PytorchExample.cpp

#include <opencv2/opencv.hpp>
#include <torch/script.h>
#include <algorithm>
#include <iostream>
#include <time.h>


std::vector<torch::Tensor> non_max_suppression(torch::Tensor preds, float score_thresh = 0.5, float iou_thresh = 0.5)
{
    std::vector<torch::Tensor> output;
    for (size_t i = 0; i < preds.sizes()[0]; ++i)
    {
        torch::Tensor pred = preds.select(0, i);

        // Filter by scores
        torch::Tensor scores = pred.select(1, 4) * std::get<0>(torch::max(pred.slice(1, 5, pred.sizes()[1]), 1));
        pred = torch::index_select(pred, 0, torch::nonzero(scores > score_thresh).select(1, 0));
        if (pred.sizes()[0] == 0) continue;

        // (center_x, center_y, w, h) to (left, top, right, bottom)
        pred.select(1, 0) = pred.select(1, 0) - pred.select(1, 2) / 2;
        pred.select(1, 1) = pred.select(1, 1) - pred.select(1, 3) / 2;
        pred.select(1, 2) = pred.select(1, 0) + pred.select(1, 2);
        pred.select(1, 3) = pred.select(1, 1) + pred.select(1, 3);

        // Computing scores and classes
        std::tuple<torch::Tensor, torch::Tensor> max_tuple = torch::max(pred.slice(1, 5, pred.sizes()[1]), 1);
        pred.select(1, 4) = pred.select(1, 4) * std::get<0>(max_tuple);
        pred.select(1, 5) = std::get<1>(max_tuple);

        torch::Tensor  dets = pred.slice(1, 0, 6);

        torch::Tensor keep = torch::empty({ dets.sizes()[0] });
        torch::Tensor areas = (dets.select(1, 3) - dets.select(1, 1)) * (dets.select(1, 2) - dets.select(1, 0));
        std::tuple<torch::Tensor, torch::Tensor> indexes_tuple = torch::sort(dets.select(1, 4), 0, 1);
        torch::Tensor v = std::get<0>(indexes_tuple);
        torch::Tensor indexes = std::get<1>(indexes_tuple);
        int count = 0;
        while (indexes.sizes()[0] > 0)
        {
            keep[count] = (indexes[0].item().toInt());
            count += 1;

            // Computing overlaps
            torch::Tensor lefts = torch::empty(indexes.sizes()[0] - 1);
            torch::Tensor tops = torch::empty(indexes.sizes()[0] - 1);
            torch::Tensor rights = torch::empty(indexes.sizes()[0] - 1);
            torch::Tensor bottoms = torch::empty(indexes.sizes()[0] - 1);
            torch::Tensor widths = torch::empty(indexes.sizes()[0] - 1);
            torch::Tensor heights = torch::empty(indexes.sizes()[0] - 1);
            for (size_t i = 0; i < indexes.sizes()[0] - 1; ++i)
            {
                lefts[i] = std::max(dets[indexes[0]][0].item().toFloat(), dets[indexes[i + 1]][0].item().toFloat());
                tops[i] = std::max(dets[indexes[0]][1].item().toFloat(), dets[indexes[i + 1]][1].item().toFloat());
                rights[i] = std::min(dets[indexes[0]][2].item().toFloat(), dets[indexes[i + 1]][2].item().toFloat());
                bottoms[i] = std::min(dets[indexes[0]][3].item().toFloat(), dets[indexes[i + 1]][3].item().toFloat());
                widths[i] = std::max(float(0), rights[i].item().toFloat() - lefts[i].item().toFloat());
                heights[i] = std::max(float(0), bottoms[i].item().toFloat() - tops[i].item().toFloat());
            }
            torch::Tensor overlaps = widths * heights;

            // FIlter by IOUs
            torch::Tensor ious = overlaps / (areas.select(0, indexes[0].item().toInt()) + torch::index_select(areas, 0, indexes.slice(0, 1, indexes.sizes()[0])) - overlaps);
            indexes = torch::index_select(indexes, 0, torch::nonzero(ious <= iou_thresh).select(1, 0) + 1);
        }
        keep = keep.toType(torch::kInt64);
        output.push_back(torch::index_select(dets, 0, keep.slice(0, 0, count)));
    }
    return output;
}


int main()
{
    // Loading  Module
    torch::jit::script::Module module = torch::jit::load("C:/best.pt");

    std::vector<std::string> classnames;
    std::ifstream f("C:/coco.names");
    std::string name = "";
    while (std::getline(f, name))
    {
        classnames.push_back(name);
    }

    cv::VideoCapture cap = cv::VideoCapture(0);
    cap.set(cv::CAP_PROP_FRAME_WIDTH, 1920);
    cap.set(cv::CAP_PROP_FRAME_HEIGHT, 1080);
    cv::Mat frame, img;
    while (cap.isOpened())
    {
        clock_t start = clock();
        cap.read(frame);
        if (frame.empty())
        {
            std::cout << "Read frame failed!" << std::endl;
            break;
        }

        // Preparing input tensor
        cv::resize(frame, img, cv::Size(640, 384));
        cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
        torch::Tensor imgTensor = torch::from_blob(img.data, { img.rows, img.cols,3 }, torch::kByte);
        imgTensor = imgTensor.permute({ 2,0,1 });
        imgTensor = imgTensor.toType(torch::kFloat);
        imgTensor = imgTensor.div(255);
        imgTensor = imgTensor.unsqueeze(0);

        // preds: [?, 15120, 9]
        torch::Tensor preds = module.forward({ imgTensor }).toTuple()->elements()[0].toTensor();
        std::vector<torch::Tensor> dets = non_max_suppression(preds, 0.4, 0.5);
        if (dets.size() > 0)
        {
            // Visualize result
            for (size_t i = 0; i < dets[0].sizes()[0]; ++i)
            {
                float left = dets[0][i][0].item().toFloat() * frame.cols / 640;
                float top = dets[0][i][1].item().toFloat() * frame.rows / 384;
                float right = dets[0][i][2].item().toFloat() * frame.cols / 640;
                float bottom = dets[0][i][3].item().toFloat() * frame.rows / 384;
                float score = dets[0][i][4].item().toFloat();
                int classID = dets[0][i][5].item().toInt();

                cv::rectangle(frame, cv::Rect(left, top, (right - left), (bottom - top)), cv::Scalar(0, 255, 0), 2);

                cv::putText(frame,
                    classnames[classID] + ": " + cv::format("%.2f", score),
                    cv::Point(left, top),
                    cv::FONT_HERSHEY_SIMPLEX, (right - left) / 200, cv::Scalar(0, 255, 0), 2);
            }
        }
        cv::putText(frame, "FPS: " + std::to_string(int(1e7 / (clock() - start))),
            cv::Point(50, 50),
            cv::FONT_HERSHEY_SIMPLEX, 1, cv::Scalar(0, 255, 0), 2);
        cv::imshow("", frame);
        if (cv::waitKey(1) == 27) break;
    }
    return 0;
}