前言
首先感谢無證騎士博主的大力支持，本文的也是根据他的文章魔改。
因项目需求，在TX2上部署yolov5算法实时检测目标。之前在ubantu上做过yolov5的图片检测，实时检测与之类似，但也是耗费一周的时间，原谅我是个小白。
一、环境安装
附上無證騎士博主的博客地址：
https://blog.csdn.net/hahasl555/article/details/116500763
二、opencv的安装
本人使用的opencv并不是按照上述博客安装的，而是使用的是opencv=4.2.0,附上安装的博客：https://blog.csdn.net/weixin_41868104/article/details/115010947?ops_request_misc=&request_id=&biz_id=102&utm_term=ubantu18%20%E5%AE%89%E8%A3%85opencv4.2+&utm_medium=distribute.pc_search_result.none-task-blog-2~all~sobaiduweb~default-9-.pc_search_result_before_js&spm=1018.2226.3001.4187
注：（1）在执行 cd build 后，先安装博客中“无痛安装NB”中的内容，在执行
cmake -D CMAKE_BUILD_TYPE=Release -D CMAKE_INSTALL_PREFIX=/usr/local -D OPENCV_EXTRA_MODULES_PATH=../opencv_contrib/modules ..
然后执行编译
sudo make install
最后按照大佬博客中的内容继续安装，直到结束。本人按照2中的博客成功安装两次。
三、 调用板载摄像头进行实时yolov5目标检测
此时，按照無證騎士博主的博客内容，已经同样到达第三步。我们需要调用板载摄像头，不涉及USB摄像头的内容。我们只需要根据無證騎士博主的博客内容魔改一下即可。将下面的附录代码复制到yolov5s.cpp中。
最后在../yolov5文件下执行
make将生成的yolov5s.engine复制到../yolov5/build中，执行
sudo ./yolov5 -v yolov5s.engine附录代码
#include <iostream>
#include <chrono>
#include "cuda_utils.h"
#include "logging.h"
#include "common.hpp"
#include "utils.h"
#include "calibrator.h"
#define USE_FP16 // set USE_INT8 or USE_FP16 or USE_FP32
#define DEVICE 0 // GPU id
#define NMS_THRESH 0.4
#define CONF_THRESH 0.5
#define BATCH_SIZE 1
// stuff we know about the network and the input/output blobs
static const int INPUT_H = Yolo::INPUT_H;
static const int INPUT_W = Yolo::INPUT_W;
static const int CLASS_NUM = Yolo::CLASS_NUM;
static const int OUTPUT_SIZE = Yolo::MAX_OUTPUT_BBOX_COUNT * sizeof(Yolo::Detection) / sizeof(float) + 1; // we assume the yololayer outputs no more than MAX_OUTPUT_BBOX_COUNT boxes that conf >= 0.1
const char* INPUT_BLOB_NAME = "data";
const char* OUTPUT_BLOB_NAME = "prob";
static Logger gLogger;
const char* gst = "nvarguscamerasrc ! "
"video/x-raw(memory:NVMM), "
"width=(int)1280, height=(int)720, "
"format=(string)NV12, framerate=(fraction)60/1 ! "
"nvvidconv flip-method=0 ! "
"video/x-raw, width=(int)1280, height=(int)720, format=(string)BGRx ! "
"videoconvert ! "
"video/x-raw, format=(string)BGR ! appsink";
char *my_classes[]={ "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
"fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
"elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard","surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair drier", "toothbrush" };
static int get_width(int x, float gw, int divisor = 8) {
//return math.ceil(x / divisor) * divisor
if (int(x * gw) % divisor == 0) {
return int(x * gw);
}
return (int(x * gw / divisor) + 1) * divisor;
}
static int get_depth(int x, float gd) {
if (x == 1) {
return 1;
} else {
return round(x * gd) > 1 ? round(x * gd) : 1;
}
}
ICudaEngine* build_engine(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
INetworkDefinition* network = builder->createNetworkV2(0U);
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
assert(data);
std::map<std::string, Weights> weightMap = loadWeights(wts_name);
/* ------ yolov5 backbone------ */
auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");
auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
auto bottleneck_CSP2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
auto conv3 = convBlock(network, weightMap, *bottleneck_CSP2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
auto bottleneck_csp4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");
auto conv5 = convBlock(network, weightMap, *bottleneck_csp4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
auto bottleneck_csp6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
auto conv7 = convBlock(network, weightMap, *bottleneck_csp6->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.7");
auto spp8 = SPP(network, weightMap, *conv7->getOutput(0), get_width(1024, gw), get_width(1024, gw), 5, 9, 13, "model.8");
/* ------ yolov5 head ------ */
auto bottleneck_csp9 = C3(network, weightMap, *spp8->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.9");
auto conv10 = convBlock(network, weightMap, *bottleneck_csp9->getOutput(0), get_width(512, gw), 1, 1, 1, "model.10");
auto upsample11 = network->addResize(*conv10->getOutput(0));
assert(upsample11);
upsample11->setResizeMode(ResizeMode::kNEAREST);
upsample11->setOutputDimensions(bottleneck_csp6->getOutput(0)->getDimensions());
ITensor* inputTensors12[] = { upsample11->getOutput(0), bottleneck_csp6->getOutput(0) };
auto cat12 = network->addConcatenation(inputTensors12, 2);
auto bottleneck_csp13 = C3(network, weightMap, *cat12->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.13");
auto conv14 = convBlock(network, weightMap, *bottleneck_csp13->getOutput(0), get_width(256, gw), 1, 1, 1, "model.14");
auto upsample15 = network->addResize(*conv14->getOutput(0));
assert(upsample15);
upsample15->setResizeMode(ResizeMode::kNEAREST);
upsample15->setOutputDimensions(bottleneck_csp4->getOutput(0)->getDimensions());
ITensor* inputTensors16[] = { upsample15->getOutput(0), bottleneck_csp4->getOutput(0) };
auto cat16 = network->addConcatenation(inputTensors16, 2);
auto bottleneck_csp17 = C3(network, weightMap, *cat16->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.17");
// yolo layer 0
IConvolutionLayer* det0 = network->addConvolutionNd(*bottleneck_csp17->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.0.weight"], weightMap["model.24.m.0.bias"]);
auto conv18 = convBlock(network, weightMap, *bottleneck_csp17->getOutput(0), get_width(256, gw), 3, 2, 1, "model.18");
ITensor* inputTensors19[] = { conv18->getOutput(0), conv14->getOutput(0) };
auto cat19 = network->addConcatenation(inputTensors19, 2);
auto bottleneck_csp20 = C3(network, weightMap, *cat19->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.20");
//yolo layer 1
IConvolutionLayer* det1 = network->addConvolutionNd(*bottleneck_csp20->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.1.weight"], weightMap["model.24.m.1.bias"]);
auto conv21 = convBlock(network, weightMap, *bottleneck_csp20->getOutput(0), get_width(512, gw), 3, 2, 1, "model.21");
ITensor* inputTensors22[] = { conv21->getOutput(0), conv10->getOutput(0) };
auto cat22 = network->addConcatenation(inputTensors22, 2);
auto bottleneck_csp23 = C3(network, weightMap, *cat22->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
IConvolutionLayer* det2 = network->addConvolutionNd(*bottleneck_csp23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.24.m.2.weight"], weightMap["model.24.m.2.bias"]);
auto yolo = addYoLoLayer(network, weightMap, "model.24", std::vector<IConvolutionLayer*>{det0, det1, det2});
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*yolo->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
#if defined(USE_FP16)
config->setFlag(BuilderFlag::kFP16);
#elif defined(USE_INT8)
std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
assert(builder->platformHasFastInt8());
config->setFlag(BuilderFlag::kINT8);
Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
config->setInt8Calibrator(calibrator);
#endif
std::cout << "Building engine, please wait for a while..." << std::endl;
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "Build engine successfully!" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto& mem : weightMap)
{
free((void*)(mem.second.values));
}
return engine;
}
ICudaEngine* build_engine_p6(unsigned int maxBatchSize, IBuilder* builder, IBuilderConfig* config, DataType dt, float& gd, float& gw, std::string& wts_name) {
INetworkDefinition* network = builder->createNetworkV2(0U);
// Create input tensor of shape {3, INPUT_H, INPUT_W} with name INPUT_BLOB_NAME
ITensor* data = network->addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });
assert(data);
std::map<std::string, Weights> weightMap = loadWeights(wts_name);
/* ------ yolov5 backbone------ */
auto focus0 = focus(network, weightMap, *data, 3, get_width(64, gw), 3, "model.0");
auto conv1 = convBlock(network, weightMap, *focus0->getOutput(0), get_width(128, gw), 3, 2, 1, "model.1");
auto c3_2 = C3(network, weightMap, *conv1->getOutput(0), get_width(128, gw), get_width(128, gw), get_depth(3, gd), true, 1, 0.5, "model.2");
auto conv3 = convBlock(network, weightMap, *c3_2->getOutput(0), get_width(256, gw), 3, 2, 1, "model.3");
auto c3_4 = C3(network, weightMap, *conv3->getOutput(0), get_width(256, gw), get_width(256, gw), get_depth(9, gd), true, 1, 0.5, "model.4");
auto conv5 = convBlock(network, weightMap, *c3_4->getOutput(0), get_width(512, gw), 3, 2, 1, "model.5");
auto c3_6 = C3(network, weightMap, *conv5->getOutput(0), get_width(512, gw), get_width(512, gw), get_depth(9, gd), true, 1, 0.5, "model.6");
auto conv7 = convBlock(network, weightMap, *c3_6->getOutput(0), get_width(768, gw), 3, 2, 1, "model.7");
auto c3_8 = C3(network, weightMap, *conv7->getOutput(0), get_width(768, gw), get_width(768, gw), get_depth(3, gd), true, 1, 0.5, "model.8");
auto conv9 = convBlock(network, weightMap, *c3_8->getOutput(0), get_width(1024, gw), 3, 2, 1, "model.9");
auto spp10 = SPP(network, weightMap, *conv9->getOutput(0), get_width(1024, gw), get_width(1024, gw), 3, 5, 7, "model.10");
auto c3_11 = C3(network, weightMap, *spp10->getOutput(0), get_width(1024, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.11");
/* ------ yolov5 head ------ */
auto conv12 = convBlock(network, weightMap, *c3_11->getOutput(0), get_width(768, gw), 1, 1, 1, "model.12");
auto upsample13 = network->addResize(*conv12->getOutput(0));
assert(upsample13);
upsample13->setResizeMode(ResizeMode::kNEAREST);
upsample13->setOutputDimensions(c3_8->getOutput(0)->getDimensions());
ITensor* inputTensors14[] = { upsample13->getOutput(0), c3_8->getOutput(0) };
auto cat14 = network->addConcatenation(inputTensors14, 2);
auto c3_15 = C3(network, weightMap, *cat14->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.15");
auto conv16 = convBlock(network, weightMap, *c3_15->getOutput(0), get_width(512, gw), 1, 1, 1, "model.16");
auto upsample17 = network->addResize(*conv16->getOutput(0));
assert(upsample17);
upsample17->setResizeMode(ResizeMode::kNEAREST);
upsample17->setOutputDimensions(c3_6->getOutput(0)->getDimensions());
ITensor* inputTensors18[] = { upsample17->getOutput(0), c3_6->getOutput(0) };
auto cat18 = network->addConcatenation(inputTensors18, 2);
auto c3_19 = C3(network, weightMap, *cat18->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.19");
auto conv20 = convBlock(network, weightMap, *c3_19->getOutput(0), get_width(256, gw), 1, 1, 1, "model.20");
auto upsample21 = network->addResize(*conv20->getOutput(0));
assert(upsample21);
upsample21->setResizeMode(ResizeMode::kNEAREST);
upsample21->setOutputDimensions(c3_4->getOutput(0)->getDimensions());
ITensor* inputTensors21[] = { upsample21->getOutput(0), c3_4->getOutput(0) };
auto cat22 = network->addConcatenation(inputTensors21, 2);
auto c3_23 = C3(network, weightMap, *cat22->getOutput(0), get_width(512, gw), get_width(256, gw), get_depth(3, gd), false, 1, 0.5, "model.23");
auto conv24 = convBlock(network, weightMap, *c3_23->getOutput(0), get_width(256, gw), 3, 2, 1, "model.24");
ITensor* inputTensors25[] = { conv24->getOutput(0), conv20->getOutput(0) };
auto cat25 = network->addConcatenation(inputTensors25, 2);
auto c3_26 = C3(network, weightMap, *cat25->getOutput(0), get_width(1024, gw), get_width(512, gw), get_depth(3, gd), false, 1, 0.5, "model.26");
auto conv27 = convBlock(network, weightMap, *c3_26->getOutput(0), get_width(512, gw), 3, 2, 1, "model.27");
ITensor* inputTensors28[] = { conv27->getOutput(0), conv16->getOutput(0) };
auto cat28 = network->addConcatenation(inputTensors28, 2);
auto c3_29 = C3(network, weightMap, *cat28->getOutput(0), get_width(1536, gw), get_width(768, gw), get_depth(3, gd), false, 1, 0.5, "model.29");
auto conv30 = convBlock(network, weightMap, *c3_29->getOutput(0), get_width(768, gw), 3, 2, 1, "model.30");
ITensor* inputTensors31[] = { conv30->getOutput(0), conv12->getOutput(0) };
auto cat31 = network->addConcatenation(inputTensors31, 2);
auto c3_32 = C3(network, weightMap, *cat31->getOutput(0), get_width(2048, gw), get_width(1024, gw), get_depth(3, gd), false, 1, 0.5, "model.32");
/* ------ detect ------ */
IConvolutionLayer* det0 = network->addConvolutionNd(*c3_23->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.0.weight"], weightMap["model.33.m.0.bias"]);
IConvolutionLayer* det1 = network->addConvolutionNd(*c3_26->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.1.weight"], weightMap["model.33.m.1.bias"]);
IConvolutionLayer* det2 = network->addConvolutionNd(*c3_29->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.2.weight"], weightMap["model.33.m.2.bias"]);
IConvolutionLayer* det3 = network->addConvolutionNd(*c3_32->getOutput(0), 3 * (Yolo::CLASS_NUM + 5), DimsHW{ 1, 1 }, weightMap["model.33.m.3.weight"], weightMap["model.33.m.3.bias"]);
auto yolo = addYoLoLayer(network, weightMap, "model.33",std::vector<IConvolutionLayer*>{det0, det1, det2,det3});
yolo->getOutput(0)->setName(OUTPUT_BLOB_NAME);
network->markOutput(*yolo->getOutput(0));
// Build engine
builder->setMaxBatchSize(maxBatchSize);
config->setMaxWorkspaceSize(16 * (1 << 20)); // 16MB
#if defined(USE_FP16)
config->setFlag(BuilderFlag::kFP16);
#elif defined(USE_INT8)
std::cout << "Your platform support int8: " << (builder->platformHasFastInt8() ? "true" : "false") << std::endl;
assert(builder->platformHasFastInt8());
config->setFlag(BuilderFlag::kINT8);
Int8EntropyCalibrator2* calibrator = new Int8EntropyCalibrator2(1, INPUT_W, INPUT_H, "./coco_calib/", "int8calib.table", INPUT_BLOB_NAME);
config->setInt8Calibrator(calibrator);
#endif
std::cout << "Building engine, please wait for a while..." << std::endl;
ICudaEngine* engine = builder->buildEngineWithConfig(*network, *config);
std::cout << "Build engine successfully!" << std::endl;
// Don't need the network any more
network->destroy();
// Release host memory
for (auto& mem : weightMap)
{
free((void*)(mem.second.values));
}
return engine;
}
void APIToModel(unsigned int maxBatchSize, IHostMemory** modelStream, float& gd, float& gw, std::string& wts_name) {
// Create builder
IBuilder* builder = createInferBuilder(gLogger);
IBuilderConfig* config = builder->createBuilderConfig();
// Create model to populate the network, then set the outputs and create an engine
ICudaEngine* engine = build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);
assert(engine != nullptr);
// Serialize the engine
(*modelStream) = engine->serialize();
// Close everything down
engine->destroy();
builder->destroy();
config->destroy();
}
void doInference(IExecutionContext& context, cudaStream_t& stream, void **buffers, float* input, float* output, int batchSize) {
// DMA input batch data to device, infer on the batch asynchronously, and DMA output back to host
CUDA_CHECK(cudaMemcpyAsync(buffers[0], input, batchSize * 3 * INPUT_H * INPUT_W * sizeof(float), cudaMemcpyHostToDevice, stream));
context.enqueue(batchSize, buffers, stream, nullptr);
CUDA_CHECK(cudaMemcpyAsync(output, buffers[1], batchSize * OUTPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost, stream));
cudaStreamSynchronize(stream);
}
bool parse_args(int argc, char** argv, std::string& engine) {
if (argc < 3) return false;
if (std::string(argv[1]) == "-v" && argc == 3) {
engine = std::string(argv[2]);
} else {
return false;
}
return true;
}
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,engine_name)){
std::cerr << "arguments not right!" << std::endl;
std::cerr << "./yolov5 -v [.engine] // run inference with camera" << std::endl;
return -1;
}
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();
// 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];
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() == 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);
const int outputIndex = engine->getBindingIndex(OUTPUT_BLOB_NAME);
assert(inputIndex == 0);
assert(outputIndex == 1);
// Create GPU buffers on device
CUDA_CHECK(cudaMalloc(&buffers[inputIndex], BATCH_SIZE * 3 * INPUT_H * INPUT_W * sizeof(float)));
CUDA_CHECK(cudaMalloc(&buffers[outputIndex], BATCH_SIZE * OUTPUT_SIZE * sizeof(float)));
// Create stream
cudaStream_t stream;
CUDA_CHECK(cudaStreamCreate(&stream));
cv::VideoCapture capture(gst);
//cv::VideoCapture capture("../overpass.mp4");
//int fourcc = cv::VideoWriter::fourcc('M','J','P','G');
//capture.set(cv::CAP_PROP_FOURCC, fourcc);
if(!capture.isOpened()){
std::cout << "Error opening video stream or file" << std::endl;
return -1;
}
int key;
int fcount = 0;
while(1)
{
cv::Mat frame;
capture >> frame; //从capture读取数据到frame
if(frame.empty())
{
std::cout << "Fail to read image from camera!" << std::endl;
break;
}
fcount++;
//if (fcount < BATCH_SIZE && f + 1 != (int)file_names.size()) continue;
for (int b = 0; b < fcount; b++) {
//cv::Mat img = cv::imread(img_dir + "/" + file_names[f - fcount + 1 + b]);
cv::Mat img = frame;
if (img.empty()) continue;
cv::Mat pr_img = preprocess_img(img, INPUT_W, INPUT_H); // letterbox BGR to RGB
int i = 0;
for (int row = 0; row < INPUT_H; ++row) {
uchar* uc_pixel = pr_img.data + row * pr_img.step;
for (int col = 0; col < INPUT_W; ++col) {
data[b * 3 * INPUT_H * INPUT_W + i] = (float)uc_pixel[2] / 255.0;
data[b * 3 * INPUT_H * INPUT_W + i + INPUT_H * INPUT_W] = (float)uc_pixel[1] / 255.0;
data[b * 3 * INPUT_H * INPUT_W + i + 2 * INPUT_H * INPUT_W] = (float)uc_pixel[0] / 255.0;
uc_pixel += 3;
++i;
}
}
}
// Run inference
auto start = std::chrono::system_clock::now();
doInference(*context, stream, buffers, data, prob, BATCH_SIZE);
auto end = std::chrono::system_clock::now();
//std::cout << std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count() << "ms" << std::endl;
int fps = 1000.0/std::chrono::duration_cast<std::chrono::milliseconds>(end - start).count();
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);
}
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]);
for (size_t j = 0; j < res.size(); j++) {
cv::Rect r = get_rect(frame, res[j].bbox);
cv::rectangle(frame, r, cv::Scalar(0x27, 0xC1, 0x36), 2);
std::string label = my_classes[(int)res[j].class_id];
cv::putText(frame, label, cv::Point(r.x, r.y - 1), cv::FONT_HERSHEY_PLAIN, 1.2, cv::Scalar(0xFF, 0xFF, 0xFF), 2);
std::string jetson_fps = "Jetson TX2 FPS: " + std::to_string(fps);
cv::putText(frame, jetson_fps, cv::Point(11,80), cv::FONT_HERSHEY_PLAIN, 3, cv::Scalar(0, 0, 255), 2, cv::LINE_AA);
}
//cv::imwrite("_" + file_names[f - fcount + 1 + b], img);
}
cv::imshow("yolov5",frame);
key = cv::waitKey(1);
if (key == 'q'){
break;
}
fcount = 0;
}
capture.release();
// Release stream and buffers
cudaStreamDestroy(stream);
CUDA_CHECK(cudaFree(buffers[inputIndex]));
CUDA_CHECK(cudaFree(buffers[outputIndex]));
// Destroy the engine
context->destroy();
engine->destroy();
runtime->destroy();
return 0;
}