-
Ubuntu 18.04.5 LTS (Bionic Beaver)
-
Intel Distribution of OpenVINO Toolkit 2020.3.1 LTS (latest)
cd ~/Downloads/
tar -xvzf l_openvino_toolkit_p_2020.3.341.tgz
cd l_openvino_toolkit_p_2020.3.341/
sudo ./install_GUI.sh
cd /opt/intel/openvino/install_dependencies
sudo -E ./install_openvino_dependencies.sh
source /opt/intel/openvino/bin/setupvars.sh
cd /opt/intel/openvino/deployment_tools/model_optimizer/install_prerequisites
sudo ./install_prerequisites.sh
cd /opt/intel/openvino/deployment_tools/demo
./demo_squeezenet_download_convert_run.sh
Run Inference Engine classification sample
Run ./classification_sample_async -d CPU -i /opt/intel/openvino/deployment_tools/demo/car.png -m /home/sergio/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
CPU
MKLDNNPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6853042 sports car, sport car
479 0.1835186 car wheel
511 0.0917199 convertible
436 0.0200693 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0069604 racer, race car, racing car
656 0.0044177 minivan
717 0.0024739 pickup, pickup truck
581 0.0017788 grille, radiator grille
468 0.0013083 cab, hack, taxi, taxicab
661 0.0007443 Model T
[ INFO ] Execution successful
cd /opt/intel/openvino/deployment_tools/demo
./demo_security_barrier_camera.sh
Run Inference Engine security_barrier_camera demo
Run ./security_barrier_camera_demo -d CPU -d_va CPU -d_lpr CPU -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m /home/sergio/openvino_models/ir/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_lpr /home/sergio/openvino_models/ir/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -m_va /home/sergio/openvino_models/ir/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
[ INFO ] InferenceEngine: 0x7fc2e79e3040
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car_1.bmp
[ INFO ] Loading device CPU
CPU
MKLDNNPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading detection model to the CPU plugin
[ INFO ] Loading Vehicle Attribs model to the CPU plugin
[ INFO ] Loading Licence Plate Recognition (LPR) model to the CPU plugin
[ INFO ] Number of InferRequests: 1 (detection), 3 (classification), 3 (recognition)
[ INFO ] 4 streams for CPU
[ INFO ] Display resolution: 1920x1080
[ INFO ] Number of allocated frames: 3
[ INFO ] Resizable input with support of ROI crop and auto resize is disabled
0.1FPS for (1 / 1) frames
Detection InferRequests usage: 100.0%
[ INFO ] Execution successful
source /opt/intel/openvino/bin/setupvars.sh
cd ~/inference_engine_samples_build/intel64/Release
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d CPU
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d CPU
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
CPU
MKLDNNPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6853042 sports car, sport car
479 0.1835186 car wheel
511 0.0917199 convertible
436 0.0200693 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0069604 racer, race car, racing car
656 0.0044177 minivan
717 0.0024739 pickup, pickup truck
581 0.0017788 grille, radiator grille
468 0.0013083 cab, hack, taxi, taxicab
661 0.0007443 Model T
[ INFO ] Execution successful
cd /opt/intel/openvino/install_dependencies/
sudo -E su
./install_NEO_OCL_driver.sh
exit
source /opt/intel/openvino/bin/setupvars.sh
cd ~/inference_engine_samples_build/intel64/Release
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d GPU
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d GPU
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
GPU
clDNNPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6679688 sports car, sport car
479 0.1914062 car wheel
511 0.1024170 convertible
436 0.0192413 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0068817 racer, race car, racing car
656 0.0045090 minivan
717 0.0026093 pickup, pickup truck
581 0.0017672 grille, radiator grille
468 0.0013123 cab, hack, taxi, taxicab
661 0.0007715 Model T
[ INFO ] Execution successful
source /opt/intel/openvino/bin/setupvars.sh
cd /opt/intel/openvino/install_dependencies
./install_NCS_udev_rules.sh
sudo usermod -a -G users "$(whoami)"
*Log out and log in.
sudo cp /opt/intel/openvino/inference_engine/external/97-myriad-usbboot.rules /etc/udev/rules.d/
sudo udevadm control --reload-rules
sudo udevadm trigger
sudo ldconfig
*Reboot the machine.
cd /opt/intel/openvino/deployment_tools/demo
./demo_squeezenet_download_convert_run.sh -d MYRIAD
Run Inference Engine classification sample
Run ./classification_sample_async -d MYRIAD -i /opt/intel/openvino/deployment_tools/demo/car.png -m /home/sergio/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
MYRIAD
myriadPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6708984 sports car, sport car
479 0.1922607 car wheel
511 0.0936890 convertible
436 0.0216064 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0075760 racer, race car, racing car
656 0.0049667 minivan
717 0.0027428 pickup, pickup truck
581 0.0019779 grille, radiator grille
468 0.0014219 cab, hack, taxi, taxicab
661 0.0008636 Model T
[ INFO ] Execution successful
cd /opt/intel/openvino/deployment_tools/demo
./demo_security_barrier_camera.sh -d MYRIAD
Run Inference Engine security_barrier_camera demo
Run ./security_barrier_camera_demo -d MYRIAD -d_va MYRIAD -d_lpr MYRIAD -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m /home/sergio/openvino_models/ir/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_lpr /home/sergio/openvino_models/ir/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -m_va /home/sergio/openvino_models/ir/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
[ INFO ] InferenceEngine: 0x7fe6c7304040
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car_1.bmp
[ INFO ] Loading device MYRIAD
MYRIAD
myriadPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading detection model to the MYRIAD plugin
[ INFO ] Loading Vehicle Attribs model to the MYRIAD plugin
[ INFO ] Loading Licence Plate Recognition (LPR) model to the MYRIAD plugin
[ INFO ] Number of InferRequests: 1 (detection), 3 (classification), 3 (recognition)
[ INFO ] Display resolution: 1920x1080
[ INFO ] Number of allocated frames: 3
[ INFO ] Resizable input with support of ROI crop and auto resize is disabled
0.3FPS for (1 / 1) frames
Detection InferRequests usage: 100.0%
[ INFO ] Execution successful
source /opt/intel/openvino/bin/setupvars.sh
cd ~/inference_engine_samples_build/intel64/Release
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d MYRIAD
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d MYRIAD
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
MYRIAD
myriadPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6708984 sports car, sport car
479 0.1922607 car wheel
511 0.0936890 convertible
436 0.0216064 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0075760 racer, race car, racing car
656 0.0049667 minivan
717 0.0027428 pickup, pickup truck
581 0.0019779 grille, radiator grille
468 0.0014219 cab, hack, taxi, taxicab
661 0.0008636 Model T
[ INFO ] Execution successful
source /opt/intel/openvino/bin/setupvars.sh
${HDDL_INSTALL_DIR}/install_IVAD_VPU_dependencies.sh
========================================
Install HDDL depencdencies sucessful
Please reboot
cd ${HDDL_INSTALL_DIR}/drivers
sudo ./setup.sh install
source /opt/intel/openvino/bin/setupvars.sh
cd /opt/intel/openvino/deployment_tools/demo
./demo_squeezenet_download_convert_run.sh -d HDDL
Run Inference Engine classification sample
Run ./classification_sample_async -d HDDL -i /opt/intel/openvino/deployment_tools/demo/car.png -m /home/sergio/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
HDDL
HDDLPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[19:07:42.8180][2716]I[main.cpp:243] ## HDDL_INSTALL_DIR: /opt/intel/openvino_2020.3.341/deployment_tools/inference_engine/external/hddl
[19:07:42.8184][2716]I[main.cpp:245] Config file '/opt/intel/openvino_2020.3.341/deployment_tools/inference_engine/external/hddl/config/hddl_service.config' has been loaded
[19:07:42.8201][2716]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_service_alive.mutex owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8203][2716]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_service_ready.mutex owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8205][2716]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_start_exit.mutex owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8262][2716]I[AutobootStarter.cpp:156] Info: No running autoboot process. Start autoboot daemon...
[19:07:42.8629][2718]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_autoboot_alive.mutex owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8632][2718]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_autoboot_ready.mutex owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8633][2718]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_autoboot_start_exit.mutex owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8635][2718]I[FileHelper.cpp:272] Set file:/tmp/hddl_autoboot_device.map owner: user-'no_change', group-'users', mode-'0660'
[19:07:42.8643][2718]I[AutoBoot.cpp:308] [Firmware Config] deviceName=default deviceNum=0 firmwarePath=/opt/intel/openvino_2020.3.341/deployment_tools/inference_engine/external/hddl/lib/mvnc/usb-ma2x8x.mvcmd
[19:07:44.1740][2727]I[AutoBoot.cpp:197] Start boot device 1.8-ma2480
[19:07:44.4334][2727]I[AutoBoot.cpp:199] Device 1.8-ma2480 boot success, firmware=/opt/intel/openvino_2020.3.341/deployment_tools/inference_engine/external/hddl/lib/mvnc/usb-ma2x8x.mvcmd
[19:07:44.4335][2727]I[AutoBoot.cpp:197] Start boot device 1.4-ma2480
[19:07:44.6188][2727]I[AutoBoot.cpp:199] Device 1.4-ma2480 boot success, firmware=/opt/intel/openvino_2020.3.341/deployment_tools/inference_engine/external/hddl/lib/mvnc/usb-ma2x8x.mvcmd
[19:08:04.6232][2716]I[AutobootStarter.cpp:85] Info: Autoboot is running.
[19:08:04.6586][2716]W[ConfigParser.cpp:269] Warning: Cannot find key, path=scheduler_config.max_graph_per_device subclass=0, use default value: 1.
[19:08:04.6588][2716]W[ConfigParser.cpp:292] Warning: Cannot find key, path=scheduler_config.use_sgad_by_default subclass=0, use default value: false.
[19:08:04.6590][2716]I[DeviceSchedulerFactory.cpp:56] Info: ## DeviceSchedulerFacotry ## Created Squeeze Device-Scheduler2.
[19:08:04.6620][2716]I[DeviceManager.cpp:551] ## SqueezeScheduler created ##
[19:08:04.6620][2716]I[DeviceManager.cpp:649] times 0: try to create worker on device(2.6)
[19:08:06.6700][2716]I[DeviceManager.cpp:670] [SUCCESS] times 0: create worker on device(2.6)
[19:08:06.6702][2716]I[DeviceManager.cpp:719] worker(Wt2.6) created on device(2.6), type(0)
[19:08:06.6703][2716]I[DeviceManager.cpp:649] times 0: try to create worker on device(2.2)
[19:08:08.6752][2716]I[DeviceManager.cpp:670] [SUCCESS] times 0: create worker on device(2.2)
[19:08:08.6753][2716]I[DeviceManager.cpp:719] worker(Wt2.2) created on device(2.2), type(0)
[19:08:08.6754][2716]I[DeviceManager.cpp:145] DEVICE FOUND : 2
[19:08:08.6754][2716]I[DeviceManager.cpp:146] DEVICE OPENED : 2
[19:08:08.6757][2716]I[DeviceManagerCreator.cpp:81] New device manager(DeviceManager0) created with subclass(0), deviceCount(2)
[19:08:09.0120][2716]I[TaskSchedulerFactory.cpp:45] Info: ## TaskSchedulerFactory ## Created Polling Task-Scheduler.
[19:08:09.0676][2716]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_snapshot.sock owner: user-'no_change', group-'users', mode-'0660'
[19:08:09.0685][2716]I[FileHelper.cpp:272] Set file:/var/tmp/hddl_service.sock owner: user-'no_change', group-'users', mode-'0660'
[19:08:09.0687][2716]I[MessageDispatcher.cpp:87] Message Dispatcher initialization finished
[19:08:09.0689][2716]I[main.cpp:103] SERVICE IS READY ...
[19:08:09.0882][2766]I[ClientManager.cpp:159] client(id:1) registered: clientName=HDDLPlugin socket=2
[19:08:09.4250][2767]I[GraphManager.cpp:491] Load graph success, graphId=1 graphName=squeezenet1.1
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6708984 sports car, sport car
479 0.1922607 car wheel
511 0.0936890 convertible
436 0.0216064 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0075760 racer, race car, racing car
656 0.0049667 minivan
717 0.0027428 pickup, pickup truck
581 0.0019779 grille, radiator grille
468 0.0014219 cab, hack, taxi, taxicab
661 0.0008636 Model T
[19:08:09.5416][2766]I[ClientManager.cpp:189] client(id:1) unregistered: clientName=HDDLPlugin socket=2
[19:08:09.5501][2767]I[GraphManager.cpp:539] graph(1) destroyed
[ INFO ] Execution successful
source /opt/intel/openvino/bin/setupvars.sh
cd /opt/intel/openvino/deployment_tools/demo
./demo_security_barrier_camera.sh -d HDDL
Run Inference Engine security_barrier_camera demo
Run ./security_barrier_camera_demo -d HDDL -d_va HDDL -d_lpr HDDL -i /opt/intel/openvino/deployment_tools/demo/car_1.bmp -m /home/sergio/openvino_models/ir/intel/vehicle-license-plate-detection-barrier-0106/FP16/vehicle-license-plate-detection-barrier-0106.xml -m_lpr /home/sergio/openvino_models/ir/intel/license-plate-recognition-barrier-0001/FP16/license-plate-recognition-barrier-0001.xml -m_va /home/sergio/openvino_models/ir/intel/vehicle-attributes-recognition-barrier-0039/FP16/vehicle-attributes-recognition-barrier-0039.xml
[ INFO ] InferenceEngine: 0x7fe2896b9040
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car_1.bmp
[ INFO ] Loading device HDDL
HDDL
HDDLPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading detection model to the HDDL plugin
[19:10:08.9352][2766]I[ClientManager.cpp:159] client(id:2) registered: clientName=HDDLPlugin socket=2
[19:10:10.3769][2767]I[GraphManager.cpp:491] Load graph success, graphId=2 graphName=vehicle-license-plate-detection-barrier-0106
[ INFO ] Loading Vehicle Attribs model to the HDDL plugin
[19:10:10.4047][2766]I[ClientManager.cpp:159] client(id:3) registered: clientName=HDDLPlugin socket=3
[19:10:10.5231][2767]I[GraphManager.cpp:491] Load graph success, graphId=3 graphName=vehicle-attributes-recognition-barrier-0039
[ INFO ] Loading Licence Plate Recognition (LPR) model to the HDDL plugin
[19:10:10.5555][2766]I[ClientManager.cpp:159] client(id:4) registered: clientName=HDDLPlugin socket=4
[19:10:10.7742][2767]I[GraphManager.cpp:491] Load graph success, graphId=4 graphName=LPRNet
[ INFO ] Number of InferRequests: 1 (detection), 3 (classification), 3 (recognition)
[ INFO ] Display resolution: 1920x1080
[ INFO ] Number of allocated frames: 3
[ INFO ] Resizable input with support of ROI crop and auto resize is disabled
0.1FPS for (3 / 1) frames
Detection InferRequests usage: 0.0%
[19:10:58.6994][2766]I[ClientManager.cpp:189] client(id:4) unregistered: clientName=HDDLPlugin socket=4
[19:10:58.7059][2767]I[GraphManager.cpp:539] graph(4) destroyed
[19:10:58.8015][2766]I[ClientManager.cpp:189] client(id:3) unregistered: clientName=HDDLPlugin socket=3
[19:10:58.8075][2767]I[GraphManager.cpp:539] graph(3) destroyed
[19:10:58.9046][2766]I[ClientManager.cpp:189] client(id:2) unregistered: clientName=HDDLPlugin socket=2
[19:10:58.9201][2767]I[GraphManager.cpp:539] graph(2) destroyed
[ INFO ] Execution successful
source /opt/intel/openvino/bin/setupvars.sh
cd ~/inference_engine_samples_build/intel64/Release
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d HDDL
./classification_sample_async -i /opt/intel/openvino/deployment_tools/demo/car.png -m ~/openvino_models/ir/public/squeezenet1.1/FP16/squeezenet1.1.xml -d HDDL
[ INFO ] InferenceEngine:
API version ............ 2.1
Build .................. 2020.3.1-3500-68236d2e44c-releases/2020/3
Description ....... API
[ INFO ] Parsing input parameters
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ] /opt/intel/openvino/deployment_tools/demo/car.png
[ INFO ] Creating Inference Engine
HDDL
HDDLPlugin version ......... 2.1
Build ........... 2020.3.1-3500-68236d2e44c-releases/2020/3
[ INFO ] Loading network files
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (787, 259) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the device
[19:12:00.9253][2766]I[ClientManager.cpp:159] client(id:5) registered: clientName=HDDLPlugin socket=2
[19:12:01.1912][2767]I[GraphManager.cpp:491] Load graph success, graphId=5 graphName=squeezenet1.1
[ INFO ] Create infer request
[ INFO ] Start inference (10 asynchronous executions)
[ INFO ] Completed 1 async request execution
[ INFO ] Completed 2 async request execution
[ INFO ] Completed 3 async request execution
[ INFO ] Completed 4 async request execution
[ INFO ] Completed 5 async request execution
[ INFO ] Completed 6 async request execution
[ INFO ] Completed 7 async request execution
[ INFO ] Completed 8 async request execution
[ INFO ] Completed 9 async request execution
[ INFO ] Completed 10 async request execution
[ INFO ] Processing output blobs
Top 10 results:
Image /opt/intel/openvino/deployment_tools/demo/car.png
classid probability label
------- ----------- -----
817 0.6708984 sports car, sport car
479 0.1922607 car wheel
511 0.0936890 convertible
436 0.0216064 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0075760 racer, race car, racing car
656 0.0049667 minivan
717 0.0027428 pickup, pickup truck
581 0.0019779 grille, radiator grille
468 0.0014219 cab, hack, taxi, taxicab
661 0.0008636 Model T
[19:12:01.3033][2766]I[ClientManager.cpp:189] client(id:5) unregistered: clientName=HDDLPlugin socket=2
[19:12:01.3114][2767]I[GraphManager.cpp:539] graph(5) destroyed
[ INFO ] Execution successful
-
Windows 10 Pro
-
Microsoft Visual Studio Community 2019
-
Microsoft Visual C++ 2015-2019 Redistributable x64
-
CMake 3.19.4
-
Python 3.6.5 x64
py -m pip install --upgrade pip
pip install pillow
pip install numpy
pip install opencv-contrib-python
py
import cv2
cv2.__version__
'4.5.1'
exit()
- Intel Distribution of OpenVINO Toolkit 2020.3.1 LTS
cd C:\Program Files (x86)\IntelSWTools\openvino\bin\
setupvars.bat
py
import cv2
cv2.__version__
'4.3.0-openvino-2020.3.0'
exit()
cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\install_prerequisites
install_prerequisites.bat
cd C:\"Program Files (x86)"\IntelSWTools\openvino\deployment_tools\demo
demo_squeezenet_download_convert_run.bat –d MYRIAD
[ INFO ] Loading network files
Top 10 results:
Image C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\demo\car.png
classid probability label
------- ----------- -----
817 0.6708984 sports car, sport car
479 0.1922607 car wheel
511 0.0936890 convertible
436 0.0216064 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
751 0.0075760 racer, race car, racing car
656 0.0049667 minivan
717 0.0027428 pickup, pickup truck
581 0.0019779 grille, radiator grille
468 0.0014219 cab, hack, taxi, taxicab
661 0.0008636 Model T
[ INFO ] Execution successful
cd C:\"Program Files (x86)"\IntelSWTools\openvino\deployment_tools\demo
demo_security_barrier_camera.bat -d MYRIAD
[ INFO ] Loading detection model to the MYRIAD plugin
[ INFO ] Loading Vehicle Attribs model to the MYRIAD plugin
[ INFO ] Loading Licence Plate Recognition (LPR) model to the MYRIAD plugin
[ INFO ] Number of InferRequests: 1 (detection), 3 (classification), 3 (recognition)
[ INFO ] Display resolution: 1920x1080
[ INFO ] Number of allocated frames: 3
[ INFO ] Resizable input with support of ROI crop and auto resize is disabled
0.1FPS for (1 / 1) frames
Detection InferRequests usage: 100.0%
[ INFO ] Execution successful
- Pillow
sudo apt-get install python3-pil
- TkInter
sudo apt-get install python3-tk
- ImageTk
sudo apt-get install python3-pil.imagetk
- Visual Studio Code
cd ~/Downloads/
sudo apt install ./code_1.54.3-1615806378_amd64.deb
- Git
sudo apt-get install git
cd ~/MVPy_MachineVisionPoka-yoke
MVPy.sh
source /opt/intel/openvino/bin/setupvars.sh
cd ~/MVPy_MachineVisionPoka-yoke
python3 MVPy.py
-
Visual Studio Code
-
Git 2.30.1
-
https://github.com/SergioVelmay/MVPy_MachineVisionPoka-yoke.git
-
C:\Users\sergi\source\repos\
cd C:\Users\sergi\source\repos\MVPy_MachineVisionPoka-yoke
MVPy.bat
cd C:\Program Files (x86)\IntelSWTools\openvino\bin\
setupvars.bat
cd C:\Users\sergi\source\repos\MVPy_MachineVisionPoka-yoke
py MVPy.py
py MVPy.py -h
usage: MVPy | Machine Vision Poka-yoke
[-h] [-d {CPU,GPU,HDDL,MYRIAD}] [-t {False,True,No,Yes,0,1}]
Edge computing application for manual assembly cells.
optional arguments:
-h, --help show this help message and exit
-d, --device device name for OpenVINO inference
-t, --training store image captures for training
example: $ py MVPy.py -d MYRIAD -t False
- Videos .mp4 without audio track
Warning: libmmd.dll couldn't be found in %PATH%.
Please check if the redistributable package for Intel(R) C++ Compiler is installed and the library path is added to the PATH environment variable.
System reboot can be required to update the system environment.
C:\Program Files (x86)\Common Files\Intel\Shared Libraries\redist\intel64_win\compiler\
libmmd.dll
Edit the system environment variables / Environment Variables... / Path / Edit... / New / "C:\..." / Ok
https://software.intel.com/content/www/us/en/develop/articles/redistributable-libraries-for-intel-c-and-fortran-2020-compilers-for-windows.html
- Prerequisites
cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer\install_prerequisites
install_prerequisites_tf.bat
- TensorFlow
pip install tensorflow==1.2
- Object Detection
cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer
py mo_tf.py --log_level=DEBUG --output_dir C:\Users\sergi\Desktop\MVPy\Models\Part4Detection\IR -b 1 --input_model C:\Users\sergi\Desktop\MVPy\Models\Part4Detection\GeneralCompact.TensorFlow\model.pb
[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: C:\Users\sergi\Desktop\MVPy\Models\Part4Detection\IR\model.xml
[ SUCCESS ] BIN file: C:\Users\sergi\Desktop\MVPy\Models\Part4Detection\IR\model.bin
[ SUCCESS ] Total execution time: 24.51 seconds.
- Image Classification
cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\model_optimizer
py mo_tf.py --log_level=DEBUG --output_dir C:\Users\sergi\Desktop\MVPy\Models\ORingClassification\IR -b 1 --input_model C:\Users\sergi\Desktop\MVPy\Models\ORingClassification\GeneralCompact.TensorFlow\model.pb
[ SUCCESS ] Generated IR version 10 model.
[ SUCCESS ] XML file: C:\Users\sergi\Desktop\MVPy\Models\ORingClassification\IR\model.xml
[ SUCCESS ] BIN file: C:\Users\sergi\Desktop\MVPy\Models\ORingClassification\IR\model.bin
[ SUCCESS ] Total execution time: 54.47 seconds.
sudo modprobe i2c_i801
sudo modprobe gpio-pca953x
echo "pca9535 0x20" > /sys/bus/i2c/devices/i2c-13/new_device
echo "sx1509q 0x3e" > /sys/bus/i2c/devices/i2c-1/new_device
sudo add-apt-repository ppa:mraa/mraa
sudo apt-get update
sudo apt-get install libmraa2 libmraa-dev libmraa-java python-mraa python3-mraa node-mraa mraa-tools
sudo apt-get install git build-essential swig3.0 cmake
sudo apt-get install python-dev python3-dev nodejs-dev libjson-c-dev
git clone https://github.com/eclipse/mraa.git
cd mraa
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX:PATH=/usr -DCMAKE_BUILD_TYPE=DEBUG
make
sudo make install
sudo ln -s /usr/lib/python2.7/site-packages/* /usr/lib/python2.7/dist-packages
sudo ln -s /usr/lib/python3.6/site-packages/* /usr/lib/python3.6/dist-packages
- Add /usr/local/lib to the default Ubuntu path:
LD_LIBRARY_PATH=/lib:/usr/lib:/usr/local/lib
sudo ldconfig
mraa-gpio version
Version v2.2.0-1-gbb1c6df on LEC-AL AI
mraa-gpio list
01 3v3:
02 5v:
03 I2C0_DAT: I2C
04 5v:
05 I2C0_CK: I2C
06 GND:
07 GPIO04: GPIO
08 UART_TXD: UART
09 GND:
10 UART_RXD: UART
11 GPIO05: GPIO
12 GPIO06: GPIO
13 GPIO07: GPIO
14 GND:
15 GPIO08: GPIO
16 GPIO09: GPIO
17 3v3:
18 GPIO10: GPIO
19 SPI_0_MOSI: SPI
20 GND:
21 SPI_0_MISO: SPI
22 GPIO11: GPIO
23 SPI_0_SCLK: SPI
24 SPI_0_CE0: SPI
25 GND:
26 SPI_0_CE1: SPI
27 I2C1_DAT: I2C
28 I2C1_CK: I2C
29 GPIO1_0: GPIO PWM
30 GND:
31 GPIO1_1: GPIO PWM
32 GPIO1_2: GPIO PWM
33 GPIO1_3: GPIO PWM
34 GND:
35 GPIO1_4: GPIO PWM
36 GPIO1_5: GPIO PWM
37 GPIO1_6: GPIO PWM
38 GPIO1_7: GPIO PWM
39 GND:
40 GPIO2_8: GPIO PWM