#include "mmdeploy/detector.hpp" #include #include #include int main(int argc, char* argv[]) { if (argc != 4) { fprintf(stderr, "usage:\n object_detection device_name model_path image_path\n"); return 1; } auto device_name = argv[1]; auto model_path = argv[2]; auto image_path = argv[3]; cv::Mat img = cv::imread(image_path); if (!img.data) { fprintf(stderr, "failed to load image: %s\n", image_path); return 1; } mmdeploy::Model model(model_path); mmdeploy::Detector detector(model, mmdeploy::Device{device_name, 0}); auto dets = detector.Apply(img); fprintf(stdout, "bbox_count=%d\n", (int)dets.size()); for (int i = 0; i < dets.size(); ++i) { const auto& box = dets[i].bbox; const auto& mask = dets[i].mask; fprintf(stdout, "box %d, left=%.2f, top=%.2f, right=%.2f, bottom=%.2f, label=%d, score=%.4f\n", i, box.left, box.top, box.right, box.bottom, dets[i].label_id, dets[i].score); // skip detections with invalid bbox size (bbox height or width < 1) if ((box.right - box.left) < 1 || (box.bottom - box.top) < 1) { continue; } // skip detections less than specified score threshold if (dets[i].score < 0.3) { continue; } // generate mask overlay if model exports masks if (mask != nullptr) { fprintf(stdout, "mask %d, height=%d, width=%d\n", i, mask->height, mask->width); cv::Mat imgMask(mask->height, mask->width, CV_8UC1, &mask->data[0]); auto x0 = std::max(std::floor(box.left) - 1, 0.f); auto y0 = std::max(std::floor(box.top) - 1, 0.f); cv::Rect roi((int)x0, (int)y0, mask->width, mask->height); // split the RGB channels, overlay mask to a specific color channel cv::Mat ch[3]; split(img, ch); int col = 0; // int col = i % 3; cv::bitwise_or(imgMask, ch[col](roi), ch[col](roi)); merge(ch, 3, img); } cv::rectangle(img, cv::Point{(int)box.left, (int)box.top}, cv::Point{(int)box.right, (int)box.bottom}, cv::Scalar{0, 255, 0}); } cv::imwrite("output_detection.png", img); return 0; }