README.md

Computer Pointer Controller

This project controls a computer's pointer using the gaze of the eyes. To accomplish this, four pre-trained models from the Intel Pre-trained Models Zoo have to be used.

• Face Detection
• Head Pose Estimation
• Facial Landmarks Detection
• Gaze Estimation Model

Project Set Up and Installation

Setting up the virtual environment

• Open a terminal.

  CTRL+ALT+T

• Setup the pip package manager.

  pip -h

  Check if the python version has pip installed if not run the following command or follow this documentation to install pip.

  python -m pip install -U pip

• Install the virtualenv package.

  pip install virtualenv

• Create the virtual environment.

  vitualenv venv

• Activate the virtual environment.

  source venv/bin/activate

• Deactivate the virtual environment.

  deactivate

The installation instructions for the OpenVINO Toolkit, can be found here

This project utilizes the InferenceEngine API from Intel's OpenVino ToolKit to build the project. The gaze estimation model requires three inputs:

• Image of the right eye
• Image of the left eye
• The head pose

Start by cloning this GitHub Repo. This repository contains all the pre-trained models required to run the project on your machine locally. In addition to that, it contains the required packages that you will have to install to run this project without encountering errors.

Luckily, for this project all the required packages have been set up in the virtual environment. There are four main directories for this project

• bin ~ This contains the video input video to test the app and also has a directory containing images illustrating how the project works

• models ~ This contains all the pre-trained models required to run the project. They have been downloaded from the Intel Pre-trained Models Zoo

  • Face Detection
  • Head Pose Estimation
  • Facial Landmarks Detection
  • Gaze Estimation Model

  Save all these models in the models/intel/ directory. For example, the directory for the face detection .xml file should be models/intel/face-detection-adas-binary-0001/FP32-INT1/face-detection-adas-binary-0001.xml

• src ~ Contains all the python files required to control the flow of data from either the video file, webcam feed or an image through the four models and obtain results that will be used to control the computer pointer. There are 7 python files all performing different tasks. Running the app, loading the four different models, loading the input file and the last one controls the computer pointer.

• venv ~ The virtual environment folder that contains all additional packages required while running the project

• There is a Readme and a requirements file

  To install the required packages listen in the requirements file use

  pip install PACKAGE_NAME
  

Demo

These are the possible ways to run this project.

• Running the main python file using a command line argument. Before that, assuming that you have already installed the OpenVINO Toolkit, source the OpenVINO Environment using

source /opt/intel/openvino/bin/setupvars.sh -pyver 3.6

Afterwards, run the main python file using either of the following command lines

• To run the project using the webcam as the input type, use the following the command line. The command line argument specifying the input type is required to tun the main python file

  python src/main.py --input_type cam
  

  While using the video or an image as the input type, a command line argument specifying the directory path of the input video or input image has to be specified by using one of the following command lines. For an image file specify the command line argument --input_type as video and add the path to the video in the --input_type command line argument

  python src/main.py --input_type video --input_file path_to_video_file
  

  For an image file specify the command line argument --input_type as image and add the path to the image in the --input_type command line argument

  python src/main.py --input_type image --input_file path_to_image_file
  

  There are optional CLI arguments that can be implemented to change the default set parameters such as

  --show_results can be given the parameter yes or no to show or hide layers benchmark results respectively while running the project. The default parameter is no, therefore, layers benchmark results will not show when the command line argument is not added and parameter specified as yes

  --device can be given the name of the target device to perform inference such as CPU, GPU, VPU or MYRIAD. The default parameter is set to run inference on CPU.

  --model_path This is a required command line argument when running the project. A directory path containing all the four models should be added as the command line argument parameter.

Documentation

The project requires different command line arguments depending on your desired input file to the project.

• While using the webcam as the input feed source, to run the main python file, a command line argument --input_type has to be added with the parameter cam to specify the input type as webcam.

• To use a video file as the source of the input feed, running the main python file though the command line, a command line argument --input_type has to be added with the parameter video to specify the input type as video. In addition to that, to use a different video or the one in the project directory, add the path to the video file to the --input_file command line argument.

• To use a image file as the source of the input feed, running the main python file though the command line, a command line argument --input_type has to be added with the parameter image to specify the input type as an image. In addition to that, add the path to the image file to the --input_file command line argument.

Benchmarks

FP32 Results

The face detection model uses one precision, that is, FP32-INT1. The loading time and the inference time for the model remains the same.

• Results for all the models have been calculated while running the project and are as shown below.

  

  • Face Detection Model Deep Learning Workbench Results using FP32-INT1 as the model precision.

    

  • Facial Landmarks Detection Model Deep Learning Workbench results using FP32 as the model precision

  • Head Pose Estimation Model Deep Learning Workbench Results using FP32 as the model precision

    

  • Gaze Estimation Model Deep Learning Workbench Results using FP32 as the model precision

    

FP16 Results

• Below are the results for running the project using FP16 as the model precision

  

  • Facial Landmarks Detection Model Deep Learning Workbench results using FP32 as the model precision

  • Head Pose Estimation Model Deep Learning Workbench Results using FP32 as the model precision

  • Gaze Estimation Model Deep Learning Workbench Results using FP32 as the model precision

FP16-INT8 Results

• Below are the results for running the project using FP16-INT8 as the model precision

  

  • Facial Landmarks Detection Model Deep Learning Workbench results using FP32 as the model precision

  • Head Pose Estimation Model Deep Learning Workbench Results using FP32 as the model precision

  • Gaze Estimation Model Deep Learning Workbench Results using FP32 as the model precision

Results

In the benchmark results, it has been illustrated that the project will take less time to run an inference and obtain results using a lower precision value of the models, that is, the FP16-INT8. As the value of the model precision used gets lower, the performance of the project in terms of fps improves.

FP32 model precision store large weight values compared to FP16 and INT8 model precision. This factor makes them resource intensive when working with such models. Storing large values makes them bigger that the other two model precisions and will take longer time to execute.

For the FP16 model precisions, store have the amount of weight values as compared to FP32 models. This makes the faster than the FP32 due to the size and less compute power required.

INT8 Models are the fastest to execute and have the best performance in terms of fps. This is because they have the least weight values requiring less resources to execute them and less storage space. Obtaining an INT8 model from either a FP32 or FP16 model can be done by weight quantization that transformed floating point weight values to quantized weights.

Stand Out Suggestions

• Build an Inference Pipeline for both video file and webcam feed as input:

  I have given the user an option to choose the type of media they would like to feed to the project by the use of a command line argument. Besides that, the user can also add the path to another video file or image file using a command line argument.

• Can you improve your inference speed without significant drop in performance by changing the precision of the models?

  It is possible to improve the inference speed without experiencing a significant drop in performance. I have run the app using different models precisions and calculated the time it takes to obtain results on different model precision combinations. I have run the project with FP32 for all models and the time taken to run inference on all 4 models and obtain results ranges from 1.2277 - 1.2416 seconds. The average is 1.23465 seconds

  When using FP16 as the models precision, the total time taken to run an inference on all models ranges from 1.2216 - 1.2251 seconds. The average is 1.22335 seconds

  Lastly, when using the FP16-INT8 models precision, the total time taken to run one inference across all models ranges from 1.2108 - 1.2259 seconds. The average is 1.21835 seconds

  Clearly, it is possible to improve the general performance of the project by reducing the model precision and the performance will almost be the same. There are no instances where a face was not detected or the mouse failed to move while running on FP16-INT8 as the models precision.

• Add a toggle to the UI to shut off the camera feed and show stats only(as well as to toggle the camera feed back on)

  I have attempted this but it works when you press the key multiple times and cuts the camera feed. After a few frames the feed continues streaming

• There will be certain edge cases that will cause the system to not function properly

  Has a detailed explanation below on the Edge Cases section

• Benchmark rhe running times different parts of the pre-processing and inference pipeline and let the user specify a CLI argument if they want to see the benchmark timing

  To view benchmark timing, add the command line argument --show_results and specify its parameter as yes. By default, when running the app, the parameter fo the --show_results CLI argument is set to no.

• Use the Vtune Amplifier to find hotspots in your Inference Engine Pipeline

  Below are the results obtained when the project was run on the Vtune profiler

  

  

Async Inference

I have used Async Inference to load each frame per model and obtain the results. This ensures that the time taken to run the app is quite small compared to when running synchronous inference requests. It is efficient in that the mouse controller does not have to wait for inference to be performed and results obtained on all the models. Inference is performed on a single frame and the results sent to the relevant model, inference starts on that model sending results to the next model and the results sent to the pointer controller python file. This reduces the time it takes to run the project, and as a result, saving the power consumed and having a significant boost on the performance.

Edge Cases

There will be certain situations that will break your inference flow. For instance, lighting changes or multiple people in the frame. Explain some of the edge cases you encountered in your project and how you solved them to make your project more robust.

There are instances that will result in the inference flow breaking. In low lighting while using the webcam, if the face detection model results no coordinates for a phase in 10 consecutive frames of the input feed, the inference flow breaks.

If there is no person on the video frame, meaning that no face will be detected for next 10 consecutive frames, the inference flow used to break and no inputs are fed to the facial landmarks detection and head pose estimation models.

I overcame this challenge by creating a function that executes on the first frame and initiates the program to check if there is a face in the frame. When there is no face detected on the frame, the program encounters an AttributeError. Immediately after the function is called there is a try block that ensures that the program runs if there is a face detected and no AttributeError experienced. In case there occurs and AttributeError, the program excepts the error and calls the function that reads video frames and feeds them to the face detection model. The program will continue running even when there is no face being detected in the current frame.