Open CV
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.
OpenCV uses machine learning algorithms to search for faces within a picture. Because faces are so complicated, there isn’t one simple test that will tell you if it found a face or not. Instead, there are thousands of small patterns and features that must be matched. The algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve. These tasks are also called classifiers.
Computer Vision: Computer Vision can be defined as a discipline that explains how to reconstruct, interrupt, and understand a 3D scene from its 2D images, in terms of the properties of the structure present in the scene. It deals with modelling and replicating human vision using computer software and hardware.
Features of OpenCV Library
Using OpenCV library, you can −
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Read and write images
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Capture and save videos
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Process images (filter, transform)
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Perform feature detection
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Detect specific objects such as faces, eyes, cars, in the videos or images.
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Analyze the video, i.e., estimate the motion in it, subtract the background, and track objects in it.
OpenCV was originally developed in C++. In addition to it, Python and Java bindings were provided. OpenCV runs on various Operating Systems such as Windows, Linux, OSx, FreeBSD, Net BSD, Open BSD, etc.
Before we install the open cv in our working environment we need to install
- Install Python
- Numpy
We are going to use pip install method.
Open CMD as administrator.
Use command:
` **pip install opencv-python**`
Fig: install OpenCV
Now check the OpenCV version
Use command:
`**import cv2**`
`**print(cv2.__version__)**`
Fig: Checking OpenCV version