MetaMOT/deploy/ncnn/cpp/src/bytetrack.cpp

#include "layer.h"
#include "net.h"

#if defined(USE_NCNN_SIMPLEOCV)
#include "simpleocv.h"
#include <opencv2/opencv.hpp>
#else
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/opencv.hpp>
#endif
#include <float.h>
#include <stdio.h>
#include <vector>
#include <chrono>
#include "BYTETracker.h"

#define YOLOX_NMS_THRESH  0.7 // nms threshold
#define YOLOX_CONF_THRESH 0.1 // threshold of bounding box prob
#define INPUT_W 1088  // target image size w after resize
#define INPUT_H 608   // target image size h after resize

Mat static_resize(Mat& img) {
    float r = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
    // r = std::min(r, 1.0f);
    int unpad_w = r * img.cols;
    int unpad_h = r * img.rows;
    Mat re(unpad_h, unpad_w, CV_8UC3);
    resize(img, re, re.size());
    Mat out(INPUT_H, INPUT_W, CV_8UC3, Scalar(114, 114, 114));
    re.copyTo(out(Rect(0, 0, re.cols, re.rows)));
    return out;
}

// YOLOX use the same focus in yolov5
class YoloV5Focus : public ncnn::Layer
{
public:
    YoloV5Focus()
    {
        one_blob_only = true;
    }

    virtual int forward(const ncnn::Mat& bottom_blob, ncnn::Mat& top_blob, const ncnn::Option& opt) const
    {
        int w = bottom_blob.w;
        int h = bottom_blob.h;
        int channels = bottom_blob.c;

        int outw = w / 2;
        int outh = h / 2;
        int outc = channels * 4;

        top_blob.create(outw, outh, outc, 4u, 1, opt.blob_allocator);
        if (top_blob.empty())
            return -100;

        #pragma omp parallel for num_threads(opt.num_threads)
        for (int p = 0; p < outc; p++)
        {
            const float* ptr = bottom_blob.channel(p % channels).row((p / channels) % 2) + ((p / channels) / 2);
            float* outptr = top_blob.channel(p);

            for (int i = 0; i < outh; i++)
            {
                for (int j = 0; j < outw; j++)
                {
                    *outptr = *ptr;

                    outptr += 1;
                    ptr += 2;
                }

                ptr += w;
            }
        }

        return 0;
    }
};

DEFINE_LAYER_CREATOR(YoloV5Focus)

struct GridAndStride
{
    int grid0;
    int grid1;
    int stride;
};

static inline float intersection_area(const Object& a, const Object& b)
{
    cv::Rect_<float> inter = a.rect & b.rect;
    return inter.area();
}

static void qsort_descent_inplace(std::vector<Object>& faceobjects, int left, int right)
{
    int i = left;
    int j = right;
    float p = faceobjects[(left + right) / 2].prob;

    while (i <= j)
    {
        while (faceobjects[i].prob > p)
            i++;

        while (faceobjects[j].prob < p)
            j--;

        if (i <= j)
        {
            // swap
            std::swap(faceobjects[i], faceobjects[j]);

            i++;
            j--;
        }
    }

    #pragma omp parallel sections
    {
        #pragma omp section
        {
            if (left < j) qsort_descent_inplace(faceobjects, left, j);
        }
        #pragma omp section
        {
            if (i < right) qsort_descent_inplace(faceobjects, i, right);
        }
    }
}

static void qsort_descent_inplace(std::vector<Object>& objects)
{
    if (objects.empty())
        return;

    qsort_descent_inplace(objects, 0, objects.size() - 1);
}

static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold)
{
    picked.clear();

    const int n = faceobjects.size();

    std::vector<float> areas(n);
    for (int i = 0; i < n; i++)
    {
        areas[i] = faceobjects[i].rect.area();
    }

    for (int i = 0; i < n; i++)
    {
        const Object& a = faceobjects[i];

        int keep = 1;
        for (int j = 0; j < (int)picked.size(); j++)
        {
            const Object& b = faceobjects[picked[j]];

            // intersection over union
            float inter_area = intersection_area(a, b);
            float union_area = areas[i] + areas[picked[j]] - inter_area;
            // float IoU = inter_area / union_area
            if (inter_area / union_area > nms_threshold)
                keep = 0;
        }

        if (keep)
            picked.push_back(i);
    }
}

static void generate_grids_and_stride(const int target_w, const int target_h, std::vector<int>& strides, std::vector<GridAndStride>& grid_strides)
{
    for (int i = 0; i < (int)strides.size(); i++)
    {
        int stride = strides[i];
        int num_grid_w = target_w / stride;
        int num_grid_h = target_h / stride;
        for (int g1 = 0; g1 < num_grid_h; g1++)
        {
            for (int g0 = 0; g0 < num_grid_w; g0++)
            {
                GridAndStride gs;
                gs.grid0 = g0;
                gs.grid1 = g1;
                gs.stride = stride;
                grid_strides.push_back(gs);
            }
        }
    }
}

static void generate_yolox_proposals(std::vector<GridAndStride> grid_strides, const ncnn::Mat& feat_blob, float prob_threshold, std::vector<Object>& objects)
{
    const int num_grid = feat_blob.h;
    const int num_class = feat_blob.w - 5;
    const int num_anchors = grid_strides.size();

    const float* feat_ptr = feat_blob.channel(0);
    for (int anchor_idx = 0; anchor_idx < num_anchors; anchor_idx++)
    {
        const int grid0 = grid_strides[anchor_idx].grid0;
        const int grid1 = grid_strides[anchor_idx].grid1;
        const int stride = grid_strides[anchor_idx].stride;

        // yolox/models/yolo_head.py decode logic
        //  outputs[..., :2] = (outputs[..., :2] + grids) * strides
        //  outputs[..., 2:4] = torch.exp(outputs[..., 2:4]) * strides
        float x_center = (feat_ptr[0] + grid0) * stride;
        float y_center = (feat_ptr[1] + grid1) * stride;
        float w = exp(feat_ptr[2]) * stride;
        float h = exp(feat_ptr[3]) * stride;
        float x0 = x_center - w * 0.5f;
        float y0 = y_center - h * 0.5f;

        float box_objectness = feat_ptr[4];
        for (int class_idx = 0; class_idx < num_class; class_idx++)
        {
            float box_cls_score = feat_ptr[5 + class_idx];
            float box_prob = box_objectness * box_cls_score;
            if (box_prob > prob_threshold)
            {
                Object obj;
                obj.rect.x = x0;
                obj.rect.y = y0;
                obj.rect.width = w;
                obj.rect.height = h;
                obj.label = class_idx;
                obj.prob = box_prob;

                objects.push_back(obj);
            }

        } // class loop
        feat_ptr += feat_blob.w;

    } // point anchor loop
}

static int detect_yolox(ncnn::Mat& in_pad, std::vector<Object>& objects, ncnn::Extractor ex, float scale)
{

    ex.input("images", in_pad);
    
    std::vector<Object> proposals;

    {
        ncnn::Mat out;
        ex.extract("output", out);

        static const int stride_arr[] = {8, 16, 32}; // might have stride=64 in YOLOX
        std::vector<int> strides(stride_arr, stride_arr + sizeof(stride_arr) / sizeof(stride_arr[0]));
        std::vector<GridAndStride> grid_strides;
        generate_grids_and_stride(INPUT_W, INPUT_H, strides, grid_strides);
        generate_yolox_proposals(grid_strides, out, YOLOX_CONF_THRESH, proposals);
    }
    // sort all proposals by score from highest to lowest
    qsort_descent_inplace(proposals);

    // apply nms with nms_threshold
    std::vector<int> picked;
    nms_sorted_bboxes(proposals, picked, YOLOX_NMS_THRESH);

    int count = picked.size();

    objects.resize(count);
    for (int i = 0; i < count; i++)
    {
        objects[i] = proposals[picked[i]];

        // adjust offset to original unpadded
        float x0 = (objects[i].rect.x) / scale;
        float y0 = (objects[i].rect.y) / scale;
        float x1 = (objects[i].rect.x + objects[i].rect.width) / scale;
        float y1 = (objects[i].rect.y + objects[i].rect.height) / scale;

        // clip
        // x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
        // y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
        // x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
        // y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);

        objects[i].rect.x = x0;
        objects[i].rect.y = y0;
        objects[i].rect.width = x1 - x0;
        objects[i].rect.height = y1 - y0;
    }

    return 0;
}

int main(int argc, char** argv)
{
    if (argc != 2)
    {
        fprintf(stderr, "Usage: %s [videopath]\n", argv[0]);
        return -1;
    }

    ncnn::Net yolox;

    //yolox.opt.use_vulkan_compute = true;
    //yolox.opt.use_bf16_storage = true;
    yolox.opt.num_threads = 20;
    //ncnn::set_cpu_powersave(0);

    //ncnn::set_omp_dynamic(0);
    //ncnn::set_omp_num_threads(20);

    // Focus in yolov5
    yolox.register_custom_layer("YoloV5Focus", YoloV5Focus_layer_creator);

    yolox.load_param("bytetrack_s_op.param");
    yolox.load_model("bytetrack_s_op.bin");
    
    ncnn::Extractor ex = yolox.create_extractor();

    const char* videopath = argv[1];

    VideoCapture cap(videopath);
	if (!cap.isOpened())
		return 0;

	int img_w = cap.get(CV_CAP_PROP_FRAME_WIDTH);
	int img_h = cap.get(CV_CAP_PROP_FRAME_HEIGHT);
    int fps = cap.get(CV_CAP_PROP_FPS);
    long nFrame = static_cast<long>(cap.get(CV_CAP_PROP_FRAME_COUNT));
    cout << "Total frames: " << nFrame << endl;

    VideoWriter writer("demo.mp4", CV_FOURCC('m', 'p', '4', 'v'), fps, Size(img_w, img_h));

    Mat img;
    BYTETracker tracker(fps, 30);
    int num_frames = 0;
    int total_ms = 1;
	for (;;)
    {
        if(!cap.read(img))
            break;
        num_frames ++;
        if (num_frames % 20 == 0)
        {
            cout << "Processing frame " << num_frames << " (" << num_frames * 1000000 / total_ms << " fps)" << endl;
        }
		if (img.empty())
			break;

        float scale = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
        Mat pr_img = static_resize(img);
        ncnn::Mat in_pad = ncnn::Mat::from_pixels_resize(pr_img.data, ncnn::Mat::PIXEL_BGR2RGB, INPUT_W, INPUT_H, INPUT_W, INPUT_H);
    
        // python 0-1 input tensor with rgb_means = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225)
        // so for 0-255 input image, rgb_mean should multiply 255 and norm should div by std.
        const float mean_vals[3] = {255.f * 0.485f, 255.f * 0.456, 255.f * 0.406f};
        const float norm_vals[3] = {1 / (255.f * 0.229f), 1 / (255.f * 0.224f), 1 / (255.f * 0.225f)};

        in_pad.substract_mean_normalize(mean_vals, norm_vals);

        std::vector<Object> objects;
        auto start = chrono::system_clock::now();
        //detect_yolox(img, objects);
        detect_yolox(in_pad, objects, ex, scale);
        vector<STrack> output_stracks = tracker.update(objects);
        auto end = chrono::system_clock::now();
        total_ms = total_ms + chrono::duration_cast<chrono::microseconds>(end - start).count();
        for (int i = 0; i < output_stracks.size(); i++)
		{
			vector<float> tlwh = output_stracks[i].tlwh;
			bool vertical = tlwh[2] / tlwh[3] > 1.6;
			if (tlwh[2] * tlwh[3] > 20 && !vertical)
			{
				Scalar s = tracker.get_color(output_stracks[i].track_id);
				putText(img, format("%d", output_stracks[i].track_id), Point(tlwh[0], tlwh[1] - 5), 
                        0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);
                rectangle(img, Rect(tlwh[0], tlwh[1], tlwh[2], tlwh[3]), s, 2);
			}
		}
        putText(img, format("frame: %d fps: %d num: %d", num_frames, num_frames * 1000000 / total_ms, output_stracks.size()), 
                Point(0, 30), 0, 0.6, Scalar(0, 0, 255), 2, LINE_AA);
        writer.write(img);
        char c = waitKey(1);
        if (c > 0)
        {
            break;
        }
    }
    cap.release();
    cout << "FPS: " << num_frames * 1000000 / total_ms << endl;

    return 0;
}