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1. 源由

从应用角度来说，图像分类是计算机视觉里面最基本的一个操作。

2. imagenet

imageNet对象接受输入图像并输出每个类别的概率。GoogleNet和ResNet-18模型在构建过程中自动下载，这些模型已在包含1000个物体的ImageNet ILSVRC数据集上进行了训练。

2.1 命令选项

$ imagenet --help
usage: imagenet [--help] [--network=NETWORK] ...input_URI [output_URI]Classify a video/image stream using an image recognition DNN.
See below for additional arguments that may not be shown above.optional arguments:--help            show this help message and exit--network=NETWORK pre-trained model to load (see below for options)--topK=N         show the topK number of class predictions (default: 1)
positional arguments:input_URI       resource URI of input stream  (see videoSource below)output_URI      resource URI of output stream (see videoOutput below)imageNet arguments:--network=NETWORK    pre-trained model to load, one of the following:* alexnet* googlenet (default)* googlenet-12* resnet-18* resnet-50* resnet-101* resnet-152* vgg-16* vgg-19* inception-v4--model=MODEL        path to custom model to load (caffemodel, uff, or onnx)--prototxt=PROTOTXT  path to custom prototxt to load (for .caffemodel only)--labels=LABELS      path to text file containing the labels for each class--input-blob=INPUT   name of the input layer (default is 'data')--output-blob=OUTPUT name of the output layer (default is 'prob')--threshold=CONF     minimum confidence threshold for classification (default is 0.01)--smoothing=WEIGHT   weight between [0,1] or number of frames (disabled by default)--profile            enable layer profiling in TensorRTvideoSource arguments:input                resource URI of the input stream, for example:* /dev/video0               (V4L2 camera #0)* csi://0                   (MIPI CSI camera #0)* rtp://@:1234              (RTP stream)* rtsp://user:pass@ip:1234  (RTSP stream)* webrtc://@:1234/my_stream (WebRTC stream)* file://my_image.jpg       (image file)* file://my_video.mp4       (video file)* file://my_directory/      (directory of images)--input-width=WIDTH    explicitly request a width of the stream (optional)--input-height=HEIGHT  explicitly request a height of the stream (optional)--input-rate=RATE      explicitly request a framerate of the stream (optional)--input-save=FILE      path to video file for saving the input stream to disk--input-codec=CODEC    RTP requires the codec to be set, one of these:* h264, h265* vp8, vp9* mpeg2, mpeg4* mjpeg--input-decoder=TYPE   the decoder engine to use, one of these:* cpu* omx  (aarch64/JetPack4 only)* v4l2 (aarch64/JetPack5 only)--input-flip=FLIP      flip method to apply to input:* none (default)* counterclockwise* rotate-180* clockwise* horizontal* vertical* upper-right-diagonal* upper-left-diagonal--input-loop=LOOP      for file-based inputs, the number of loops to run:* -1 = loop forever*  0 = don't loop (default)* >0 = set number of loopsvideoOutput arguments:output               resource URI of the output stream, for example:* file://my_image.jpg       (image file)* file://my_video.mp4       (video file)* file://my_directory/      (directory of images)* rtp://<remote-ip>:1234    (RTP stream)* rtsp://@:8554/my_stream   (RTSP stream)* webrtc://@:1234/my_stream (WebRTC stream)* display://0               (OpenGL window)--output-codec=CODEC   desired codec for compressed output streams:* h264 (default), h265* vp8, vp9* mpeg2, mpeg4* mjpeg--output-encoder=TYPE  the encoder engine to use, one of these:* cpu* omx  (aarch64/JetPack4 only)* v4l2 (aarch64/JetPack5 only)--output-save=FILE     path to a video file for saving the compressed streamto disk, in addition to the primary output above--bitrate=BITRATE      desired target VBR bitrate for compressed streams,in bits per second. The default is 4000000 (4 Mbps)--headless             don't create a default OpenGL GUI windowlogging arguments:--log-file=FILE        output destination file (default is stdout)--log-level=LEVEL      message output threshold, one of the following:* silent* error* warning* success* info* verbose (default)* debug--verbose              enable verbose logging (same as --log-level=verbose)--debug                enable debug logging   (same as --log-level=debug)

注：关于照片、视频等基本操作，详见： 《Linux 36.3 + JetPack v6.0@jetson-inference之视频操作》

2.2 下载模型

两种方式：

1. 创建imageNet对象时，初始化会自动下载
2. 通过手动将模型文件放置到data/networks/目录下

国内，由于“墙”的存在，对于我们这种处于起飞阶段的菜鸟来说就是“障碍”。有条件的朋友可以参考《apt-get通过代理更新系统》进行设置网络。

不过，NVIDIA还是很热心的帮助我们做了“Work around”，所有的模型都已经预先存放在中国大陆能访问的位置：Github - model-mirror-190618

  --network=NETWORK    pre-trained model to load, one of the following:* alexnet* googlenet (default)* googlenet-12* resnet-18* resnet-50* resnet-101* resnet-152* vgg-16* vgg-19* inception-v4--model=MODEL        path to custom model to load (caffemodel, uff, or onnx)

根据以上Model方面信息，该命令支持：

• alexnet
• googlenet (default)
• googlenet-12
• resnet-18
• resnet-50
• resnet-101
• resnet-152
• vgg-16
• vgg-19
• inception-v4
• 支持定制模型(需要用到通用的模型文件caffemodel, uff, or onnx)

作为示例，就下载一个googlenet (default)模型

$ mkdir model-mirror-190618
$ cd model-mirror-190618
$ wget https://github.com/dusty-nv/jetson-inference/releases/download/model-mirror-190618/GoogleNet.tar.gz
$ mkdir -p ../data/networks/Googlenet
$ tar -zxvf GoogleNet.tar.gz -C ../data/networks/Googlenet
$ cd ..

注：这个模型文件下载要注意，将解压缩文件放置到Googlenet目录下。

2.3 操作示例

它加载图像（或多张图像），使用TensorRT和imageNet类进行推理，然后叠加分类结果并保存输出图像。该项目附带了供您使用的示例图像，这些图像位于images/目录下。

• What’s wrong with imagenet, continous printf?

$ cd build/aarch64/bin/

2.3.1 单张照片

# C++
$ ./imagenet images/orange_0.jpg images/test/output_imagenet_cpp.jpg

# Python
$ ./imagenet.py images/strawberry_0.jpg images/test/output_imagenet_python.jpg

2.3.2 视频

# Download test video (thanks to jell.yfish.us)
$ wget https://nvidia.box.com/shared/static/tlswont1jnyu3ix2tbf7utaekpzcx4rc.mkv -O jellyfish.mkv

# C++
$ ./imagenet --network=resnet-18 ../../../jellyfish.mkv images/test/output_imagenet_jellyfish_cpp.mkv

# Python
$ ./imagenet.py --network=resnet-18 ../../../jellyfish.mkv images/test/output_imagenet_jellyfish_python.mkv

这里视频就放一份了，理论上将既然有概率性的问题求解方式，不同时间运算的结果可能会有差异。但是基于这个模型，计算机没有记忆，所以理论上是同一个概率。

那么问题来了，照片的CPP和Python两次运算概率确是是不一样的。这是什么原因呢？

3. 代码

3.1 Python

Import statements
├── sys
├── argparse
├── jetson_inference
│   └── imageNet
└── jetson_utils├── videoSource├── videoOutput├── cudaFont└── LogCommand line parsing
├── Create ArgumentParser
│   ├── description
│   ├── formatter_class
│   └── epilog
├── Add arguments
│   ├── input
│   ├── output
│   ├── --network
│   └── --topK
└── Parse arguments├── try│   └── args = parser.parse_known_args()[0]└── except├── print("")├── parser.print_help()└── sys.exit(0)Load the recognition network
└── net = imageNet(args.network, sys.argv)Optional hard-coded model loading (commented out)
└── net = imageNet(model="model/resnet18.onnx", labels="model/labels.txt", input_blob="input_0", output_blob="output_0")Create video sources & outputs
├── input = videoSource(args.input, argv=sys.argv)
├── output = videoOutput(args.output, argv=sys.argv)
└── font = cudaFont()Process frames until EOS or user exits
└── while True├── Capture the next image│   ├── img = input.Capture()│   └── if img is None│       └── continue├── Classify the image and get the topK predictions│   └── predictions = net.Classify(img, topK=args.topK)├── Draw predicted class labels│   └── for n, (classID, confidence) in enumerate(predictions)│       ├── classLabel = net.GetClassLabel(classID)│       ├── confidence *= 100.0│       ├── print(f"imagenet:  {confidence:05.2f}% class #{classID} ({classLabel})")│       └── font.OverlayText(img, text=f"{confidence:05.2f}% {classLabel}", │                            x=5, y=5 + n * (font.GetSize() + 5),│                            color=font.White, background=font.Gray40)├── Render the image│   └── output.Render(img)├── Update the title bar│   └── output.SetStatus("{:s} | Network {:.0f} FPS".format(net.GetNetworkName(), net.GetNetworkFPS()))├── Print out performance info│   └── net.PrintProfilerTimes()└── Exit on input/output EOS└── if not input.IsStreaming() or not output.IsStreaming()└── break

3.2 C++

#include statements
├── "videoSource.h"
├── "videoOutput.h"
├── "cudaFont.h"
├── "imageNet.h"
└── <signal.h>Global variables
└── bool signal_recieved = false;Function definitions
├── void sig_handler(int signo)
│   └── if (signo == SIGINT)
│       ├── LogVerbose("received SIGINT\n");
│       └── signal_recieved = true;
└── int usage()├── printf("usage: imagenet [--help] [--network=NETWORK] ...\n");├── printf("                input_URI [output_URI]\n\n");├── printf("Classify a video/image stream using an image recognition DNN.\n");├── printf("See below for additional arguments that may not be shown above.\n\n");├── printf("optional arguments:\n");├── printf("  --help            show this help message and exit\n");├── printf("  --network=NETWORK pre-trained model to load (see below for options)\n");├── printf("  --topK=N          show the topK number of class predictions (default: 1)\n");├── printf("positional arguments:\n");├── printf("    input_URI       resource URI of input stream  (see videoSource below)\n");├── printf("    output_URI      resource URI of output stream (see videoOutput below)\n\n");├── printf("%s", imageNet::Usage());├── printf("%s", videoSource::Usage());├── printf("%s", videoOutput::Usage());└── printf("%s", Log::Usage());main function
├── Parse command line
│   ├── commandLine cmdLine(argc, argv);
│   └── if (cmdLine.GetFlag("help"))
│       └── return usage();
├── Attach signal handler
│   └── if (signal(SIGINT, sig_handler) == SIG_ERR)
│       └── LogError("can't catch SIGINT\n");
├── Create input stream
│   ├── videoSource* input = videoSource::Create(cmdLine, ARG_POSITION(0));
│   └── if (!input)
│       ├── LogError("imagenet:  failed to create input stream\n");
│       └── return 1;
├── Create output stream
│   ├── videoOutput* output = videoOutput::Create(cmdLine, ARG_POSITION(1));
│   └── if (!output)
│       ├── LogError("imagenet:  failed to create output stream\n");
│       └── return 1;
├── Create font for image overlay
│   ├── cudaFont* font = cudaFont::Create();
│   └── if (!font)
│       ├── LogError("imagenet:  failed to load font for overlay\n");
│       └── return 1;
├── Create recognition network
│   ├── imageNet* net = imageNet::Create(cmdLine);
│   └── if (!net)
│       ├── LogError("imagenet:  failed to initialize imageNet\n");
│       └── return 1;
│   ├── const int topK = cmdLine.GetInt("topK", 1);  // default top result
├── Processing loop
│   └── while (!signal_recieved)
│       ├── uchar3* image = NULL;
│       ├── int status = 0;
│       ├── if (!input->Capture(&image, &status))
│       │   └── if (status == videoSource::TIMEOUT)
│       │       └── continue;
│       │   └── break; // EOS
│       ├── imageNet::Classifications classifications; // classID, confidence
│       ├── if (net->Classify(image, input->GetWidth(), input->GetHeight(), classifications, topK) < 0)
│       │   └── continue;
│       ├── for (uint32_t n=0; n < classifications.size(); n++)
│       │   ├── const uint32_t classID = classifications[n].first;
│       │   ├── const char* classLabel = net->GetClassLabel(classID);
│       │   ├── const float confidence = classifications[n].second * 100.0f;
│       │   ├── LogVerbose("imagenet:  %2.5f%% class #%i (%s)\n", confidence, classID, classLabel);
│       │   ├── char str[256];
│       │   ├── sprintf(str, "%05.2f%% %s", confidence, classLabel);
│       │   └── font->OverlayText(image, input->GetWidth(), input->GetHeight(),
│       │       str, 5, 5 + n * (font->GetSize() + 5), 
│       │       make_float4(255,255,255,255), make_float4(0,0,0,100));
│       ├── if (output != NULL)
│       │   ├── output->Render(image, input->GetWidth(), input->GetHeight());
│       │   ├── char str[256];
│       │   ├── sprintf(str, "TensorRT %i.%i.%i | %s | Network %.0f FPS", NV_TENSORRT_MAJOR, NV_TENSORRT_MINOR, NV_TENSORRT_PATCH, net->GetNetworkName(), net->GetNetworkFPS());
│       │   └── output->SetStatus(str);
│       │   └── if (!output->IsStreaming())
│       │       └── break;
│       └── net->PrintProfilerTimes();
├── Destroy resources
│   ├── LogVerbose("imagenet:  shutting down...\n");
│   ├── SAFE_DELETE(input);
│   ├── SAFE_DELETE(output);
│   ├── SAFE_DELETE(net);
└── LogVerbose("imagenet:  shutdown complete.\n");return 0;

4. 参考资料

【1】jetson-inference - Classifying Images with ImageNet

5. 补充

5.1 第一次运行模型本地适应初始化

第一次运行神经网络，虽然模型是预训练的，但是本地部署还是有个初始化过程，好像是建立一些cache的过程，具体有待进一步研究。

注：有知道为什么是这样，也请评论区告诉我，谢谢！

• imagenet can’t work as readme says, see attached log #1858
• could not find engine cache … MonoDepth-FCN-Mobilenet/monodepth_fcn_mobilenet.onnx.1.1.8602.GPU.FP16.engine ? #1855
• What’s wrong with imagenet/detectnet, continous printf?

5.2 samba软连接

注：share请替换为samba共享目录，比如：home

• ubuntu22.04 配置

[global]
allow insecure wide links = yes[share]
follow symlinks = yes
wide links = yes

• 之前的版本

[global]
unix extensions = no[share]
follow symlinks = yes
wide links = yes