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Computer Pointer Controller

This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.

Model data pipeline

Project Set Up and Installation

  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU

  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE

  • Create a virtual environment to work in and activate it: python3 -m venv cpcenv

  • cd to environment path and active it cpcenv\Scripts\activate.bat

  • Install necessary libraries pip install -r requirements.txt

  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be): C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat

  • Download models

     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel
    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>

Demo

Project directory structure

Directory structure

Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation

TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.

App arguments

Benchmarks

TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.

INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01

Results

  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.

Stand Out Suggestions

This is where you can provide information about the stand out suggestions that you have attempted.

Edge Cases

When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.

About

This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

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GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
Skip to content
BranchesTags
Repository files navigation
Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
Skip to content
BranchesTags
Repository files navigation
Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
Skip to content
BranchesTags
Repository files navigation
Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
Skip to content
BranchesTags
Repository files navigation
Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
Skip to content
BranchesTags
Repository files navigation
Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
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Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - DFRICHARD/ComputerPointerController: This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes. · GitHub
Skip to content
BranchesTags
Repository files navigation
Computer Pointer Controller
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Model data pipeline
Project Set Up and Installation
  • Device Specifications
Details
Operation SystemWindows 10 Pro
ProcessorIntel(R) Core(TM) i5-5200U CPU @ 2.20 GHz
OpenVINO™ Toolkit versionv2020.1
DeviceCPU
  • Install Intel® Distribution of OpenVINO™ toolkit. Guide to install on your platform HERE
  • Create a virtual environment to work in and activate it:
python3 -m venv cpcenv
  • cd to environment path and active it
cpcenv\Scripts\activate.bat
  • Install necessary libraries
pip install -r requirements.txt
  • set the openvino environment variable. (Since i'm using openvino 2020.1, On my Windows PC it will be):
C:\Program Files (x86)\IntelSWTools\openvino_2020.1.033\bin\setupvars.bat
  • Download models
     cd C:\Program Files (x86)\IntelSWTools\openvino\deployment_tools\tools\model_downloader\intel

    1. Face Detection Modelpython downloader.py --name face-detection-adas-binary-0001 -o <directory to save the model>
    2. Facial Landmarks Modelpython downloader.py --name landmarks-regression-retail-0009 -o <directory to save the model>
    3. Head Pose Estimation Modelpython downloader.py --name head-pose-estimation-adas-0001 -o <directory to save the model>
    4. Gaze Estimation Model python downloader.py --name gaze-estimation-adas-0002 -o <directory to save the model>
Demo
Project directory structure
Directory structure
Sample code to run the project
python3 app.py -fd <project directory>/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml -hpe <project directory>/intel/head-pose-estimation-adas-0001/FP32/head-pose-estimation-adas-0001.xml -fld <project directory>/intel/landmarks-regression-retail-0009/FP32/landmarks-regression-retail-0009.xml -ge <project directory>/intel/gaze-estimation-adas-0002/FP32/gaze-estimation-adas-0002.xml -i <project directory>/bin/demo.mp4

Documentation
TODO: Include any documentation that users might need to better understand your project code. For instance, this is a good place to explain the command line arguments that your project supports.
App arguments
Benchmarks
TODO: Include the benchmark results of running your model on multiple hardwares and multiple model precisions. Your benchmarks can include: model loading time, input/output processing time, model inference time etc.
INT8FP16FP32
Total Model loading time (s)3.021.391.29
Inference time (ms)40.1542.1350.01
Results
  • The lower the precision, the faster the inferenece. Though there is no much difference in inference time for the various precisions, with FP16 precision inference time is higher than with INT8, and FP32 higher than FP16.
  • The lower the precision, the more model loading time. At INT8 precision models took more time to load compared to FP16 and FP32 models.
Stand Out Suggestions
This is where you can provide information about the stand out suggestions that you have attempted.
Edge Cases
When running the app using camera input I made sure I was under good lighting conditions in order to avoid input errors.
About
This is a computer vision application making use of a blend of several computer vision models. This app enables controlling a mouse pointer through the movement of the head and eyes.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages