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