文章目录
这篇文章不太成功，只能算是一个失败记录吧，先不管了，怕以后忘记了，主要是也快要交毕设了，虽然不想写论文，那也得花时间去弄啊对吧，更完这篇就休息！
这篇文章需要的基础
还是延续之前的思路，需要部署一个方便进行训练和检测的平台，在电脑这边训练，然后部署就在jetson nano上来进行，尽量提高jetson nano的帧率。
1、yolov5s模型转tensorrt
1、前期准备
这里需要先安装一下torch&&torchvision，这个最好还是本地下载和安装吧，相关的文件我都放到我的网盘里面了，可以根据需要下载，口令如下：
「nano工具分享」https://www.aliyundrive.com/s/hEEe9UfsXv1
点击链接保存，或者复制本段内容，打开「阿里云盘」APP ，无需下载极速在线查看，视频原画倍速播放。
下面是分享内容
安装的话torch是直接安装，torchvision是需要编译安装的，这里下载到开发板之后进行解压
unzip xxx
之后就可以进入文件夹去安装了
pip3 install numpy torch-1.8.0-cp36-cp36m-linux_aarch64.whl
等一段时间安装完成，安装有报错的话一般是缺了什么，缺啥安装啥就行了
之后是安装我们的torchvision
cd torchvision
export BUILD_VERSION=0.9.0
python3 setup.py install --user
2、运行yolov5
首先还是从github上下载代码
git clone https://github.com/ultralytics/yolov5.git
这里我的库基本都装过了，不需要继续安装了，可以直接运行
运行的话它会自动下载我们需要的模型文件，之后就会检测里面的两张图片，可以看到一张基本是0.1s，还是不太行啊
检测效果如下所示
运行过程下载我们需要的权重。
3、TensorRT加速推理
这里需要下载两个项目，这两个项目就是后面的基础了，首先是这个tensorrt的项目
git clone https://github.com/wang-xinyu/tensorrtx.git
之后是这个，如果下载失败可以多尝试几次，还是不行就直接电脑本地下载然后传过去
git clone -b v5.0 https://github.com/ultralytics/yolov5.git
首先是进入tensorrts文件夹，把里面的一个pt转wts的代码复制到yolov5这个文件夹里面去，之后把我们下载好的yolov5的这个权重也复制进去，然后就可以生成wts文件了
cd tensorrtx
cp yolov5/gen_wts.py ~/yolov5
cd ~/yolov5
python3 gen_wts.py -m yolov5s.pt # 这里要事先把模型文件放到里面去
下面就是去生成模型文件然后进行推理了
cd ~/tensorrtx/yolov5
mkdir build && cd build
mv ~/yolov5/yolov5s.wts ./
cmake ..
make
这里看到下面结果就说明OK了
之后就是那我们的wts文件来生成engine文件了，并可以使用样例来进行一下测试
./yolov5 -s yolov5s.wts yolov5s.engine s #生成engine文件需要一段时间，请耐心等待
./yolov5 -d yolov5s.engine ../samples # 推理结果保存在samples文件夹下
检测输出，说实话也没什么提升，不知道视频咋样
实际效果
这里有一个问题，就是之前那个官方权重必须是新版的，这里的新版是指当前时间的版本，目前是yolov5 6.1版本的，估计6.0版本的也是可以的，如果用5.0版本的肯定就不行的，会报错，报错信息如下。
这里也可以理解为一个版本对应的问题，就是他们的版本不对，不管是trt那边，还是yolo这边都需要用最新的版本，或者对应的版本才行，然后就是我们训练的pt文件都是基于一个官方的pt文件来的，所以神经网络的那些层其实就都和他一样的，所以就也没啥用的，就是源头的那个模型用不了，其他的也不能用。
4、直接使用摄像头进行部署
下面参考这位大佬的方案
https://blog.csdn.net/hahasl555/article/details/116500763
将下面的内容替换yolov5文件夹下的yolov5.cpp文件
vim yolov5.cpp
之后因为是vim编辑器，输入
#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;
char* my_classes[] = {
"person" , "dog" , "bicycle" };
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(1);
//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;
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 Nano 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;
}
这里需要替换为自己的
再次进行编译和测试
cd build
make
sudo ./yolov5 -v yolov5s.engine
就可以在看到摄像头检测的图像了
2、deepstream检测
1、下载SDK
关于DeepStream的介绍参考这篇文章
https://blog.csdn.net/Tosonw/article/details/104154090?msclkid=12bd0cf4cf6911ecb107816bf39a19e5
下面就是下载了，官方链接为
https://developer.nvidia.com/deepstream-sdk?msclkid=12bd94d8cf6911ecb9a72916005a0257
不过这个是针对jp4.6.1版本的，我这里用的不是对应的版本，所以不能下载这个
后面百度搜到了这个网站，去这里下载就可以了
https://developer.nvidia.com/embedded/deepstream-on-jetson-downloads-archived
这里可以看到需要选择5.0.1版本，或者5.0版本的都可以的
这里有500多M的大小，因此决定就还是先本地下载吧
之后使用SSH远程传输到我们的jetson nano开发板上去
2、安装依赖
使用下面的命令安装所需的依赖
sudo apt-get install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4
sudo apt-get install libgstreamer1.0-0 gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio libgstrtspserver-1.0-dev gstreamer1.0-rtsp
sudo apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
sudo apt-get install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev libgstrtspserver-1.0-dev libx11-dev libgstrtspserver-1.0-dev gstreamer1.0-rtsp ffmpeg
sudo apt-get install libgstreamer-plugins-base1.0-dev libgstreamer1.0-dev libgstrtspserver-1.0-dev libx11-dev libjson-glib-dev
下面对我们的安装包进行解压
sudo tar -jxvf deepstream_sdk_v5.0.1_jetson.tbz2 -C /
这里是解压到这个位置了
进到这里，然后准备安装
cd /opt/nvidia/deepstream/deepstream-5.0/
安装
sudo ./install.sh
sudo ldconfig
3、配置环境
下面需要配置下环境变量，编辑下面的文件
sudo vim /etc/ld.so.conf
在末尾加上这个，其实这个文件就一行
/opt/nvidia/deepstream/deepstream-5.0/lib/
使我们的配置生效，退出后输入
sudo ldconfig
在将我们另一个位置配置下，就是之前配置cuda和cudnn的地方
sudo vim ~/.bashrc
在结尾处添加
unset DISPLAY
同样的使配置生效，退出后输入
source ~/.bashrc
验证安装是否成功
deepstream-app --version-all
看到下面的这个结果就说明可以了
4、测试效果
下面运行下官方的demo
cd /opt/nvidia/deepstream/deepstream
运行
deepstream-app -c samples/configs/deepstream-app/source8_1080p_dec_infer-resnet_tracker_tiled_display_fp16_nano.txt
出现下面的输出，一个多路的视频检测
运行结束，可以看到测试成功！
3、使用deepstream进行部署
暂未尝试，试了再写
4、yolov5目前比较新的版本训练和检测
1、前期准备
前面说到就是在部署的时候存在的新版和旧版的问题，这里干脆就看看新版怎么训练的，这样我们训练的模型就可以直接拿过去用了，首先还是修改配置文件，这里修改数量就行（复制yolovs的配置文件，因为用yolov5s来进行的训练）。
这里修改VOC文件，因为我们用的VOC的数据集，复制一份然后修改就行，加入训练集的目录还有标签。
修改如下所示
准备好voc2007的数据集，如下所示
2、开始训练
在train.py里面找到配置文件进行修改，加入我们之前的一些配置文件，这里有个一次训练的数目，不要选的太大了，选太大了容易出问题，就会提示你不够大什么的
最后就开始训练了，训练过程结果如下所示
3、进行检测
检测的话这里只需要修改下我们的detect.py就行了，在这里加入我们的准备好的权重文件，如下所示
训练结果如下
