object-detection-opencv | YOLO Object detection with OpenCV and Python | Computer Vision library

by arunponnusamy Python Version: v0.1 License: MIT

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kandi X-RAY | object-detection-opencv Summary

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

YOLO Object detection with OpenCV and Python.

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object-detection-opencv has a low active ecosystem.

It has 275 star(s) with 213 fork(s). There are 14 watchers for this library.

It had no major release in the last 12 months.

There are 18 open issues and 10 have been closed. On average issues are closed in 40 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of object-detection-opencv is v0.1

Quality

object-detection-opencv has 0 bugs and 0 code smells.

Security

object-detection-opencv has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

object-detection-opencv code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

object-detection-opencv is licensed under the MIT License. This license is Permissive.

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

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object-detection-opencv releases are available to install and integrate.

object-detection-opencv has no build file. You will be need to create the build yourself to build the component from source.

It has 67 lines of code, 2 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 object-detection-opencv and discovered the below as its top functions. This is intended to give you an instant insight into object-detection-opencv implemented functionality, and help decide if they suit your requirements.

Draw a prediction .
Get the list of output layers .

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object-detection-opencv Key Features

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object-detection-opencv Examples and Code Snippets

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Community Discussions

Trending Discussions on object-detection-opencv

Couldn't display YOLO result via OpenCV dnn module

QUESTION

Couldn't display YOLO result via OpenCV dnn module

Asked 2020-Jan-12 at 14:44

I'm trying to train YOLO object detection via darknet(AlexeyAB or pjreddie) and it was great for showing object via

./darknet detector test cfg/obj.data cfg/yolo-obj.cfg yolo.weights data/test.jpg

I used 160*30 images for training and testing. Also I did it via Tiny-YOLOv2 and another model and it could detect all numbers in the images.

But when I want to showing result for trained weight and a specific image like bellow, and by using Opencv dnn modules, I could not display all objects(numbers) correctly.

YOLO Object Detection with OpenCV and Python

Deep Learning based Object Detection using YOLOv3 with OpenCV ( Python / C++)

YOLO object detection with OpenCV

YOLO object detection using OpenCV and Python

Exploring OpenCV’s Deep Learning Object Detection Library

But when I check it for a bigger image(1280*760), It could show results.

correct output for plate detection:

...

ANSWER

Answered 2020-Jan-12 at 14:44

I solved it. When we want to show the result using OpenCV and DNN module, we must consider the resizing ratio of the original image. For example when I have an 160*30 input image, at first I convert it to 416*78(resizing one of the width or height to 416). Now for showing results, I resize height by multiplying it to 416/78

Source https://stackoverflow.com/questions/57337565

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

Vulnerabilities

No vulnerabilities reported

Install object-detection-opencv

You can download it from GitHub.
You can use object-detection-opencv 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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