Eye Blink Counting Detection OpenCV Python – The blinking of the eyes is a fast closure and opening of the eyelids.
Human-computer interface and human vision health care studies both benefit from eye blink detection.
Existing approaches to eye blink detection are frequently insufficient for resource-constrained eye blink detection platforms, such as Smart Glasses, which have restricted energy supplies and typically cannot afford powerful image and processing capabilities.
Importance of Eye blink Counting Detection Python OpenCV
Because of its liquid nature, your eye is prone to gathering dust and dirt.
Blinking helps to clear the ocular surface of dirt, thus blinking half as frequently won’t get your eyes as clean as they need to be.
This can harm the surface structures of your eye over time.
Eye Blink Counting Detection In Python: Project Details and Technology
| Project Name: | Eye Blink Counting Detection Project In Python With Source Code |
| Abstract | Eye Blink Counting Detection Python OpenCV Human computer interface and human vision health care studies both benefit from eye blink detection. |
| Language/s Used: | Python Deep Learning |
| Python version (Recommended): | 3.8/3.9 |
| Database: | None |
| Type: | Desktop Application |
| Developer: | Source Code Hero |
| Updates: | 0 |
Eye Blink Counting Detection OpenCV Python: About the project
This Eye Blink Counting Detection OpenCV Python was created with Python OpenCV, and in this Python OpenCV Project With Source Code, we’ll build on that expertise to create a computer vision application that can identify and count blinks in video streams using facial landmarks and OpenCV.
Eye Blink Detection OpenCV Python focuses entirely on determining if a person has blinked in a video stream using the eye aspect ratio as a quantitative parameter.
However, due to noise in the video stream, poor facial landmark detection, or rapid changes in viewing angle, a basic eye aspect ratio threshold could create a false-positive detection, reporting a blink when the individual had not blinked.
This Eye Blink Counting Detection In Python also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
To start executing a Eye Blink Counting Detection OpenCV Python With Source Code, make sure that you have installed Python in your computer.
Eye Blink Counting Detection OpenCV Python With Source Code : Steps on how to run the project
Time needed: 5 minutes
These are the steps on how to run Eye Blink Counting Detection OpenCV Python With Source Code
- Step 1: Download Source Code
First, download the provided source code below.
- Step 2: Extract File
Next, after the download finished extract the zip file.
- Step 3: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 4: Install all Libraries.
Next, install all libraries given below.
- Step 5: Open cmd.
Next, click the folder directory of the project and type cmd.
- Step 6: Run Project.
last, run the project with the command “py main.py”
Download the Source Code below
Summary
In this article, I demonstrated how to execute a blink detector using OpenCV, Python, and dlib.
The first step in building a blink detector is to perform facial landmark detection to localize the eyes in a given frame from a video stream.
Once we have the facial landmarks for both eyes, we compute the eye aspect ratio for each eye, which gives us a singular value, relating the distances between the vertical eye landmark points to the distances between the horizontal landmark points.
Once we have the eye aspect ratio, we can threshold it to determine if a person is blinking — the eye aspect ratio will remain approximately constant when the eyes are open and then will rapidly approach zero during a blink, then increase again as the eye opens.
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Inquiries
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