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- #include <fstream>
- #include <iostream>
- #include <sstream>
- #include <numeric>
- #include <chrono>
- #include <vector>
- #include <opencv2/opencv.hpp>
- #include <dirent.h>
- #include "NvInfer.h"
- #include "cuda_runtime_api.h"
- #include "logging.h"
- #include "BYTETracker.h"
-
- #define CHECK(status) \
- do\
- {\
- auto ret = (status);\
- if (ret != 0)\
- {\
- cerr << "Cuda failure: " << ret << endl;\
- abort();\
- }\
- } while (0)
-
- #define DEVICE 0 // GPU id
- #define NMS_THRESH 0.7
- #define BBOX_CONF_THRESH 0.1
-
- using namespace nvinfer1;
-
- // stuff we know about the network and the input/output blobs
- static const int INPUT_W = 1088;
- static const int INPUT_H = 608;
- const char* INPUT_BLOB_NAME = "input_0";
- const char* OUTPUT_BLOB_NAME = "output_0";
- static Logger gLogger;
-
- 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;
- }
-
- struct GridAndStride
- {
- int grid0;
- int grid1;
- int stride;
- };
-
- static void generate_grids_and_stride(const int target_w, const int target_h, vector<int>& strides, vector<GridAndStride>& grid_strides)
- {
- for (auto stride : strides)
- {
- 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++)
- {
- grid_strides.push_back((GridAndStride){g0, g1, stride});
- }
- }
- }
- }
-
- static inline float intersection_area(const Object& a, const Object& b)
- {
- Rect_<float> inter = a.rect & b.rect;
- return inter.area();
- }
-
- static void qsort_descent_inplace(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
- 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(vector<Object>& objects)
- {
- if (objects.empty())
- return;
-
- qsort_descent_inplace(objects, 0, objects.size() - 1);
- }
-
- static void nms_sorted_bboxes(const vector<Object>& faceobjects, vector<int>& picked, float nms_threshold)
- {
- picked.clear();
-
- const int n = faceobjects.size();
-
- 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_yolox_proposals(vector<GridAndStride> grid_strides, float* feat_blob, float prob_threshold, vector<Object>& objects)
- {
- const int num_class = 1;
-
- const int num_anchors = grid_strides.size();
-
- 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;
-
- const int basic_pos = anchor_idx * (num_class + 5);
-
- // yolox/models/yolo_head.py decode logic
- float x_center = (feat_blob[basic_pos+0] + grid0) * stride;
- float y_center = (feat_blob[basic_pos+1] + grid1) * stride;
- float w = exp(feat_blob[basic_pos+2]) * stride;
- float h = exp(feat_blob[basic_pos+3]) * stride;
- float x0 = x_center - w * 0.5f;
- float y0 = y_center - h * 0.5f;
-
- float box_objectness = feat_blob[basic_pos+4];
- for (int class_idx = 0; class_idx < num_class; class_idx++)
- {
- float box_cls_score = feat_blob[basic_pos + 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
-
- } // point anchor loop
- }
-
- float* blobFromImage(Mat& img){
- cvtColor(img, img, COLOR_BGR2RGB);
-
- float* blob = new float[img.total()*3];
- int channels = 3;
- int img_h = img.rows;
- int img_w = img.cols;
- vector<float> mean = {0.485, 0.456, 0.406};
- vector<float> std = {0.229, 0.224, 0.225};
- for (size_t c = 0; c < channels; c++)
- {
- for (size_t h = 0; h < img_h; h++)
- {
- for (size_t w = 0; w < img_w; w++)
- {
- blob[c * img_w * img_h + h * img_w + w] =
- (((float)img.at<Vec3b>(h, w)[c]) / 255.0f - mean[c]) / std[c];
- }
- }
- }
- return blob;
- }
-
-
- static void decode_outputs(float* prob, vector<Object>& objects, float scale, const int img_w, const int img_h) {
- vector<Object> proposals;
- vector<int> strides = {8, 16, 32};
- vector<GridAndStride> grid_strides;
- generate_grids_and_stride(INPUT_W, INPUT_H, strides, grid_strides);
- generate_yolox_proposals(grid_strides, prob, BBOX_CONF_THRESH, proposals);
- //std::cout << "num of boxes before nms: " << proposals.size() << std::endl;
-
- qsort_descent_inplace(proposals);
-
- vector<int> picked;
- nms_sorted_bboxes(proposals, picked, NMS_THRESH);
-
-
- int count = picked.size();
-
- //std::cout << "num of boxes: " << count << std::endl;
-
- 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;
- }
- }
-
- const float color_list[80][3] =
- {
- {0.000, 0.447, 0.741},
- {0.850, 0.325, 0.098},
- {0.929, 0.694, 0.125},
- {0.494, 0.184, 0.556},
- {0.466, 0.674, 0.188},
- {0.301, 0.745, 0.933},
- {0.635, 0.078, 0.184},
- {0.300, 0.300, 0.300},
- {0.600, 0.600, 0.600},
- {1.000, 0.000, 0.000},
- {1.000, 0.500, 0.000},
- {0.749, 0.749, 0.000},
- {0.000, 1.000, 0.000},
- {0.000, 0.000, 1.000},
- {0.667, 0.000, 1.000},
- {0.333, 0.333, 0.000},
- {0.333, 0.667, 0.000},
- {0.333, 1.000, 0.000},
- {0.667, 0.333, 0.000},
- {0.667, 0.667, 0.000},
- {0.667, 1.000, 0.000},
- {1.000, 0.333, 0.000},
- {1.000, 0.667, 0.000},
- {1.000, 1.000, 0.000},
- {0.000, 0.333, 0.500},
- {0.000, 0.667, 0.500},
- {0.000, 1.000, 0.500},
- {0.333, 0.000, 0.500},
- {0.333, 0.333, 0.500},
- {0.333, 0.667, 0.500},
- {0.333, 1.000, 0.500},
- {0.667, 0.000, 0.500},
- {0.667, 0.333, 0.500},
- {0.667, 0.667, 0.500},
- {0.667, 1.000, 0.500},
- {1.000, 0.000, 0.500},
- {1.000, 0.333, 0.500},
- {1.000, 0.667, 0.500},
- {1.000, 1.000, 0.500},
- {0.000, 0.333, 1.000},
- {0.000, 0.667, 1.000},
- {0.000, 1.000, 1.000},
- {0.333, 0.000, 1.000},
- {0.333, 0.333, 1.000},
- {0.333, 0.667, 1.000},
- {0.333, 1.000, 1.000},
- {0.667, 0.000, 1.000},
- {0.667, 0.333, 1.000},
- {0.667, 0.667, 1.000},
- {0.667, 1.000, 1.000},
- {1.000, 0.000, 1.000},
- {1.000, 0.333, 1.000},
- {1.000, 0.667, 1.000},
- {0.333, 0.000, 0.000},
- {0.500, 0.000, 0.000},
- {0.667, 0.000, 0.000},
- {0.833, 0.000, 0.000},
- {1.000, 0.000, 0.000},
- {0.000, 0.167, 0.000},
- {0.000, 0.333, 0.000},
- {0.000, 0.500, 0.000},
- {0.000, 0.667, 0.000},
- {0.000, 0.833, 0.000},
- {0.000, 1.000, 0.000},
- {0.000, 0.000, 0.167},
- {0.000, 0.000, 0.333},
- {0.000, 0.000, 0.500},
- {0.000, 0.000, 0.667},
- {0.000, 0.000, 0.833},
- {0.000, 0.000, 1.000},
- {0.000, 0.000, 0.000},
- {0.143, 0.143, 0.143},
- {0.286, 0.286, 0.286},
- {0.429, 0.429, 0.429},
- {0.571, 0.571, 0.571},
- {0.714, 0.714, 0.714},
- {0.857, 0.857, 0.857},
- {0.000, 0.447, 0.741},
- {0.314, 0.717, 0.741},
- {0.50, 0.5, 0}
- };
-
- void doInference(IExecutionContext& context, float* input, float* output, const int output_size, Size input_shape) {
- const ICudaEngine& engine = context.getEngine();
-
- // Pointers to input and output device buffers to pass to engine.
- // Engine requires exactly IEngine::getNbBindings() number of buffers.
- assert(engine.getNbBindings() == 2);
- void* buffers[2];
-
- // In order to bind the buffers, we need to know the names of the input and output tensors.
- // Note that indices are guaranteed to be less than IEngine::getNbBindings()
- const int inputIndex = engine.getBindingIndex(INPUT_BLOB_NAME);
-
- assert(engine.getBindingDataType(inputIndex) == nvinfer1::DataType::kFLOAT);
- const int outputIndex = engine.getBindingIndex(OUTPUT_BLOB_NAME);
- assert(engine.getBindingDataType(outputIndex) == nvinfer1::DataType::kFLOAT);
- int mBatchSize = engine.getMaxBatchSize();
-
- // Create GPU buffers on device
- CHECK(cudaMalloc(&buffers[inputIndex], 3 * input_shape.height * input_shape.width * sizeof(float)));
- CHECK(cudaMalloc(&buffers[outputIndex], output_size*sizeof(float)));
-
- // Create stream
- cudaStream_t stream;
- CHECK(cudaStreamCreate(&stream));
-
- // DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
- CHECK(cudaMemcpyAsync(buffers[inputIndex], input, 3 * input_shape.height * input_shape.width * sizeof(float), cudaMemcpyHostToDevice, stream));
- context.enqueue(1, buffers, stream, nullptr);
- CHECK(cudaMemcpyAsync(output, buffers[outputIndex], output_size * sizeof(float), cudaMemcpyDeviceToHost, stream));
- cudaStreamSynchronize(stream);
-
- // Release stream and buffers
- cudaStreamDestroy(stream);
- CHECK(cudaFree(buffers[inputIndex]));
- CHECK(cudaFree(buffers[outputIndex]));
- }
-
- int main(int argc, char** argv) {
- cudaSetDevice(DEVICE);
-
- // create a model using the API directly and serialize it to a stream
- char *trtModelStream{nullptr};
- size_t size{0};
-
- if (argc == 4 && string(argv[2]) == "-i") {
- const string engine_file_path {argv[1]};
- ifstream file(engine_file_path, ios::binary);
- if (file.good()) {
- file.seekg(0, file.end);
- size = file.tellg();
- file.seekg(0, file.beg);
- trtModelStream = new char[size];
- assert(trtModelStream);
- file.read(trtModelStream, size);
- file.close();
- }
- } else {
- cerr << "arguments not right!" << endl;
- cerr << "run 'python3 tools/trt.py -f exps/example/mot/yolox_s_mix_det.py -c pretrained/bytetrack_s_mot17.pth.tar' to serialize model first!" << std::endl;
- cerr << "Then use the following command:" << endl;
- cerr << "cd demo/TensorRT/cpp/build" << endl;
- cerr << "./bytetrack ../../../../YOLOX_outputs/yolox_s_mix_det/model_trt.engine -i ../../../../videos/palace.mp4 // deserialize file and run inference" << std::endl;
- return -1;
- }
- const string input_video_path {argv[3]};
-
- IRuntime* runtime = createInferRuntime(gLogger);
- assert(runtime != nullptr);
- ICudaEngine* engine = runtime->deserializeCudaEngine(trtModelStream, size);
- assert(engine != nullptr);
- IExecutionContext* context = engine->createExecutionContext();
- assert(context != nullptr);
- delete[] trtModelStream;
- auto out_dims = engine->getBindingDimensions(1);
- auto output_size = 1;
- for(int j=0;j<out_dims.nbDims;j++) {
- output_size *= out_dims.d[j];
- }
- static float* prob = new float[output_size];
-
- VideoCapture cap(input_video_path);
- 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 = 0;
- while (true)
- {
- 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;
- Mat pr_img = static_resize(img);
-
- float* blob;
- blob = blobFromImage(pr_img);
- float scale = min(INPUT_W / (img.cols*1.0), INPUT_H / (img.rows*1.0));
-
- // run inference
- auto start = chrono::system_clock::now();
- doInference(*context, blob, prob, output_size, pr_img.size());
- vector<Object> objects;
- decode_outputs(prob, objects, scale, img_w, img_h);
- 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);
-
- delete blob;
- char c = waitKey(1);
- if (c > 0)
- {
- break;
- }
- }
- cap.release();
- cout << "FPS: " << num_frames * 1000000 / total_ms << endl;
- // destroy the engine
- context->destroy();
- engine->destroy();
- runtime->destroy();
- return 0;
- }
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