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OpenCV+yolov3实现目标检测(C++,Python)_yolo实时检测c++怎么看fps

作者：Monodyee | 2024-04-09 06:53:03

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yolo实时检测c++怎么看fps

OpenCV+yolov3实现目标检测(C++,Python)

目标检测算法主要分为两类：一类是基于Region Proposal（候选区域）的算法，如R-CNN系算法（R-CNN，Fast R-CNN, Faster R-CNN），它们是two-stage（两步法）的，需要先使用Selective search或者CNN网络（RPN）产生Region Proposal，然后再在Region Proposal上做分类与回归。而另一类是Yolo，SSD这类one-stage算法（一步法），其仅仅使用一个CNN网络直接预测不同目标的类别与位置。第一类方法是准确度高一些，但是速度慢，而第二类算法是速度快，但是准确性要低一些。

YOLO是一种比SSD还要快的目标检测网络模型，作者在其论文中说FPS是Fast R-CNN的100倍，这里首先简单的介绍一下YOLO网络基本结构，然后通过OpenCV C++调用Darknet的，实现目标检测。OpenCV在3.3.1的版本中开始正式支持Darknet网络框架并且支持YOLO1与YOLO2以及YOLO Tiny网络模型的导入与使用。后面测试，OpenCV3.4.2也支持YOLO3 。另外，OpenCV dnn模块目前支持Caffe、TensorFlow、Torch、PyTorch等深度学习框架，关于《OpenCV调用TensorFlow预训练模型》可参考鄙人的另一份博客：https://blog.csdn.net/guyuealian/article/details/80570120

关于《OpenCV+yolov2-tiny实现目标检测(C++)》请参考我的另一篇博客：https://blog.csdn.net/guyuealian/article/details/82950283

本博客源码都放在Github上：https://github.com/PanJinquan/opencv-learning-tutorials/tree/master/dnn_tutorial，麻烦给个“Star”哈

参考资料：

《Deep Learning based Object Detection using YOLOv3 with OpenCV ( Python / C++ )》：

官网博客：https://www.learnopencv.com/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/

《YOLOv3 + OpenCV 实现目标检测（Python / C ++）》：https://blog.csdn.net/haoqimao_hard/article/details/82081285

Github参考源码：https://github.com/spmallick/learnopencv/tree/master/ObjectDetection-YOLO

darknt yolo官网：https://pjreddie.com/darknet/yolo/

OpenCV+yolov3实现目标检测(C++,Python)

1、YOLO网络

（1）YOLO网络结构

2、OpenCV使用YOLO v3实现目标检测

1、YOLOv3网络

相比YOLOv2和YOLOv1，YOLOv3最大的变化包括两点：使用残差模型和采用FPN架构。YOLOv3的特征提取器是一个残差模型，因为包含53个卷积层，所以称为Darknet-53，从网络结构上看，相比Darknet-19网络使用了残差单元，所以可以构建得更深。另外一个点是采用FPN架构（Feature Pyramid Networks for Object Detection）来实现多尺度检测

YOLOv3是到目前为止，速度和精度最均衡的目标检测网络。通过多种先进方法的融合，将YOLO系列的短板（速度很快，不擅长检测小物体等）全部补齐。

1.1 YOLOv3网络结构

参考资料：

《深入理解目标检测与YOLO（从v1到v3）》：https://blog.csdn.net/qq_39521554/article/details/80694512

https://blog.csdn.net/leviopku/article/details/82660381

2、OpenCV使用YOLO v3实现目标检测

yolov3模型下载地址：链接: https://pan.baidu.com/s/1TugNSWRZaJz1R6IejRtNiA 提取码: 46mh

2.1 C++代码


// This code is written at BigVision LLC. It is based on the OpenCV project.
//It is subject to the license terms in the LICENSE file found in this distribution and at http://opencv.org/license.html
 
// Usage example:  ./object_detection_yolo.out --video=run.mp4
//                 ./object_detection_yolo.out --image=bird.jpg
#include <fstream>
#include <sstream>
#include <iostream>
 
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
;
using namespace cv;
using namespace dnn;
using namespace std;
 
string pro_dir = "E:/opencv-learning-tutorials/"; //项目根目录
 
// 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;
 
// Remove the bounding boxes with low confidence using non-maxima suppression
void postprocess(Mat& frame, const vector<Mat>& out);
 
// 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);
 
void detect_image(string image_path, string modelWeights, string modelConfiguration, string classesFile);
 
void detect_video(string video_path, string modelWeights, string modelConfiguration, string classesFile);
 
 
int main(int argc, char** argv)
{
	// Give the configuration and weight files for the model
	String modelConfiguration = pro_dir + "data/models/yolov3/yolov3.cfg";
	String modelWeights = pro_dir + "data/models/yolov3/yolov3.weights";
	string image_path = pro_dir + "data/images/bird.jpg";
	string classesFile = pro_dir + "data/models/yolov3/coco.names";// "coco.names";
	//detect_image(image_path, modelWeights, modelConfiguration, classesFile);
	string video_path = pro_dir + "data/images/run.mp4";
	detect_video(video_path, modelWeights, modelConfiguration, classesFile);
	cv::waitKey(0);
	return 0;
}
 
void detect_image(string image_path, string modelWeights, string modelConfiguration, string classesFile) {
	// Load names of classes
	ifstream ifs(classesFile.c_str());
	string line;
	while (getline(ifs, line)) classes.push_back(line);
 
	// Load the network
	Net net = readNetFromDarknet(modelConfiguration, modelWeights);
	net.setPreferableBackend(DNN_BACKEND_OPENCV);
	net.setPreferableTarget(DNN_TARGET_OPENCL);
 
	// Open a video file or an image file or a camera stream.
	string str, outputFile;
	cv::Mat frame = cv::imread(image_path);
	// Create a window
	static const string kWinName = "Deep learning object detection in OpenCV";
	namedWindow(kWinName, WINDOW_NORMAL);
 
	// Stop the program if reached end of video
	// Create a 4D blob from a frame.
	Mat blob;
	blobFromImage(frame, blob, 1 / 255.0, cvSize(inpWidth, inpHeight), Scalar(0, 0, 0), true, false);
 
	//Sets the input to the network
	net.setInput(blob);
 
	// Runs the forward pass to get output of the output layers
	vector<Mat> outs;
	net.forward(outs, getOutputsNames(net));
 
	// Remove the bounding boxes with low confidence
	postprocess(frame, outs);
	// 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);
	putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255));
	// Write the frame with the detection boxes
	imshow(kWinName, frame);
	cv::waitKey(30);
}
 
void detect_video(string video_path, string modelWeights, string modelConfiguration, string classesFile) {
	string outputFile  = "./yolo_out_cpp.avi";;
	// Load names of classes
	ifstream ifs(classesFile.c_str());
	string line;
	while (getline(ifs, line)) classes.push_back(line);
 
	// 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.
	VideoCapture cap;
	//VideoWriter video;
	Mat frame, blob;
 
	try {
		// Open the video file
		ifstream ifile(video_path);
		if (!ifile) throw("error");
		cap.open(video_path);
	}
	catch (...) {
		cout << "Could not open the input image/video stream" << endl;
		return ;
	}
 
	// Get the video writer initialized to save the output video
	//video.open(outputFile, 
	//	VideoWriter::fourcc('M', 'J', 'P', 'G'), 
	//	28, 
	//	Size(cap.get(CAP_PROP_FRAME_WIDTH), cap.get(CAP_PROP_FRAME_HEIGHT)));
 
	// Create a window
	static const string kWinName = "Deep learning object detection in OpenCV";
	namedWindow(kWinName, WINDOW_NORMAL);
 
	// Process frames.
	while (waitKey(1) < 0)
	{
		// get frame from the video
		cap >> frame;
 
		// Stop the program if reached end of video
		if (frame.empty()) {
			cout << "Done processing !!!" << endl;
			cout << "Output file is stored as " << outputFile << endl;
			waitKey(3000);
			break;
		}
		// Create a 4D blob from a frame.
		blobFromImage(frame, blob, 1 / 255.0, cvSize(inpWidth, inpHeight), Scalar(0, 0, 0), true, false);
 
		//Sets the input to the network
		net.setInput(blob);
 
		// Runs the forward pass to get output of the output layers
		vector<Mat> outs;
		net.forward(outs, getOutputsNames(net));
 
		// Remove the bounding boxes with low confidence
		postprocess(frame, outs);
 
		// 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);
		putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 0, 255));
 
		// Write the frame with the detection boxes
		Mat detectedFrame;
		frame.convertTo(detectedFrame, CV_8U);
		//video.write(detectedFrame);
		imshow(kWinName, frame);
 
	}
 
	cap.release();
	//video.release();
 
}
 
// Remove the bounding boxes with low confidence using non-maxima suppression
void postprocess(Mat& frame, const vector<Mat>& outs)
{
	vector<int> classIds;
	vector<float> confidences;
	vector<Rect> boxes;
 
	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);
	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);
	}
}
 
// Draw the predicted bounding box
void 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, 0.75, Scalar(0, 0, 0), 1);
}
 
// Get the names of the output layers
vector<String> getOutputsNames(const Net& net)
{
	static vector<String> names;
	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
		names.resize(outLayers.size());
		for (size_t i = 0; i < outLayers.size(); ++i)
			names[i] = layersNames[outLayers[i] - 1];
	}
	return names;
}

2.2 Python代码

使用cv_dnn_forward获得预测输出outs是三个二维的数组，每个二维数组是一个feature_map的输出结果，feature_map中每一行是一个预测值：

outs:[507*85 =13*13*3*(5+80),
2028*85=26*26*3*(5+80),
8112*85=52*52*3*(5+80)]

每一个行：85=[x,y,w,h,confs,class_probs_0,class_probs_1,..,class_probs_78,class_probs_79]


# -*-coding: utf-8 -*-
"""
    @Project: tensorflow-yolov3
    @File   : opencv_dnn_yolov3.py
    @Author : panjq
    @E-mail : pan_jinquan@163.com
    @Date   : 2019-01-28 14:36:00
"""
 
import cv2 as cv
import numpy as np
 
def read_class(file):
    with open(file, 'rt') as f:
        classes = f.read().rstrip('\n').split('\n')
    return classes
 
 
class cv_yolov3(object):
    def __init__(self,class_path,net_width,net_height,confThreshold,nmsThreshold):
        '''
        Initialize the parameters
        :param class_path:
        :param net_width: default 416, Width of network's input image
        :param net_height: default 416,Height of network's input image
        :param confThreshold: default 0.5, Confidence threshold
        :param nmsThreshold: default 0.5,Non-maximum suppression threshold
        '''
        self.classes = read_class(class_path)
        self.net_width=net_width
        self.net_height=net_height
        self.confThreshold=confThreshold
        self.nmsThreshold=nmsThreshold
 
    def cv_dnn_init(self,modelConfiguration,modelWeights):
        '''
        Give the configuration and weight files for the model and load the network using them.
        eg:
        modelConfiguration = "checkpoint-bk/yolov3.cfg";
        modelWeights = "checkpoint-bk/yolov3.weights";
        :param modelConfiguration:
        :param modelWeights:
        :return:
        '''
        self.net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
        self.net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        self.net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
 
    def getOutputsNames(self,net):
        '''
        Get the names of the output layers
        :param net:
        :return:
        '''
        # Get the names of all the layers in the network
        layersNames = net.getLayerNames()
        # Get the names of the output layers, i.e. the layers with unconnected outputs
        return [layersNames[i[0] - 1] for i in net.getUnconnectedOutLayers()]
 
 
    def drawPred(self,frame,classes,classId, conf, left, top, right, bottom):
        '''
        Draw the predicted bounding box
        :param frame:
        :param classes:
        :param classId:
        :param conf:
        :param left:
        :param top:
        :param right:
        :param bottom:
        :return:
        '''
        # Draw a bounding box.
        cv.rectangle(frame, (left, top), (right, bottom), (255, 178, 50), 3)
 
        label = '%.2f' % conf
 
        # Get the label for the class name and its confidence
        if classes:
            assert (classId < len(classes))
            label = '%s:%s' % (classes[classId], label)
 
        # Display the label at the top of the bounding box
        labelSize, baseLine = cv.getTextSize(label, cv.FONT_HERSHEY_SIMPLEX, 0.5, 1)
        top = max(top, labelSize[1])
        cv.rectangle(frame, (left, top - round(1.5 * labelSize[1])), (left + round(1.5 * labelSize[0]), top + baseLine),
                     (255, 255, 255), cv.FILLED)
        cv.putText(frame, label, (left, top), cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 1)
 
 
    def postprocess(self,frame, classes,outs):
        '''
        Remove the bounding boxes with low confidence using non-maxima suppression
        :param frame:
        :param classes:
        :return: outs:[507*85 =(13*13*3)*(5+80),
                 2028*85=(26*26*3)*(5+80),
                 8112*85=(52*52*3)*(5+80)]
        outs中每一行是一个预测值：[x,y,w,h,confs,class_probs_0,class_probs_1,..,class_probs_78,class_probs_79]
        :return:
        '''
        frameHeight = frame.shape[0]
        frameWidth = frame.shape[1]
        # 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.
        classIds = []
        confidences = []
        boxes = []
        for out in outs:
            for detection in out:
                scores = detection[5:]
                classId = np.argmax(scores)
                confidence = scores[classId]
                if confidence > self.confThreshold:
                    center_x = int(detection[0] * frameWidth)
                    center_y = int(detection[1] * frameHeight)
                    width = int(detection[2] * frameWidth)
                    height = int(detection[3] * frameHeight)
                    left = int(center_x - width / 2)
                    top = int(center_y - height / 2)
                    classIds.append(classId)
                    confidences.append(float(confidence))
                    boxes.append([left, top, width, height])
 
        # Perform non maximum suppression to eliminate redundant overlapping boxes with
        # lower confidences.
        indices = cv.dnn.NMSBoxes(boxes, confidences, self.confThreshold, self.nmsThreshold)
        for i in indices:
            i = i[0]
            box = boxes[i]
            left = box[0]
            top = box[1]
            width = box[2]
            height = box[3]
            self.drawPred(frame,classes,classIds[i], confidences[i], left, top, left + width, top + height)
 
    def cv_dnn_forward(self,frame):
        '''
        :param frame:
        :return: outs:[507*85 =13*13*3*(5+80),
                       2028*85=26*26*3*(5+80),
                       8112*85=52*52*3*(5+80)]
        '''
        # Create a 4D blob from a frame.
        blob = cv.dnn.blobFromImage(frame, 1 / 255, (self.net_width, self.net_height), [0, 0, 0], 1, crop=False)
        # Sets the input to the network
        self.net.setInput(blob)
        # Runs the forward pass to get output of the output layers
        outs = self.net.forward(self.getOutputsNames(self.net))
        # Put efficiency information. The function getPerfProfile returns the overall time for inference(t) and the timings for each of the layers(in layersTimes)
        runtime, _ = self.net.getPerfProfile()
        return outs,runtime
 
    def yolov3_predict(self,image_path):
        '''
        :param image_path:
        :return:
        '''
        # Process inputs
        winName = 'Deep learning object detection in OpenCV'
        cv.namedWindow(winName, cv.WINDOW_NORMAL)
 
        frame=cv.imread(image_path)
        outs,runtime=self.cv_dnn_forward(frame)
        # Remove the bounding boxes with low confidence
        self.postprocess(frame, self.classes, outs)
 
        label = 'Inference time: %.2f ms' % (runtime * 1000.0 / cv.getTickFrequency())
        cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
 
        cv.imshow(winName, frame)
        cv.waitKey(0)
 
if __name__=="__main__":
    confThreshold = 0.5  # Confidence threshold
    nmsThreshold = 0.5  # Non-maximum suppression threshold
    net_input_width = 416  # Width of network's input image
    net_input_height = 416  # Height of network's input image
    image_path = "./data/demo_data/dog.jpg"
    # anchors_path = './data/coco_anchors.txt'
    classesFile = './data/coco.names'
    modelConfiguration = "model/yolov3.cfg";
    modelWeights = "model/yolov3.weights";
    cv_model=cv_yolov3(classesFile,net_input_width,net_input_height,confThreshold,nmsThreshold)
    cv_model.cv_dnn_init(modelConfiguration,modelWeights)
    cv_model.yolov3_predict(image_path)

3、YOLO的缺点

YOLO对相互靠的很近的物体，还有很小的群体检测效果不好，这是因为一个网格中只预测了两个框，并且只属于一类。
对测试图像中，同一类物体出现的新的不常见的长宽比和其他情况是。泛化能力偏弱。
由于损失函数的问题，定位误差是影响检测效果的主要原因。尤其是大小物体的处理上，还有待加强。

4、参考资料：

[1].《论文阅读笔记：You Only Look Once: Unified, Real-Time Object Detection》https://blog.csdn.net/tangwei2014/article/details/50915317

[2]. https://blog.csdn.net/xiaohu2022/article/details/79211732

[3]. https://blog.csdn.net/u014380165/article/details/72616238

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