Pedestrian-Detection | repo content all dataset , record | Computer Vision library

by Tourenathan-G5organisation Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | Pedestrian-Detection Summary

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

This repo content all the dataset, the record and the config that were used in training a TensorFlow pedestrian detector model.

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Support

Pedestrian-Detection has a low active ecosystem.

It has 12 star(s) with 5 fork(s). There are no watchers for this library.

It had no major release in the last 6 months.

Pedestrian-Detection has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Pedestrian-Detection is current.

Quality

Pedestrian-Detection has no bugs reported.

Security

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

License

Pedestrian-Detection does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

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

Top functions reviewed by kandi - BETA

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

Create a tf example
Converts a class label text to an integer
Convert xml file to csv
Split a Pandas DataFrame

Get all kandi verified functions for this library.

Pedestrian-Detection Key Features

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

Pedestrian-Detection Examples and Code Snippets

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

Community Discussions

Trending Discussions on Pedestrian-Detection

c4-pedestrian-detector.cpp:283:8: error: ‘Show_Detection_Steps’ was not declared in this scope

The correct header file to access the cvLoadImage() function

Python and openCV : HOG descriptor detect multiscale returns negative bounding box

QUESTION

c4-pedestrian-detector.cpp:283:8: error: ‘Show_Detection_Steps’ was not declared in this scope

Asked 2020-May-23 at 17:16

I am trying to compile the CPP file present here and it is giving me the following error. I am new to this. Any help will be highly appreciated. Also, any other way to compile and run it will be helpful if I am making it wrong.

...

ANSWER

Answered 2020-May-23 at 17:16

The code is indeed broken; Show_Detection_Steps is used before it is declared.

Some compilers let you get away with it due to different methods of implementing templates, which is probably why the author missed it. But the code is, per the C++ language, broken.

You should report this as a bug to the author, using the GitHub project page you linked to.

Source https://stackoverflow.com/questions/61975715

QUESTION

The correct header file to access the cvLoadImage() function

Asked 2018-Dec-04 at 11:56

I'm trying to get the algorithm provided in this repository to work on windows. After countless issues, I'm only left with one unrecognized function cvLoadImage which is apprently depricated. I was instructed to work with the c++ API instead but the problem is that I will have to rewrite other parts of the code as well and I might end up breaking it.

...

ANSWER

Answered 2018-Dec-04 at 11:56

Indeed, that is the old OpenCV C API. You will need to port the old C functions to the c++ OpenCV API, e.g.:

cvNamedWindow -> cv::namedWindow
cvRectangle -> cv::rectangle
cvPoint -> cv::Point

etc.

The code you're using is actually a mix of the old C API and the newer c++ API. It's just a matter of going through all the C API calls in that repo and manually port them to the c++ API. As you can see above, most of the time that is fairly intuitive. When in doubt search the OpenCV documentation.

Additionally you should look into YOLOv2 for Pedestrian detection.

Update:

There are multiple forks of this repository and it looks like Berak already has already removed the C API calls. His changes were merged, so you should to pull the latest changes and rebuild:

Source https://stackoverflow.com/questions/53537475

QUESTION

Python and openCV : HOG descriptor detect multiscale returns negative bounding box

Asked 2017-Aug-28 at 11:58

I am using OpenCV's HOG detector to detect pedestrians in a video. But the bounding boxes returned by it's detectMultiScale() have one negative value. I couldn't find anything helpful or useful so far on internet to understand and solve this problem. I do not even know why is this problem occurring. that's the output.

...

ANSWER

Answered 2017-Aug-28 at 11:58

After trying almost everything on internet when I couldn't find answer anywhere I started analyzing this problem myself and found the solution to it.

Source https://stackoverflow.com/questions/45448606

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

Vulnerabilities

No vulnerabilities reported

Install Pedestrian-Detection

You can download it from GitHub.
You can use Pedestrian-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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