本文是文章傳統(tǒng)圖像處理方法實(shí)現(xiàn)車(chē)輛計(jì)數(shù)的后續(xù)。這里用OpenCV實(shí)現(xiàn)了基于yolov5檢測(cè)器的單向車(chē)輛計(jì)數(shù)功能，方法是撞線(xiàn)計(jì)數(shù)。該代碼只能演示視頻demo效果，一些功能未完善，離實(shí)際工程應(yīng)用還有距離。
實(shí)現(xiàn)流程：
（1）訓(xùn)練yolov5模型，這里就沒(méi)有自己訓(xùn)練了，直接使用官方的開(kāi)源模型yolov5s.pt；
（2）運(yùn)行yolov5工程下面的export.py，將pt模型轉(zhuǎn)成onnx模型；
（3）編寫(xiě)yolov5部署的C++工程，包括前處理、推理和后處理部分；
（4）讀取視頻第一幀，用yolov5檢測(cè)第一幀圖像的車(chē)輛目標(biāo)，計(jì)算這些檢測(cè)框的中心點(diǎn)，
（5）讀取視頻的后續(xù)幀，用yolov5檢測(cè)每幀圖像上的車(chē)輛目標(biāo)，計(jì)算新目標(biāo)和上一幀圖像中檢測(cè)框中心點(diǎn)的距離矩陣；
（6）通過(guò)距離矩陣確定新舊目標(biāo)檢測(cè)框之間的對(duì)應(yīng)關(guān)系；
（7）計(jì)算對(duì)應(yīng)新舊目標(biāo)檢測(cè)框中心點(diǎn)之間的連線(xiàn)，判斷和事先設(shè)置的虛擬撞線(xiàn)是否相交，若相交則計(jì)數(shù)加1；
（8）重復(fù)（5）-（7）。
由于程序在CPU端運(yùn)行，為了提速，實(shí)際實(shí)現(xiàn)的時(shí)候采取的是隔幀判斷而不是使用相鄰幀，v1的代碼實(shí)現(xiàn)如下：

#include <iostream> #include <fstream> #include <opencv2/opencv.hpp>// 常量 const float INPUT_WIDTH = 640.0; const float INPUT_HEIGHT = 640.0; const float SCORE_THRESHOLD = 0.5; const float NMS_THRESHOLD = 0.45; const float CONFIDENCE_THRESHOLD = 0.45;const std::vector<std::string> class_name = { "person", "bicycle", "car", "motorcycle", "airplane", "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", "couch", "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" };// 畫(huà)框函數(shù) void draw_label(cv::Mat& input_image, std::string label, int left, int top) {int baseLine;cv::Size label_size = cv::getTextSize(label, 0.7, 0.7, 1, &baseLine);top = std::max(top, label_size.height);cv::Point tlc = cv::Point(left, top);cv::Point brc = cv::Point(left , top + label_size.height + baseLine);cv::putText(input_image, label, cv::Point(left, top + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 255), 1); }// 預(yù)處理 std::vector<cv::Mat> preprocess(cv::Mat& input_image, cv::dnn::Net& net) {cv::Mat blob;cv::dnn::blobFromImage(input_image, blob, 1. / 255., cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(), true, false);net.setInput(blob);std::vector<cv::Mat> preprcess_image;net.forward(preprcess_image, net.getUnconnectedOutLayersNames());return preprcess_image; }// 后處理 std::vector<cv::Rect> postprocess(std::vector<cv::Mat>& preprcess_image, cv::Mat& output_image) {std::vector<int> class_ids;std::vector<float> confidences;std::vector<cv::Rect> boxes;std::vector<cv::Rect> boxes_nms;float x_factor = output_image.cols / INPUT_WIDTH;float y_factor = output_image.rows / INPUT_HEIGHT;float* data = (float*)preprcess_image[0].data;const int dimensions = 85;const int rows = 25200;for (int i = 0; i < rows; ++i){float confidence = data[4];if (confidence >= CONFIDENCE_THRESHOLD){float* classes_scores = data + 5;cv::Mat scores(1, class_name.size(), CV_32FC1, classes_scores);cv::Point class_id;double max_class_score;cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id);if (max_class_score > SCORE_THRESHOLD){confidences.push_back(confidence);class_ids.push_back(class_id.x);float cx = data[0];float cy = data[1];float w = data[2];float h = data[3];int left = int((cx - 0.5 * w) * x_factor);int top = int((cy - 0.5 * h) * y_factor);int width = int(w * x_factor);int height = int(h * y_factor);boxes.push_back(cv::Rect(left, top, width, height));}}data += 85;}std::vector<int> indices;cv::dnn::NMSBoxes(boxes, confidences, SCORE_THRESHOLD, NMS_THRESHOLD, indices);for (size_t i = 0; i < indices.size(); i++){int idx = indices[i];cv::Rect box = boxes[idx];boxes_nms.push_back(box);int left = box.x;int top = box.y;int width = box.width;int height = box.height;cv::rectangle(output_image, cv::Point(left, top), cv::Point(left + width, top + height), cv::Scalar(255, 0, 0), 1);std::string label = cv::format("%.2f", confidences[idx]);label = class_name[class_ids[idx]] + ":" + label;draw_label(output_image, label, left, top);}return boxes_nms; }std::vector<cv::Point> get_centers(std::vector<cv::Rect> detections) {std::vector<cv::Point> detections_centers(detections.size());for (size_t i = 0; i < detections.size(); i++){detections_centers[i] = cv::Point(detections[i].x + detections[i].width / 2, detections[i].y + detections[i].height / 2);}return detections_centers; }float get_distance(cv::Point p1, cv::Point p2) {return sqrt(pow(p1.x - p2.x, 2) + pow(p1.y - p2.y, 2)); }bool is_cross(cv::Point p1, cv::Point p2) {if (p1.x == p2.x) return false;int y = 500; //line1: y = 500float k = (p1.y - p2.y) / (p1.x - p2.x); //float b = p1.y - k * p1.x; //line2: y = kx + bfloat x = (y - b) / k;return (x > std::min(p1.x, p2.x) && x < std::max(p1.x, p2.x)); }int main(int argc, char** argv) {cv::VideoCapture capture("test.mp4");cv::Mat frame;cv::dnn::Net net = cv::dnn::readNet("yolov5s-f32.onnx");int frame_num = 0;int count = 0;std::vector<cv::Point> detections_centers_old;std::vector<cv::Point> detections_centers_new;while(cv::waitKey(1) < 0){capture >> frame;if (frame.empty())break;std::cout << "******************************************************************* frame_num: " << frame_num << std::endl;cv::Mat image = frame.clone();std::vector<cv::Mat> preprcess_image = preprocess(image, net);std::vector<cv::Rect> detections = postprocess(preprcess_image, image);if (frame_num == 0){detections_centers_old = get_centers(detections);std::cout << "detections_center:" << std::endl;for (size_t i = 0; i < detections_centers_old.size(); i++){std::cout << detections_centers_old[i] << std::endl;}}else if (frame_num % 2 == 0){detections_centers_new = get_centers(detections);std::cout << "detections_center:" << std::endl;for (size_t i = 0; i < detections_centers_new.size(); i++){std::cout << detections_centers_new[i] << std::endl;}std::vector<std::vector<float>> distance_matrix(detections_centers_new.size(), std::vector<float>(detections_centers_old.size()));std::cout << "distance_matrix:" << std::endl;for (size_t i = 0; i < detections_centers_new.size(); i++){for (size_t j = 0; j < detections_centers_old.size(); j++){distance_matrix[i][j] = get_distance(detections_centesr_new[i], detections_centers_old[j]); //std::cout << distance_matrix[i][j] << " ";}std::cout << std::endl;}std::cout << "min_index:" << std::endl;std::vector<float> min_indices(detections_centers_new.size());for (size_t i = 0; i < detections_centers_new.size(); i++){std::vector<float> distance_vector = distance_matrix[i];int min_index = std::min_element(distance_vector.begin(), distance_vector.end()) - distance_vector.begin();min_indices[i] = min_index;std::cout << min_index << " ";}std::cout << std::endl;for (size_t i = 0; i < detections_centers_new.size(); i++){cv::Point p1 = detections_centers_new[i];cv::Point p2 = detections_centers_old[min_indices[i]];std::cout << p1 << " " << p2 << std::endl;if (is_cross(p1, p2)){std::cout << "is_cross" << p1 << " " << p2 << std::endl;count++;}}detections_centers_old = detections_centers_new;}frame_num++;cv::putText(image, "car num: " + std::to_string(count), cv::Point(20, 50), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 255), 1);cv::line(image, cv::Point(0, 500), cv::Point(1280, 500) , cv::Scalar(0, 0, 255));cv::imshow("output", image);cv::imwrite(std::to_string(frame_num) + ".jpg", image);}capture.release();return 0; }

在調(diào)試中，發(fā)現(xiàn)v1的實(shí)現(xiàn)存在如下問(wèn)題：出現(xiàn)新目標(biāo)的時(shí)候，計(jì)算新舊檢測(cè)框的對(duì)應(yīng)關(guān)系出現(xiàn)匹配錯(cuò)誤，導(dǎo)致計(jì)數(shù)偏多。因此在v2中設(shè)置匹配的距離閾值，并簡(jiǎn)化了判斷檢測(cè)框中心點(diǎn)連線(xiàn)和撞線(xiàn)是否相交的方法。
v2的代碼實(shí)現(xiàn)如下：

#include <iostream> #include <opencv2/opencv.hpp>//#define DEBUG// 常量 const float INPUT_WIDTH = 640.0; const float INPUT_HEIGHT = 640.0; const float SCORE_THRESHOLD = 0.5; const float NMS_THRESHOLD = 0.25; const float CONFIDENCE_THRESHOLD = 0.5;const std::vector<std::string> class_name = {"person", "bicycle", "car", "motorcycle", "airplane", "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", "couch","potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone","microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear","hair drier", "toothbrush" };const int IMAGE_WIDTH = 1280; const int IMAGE_HEIGHT = 720; const int LINE_HEIGHT = IMAGE_HEIGHT / 2;//畫(huà)出檢測(cè)框和標(biāo)簽 void draw_label(cv::Mat& input_image, std::string label, int left, int top) {int baseLine;cv::Size label_size = cv::getTextSize(label, 0.7, 0.7, 1, &baseLine);top = std::max(top, label_size.height);cv::Point tlc = cv::Point(left, top);cv::Point brc = cv::Point(left , top + label_size.height + baseLine);cv::putText(input_image, label, cv::Point(left, top + label_size.height), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 255, 255), 1); }//預(yù)處理 std::vector<cv::Mat> preprocess(cv::Mat& input_image, cv::dnn::Net& net) {cv::Mat blob;cv::dnn::blobFromImage(input_image, blob, 1. / 255., cv::Size(INPUT_WIDTH, INPUT_HEIGHT), cv::Scalar(), true, false);net.setInput(blob);std::vector<cv::Mat> preprcess_image;net.forward(preprcess_image, net.getUnconnectedOutLayersNames());return preprcess_image; }//后處理 std::vector<cv::Rect> postprocess(std::vector<cv::Mat>& preprcess_image, cv::Mat& output_image) {std::vector<int> class_ids;std::vector<float> confidences;std::vector<cv::Rect> boxes;std::vector<cv::Rect> boxes_nms;float x_factor = output_image.cols / INPUT_WIDTH;float y_factor = output_image.rows / INPUT_HEIGHT;float* data = (float*)preprcess_image[0].data;const int dimensions = 85;const int rows = 25200;for (int i = 0; i < rows; ++i){float confidence = data[4];if (confidence >= CONFIDENCE_THRESHOLD){float* classes_scores = data + 5;cv::Mat scores(1, class_name.size(), CV_32FC1, classes_scores);cv::Point class_id;double max_class_score;cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id);if (max_class_score > SCORE_THRESHOLD){confidences.push_back(confidence);class_ids.push_back(class_id.x);float cx = data[0];float cy = data[1];float w = data[2];float h = data[3];int left = int((cx - 0.5 * w) * x_factor);int top = int((cy - 0.5 * h) * y_factor);int width = int(w * x_factor);int height = int(h * y_factor);boxes.push_back(cv::Rect(left, top, width, height));}}data += 85;}std::vector<int> indices;cv::dnn::NMSBoxes(boxes, confidences, SCORE_THRESHOLD, NMS_THRESHOLD, indices);for (size_t i = 0; i < indices.size(); i++){int idx = indices[i];cv::Rect box = boxes[idx];boxes_nms.push_back(box);int left = box.x;int top = box.y;int width = box.width;int height = box.height;cv::rectangle(output_image, cv::Point(left, top), cv::Point(left + width, top + height), cv::Scalar(255, 0, 0), 1);std::string label = cv::format("%.2f", confidences[idx]);//label = class_name[class_ids[idx]] + ":" + label;label = "car";draw_label(output_image, label, left, top);}return boxes_nms; }//計(jì)算檢測(cè)框的中心 std::vector<cv::Point> get_centers(std::vector<cv::Rect> detections) {std::vector<cv::Point> detections_centers(detections.size());for (size_t i = 0; i < detections.size(); i++){detections_centers[i] = cv::Point(detections[i].x + detections[i].width / 2, detections[i].y + detections[i].height / 2);}return detections_centers; }//計(jì)算兩點(diǎn)間距離 float get_distance(cv::Point p1, cv::Point p2) {return sqrt(pow(p1.x - p2.x, 2) + pow(p1.y - p2.y, 2)); }//判斷連接相鄰兩幀對(duì)應(yīng)檢測(cè)框中心的線(xiàn)段是否與紅線(xiàn)相交 bool is_cross(cv::Point p1, cv::Point p2) {return (p1.y <= LINE_HEIGHT && p2.y > LINE_HEIGHT) || (p1.y > LINE_HEIGHT && p2.y <= LINE_HEIGHT); }int main(int argc, char** argv) {cv::VideoCapture capture("test.mp4");cv::Mat frame;cv::dnn::Net net = cv::dnn::readNet("yolov5s-f32.onnx");int frame_num = 0;int count = 0;std::vector<cv::Point> detections_centers_old;std::vector<cv::Point> detections_centers_new;while(cv::waitKey(1) < 0){capture >> frame;if (frame.empty())break;std::cout << "******************************************************************* frame_num: " << frame_num << std::endl;cv::Mat image = frame.clone();std::vector<cv::Mat> preprcess_image = preprocess(image, net);std::vector<cv::Rect> detections = postprocess(preprcess_image, image);if (frame_num == 0){detections_centers_old = get_centers(detections);#ifdef DEBUGstd::cout << "detections_center:" << std::endl;for (size_t i = 0; i < detections_centers_old.size(); i++){std::cout << detections_centers_old[i] << std::endl;} #endif // DEBUG}else if (frame_num % 2 == 0){detections_centers_new = get_centers(detections);#ifdef DEBUGstd::cout << "detections_center:" << std::endl;for (size_t i = 0; i < detections_centers_new.size(); i++){std::cout << detections_centers_new[i] << std::endl;} #endif // DEBUGstd::vector<std::vector<float>> distance_matrix(detections_centers_new.size(), std::vector<float>(detections_centers_old.size())); //距離矩陣for (size_t i = 0; i < detections_centers_new.size(); i++){for (size_t j = 0; j < detections_centers_old.size(); j++){distance_matrix[i][j] = get_distance(detections_centers_new[i], detections_centers_old[j]); }}#ifdef DEBUGstd::cout << "min_index:" << std::endl; #endif // DEBUGstd::vector<float> min_indices(detections_centers_new.size());for (size_t i = 0; i < detections_centers_new.size(); i++){std::vector<float> distance_vector = distance_matrix[i];float min_val = *std::min_element(distance_vector.begin(), distance_vector.end());int min_index = -1;if (min_val < LINE_HEIGHT / 5)min_index = std::min_element(distance_vector.begin(), distance_vector.end()) - distance_vector.begin();min_indices[i] = min_index; #ifdef DEBUGstd::cout << min_index << " "; #endif // DEBUG}std::cout << std::endl;for (size_t i = 0; i < detections_centers_new.size(); i++){if (min_indices[i] < 0)continue;cv::Point p1 = detections_centers_new[i];cv::Point p2 = detections_centers_old[min_indices[i]];#ifdef DEBUGstd::cout << p1 << " " << p2 << std::endl; #endif // DEBUGif (is_cross(p1, p2)){ #ifdef DEBUGstd::cout << "is_cross" << p1 << " " << p2 << std::endl; #endif // DEBUGcount++;}}detections_centers_old = detections_centers_new;}cv::putText(image, "car num: " + std::to_string(count), cv::Point(20, 50), cv::FONT_HERSHEY_SIMPLEX, 0.7, cv::Scalar(0, 0, 255), 1);cv::line(image, cv::Point(0, LINE_HEIGHT), cv::Point(IMAGE_WIDTH, LINE_HEIGHT), cv::Scalar(0, 0, 255));cv::imshow("output", image);#ifdef DEBUGif (frame_num % 2 == 0)cv::imwrite(std::to_string(frame_num) + ".jpg", image); #endif // DEBUGframe_num++;}capture.release();return 0; }

檢測(cè)效果如下圖，效果還是可以的，比傳統(tǒng)方法有大幅提升。完整視頻中有一次計(jì)數(shù)異常(總共52輛向圖像上方行駛的車(chē)輛，檢出53輛)，是因?yàn)闄z測(cè)器不準(zhǔn)導(dǎo)致車(chē)輛檢測(cè)框位置漂移，可以后續(xù)優(yōu)化。注：由于官方提供的coco80類(lèi)的開(kāi)源權(quán)重文件用于車(chē)輛檢測(cè)效果不是很好，LZ把檢測(cè)出的類(lèi)別直接固定為car，實(shí)際應(yīng)自己重新訓(xùn)練一個(gè)車(chē)輛檢測(cè)的模型。

更詳細(xì)注釋的代碼、測(cè)試視頻和轉(zhuǎn)好的權(quán)重文件放在下載鏈接：點(diǎn)擊跳轉(zhuǎn)

總結(jié)

以上是生活随笔為你收集整理的两百行C++代码实现yolov5车辆计数部署（通俗易懂版）的全部?jī)?nèi)容，希望文章能夠幫你解決所遇到的問(wèn)題。

如果覺(jué)得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。