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Ubuntu 18conda环境python3.9cuda10.2#xff0c;硬件平台是Jetson tx2 nx 前提你已经能运行YOLOV5代码后#xff0c;再配置tensorRT进行加速。 目前只试了图片检测和C打开USB摄像头进行视频检测#xff0c;希望是使用python配合D435i深度相机来实现检测#xff…环境说明
Ubuntu 18conda环境python3.9cuda10.2硬件平台是Jetson tx2 nx 前提你已经能运行YOLOV5代码后再配置tensorRT进行加速。 目前只试了图片检测和C打开USB摄像头进行视频检测希望是使用python配合D435i深度相机来实现检测后续再更新。
一、安装TensorRT
安装git和cmake已经安装了忽略这一步
sudo apt-get install libpython3-dev python3-numpy克隆源码 连接不上就挂个梯子
git clone https://github.com/dusty-nv/jetson-inference安装附属文件
git submodule update --init添加jetson-inference需要的包
先下载相关包百度网盘分享 提取码s75z 下载后拷贝到jetson tx2中将所有包复制到刚刚克隆的jetson-inference下的data/networks 然后cd进入到data/networks文件夹解压相关包
cd jetson-inference/data/networks
for tar in *.tar.gz; do tar xvf $tar; done再编辑 jetson-inference/CMakePrebuild.sh文件把./download-models.sh 注释掉
编译
在 jetson-inference文件夹里面创建build文件夹
mkdir build进入build中进行cmake
cd build
cmake ../运行过程中弹出该页面跳过即可 cmake过程中报错克隆不成功就删除jetson-inference文件夹再试一遍
cmake成功后
# 可能有点慢耐心等待
make make成功后
sudo make install测试 安装成功后进行测试
cd jetson-inference/build/aarch64/bin./imagenet-console ./images/bird_0.jpg output.jpg参考博主https://blog.csdn.net/qq_42078934/article/details/129669965?spm1001.2014.3001.5506
如果出现在下载Googlenet.tar.gz相关并且最终下载失败报错需要先中断执行然后在networks文件夹中新建Googlenet文件夹将networks文件夹中的bvlc_googlenet.caffemodel、googlenet.prototxt和googlenet_noprob.prototxt剪切到新建的Googlenet文件夹最后在Googlenet文件夹新建networks文件夹将ilsvrc12_synset_words.txt剪切到新建networks文件夹
再次进行测试
二、TensorRT加速YOLOV5
安装pycuda包 这个包是使用python编写加速的一个包本文还只实验了C版本的但可以先把这个包安装上
python3 -m pip install pycuda2020.1用这个命令直接安装的2019.1版本的网上有些教程是下载包再进行安装我这样发现安装的包在虚拟环境中用不了建议直接进入到需要安装的虚拟环境中直接用这条命令进行安装。 安装完后测试
下载tensorrt的YOLOV5代码 这里我是想把自己训练好的模型用tensorrt做一个加速自己训练模型的yolov5版本是6.0所以这里也下载6.0版本的tensorrt yolov5。下载链接 生成wts文件 自己训练的模型是.pt这里先转换成.wts文件。
把刚刚下载的tensorrt yolo文件中yolov5下的gen_wts.py复制到你自己yolov5代码的文件夹下这里应该存放了你自己训练的.pt权重文件 执行gen_wts.py生成.wts文件。
python3 gen_wts.py weights/yolov5s.pt # 后面是自己的权重的名字这里如果遇到报错参考解决办法解决办法
生成部署引擎
先将yolov5s.wts文件(上一步生成的文件)放到tensorrtx-yolov5-v6.0/yolov5文件夹中。然后打开yololayer.h文件修改num总数根据你训练模型的类个数来这里我是两类所以改为2 编译相关
cd tensorrtx-yolov5-v6.0/yolov5
mkdir build
cd build
cmake ..
makesudo ./yolov5 -s ../yolov5s.wts yolov5s.engine s
# sudo ./yolov5 -s [.wts] [.engine] [s/m/l/x/s6/m6/l6/x6 or c/c6 gd gw]
# s代表用的是yolov5s是什么就改成什么到这里便通过tensorrt生成了基于C的engine部署引擎文件后缀.engine
使用图片测试 将yolov5源代码的data文件夹中的images文件夹整个复制到tensorrtx/yolov5文件夹在build文件夹里执行下面的代码。
sudo ./yolov5 -d yolov5s.engine ../samples
#sudo ./yolov5 -d [.engine] [image folder]执行后结果会在build中看到。如果图形没有画框可能是因为s模型所产生的置信度一般在0.2-0.4之间在yolov5.cpp文件中置信度conf_thresh设置在0.5低于0.5的检测框会被排除。
使用USB摄像头 这里是采用的C的版本替换tensorrtx-yolov5-v6.0\yolov5\yolov5.cpp文件中的内容为注意修改为自己的分类类别
#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;//修改为自己的类别
char *my_classes[]{person, bicycle};static int get_width(int x, float gw, int divisor 8) {//return math.ceil(x / divisor) * divisorif (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;}
}//#创建engine和network
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_NAMEITensor* data network-addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });assert(data);std::mapstd::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 0IConvolutionLayer* 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 1IConvolutionLayer* 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::vectorIConvolutionLayer*{det0, det1, det2});yolo-getOutput(0)-setName(OUTPUT_BLOB_NAME);network-markOutput(*yolo-getOutput(0));// Build enginebuilder-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);
#endifstd::cout Building engine, please wait for a while... std::endl;ICudaEngine* engine builder-buildEngineWithConfig(*network, *config);std::cout Build engine successfully! std::endl;// Dont need the network any morenetwork-destroy();// Release host memoryfor (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_NAMEITensor* data network-addInput(INPUT_BLOB_NAME, dt, Dims3{ 3, INPUT_H, INPUT_W });assert(data);std::mapstd::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::vectorIConvolutionLayer*{det0, det1, det2, det3});yolo-getOutput(0)-setName(OUTPUT_BLOB_NAME);network-markOutput(*yolo-getOutput(0));// Build enginebuilder-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);
#endifstd::cout Building engine, please wait for a while... std::endl;ICudaEngine* engine builder-buildEngineWithConfig(*network, *config);std::cout Build engine successfully! std::endl;// Dont need the network any morenetwork-destroy();// Release host memoryfor (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 builderIBuilder* builder createInferBuilder(gLogger);IBuilderConfig* config builder-createBuilderConfig();// Create model to populate the network, then set the outputs and create an engineICudaEngine* engine build_engine(maxBatchSize, builder, config, DataType::kFLOAT, gd, gw, wts_name);assert(engine ! nullptr);// Serialize the engine(*modelStream) engine-serialize();// Close everything downengine-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 hostCUDA_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 deviceCUDA_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 streamcudaStream_t stream;CUDA_CHECK(cudaStreamCreate(stream));//#读取本地视频//cv::VideoCapture capture(/home/nano/Videos/video.mp4);//#调用本地usb摄像头,我的默认参数为1,如果1报错,可修改为0.cv::VideoCapture capture(0);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 RGBint 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 inferenceauto start std::chrono::system_clock::now();//#获取模型推理开始时间doInference(*context, stream, buffers, data, prob, BATCH_SIZE);auto end std::chrono::system_clock::now();//#结束时间//std::cout std::chrono::duration_caststd::chrono::milliseconds(end - start).count() ms std::endl;int fps 1000.0 / std::chrono::duration_caststd::chrono::milliseconds(end - start).count();std::vectorstd::vectorYolo::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 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 bufferscudaStreamDestroy(stream);CUDA_CHECK(cudaFree(buffers[inputIndex]));CUDA_CHECK(cudaFree(buffers[outputIndex]));// Destroy the enginecontext-destroy();engine-destroy();runtime-destroy();return 0;
}
修改完后执行
cd build
make
sudo ./yolov5 -v yolov5s.engine # 后面是自己生成的部署引擎文件基于python的有时间了再弄
