OpenCV SDK for Dart

The OpenCV SDK for Dart is a powerful and versatile computer vision library that allows developers to integrate OpenCV functionality into their Dart applications. This SDK provides a bridge between Dart and the OpenCV library, enabling you to leverage advanced computer vision techniques seamlessly.

Features

• High-Performance: Benefit from the high-performance capabilities of the OpenCV library directly from your Dart applications.

• Wide Range of Algorithms: Access a comprehensive collection of image processing and computer vision algorithms, including filtering, feature detection, object tracking, and more.

• Easy Integration: The SDK provides a user-friendly interface to easily integrate OpenCV functionalities into your Dart projects.

TODO

TODO:

• Image Reading and Writing:

  • Read image: imread()
  • Write image: imwrite()

• Image Properties:

  • Get image size: size()
  • Determine image type: type()
  • Access and modify pixel values on the image: at(), set()

• Morphological Operation:

  • Eroding
  • Dilate

• Color Conversions:

  • Convert color spaces: cvtColor() for grayscale
  • Define constants for color space conversions: COLOR_*

• Filtering and Edge Detection:

  • Gaussian blur: GaussianBlur()
  • Average blur: Average()
  • Bileteral blur: Bileteral()
  • Average blur: Average()
  • Median blur: medianBlur()

• Edge Detection:

  • Edge detection: Canny()
  • Laplace: Laplace()
  • Sobel: Sobel()

• Geometric Transformations:

  • Perspective transformation: warpPerspective()
  • Scaling: resize()
  • Rotation: rotate()

• Hough Detection Transformations:

  • Hough Circle:
  • Hough Line:

• Template Matching:

  • Template matching operation: matchTemplate()

• Contour Detection:

  • Contour detection: findContours()
  • Compute contour properties: contourArea(), arcLength(), boundingRect()

• Object Detection:

  • Face detection: CascadeClassifier()
  • Object detection: detectMultiScale()

• Computational Operations:

  • Mathematical operations: add(), subtract(), multiply(), divide()
  • Histogram calculation: calcHist()

• Image Processing Helpers:

  • Bitwise masking operations: bitwise_and(), bitwise_or(), bitwise_not()
  • Splitting and merging images: split(), merge()
  • Defining Regions of Interest (ROI): Rect()

• Graphical User Interface (GUI) Helpers:

  • Display image on the screen: imshow()
  • Detect keyboard or mouse interactions: waitKey()

Usage

TODO: Include short and useful examples for package users. Add longer examples to /example folder.