Traffic-Sign-Detection | Traffic signs detection and classification in real time | Computer Vision library

by hoanglehaithanh Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Traffic-Sign-Detection Summary

Traffic-Sign-Detection is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Tensorflow, OpenCV applications. Traffic-Sign-Detection has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Traffic-Sign-Detection build file is not available. You can download it from GitHub.

This project is a traffic signs detection and classification system on videos using OpenCV. The detection phase uses Image Processing techniques that create contours on each video frame and find all ellipses or circles among those contours. They are marked as candidates for traffic signs. In the next phase - classification phase, a list of images are created by cropping from the original frame based on candidates' coordinate. A pre-trained SVM model will classify these images to find out which type of traffic sign they are.

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Quality

Security

License

Reuse

Support

Traffic-Sign-Detection has a low active ecosystem.

It has 177 star(s) with 84 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 10 have been closed. On average issues are closed in 51 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Traffic-Sign-Detection is current.

Quality

Traffic-Sign-Detection has 0 bugs and 0 code smells.

Security

Traffic-Sign-Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Traffic-Sign-Detection code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Traffic-Sign-Detection is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

Traffic-Sign-Detection releases are not available. You will need to build from source code and install.

Traffic-Sign-Detection has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

It has 598 lines of code, 57 functions and 3 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed Traffic-Sign-Detection and discovered the below as its top functions. This is intended to give you an instant insight into Traffic-Sign-Detection implemented functionality, and help decide if they suit your requirements.

Visualize the image
Find the largest sign in a list of contours
Compute the label for the given image
Find contours in an image
Evaluate the given model
Splits an iterable
Convenience function to create numpy array of images
Train data
Create a hog descriptor
Load the network
Find all contours in contours that are in contours
Crop a contour
Convert rectangle to mx coordinates
Convert array to rectangular coordinates
Calls cv2
Compute the lookat of the target
Context manager for Timer
Removes png images

Get all kandi verified functions for this library.

Traffic-Sign-Detection Key Features

No Key Features are available at this moment for Traffic-Sign-Detection.

Traffic-Sign-Detection Examples and Code Snippets

No Code Snippets are available at this moment for Traffic-Sign-Detection.

Community Discussions

Trending Discussions on Traffic-Sign-Detection

powerful IDE to reduce time in training datasets in yolo/darkflow

Python.h not found even after I used sudo install

QUESTION

powerful IDE to reduce time in training datasets in yolo/darkflow

Asked 2021-Apr-04 at 20:39

I'm a beginner in Machine Learning.
I've been learning about YOLO and DarkFlow from the following links with Ubuntu 20.04: darkflow and Tiny YOLO.

I successfully executed the code, and got the results like this:

Statistics:
car: 436
person: 73
Dataset size: 2599
Dataset of 2599 instance(s)
Training statistics:
Learning rate : 1e-05
Batch size : 16
Epoch number : 1000
Backup every : 2000

It's not bad, but the training's taking way too much time.
But I wanna know if there is any powerful IDE or other tools that can help me reduce time.
I searched in google and tried to find many ways to improve.
I heard that there are many ways to make training faster,(including Azure AI ML service) but since I'm a beginner, I cannot decide which will be the perfect choice to run YOLO and DarkFlow.

I would appreciate advices about robust development environments, especially the ones that would be suitable in my current specific condition.
Thanks in advance!

++) Since I'm a mere sophomore, the level of the hardware that I can use is very limited. I would also appreciate tools that can help me overcome the limitations of my hardware!

...

ANSWER

Answered 2021-Apr-04 at 20:39

The IDE usually won't decrease computing time, but is rather based around the compiler. Likely, you'll just have to deal with intensive training times if you can't get better hardware. However, you might look into using a gpu to do computations(if you have one) rather than the standard way it runs on the cpu. Here's a link as to how to achieve this in Tensorflow: https://stackoverflow.com/a/51307381/14392018 . The general rule is the more data and the more complex the ML/DL model(i.e. the more layers in a neural network), the longer it takes to compute 1 epoch on the dataset. Also, the more data you're working with, the higher the computational intensity.

Source https://stackoverflow.com/questions/66945435

QUESTION

Python.h not found even after I used sudo install

Asked 2021-Apr-04 at 10:58

I've been studying darkflow from the following link; https://github.com/thtrieu/darkflow
on Ubuntu 20.04
I thought there was a problem in my flow file, so I tried to rebuild the build file by the following code

...

ANSWER

Answered 2021-Apr-04 at 10:58

You can try :

Source https://stackoverflow.com/questions/66940268

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Traffic-Sign-Detection

You can download it from GitHub.
You can use Traffic-Sign-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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