Files
HSAP/algorithms/dms_yolo/code.embedded.bak/examples/YOLOv8-OpenVINO-CPP-Inference/main.cc
Chengfang Lu e72bc061c5 feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation
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)
2026-06-03 11:40:21 +08:00

43 lines
1.1 KiB
C++

#include "inference.h"
#include <iostream>
#include <opencv2/highgui.hpp>
int main(int argc, char **argv) {
// Check if the correct number of arguments is provided
if (argc != 3) {
std::cerr << "usage: " << argv[0] << " <model_path> <image_path>" << std::endl;
return 1;
}
// Get the model and image paths from the command-line arguments
const std::string model_path = argv[1];
const std::string image_path = argv[2];
// Read the input image
cv::Mat image = cv::imread(image_path);
// Check if the image was successfully loaded
if (image.empty()) {
std::cerr << "ERROR: image is empty" << std::endl;
return 1;
}
// Define the confidence and NMS thresholds
const float confidence_threshold = 0.5;
const float NMS_threshold = 0.5;
// Initialize the YOLO inference with the specified model and parameters
yolo::Inference inference(model_path, cv::Size(640, 640), confidence_threshold, NMS_threshold);
// Run inference on the input image
inference.RunInference(image);
// Display the image with the detections
cv::imshow("image", image);
cv::waitKey(0);
cv::destroyAllWindows();
return 0;
}