48 lines
1.3 KiB
C++
48 lines
1.3 KiB
C++
#include "mmdeploy/detector.hpp"
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#include "opencv2/imgcodecs/imgcodecs.hpp"
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#include "utils/argparse.h"
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#include "utils/visualize.h"
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DEFINE_ARG_string(model, "Model path");
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DEFINE_ARG_string(image, "Input image path");
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DEFINE_string(device, "cpu", R"(Device name, e.g. "cpu", "cuda")");
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DEFINE_string(output, "detector_output.jpg", "Output image path");
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DEFINE_double(det_thr, .5, "Detection score threshold");
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int main(int argc, char* argv[]) {
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if (!utils::ParseArguments(argc, argv)) {
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return -1;
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}
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cv::Mat img = cv::imread(ARGS_image);
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if (img.empty()) {
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fprintf(stderr, "failed to load image: %s\n", ARGS_image.c_str());
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return -1;
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}
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// construct a detector instance
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mmdeploy::Detector detector(mmdeploy::Model{ARGS_model}, mmdeploy::Device{FLAGS_device});
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// apply the detector, the result is an array-like class holding references to
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// `mmdeploy_detection_t`, will be released automatically on destruction
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mmdeploy::Detector::Result dets = detector.Apply(img);
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// visualize
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utils::Visualize v;
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auto sess = v.get_session(img);
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int count = 0;
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for (const mmdeploy_detection_t& det : dets) {
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if (det.score > FLAGS_det_thr) { // filter bboxes
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sess.add_det(det.bbox, det.label_id, det.score, det.mask, count++);
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}
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}
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if (!FLAGS_output.empty()) {
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cv::imwrite(FLAGS_output, sess.get());
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}
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return 0;
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}
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