Image Processing Using OpenCV

Introduction

• OpenCV is an open-source tool widely used for computer vision and image processing tasks.
• Applications include face detection, video capturing, tracking moving objects, object disclosure, face mask detection, social distancing, etc.

Key Techniques and Implementations

Edge Detection and Image Gradients

• Fundamental technique in image processing.
• Uses Sobel and Laplacian methods to detect edges.
• Canny edge detection uses thresholding to classify gradients as edges.

Dilation, Opening, Closing, And Erosion

• Fundamental operations for removing noise and detecting intensity changes.
• Practical implementation using erosion, dilation, opening, and closing.

Perspective Transformation

• Used to change the viewpoint of an image.
• Utilize getPerspectiveTransform() and warpPerspective() functions.

Image Pyramids

• Useful for scaling in object detection.
• Implements Gaussian and Laplacian pyramids using pyrUp() and pyrDown().

Cropping

• Extracts a specific part of an image using coordinates.

Scaling, Interpolations, and Re-Sizing

• resize() function in OpenCV with options for different types of interpolation.

Thresholding Techniques

• Includes binary, adaptive mean, and Otsu's thresholding for image binarization.

Sharpening

• Uses a kernel sharpening technique to enhance image clarity.

Blurring

• Implemented using different kernel sizes for smoothing effects.

Contours

• Identifies structural outlines of an object.
• Uses findContours with Canny edges.

Line Detection Using Hough Lines

• Detects lines in images using Hough transform.

Finding Corners

• Detects image corners using cornerHarris function.

Counting Circles and Ellipses

• Utilizes SimpleBlobDetector to count circular shapes in an image.

Conclusion

• The article provides a comprehensive overview of image processing using OpenCV with practical examples.
• The techniques discussed are essential for various computer vision applications.

Additional Information

• Links to further reading and resources are included for each discussed topic.
• The article was part of the Data Science Blogathon on Analytics Vidhya.