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#include "yolov5.hpp"
int main(int argc, char** argv) {
cudaSetDevice(DEVICE);
std::string wts_name = "";
std::string engine_name = "";
float gd = 0.0f, gw = 0.0f;
std::string img_dir;
if (!parse_args(argc, argv, wts_name, engine_name, gd, gw, img_dir)) {
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./yolov5 -s [.wts] [.engine] [s/m/l/x or c gd gw] // serialize model to plan file" << std::endl;
std::cerr << "./yolov5 -d [.engine] ../samples // deserialize plan file and run inference" << std::endl;
return -1;
}
// create a model using the API directly and serialize it to a stream
if (!wts_name.empty()) {
IHostMemory* modelStream{ nullptr };
APIToModel(BATCH_SIZE, &modelStream, gd, gw, wts_name);
assert(modelStream != nullptr);
std::ofstream p(engine_name, std::ios::binary);
if (!p) {
std::cerr << "could not open plan output file" << std::endl;
return -1;
}
p.write(reinterpret_cast<const char*>(modelStream->data()), modelStream->size());
modelStream->destroy();
return 0;
}
// deserialize the .engine and run inference
std::ifstream file(engine_name, std::ios::binary);
if (!file.good()) {
std::cerr << "read " << engine_name << " error!" << std::endl;
return -1;
}
char *trtModelStream = nullptr;
size_t size = 0;
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();
std::vector<std::string> file_names;
if (read_files_in_dir(img_dir.c_str(), file_names) < 0) {
std::cerr << "read_files_in_dir failed." << std::endl;
return -1;
}
// prepare input data ---------------------------
static float data[BATCH_SIZE * 3 * INPUT_H * INPUT_W];
//for (int i = 0; i < 3 * INPUT_H * INPUT_W; i++)
// data[i] = 1.0;
static float prob[BATCH_SIZE * OUTPUT_SIZE];
static int seg_out[BATCH_SIZE * IMG_H * IMG_W];
static int lane_out[BATCH_SIZE * IMG_H * IMG_W];
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;
assert(engine->getNbBindings() == 4);
void* buffers[4];
// 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);
const int output_det_index = engine->getBindingIndex(OUTPUT_DET_NAME);
const int output_seg_index = engine->getBindingIndex(OUTPUT_SEG_NAME);
const int output_lane_index = engine->getBindingIndex(OUTPUT_LANE_NAME);
assert(inputIndex == 0);
assert(output_det_index == 1);
assert(output_seg_index == 2);
assert(output_lane_index == 3);
// Create GPU buffers on device
CUDA_CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
CUDA_CHECK(cudaMalloc(&buffers[output_det_index], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
CUDA_CHECK(cudaMalloc(&buffers[output_seg_index], BATCH_SIZE * IMG_H * IMG_W * sizeof(int)));
CUDA_CHECK(cudaMalloc(&buffers[output_lane_index], BATCH_SIZE * IMG_H * IMG_W * sizeof(int)));
// Create stream
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
// store seg results
cv::Mat tmp_seg(IMG_H, IMG_W, CV_32S, seg_out);
// store lane results
cv::Mat tmp_lane(IMG_H, IMG_W, CV_32S, lane_out);
// PrintMat(tmp_seg);
std::vector<cv::Vec3b> segColor;
segColor.push_back(cv::Vec3b(0, 0, 0));
segColor.push_back(cv::Vec3b(0, 255, 0));
segColor.push_back(cv::Vec3b(255, 0, 0));
std::vector<cv::Vec3b> laneColor;
laneColor.push_back(cv::Vec3b(0, 0, 0));
laneColor.push_back(cv::Vec3b(0, 0, 255));
laneColor.push_back(cv::Vec3b(0, 0, 0));
int fcount = 0; // set for batch-inference
for (int f = 0; f < (int)file_names.size(); f++) {
fcount++;
if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
// preprocess ~3ms
for (int b = 0; b < fcount; b++) {
cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]); // load image takes ~17ms
if (img.empty()) continue;
//cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H); // letterbox
int i = 0;
// BGR to RGB and normalize
for (int row = 0; row < INPUT_H; ++row) {
float* uc_pixel = pr_img.ptr<float>(row);
for (int col = 0; col < INPUT_W; ++col) {
data[b * 3 * INPUT_H * INPUT_W + i] = uc_pixel[0];
data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = uc_pixel[1];
data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = uc_pixel[2];
uc_pixel += 3;
++i;
}
}
}
// Run inference
auto start = std::chrono::system_clock::now();
doInferenceCpu(*context, stream, buffers, data, prob, seg_out, lane_out, BATCH_SIZE);
auto end = std::chrono::system_clock::now();
std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
// postprocess ~0ms
std::vector<std::vector<Yolo::Detection>> batch_res(fcount);
for (int b = 0; b < fcount; b++) {
auto& res = batch_res[b];
nms(res, &prob[b * OUTPUT_SIZE], CONF_THRESH, NMS_THRESH);
}
// show results
for (int b = 0; b < fcount; ++b) {
auto& res = batch_res[b];
//std::cout << res.size() << std::endl;
cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
// unsigned cnt = 0;
// for (const auto &item : seg_out)
// if (item == 1)
// ++cnt;
// std::cout << cnt << std::endl;
// handling seg and lane results
cv::Mat seg_res(img.rows, img.cols, CV_32S);
cv::resize(tmp_seg, seg_res, seg_res.size(), 0, 0, cv::INTER_NEAREST);
cv::Mat lane_res(img.rows, img.cols, CV_32S);
cv::resize(tmp_lane, lane_res, lane_res.size(), 0, 0, cv::INTER_NEAREST);
for (int row = 0; row < img.rows; ++row) {
uchar* pdata = img.data + row * img.step;
for (int col = 0; col < img.cols; ++col) {
int seg_idx = seg_res.at<int>(row, col);
int lane_idx = lane_res.at<int>(row, col);
//std::cout << "enter" << ix << std::endl;
for (int i = 0; i < 3; ++i) {
if (lane_idx) {
if (i != 2)
pdata[i] = pdata[i] / 2 + laneColor[lane_idx][i] / 2;
}
else if (seg_idx)
pdata[i] = pdata[i] / 2 + segColor[seg_idx][i] / 2;
}
pdata += 3;
}
}
// handling det results
for (size_t j = 0; j < res.size(); ++j) {
cv::Rect r = get_rect(img, res[j].bbox);
cv::rectangle(img, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
cv::putText(img, std::to_string((int)res[j].class_id), cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
}
cv::imwrite("../results/_" + file_names[f - fcount + 1 + b], img);
}
fcount = 0;
}
// Release stream and buffers
cudaStreamDestroy(stream);
CUDA_CHECK(cudaFree(buffers[inputIndex]));
CUDA_CHECK(cudaFree(buffers[output_det_index]));
CUDA_CHECK(cudaFree(buffers[output_seg_index]));
CUDA_CHECK(cudaFree(buffers[output_lane_index]));
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}
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