Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
73 lines
2.3 KiB
C++
73 lines
2.3 KiB
C++
// Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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#include <iostream>
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#include <vector>
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#include <getopt.h>
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#include <opencv2/opencv.hpp>
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#include "inference.h"
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using namespace std;
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using namespace cv;
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int main(int argc, char **argv)
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{
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std::string projectBasePath = "/home/user/ultralytics"; // Set your ultralytics base path
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bool runOnGPU = true;
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//
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// Pass in either:
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//
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// "yolov8s.onnx" or "yolov5s.onnx"
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//
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// To run Inference with yolov8/yolov5 (ONNX)
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//
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// Note that in this example the classes are hard-coded and 'classes.txt' is a place holder.
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Inference inf(projectBasePath + "/yolov8s.onnx", cv::Size(640, 640), "classes.txt", runOnGPU);
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std::vector<std::string> imageNames;
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imageNames.push_back(projectBasePath + "/ultralytics/assets/bus.jpg");
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imageNames.push_back(projectBasePath + "/ultralytics/assets/zidane.jpg");
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for (int i = 0; i < imageNames.size(); ++i)
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{
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cv::Mat frame = cv::imread(imageNames[i]);
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// Inference starts here...
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std::vector<Detection> output = inf.runInference(frame);
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int detections = output.size();
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std::cout << "Number of detections:" << detections << std::endl;
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for (int i = 0; i < detections; ++i)
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{
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Detection detection = output[i];
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cv::Rect box = detection.box;
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cv::Scalar color = detection.color;
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// Detection box
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cv::rectangle(frame, box, color, 2);
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// Detection box text
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std::string classString = detection.className + ' ' + std::to_string(detection.confidence).substr(0, 4);
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cv::Size textSize = cv::getTextSize(classString, cv::FONT_HERSHEY_DUPLEX, 1, 2, 0);
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cv::Rect textBox(box.x, box.y - 40, textSize.width + 10, textSize.height + 20);
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cv::rectangle(frame, textBox, color, cv::FILLED);
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cv::putText(frame, classString, cv::Point(box.x + 5, box.y - 10), cv::FONT_HERSHEY_DUPLEX, 1, cv::Scalar(0, 0, 0), 2, 0);
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}
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// Inference ends here...
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// This is only for preview purposes
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float scale = 0.8;
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cv::resize(frame, frame, cv::Size(frame.cols*scale, frame.rows*scale));
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cv::imshow("Inference", frame);
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cv::waitKey(-1);
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}
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}
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