box.x, box.y,
box.x + box.width, box.y + box.height, frame);
//保存符合条件的box
boxParameters midBox;
midBox.confidence = confidences[idx];
midBox.roi = box;
midBox.species = classes[classIds[idx]];
boxsResult.push_back(midBox);
}
}
// Draw the predicted bounding box
void CMyVisionDetect::drawPred(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
{
//Draw a rectangle displaying the bounding box
rectangle(frame, Point(left, top), Point(right, bottom), Scalar(255, 178, 50), 3);
//Get the label for the class name and its confidence
string label = format("%.2f", conf);
if (!classes.empty())
{
CV_Assert(classId < (int)classes.size());
label = classes[classId] + ":" + label;
}
//Display the label at the top of the bounding box
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
top = max(top, labelSize.height);
rectangle(frame, Point(left, top - round(1.5*labelSize.height)), Point(left + round(1.5*labelSize.width), top + baseLine), Scalar(255, 255, 255), FILLED);
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 1.5, Scalar(0, 0, 0), 2);
}
// Get the names of the output layers
vector<String> CMyVisionDetect::getOutputsNames(const Net& net)
{
static vector<String> names = {
};
/*vector<String> names(0);
vector<String> names1;*/
if (names.empty())
{
//Get the indices of the output layers, i.e. the layers with unconnected outputs
vector<int> outLayers = net.getUnconnectedOutLayers();
//get the names of all the layers in the network
vector<String> layersNames = net.getLayerNames();
// Get the names of the output layers in names
if (outLayers.size() == 0)
{
return names;
}
names.resize(outLayers.size());
for (size_t i = 0; i < outLayers.size(); ++i)
{
//names[i] = layersNames[outLayers[i] - 1];
names.push_back(layersNames[outLayers[i] - 1]);
}
}
//vector<String> names1;
//for (size_t i = 0; i < names.size(); ++i)
//{
// //names[i] = layersNames[outLayers[i] - 1];
// names1.push_back(names[i]);
//}
return names;
}
visionDetect.h
#pragma once //避免重复编译
//#ifndef __CDLL_H__
//#define __CDLL_H__
#include"MyVisionDetect.h"
typedef unsigned char byte;
#ifndef _OUT
#define _OUT
#endif
#ifndef _IN
#define _IN
#endif
struct detectParameter
{
uint inputSize;//缓冲区大小
detectParameter()
{
inputSize = 0;
}
};
struct detectedBox
{
int x;
int y;
int width;
int height;
double confidence;
string species;
detectedBox()
{
x = 0;
y = 0;
width = 0;
height = 0;
confidence = 0;
species = "";
}
};
struct detectResult
{
byte* resultImage;//输出结果
uint resultSize;//输出大小
int boxCount;
detectedBox boxs[64];
detectResult()
{
resultImage = NULL;
resultSize = 0;
boxCount = 0;
}
};
extern "C" _declspec(dllexport) int __stdcall MV_SDK_ObjectiveDetect(byte *inputImage, _IN detectParameter input, _OUT detectResult* output);
//#endif
visionDetect.cpp
#include"visionDetect.h"
CMyVisionDetect* m_pcMyVisionDetect = new CMyVisionDetect();
int __stdcall MV_SDK_ObjectiveDetect(byte *inputImage, _IN detectParameter input, _OUT detectResult* output)
{
//解码
vector<byte> buff;
for (uint i = 0; i < input.inputSize; i++)
{
buff.push_back(inputImage[i]);
}
Mat srcImage = imdecode(buff, IMREAD_COLOR);
if (srcImage.empty())
{
return -1;
}
#ifdef SHOW_DEBUG_INFO
namedWindow("原图", WINDOW_NORMAL);
imshow("原图", srcImage);
#endif
Mat result;
vector<boxParameters> boxsResult;
m_pcMyVisionDetect->detectPicture(srcImage, result, boxsResult);
#ifdef SHOW_DEBUG_INFO
std::cout << "最后输出的box数：" << boxsResult.size() << std::endl;
#endif
output->boxCount = boxsResult.size();
for (size_t i = 0; i < boxsResult.size(); ++i)
{
//保存符合条件的box
output->boxs[i].confidence = boxsResult[i].confidence;
output->boxs[i].species = boxsResult[i].species;
output->boxs[i].x = boxsResult[i].roi.x;
output->boxs[i].y = boxsResult[i].roi.y;
output->boxs[i].width = boxsResult[i].roi.width;
output->boxs[i].height = boxsResult[i].roi.height;
}
//编码
vector<int> param = vector<int>(2);
param[0] = IMWRITE_JPEG_QUALITY;
param[1] = 95;//default(95) 0-100
vector<byte> inImage;
imencode(".jpg", result, inImage, param);
output->resultSize = inImage.size();
output->resultImage = new byte[output->resultSize];
for (uint i = 0; i < output->resultSize; i++)
{
output->resultImage[i] = inImage[i];
//cout << resultImage[i] << endl;
}
//解码
/*vector<byte> buff1;
for (uint i = 0; i<resultSize; i++)
{
buff1.push_back(resultImage[i]);
}
Mat show = imdecode(buff1, IMREAD_COLOR);
namedWindow("结果图", WINDOW_NORMAL);
imshow("结果图", show);*/
//delete m_pcMyVisionDetect;
//m_pcMyVisionDetect = NULL;
//cv::waitKey(0);
return 0;
}
c++接口调用测试
#include <fstream>
#include <sstream>
#include <iostream>
#include<Windows.h>
//#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
//#define SHOW_DEBUG_INFO
using namespace cv;
//using namespace dnn;
using namespace std;
typedef unsigned char byte;
#ifndef _OUT
#define _OUT
#endif
#ifndef _IN
#define _IN
#endif
struct detectParameter
{
uint inputSize;
detectParameter()
{
inputSize = 0;
}
};
struct detectedBox
{
int x;
int y;
int width;
int height;
double confidence;
string species;
detectedBox()
{
x = 0;
y = 0;
width = 0;
height = 0;
confidence = 0;
species = "";
}
};
struct detectResult
{
byte* resultImage;
uint resultSize;
int boxCount;
detectedBox boxs[64];
detectResult()
{
resultImage = NULL;
resultSize = 0;
boxCount = 0;
}
};
#pragma comment(lib,"VisionDetect.lib")
extern "C" _declspec(dllimport) int __stdcall MV_SDK_ObjectiveDetect(byte *inputImage, _IN detectParameter input, _OUT detectResult* output); // 加载模型
int main()
{
Mat tstMat = imread("test.jpg");
// imshow("picture",tstMat);
namedWindow("原图", WINDOW_NORMAL);
imshow("原图", tstMat);
//编码
vector<byte> inImage;
vector<int> param = vector<int>(2);
param[0] = IMWRITE_JPEG_QUALITY;
param[1] = 95;//default(95) 0-100
imencode(".jpg", tstMat, inImage, param);
uint inputSize = inImage.size();
std::cout << "编码大小：" << inputSize << std::endl;
byte *inputImage = new byte[inputSize];
for (uint i = 0; i<inputSize; i++)
{
inputImage[i] = inImage[i];
//cout << inputSize[i] << endl;
}
//byte* resultImage=new byte[900000];
//uint resultSize;
detectParameter input;
detectResult* output=new detectResult();
input.inputSize = inputSize;
output->resultImage = new byte[900000];
DWORD start_time = GetTickCount();//开始计时
//detect(inputImage,inputSize, resultImage, resultSize);
MV_SDK_ObjectiveDetect(inputImage, input, output);
DWORD end_time = GetTickCount();//结束计时
cout << "The run time is:" << (end_time - start_time) << "ms!" << endl;
std::cout << "输出box数：" << output->boxCount << std::endl;
for (int i=0;i<output->boxCount;i++)
{
std::cout << "第" << i+1 << "个:"<<std::endl;
std::cout << "类别：" << output->boxs[i].species << std::endl;
std::cout << "置信度：" << output->boxs[i].confidence << std::endl;
std::cout << "(x,y,width,height)=(" << output->boxs[i].x<<","<< output->boxs[i].y<<","<< output->boxs[i].width<<","<< output->boxs[i].height<< ")"<<std::endl;
}
//解码
std::cout << "解码大小：" << output->resultSize << std::endl;
vector<byte> buff;
for (uint i = 0; i<output->resultSize; i++)
{
buff.push_back(output->resultImage[i]);
}
Mat show = imdecode(buff, IMREAD_COLOR);
namedWindow("结果图", WINDOW_NORMAL);
imshow("结果图", show);
imwrite("save.jpg",show);
waitKey();
system("pause");
return 0;
}