OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture and analysis including features like face detection and object detection. In this tutorial, we explain how you can use OpenCV in your applications.
- Install python 3.6+
Create virtual envionment with pipenv.
pip install pipenv #only for one time
pipenv install -r requirements.txt
pipenv shellComputer vision allows the computer to perform the same kind of tasks as humans with the same efficiency. There are a two main task which are defined below:
- Object Classification - In the object classification, we train a model on a dataset of particular objects, and the model classifies new objects as belonging to one or more of your training categories.
- Object Identification - In the object identification, our model will identify a particular instance of an object - for example, parsing two faces in an image and tagging one as Virat Kohli and other one as Rohit Sharma.
contributers
More Data Analysis With Kaggle and improvement in previous Codes
Before submitting a bug, please do the following:
Perform basic troubleshooting steps:
- Make sure you are on the latest version. If you are not on the most recent version, your problem may have been solved already! Upgrading is always the best first step.
- Try older versions. If you are already on the latest release, try rolling back a few minor versions (e.g. if on 1.7, try 1.5 or 1.6) and see if the problem goes away. This will help the devs narrow down when the problem first arose in the commit log.
- Try switching up dependency versions. If the software in question has dependencies (other libraries, etc) try upgrading/downgrading those as well.
Show your appreciation to those who have contributed to the project.
For open source projects,Under MIT License.
- Project : OpenCv Tutorial
- Author : Py-Contributors
- Language : Python
- Github : https://github.com/codePerfectPlus
- Website : http://codeperfectplus.github.io