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  • linux install Openvino

    recommend centos7

    github

    Openvino

    tooltiks

    1. download

    openvino addational installation for ncs2

    ncs2 get start

    browser download https://pan.baidu.com/s/1jN3gP2TDndeguqqGFS78GQ  to ~/obama.mp4

    2. install ui 

    install gnome UI

    Report error:

    Transaction check error:
      file /boot/efi/EFI/centos from install of fwupdate-efi-12-5.el7.centos.x86_64 conflicts with file from package grub2-common-1:2.02-0.65.el7.centos.2.noarch
    

    resolve by  fwupdate-efi conflicts with grub2-common

    centOS7下安装GUI图形界面

    install rdp

    2. Movidius

    视频介绍

    install in a VM

    org doc 

    ncsdk 和 openvino 没有关系。

    doc1: 

    cd /opt/intel/computer_vision_sdk/deployment_tools/documentation
    python3 -m http.server
    

    doc2:

    /opt/intel/computer_vision_sdk/deployment_tools/intel_models
    python3 -m http.server 8001 
    

      

      

    3. build exmaple

    cd /opt/intel/computer_vision_sdk/deployment_tools/inference_engine/samples
    ./build_samples.sh
    echo "PATH=$PATH:$HOME/inference_engine_samples_build/intel64/Release" >> ~/.bashrc
    source ~/.bashrc
    
    # Build completed, you can find binaries for all samples in the /home/user/inference_engine_samples_build/intel64/Release subfolder.
     
    

      

    ls /opt/intel/computer_vision_sdk/deployment_tools/intel_models

      

    4.  Pre-Trained Models (Open Model Zoo)

    echo "MZOOPATH=/opt/intel/computer_vision_sdk/deployment_tools/intel_models" >> ~/.bashrc
    source ~/.bashrc                 
    

      

     5. download new Model

    cd /opt/intel/computer_vision_sdk/deployment_tools/model_downloader
    echo "PATH=$PATH:/opt/intel/computer_vision_sdk/deployment_tools/model_downloader" >> ~/.bashrc
    echo "PATH=$PATH:/opt/intel/computer_vision_sdk/deployment_tools/model_optimizer" >> ~/.bashrc
    source ~/.bashrc
    # python3 downloader.py --name alexnet
    downloader.py --name alexnet
    cd $HOME/classification/alexnet/caffe/
    # python3 mo.py --input_model alexnet.caffemodel
    mo.py --input_model alexnet.caffemodel
    

    6. classification   

    wget  https://www.petmd.com/sites/default/files/what-does-it-mean-when-cat-wags-tail.jpg -O cat.jpg
    
    classification_sample -i cat.jpg -m alexnet.xml -nt 5
    

    7.  Security Barrier Camera Demo

    cd $MZOOPATH
    
    security_barrier_camera_demo -i vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-1.png vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-2.png  -m vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml -m_va vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.xml -m_lpr license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.xml
    
    security_barrier_camera_demo -i  vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-2.png  -m vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml -m_va vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.xml -m_lpr license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.xml
    
    security_barrier_camera_demo -i vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-1.png vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-2.png  -m vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml -m_va vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.xml -m_lpr license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.xml
    
    security_barrier_camera_demo -i vehicle-license-plate-detection-barrier-0106/description/vehicle-license-plate-detection-barrier-0106.jpeg vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-2.png  -m vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml -m_va vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.xml -m_lpr license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.xml
    
    security_barrier_camera_demo -i license-plate-recognition-barrier-0001/description/license-plate-recognition-barrier-0001.png vehicle-attributes-recognition-barrier-0039/description/vehicle-attributes-recognition-barrier-0039-2.png  -m vehicle-license-plate-detection-barrier-0106/FP32/vehicle-license-plate-detection-barrier-0106.xml -m_va vehicle-attributes-recognition-barrier-0039/FP32/vehicle-attributes-recognition-barrier-0039.xml -m_lpr license-plate-recognition-barrier-0001/FP32/license-plate-recognition-barrier-0001.xml
    

    8. Object Detection for Faster R-CNN Demo

    mkdir -p ~/ObjDetection/faster_rcnn/caffe
    cd ~/ObjDetection/faster_rcnn/caffe
    
    wget https://raw.githubusercontent.com/rbgirshick/py-faster-rcnn/master/models/pascal_voc/VGG16/faster_rcnn_end2end/test.prototxt
    
    # curl -k -O -L  https://dl.dropboxusercontent.com/s/o6ii098bu51d139/faster_rcnn_models.tgz?dl=0
    
    mv faster_rcnn_models.tgz* faster_rcnn_models.tgz
    tar -zxvf faster_rcnn_models.tgz
    # cd faster_rcnn_models/
    mo_caffe.py --input_model  faster_rcnn_models/VGG16_faster_rcnn_final.caffemodel --input_proto test.prototxt
    
    object_detection_demo -i $MZOOPATH/person-detection-retail-0002/description/person-detection-retail-0002.png -m VGG16_faster_rcnn_final.xml
    
    cd $MZOOPATH
    object_detection_demo -i $MZOOPATH/person-detection-retail-0002/description/person-detection-retail-0002.png -m person-detection-retail-0002/FP32/person-detection-retail-0002.xml --bbox_name detector/bbox/ave_pred -d CPU
    

    8. Object Detection SSD Demo, Async API Performance Showcase

    object_detection_demo_ssd_async -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/ssd.xml -d GPU

    9. Object Detection with SSD-VGG Sample

    object_detection_sample_ssd -i $MZOOPATH/person-detection-retail-0013/description/person-detection-retail-0013.png -m $MZOOPATH/person-detection-retail-0013/FP32/person-detection-retail-0013.xml
    

    10. TensorFlow* Object Detection Mask R-CNNs Segmentation Demo

    ./mask_rcnn_demo -i <path_to_image>/inputImage.bmp -m <path_to_model>/faster_rcnn.xml

      

    11. Automatic Speech Recognition Sample

     通俗理解生成对抗网络GAN

    mkdir -p ~/kaldi/gna/
    cd ~/kaldi/gna/     
    wget https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/wsj_dnn5b_smbr/wsj_dnn5b.counts
    
    wget https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/wsj_dnn5b_smbr/wsj_dnn5b.nnet
    
    wget https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/wsj_dnn5b_smbr/dev93_scores_10.ark
    
    wget https://download.01.org/openvinotoolkit/2018_R3/models_contrib/GNA/wsj_dnn5b_smbr/dev93_10.ark
    
    mo.py --framework kaldi --input_model wsj*.nnet  --counts wsj*.counts --remove_output_softmax
    
    speech_sample -d GNA_AUTO -bs 2 -i dev93_10.ark -m wsj_dnn5b.xml -o scores.ark -r dev93_scores_10.ark
    

      

    12. Use of Sample in Kaldi* Speech Recognition Pipeline

    普及 Kaldi

    Kaldi(A1)语音识别原理

    kaldi上第一个免费的中文语音识别例子

    ...

    13. Neural Style Transfer Sample

    $ locate  cat.jpg
    /home/user/ncappzoo/data/images/cat.jpg
    /home/user/ncsdk/examples/data/images/cat.jpg
    /opt/movidius/ssd-caffe/examples/images/cat.jpg
    

    ./style_transfer_sample -i <path_to_image>/cat.bmp -m <path_to_model>/1_decoder_FP32.xml

    14. Hello Infer Request Classification Sample

    cd $HOME/classification/alexnet/caffe/
    hello_request_classification alexnet.xml /home/user/ncsdk/examples/data/images/cat.jpg CPU
    

      

    15. Interactive Face Detection Demo

    16. Image Segmentation Demo

    17. Crossroad Camera Demo 

    cd $MZOOPATH
    crossroad_camera_demo -i vdieo.mp4 -m person-vehicle-bike-detection-crossroad-0078/FP32/person-vehicle-bike-detection-crossroad-0078.xml -m_pa person-attributes-recognition-crossroad-0200/FP32/person-attributes-recognition-crossroad-0200.xml -m_reid person-reidentification-retail-0079/FP32/person-reidentification-retail-0079.xml 
    

      

    18. Multi-Channel Face Detection Demo 

    multi-channel-demo -m $MZOOPATH/face-detection-retail-0004/FP32/face-detection-retail-0004.xml 
    -l $HOME/inference_engine_samples_build/intel64/Release/lib/libcpu_extension.so 
    -nc 1 -duplicate_num 3
      
    

     

    19. Hello Autoresize Classification Sample

    cd $HOME/classification/alexnet/caffe/
    hello_autoresize_classification alexnet.xml /home/user/ncsdk/examples/data/images/cat.jpg CPU
    

      

    20. Hello Shape Infer Sample

     ./hello_shape_infer_ssd <path_to_model>/ssd_300.xml <path_to_image>/500x500.bmp CPU 3

    21. Human Pose Estimation Demo 

    human_pose_estimation_demo -i ~/obama.mp4 -m $MZOOPATH/human-pose-estimation-0001/FP32/human-pose-estimation-0001.xml -d CPU
    

    22. Object Detection YOLO* V3 Demo, Async API Performance Showcase

     object_detection_demo_yolov3_async -i <path_to_video>/inputVideo.mp4 -m <path_to_model>/yolo_v3.xml -d GPU

    23. Pedestrian Tracker Demo

    pedestrian_tracker_demo -i ~/obama.mp4 -m_det $MZOOPATH/person-detection-retail-0013/FP32/person-detection-retail-0013.xml -m_reid $MZOOPATH/person-reidentification-retail-0031/FP32/person-reidentification-retail-0031.xml
    

      

    24. Smart Classroom Demo

    ./smart_classroom_demo -m_act <path to the person/action detection retail model .xml file> -m_fd <path to the face detection retail model .xml file> -m_reid <path to the face reidentification retail model .xml file> -m_lm <path to the landmarks regression retail model .xml file> -fg <path to faces_gallery.json> -i <path to the input video>

    25. Super Resolution Demo

    ./super_resolution_demo -i <path_to_image>/image.bmp -m <path_to_model>/model.xml

    26. Using the Validation Application to Check Accuracy on a Dataset

    cd ~
    git clone -b ssd https://github.com/weiliu89/caffe.git
    cd caffe
    git branch
    cd ..
    
    
    wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar   
    tar -xvf VOCtest_06-Nov-2007.tar
    

      

    sed -i -e "s/^(INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include)/1 /usr/incl
    ude/hdf5/serial//" Makefile.config
    
    sed -i -e "s/hdf5_hl hdf5/hdf5_serial_hl hdf5_serial/" Makefile
    

      

    Hardware-accelerated Function-as-a-Service Using AWS Greengrass* (Beta)

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  • 原文地址:https://www.cnblogs.com/shaohef/p/10189625.html
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