Drowsiness-detection | Application can detect drowsiness and raise alarm | Machine Learning library
kandi X-RAY | Drowsiness-detection Summary
kandi X-RAY | Drowsiness-detection Summary
Drowsiness-detection is a Python library typically used in Artificial Intelligence, Machine Learning, OpenCV, Arduino applications. Drowsiness-detection has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However Drowsiness-detection build file is not available. You can download it from GitHub.
Application can detect drowsiness and raise alarm.
Application can detect drowsiness and raise alarm.
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Quality
Security
License
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Support
Drowsiness-detection has a low active ecosystem.
It has 6 star(s) with 7 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 172 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of Drowsiness-detection is current.
Quality
Drowsiness-detection has 0 bugs and 0 code smells.
Security
Drowsiness-detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
Drowsiness-detection code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
Drowsiness-detection is licensed under the GPL-3.0 License. This license is Strong Copyleft.
Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.
Reuse
Drowsiness-detection releases are not available. You will need to build from source code and install.
Drowsiness-detection has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
It has 159 lines of code, 5 functions and 1 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed Drowsiness-detection and discovered the below as its top functions. This is intended to give you an instant insight into Drowsiness-detection implemented functionality, and help decide if they suit your requirements.
- Visualize the face landmarks .
- Calculates the aspect ratio of an eye .
- Convert a bounding box to a bounding box .
- Convert a shape to a numpy array .
- Play sound .
Get all kandi verified functions for this library.
Drowsiness-detection Key Features
No Key Features are available at this moment for Drowsiness-detection.
Drowsiness-detection Examples and Code Snippets
No Code Snippets are available at this moment for Drowsiness-detection.
Community Discussions
Trending Discussions on Drowsiness-detection
QUESTION
[Unhandled promise rejection: Error: Size(150528) must match the product of shape 1]
Asked 2020-Sep-11 at 04:16
I am using a custom model I built using transfer learning with MobileNetV2 then converted to TF.js format.
Model takes in input of (batch_size, 224,224,3) I am not sure where the size(150528) is from cause it is not present in my model.summary()
Link to model summary as it is too long to post here
Code for the model in Python:
...ANSWER
Answered 2020-Sep-11 at 04:16It was an issue regarding the input shape I was passing to make predictions and was solved with the following code:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Drowsiness-detection
You can download it from GitHub.
You can use Drowsiness-detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use Drowsiness-detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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