文章目录
- 前期准备
- 处理步骤
- 效果
- 代码
参考：
YOLO官网： https://pjreddie/darknet/yolo/
OpenCV官方文档： https://docs.opencv/3.4.5/da/d9d/tutorial_dnn_yolo.html
大佬博客： https://www.learnopencv/deep-learning-based-object-detection-using-yolov3-with-opencv-python-c/
大佬代码： https://github/spmallick/learnopencv/blob/master/ObjectDetection-YOLO/object_detection_yolo.cpp
前期准备
本人用的是VS2015+OpenCV3.4.5（版本太低的话无法支持yolo3）
YOLO3模型下载：
1.（yolov3.weights）权重文件：https://pjreddie/media/files/yolov3.weights
2.（yolov3.cfg）配置文件：https://github/pjreddie/darknet/blob/master/cfg/yolov3.cfg
3.（coco.names）对象名称文件：https://github/pjreddie/darknet/blob/master/data/coco.names
**提供我的百度云打包下载(*￣︶￣)：链接：https://pan.baidu/s/1S--B32JVWJEKVb7L97mtJA 提取码：1r04
把3个模型文件放到项目文件中，之后程序通过路径调用。
（本人是在项目文件中新建了一个yolo3的文件，因此在程序中调用时记得修改路径）
顺便把检测对象（图片或视频）也放到同一个路径里面。
处理步骤
（这部分主要是参考翻译自大佬的文章，如果只是想跑程序，不想了解过程，可以直接跳过这一节）
1.初始化参数
YOLOv3算法生成边界框作为预测的检测输出，每个预测框都与置信度得分相关。主要涉及以下几个参数：
（1）置信阈值参数（confThreshold）：首先，将忽略置信阈值参数下的所有框以进行进一步处理，置信得分在该阈值以下的识别对象会被去除掉；
（2）非最大抑制参数（nmsThreshold）：之后，剩下的框将进行非最大抑制，以删除多余的重叠边界框。该参数如果太低的话会检测不到有重叠的对象，参数太高可能会出现同一个对象有几个重复的框；
（3）宽度（inpWidth）和高度（inpHeight）：接下来，设置网络输入图像的输入宽度和高度的默认值。将它们中的每一个设置为416，这样就可以将我们的运行与Yolov3的作者给出的Darknet的C代码进行比较。（还可以将这两个选项都更改为320以获得更快的结果，或者更改为608以获得更准确的结果）
// Initialize the parameters
floatconfThreshold = 0.5;// Confidence threshold
floatnmsThreshold = 0.4;// Non-maximum suppression threshold
intinpWidth = 416;// Width of network's input image
intinpHeight = 416;// Height of network's input image
2.导入模型和类
之前我们准备的YOLO3模型3个文件在这里导入，包括：（coco.names）对象名称文件，（yolov3.weights）权重文件，（yolov3.cfg）配置文件。（记得修改路径）
这里将DNN后端设置为OpenCV，目标为CPU。这里可以尝试将首选目标设置为cv.dnn.dnn_target_opencl以在GPU上运行。但是，当前的opencv版本只能在英特尔的GPU上测试，如果没有英特尔的GPU，它会自动切换到CPU。
// Load names of classes
string classesFile = "coco.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);
3.读取输入
这一步就是OpenCV的常规操作，可以读取图片、视频或者是摄像头，另外就是还可以设置一个输出来保存我们检测的效果。
if (parser.has("image"))
{
// Open the image file
str = parser.get<String>("image");
ifstream ifile(str);
if (!ifile) throw("error");
cap.open(str);
str.replace(str.end()-4, str.end(), "_yolo_out.jpg");
outputFile = str;
}
else if (parser.has("video"))
{
// Open the video file
str = parser.get<String>("video");
ifstream ifile(str);
if (!ifile) throw("error");
cap.open(str);
str.replace(str.end()-4, str.end(), "_yolo_out.avi");
outputFile = str;
}
// Open the webcaom
else cap.open(parser.get<int>("device"));
4.处理每一帧图像
神经网络的输入图像需要采用一种称为blob的特定格式。
从输入图像或视频流中读取帧后，将通过blobFromImage函数将其转换为神经网络的输入blob。 在此过程中，它使用比例因子1/255将图像像素值缩放到0到1的目标范围。 它还将图像的大小调整为给定大小（416,416）而不进行裁剪。
(PS：我们不在此处执行任何均值减法，因此将[0,0,0]传递给函数的mean参数，并将swapRB参数保持为其默认值1。)
之后输出blob作为输入传递到网络，并运行正向传递以获得预测边界框列表作为网络输出。 这些框经过后处理步骤，滤除了低置信度分数。 这里在图像左上角打印出每帧的推理时间， 然后将检测图像输出。
// 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);
if (parser.has("image")) imwrite(outputFile, detectedFrame);
else video.write(detectedFrame);
}
下面介绍一些代码中的调用函数。
4a.获得输出层名称
OpenCV的Net类中的 forward函数 需要知道结束层，它应该在网络中运行。 由于我们想要遍历整个网络，因此需要确定网络的最后一层。 我们通过使用函数 getUnconnectedOutLayers() 来实现这一点，该函数给出了未连接的输出层的名称，这些输出层基本上是网络的最后一层。 然后我们运行网络的正向传递以从输出层获得输出，如前面的代码片段net.forward(outs, getOutputsNames(net))。
// 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;
}
4b.对网络输出进行后处理
网络输出边界框均由类的数量+5长度的向量表示。
前5个元素分别表示 中心x、 中心y、 宽度 、 高度 和边界框包围对象的 置信度。
其余的元素是与每个类（即对象类型）相关联的置信度，最后该框被分配给对应于最高置信度分数的类。
一个边界框中的最高分数也被称为 置信度 。如果该框的置信度小于给定阈值，则边界框将被删除，不考虑进一步处理。
置信度等于或大于置信阈值的方框将受到非最大抑制参数的影响，以减少重叠框的数量。
// 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);
}
}
4c.绘制预测框
最后，我们在输入图像上绘制通过非最大抑制参数过滤后的框，并给出它们对应的类标签和置信度。
// 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(0, 0, 255));
//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);
putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255,255,255));
}
效果
街道图片：
桌面图片（左边那个鼠标识别错了）：
电视剧（视频）：
博主做了一个YOLO检测视频的效果，内容是《逃避可耻》片尾曲《恋》的MV，感兴趣的可以通过以下链接观看：
https://www.bilibili/video/av50257274
记得投个硬币(*￣︶￣)
YOLOv3官方模型可以识别80种物体，分别如下：（大家可以都试试）
| person | bicycle | car | motorbike | aeroplane |
| bus | train | truck | boat | traffic light |
| fire hydrant | stop sign | parking meter | bench | bird |
| cat | dog | horse | sheep | cow |
| elephant | bear | zebra | giraffe | backpack |
| umbrella | handbag | tie | suitcase | frisbee |
| skis | snowboard | sports ball | kite | baseball bat |
| baseball glove | skateboard | surfboard | tennis racket | bottle |
| wine glass | cup | fork | knife | spoon |
| bowl | banana | apple | sandwich | orange |
| broccoli | carrot | hot dog | pizza | donut |
| cake | chair | sofa | pottedplant | bed |
| diningtable | toilet | tvmonitor | laptop | mouse |
| remote | keyboard | cell phone | microwave | oven |
| toaster | sink | refrigerator | book | clock |
| vase | scissors | teddy bear | hair drier | toothbrush |
代码
#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;
//**************************** You should change ******************************//
//Dir of object (choose the input source, image or video)
const char* keys = "{image | yolo3/table.jpg | input image }"
"{video | yolo3/people.mp4 | input video }"
"{device | 0 | input video }";
//Dir of yolo3 model
string classesFile = "yolo3/coco.names"; //Names of classes
String modelConfiguration = "yolo3/yolov3.cfg"; //Configuration file
String modelWeights = "yolo3/yolov3.weights"; //Weight file
// Initialize the parameters
float confThreshold = 0.4; // Confidence threshold
float nmsThreshold = 0.3; // 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; // Name of 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);
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, keys);
parser.about("Use this script to run object detection using YOLO3 in OpenCV.");
// 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.
string str, outputFile;
VideoCapture cap;
VideoWriter video;
Mat frame, blob;
try {
outputFile = "yolo_out_cpp.avi";
if (parser.has("image"))
{
// Open the image file
str = parser.get<String>("image");
ifstream ifile(str);
if (!ifile) throw("error");
cap.open(str);
str.replace(str.end() - 4, str.end(), "_yolo_out_cpp.jpg");
outputFile = str;
}
else if (parser.has("video"))
{
// Open the video file
str = parser.get<String>("video");
ifstream ifile(str);
if (!ifile) throw("error");
cap.open(str);
str.replace(str.end() - 4, str.end(), "_yolo_out_cpp.avi");
outputFile = str;
}
// Open the webcaom
else cap.open(parser.get<int>("device"));
}
catch (...) {
cout << "Could not open the input image/video stream" << endl;
waitKey(0);
return 0;
}
// Get the video writer initialized to save the output video
if (!parser.has("image")) {
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_AUTOSIZE);
// 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);
if (parser.has("image")) imwrite(outputFile, detectedFrame);
else video.write(detectedFrame);
imshow(kWinName, frame);
}
cap.release();
if (!parser.has("image")) video.release();
waitKey(0);
return 0;
}
// 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(208, 244, 64), 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), 2);
}
// 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;
}
代码说明：
//**************************** You should change ******************************//
//Dir of object (choose the input source, image or video)
const char* keys = "{image | yolo3/table.jpg | input image }"
"{video | yolo3/people.mp4 | input video }"
"{device | 0 | input video }";
//Dir of yolo3 model
string classesFile = "yolo3/coco.names"; //Names of classes
String modelConfiguration = "yolo3/yolov3.cfg"; //Configuration file
String modelWeights = "yolo3/yolov3.weights"; //Weight file
// Initialize the parameters
float confThreshold = 0.4; // Confidence threshold
float nmsThreshold = 0.3; // Non-maximum suppression threshold
int inpWidth = 416; // Width of network's input image
int inpHeight = 416; // Height of network's input image
//*****************************************************************************//
代码开头的这部分需要修改，分为3部分：
（1）第一部分为图片或视频输入的路径，这里默认是图片输入，如果要视频输入的话，将图片路径改为“none”，例如：
const char* keys = "{image | <none> | input image }"
"{video | yolo3/people.mp4 | input video }"
"{device | 0 | input video }";
如果要改成摄像头输入的话，把图片和视频都改成“none”，例如：
const char* keys = "{image | <none> | input image }"
"{video | <none> | input video }"
"{device | 0 | input video }";
（2）第二部分为YOLO模型的三个文件输入路径，这个前面有说明。
（3）第三部分是参数设置，这部分在前面也有说明。
如果错误，欢迎指正！
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