https://github.com/sidhyaashu/open_cv_fundamentals

Fundamentals about OpenCV with Python
https://github.com/sidhyaashu/open_cv_fundamentals

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Fundamentals about OpenCV with Python

• Host: GitHub
• URL: https://github.com/sidhyaashu/open_cv_fundamentals
• Owner: sidhyaashu
• License: mit
• Created: 2024-09-12T04:24:57.000Z (4 months ago)
• Default Branch: sidhya
• Last Pushed: 2024-12-02T15:57:01.000Z (about 1 month ago)
• Last Synced: 2024-12-02T16:52:54.884Z (about 1 month ago)
• Language: Python
• Size: 3.32 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# OpenCV Modules Overview

OpenCV (Open Source Computer Vision Library) is a powerful library for computer vision and image processing. It contains several modules, each dedicated to specific functionalities. Below is an overview of the key modules of OpenCV:

## 1. Core (`core`)

This module includes basic data structures (e.g., `Mat`, `Scalar`) and functions used by other modules. It provides support for matrix operations, linear algebra, and utility functions.

## 2. Image Processing (`imgproc`)

Contains functions for image processing tasks such as:

- Filtering

- Edge detection (e.g., Canny)

- Histogram equalization

- Color space conversions

- Geometric transformations

## 3. Video I/O (`videoio`)

Handles capturing and reading videos from various devices and file formats. It supports:

- Camera inputs

- Video file reading and writing

## 4. GUI (`highgui`)

Provides functionalities for:

- Simple GUI window management

- Image and video display

- Drawing functions

## 5. Video Analysis (`video`)

Focuses on video-related tasks, including:

- Background subtraction

- Motion analysis

- Object tracking

- Optical flow

## 6. Calibration and 3D (`calib3d`)

Includes functions for:

- Camera calibration

- Stereo vision

- 3D reconstruction (e.g., finding essential and fundamental matrices)

## 7. Features2D (`features2d`)

Contains feature detection, description, and matching methods, such as:

- SIFT

- SURF

- ORB

- BRIEF

## 8. Object Detection (`objdetect`)

Offers methods for detecting objects, including:

- Face detection using Haar cascades

- QR code detection and decoding

## 9. Machine Learning (`ml`)

Provides implementations for various machine learning algorithms like:

- K-Nearest Neighbors (KNN)

- Support Vector Machines (SVM)

- Decision Trees

## 10. Deep Neural Networks (`dnn`)

Supports the import, inference, and deployment of deep neural networks. It can handle models from frameworks like:

- Caffe

- TensorFlow

- ONNX

## 11. Photo Processing (`photo`)

Includes advanced algorithms for:

- Image restoration

- Deblurring

- Inpainting

- Denoising

## 12. Object Tracking (`tracking`)

Offers implementations for tracking objects in a video feed, including popular algorithms like:

- KCF

- MIL

- MOSSE

## 13. Stitching (`stitching`)

Used for stitching multiple images together to create panoramic images.

## 14. Structured Light (`structured_light`)

For capturing and processing 3D scans using structured light techniques.

## 15. Text (`text`)

Provides Optical Character Recognition (OCR) functionalities using libraries like Tesseract.

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Each of these modules has a specific purpose and can be used to handle various aspects of computer vision and machine learning tasks.