Image Processing

You must have heard a famous quote saying “a picture is worth a thousand words” and in recent years, Image processing has already begun to move our world. You can find a number of applications in almost every field like medical, Engineering, Agriculture, Security, etc. So, it’s worth putting your efforts to learn image processing.

What is on this blog?

In this blog, you will not only learn the theory behind fundamental image processing tasks but will also learn how to perform these key processing tasks in practice using Python OpenCV.

Who should read this blog?

This blog is for everyone. The only thing you need is a little bit of knowledge about maths(Linear Algebra) and python.

CONTENTS

  1. Installing Python OpenCV and other libraries.
  2. What is a digital image?
    1. Read, Write and Display Images with OpenCV
    2. Read, Write and Display Videos with OpenCV Python
  3. Greyscale and Color Image
    1. Understanding Images with OpenCV-Python
    2. Understanding Image Histograms
  4. Bayer Filter
  5. Image Demosaicing or Interpolation methods
    1. Nearest Neighbor Interpolation
    2. Bilinear Interpolation
    3. Bicubic Interpolation
    4. Image Interpolation using OpenCV-Python
  6. Geometric Transformations
    1. Translation
    2. Rotation
    3. Affine Transformation
    4. Perspective Transformation
  7. Color Models
    1. Understanding Color Models using OpenCV-Python
  8. Image Enhancement
  9. Arithmetic operations for image enhancement
  10. Intensity Transformation
    1. Image Negatives
    2. Log Transformation
    3. Gamma Correction
    4. What is Contrast in Image Processing?
    5. Contrast Stretching
      1. Detecting low contrast images using Scikit-image
    6. Intensity Level Slicing
    7. Bit-plane Slicing
  11. Image Histograms
    1. Understanding Image Histograms
    2.  2D Histograms
    3. Histogram Equalization
    4. Histogram Matching (Specification)
    5. Adaptive Histogram Equalization (AHE)
    6. Histogram Backprojection
    7. Histogram Comparison
    8. Earth mover’s distance or Wasserstein metric
  12. Spatial Filtering
    1. Understanding Frequency in Images
    2. Add different noise to an image
    3. Smoothing Filters
    4. Gaussian Blurring
    5. Bilateral Filtering
    6. Understanding Image Gradients
    7. Difference of Gaussians (DoG)
    8. Unsharp Masking and Highboost filtering
    9. Canny Edge Detector
    10. First-order Derivative kernels for Edge Detection
    11. Laplacian of Gaussian (LoG)
      1. Blur Detection using the variance of the Laplacian
  13. Image Thresholding
    1. Global Thresholding
    2. Otsu’s Binarization
    3. Balanced Histogram Thresholding
    4. Methods for Improving Global Thresholding
    5. Adaptive Thresholding
    6. Thresholding using cv2.inRange
  14. Morphological image processing
    1. Erosion
    2. Dilation
    3. Opening and Closing
    4. Morphological gradient and Top-hat operators
    5. Hit-or-Miss Transformation
    6. Thinning (Skeletonisation) and Thickening
    7. Understanding Morphological operations with Trackbars
  15. Image Pyramids
    1. Image Blending using Image Pyramids
  16. Integral Images or Summed Area Table
  17. Contour Tracing algorithms
    1. OpenCV Contour tracing Suzuki’s algorithm
    2. Find and Draw Contours using OpenCV-Python
    3. Simple Shape Detection using Contour approximation
    4. Finding Convex Hull OpenCV Python
    5. Image Moments
    6. OpenCV Minimum Area Rectangle
    7. Convexity Defects OpenCV
  18. Hough Transform
    1. Hough Line Transform
    2. Hough Circle Transform
  19. Watershed Algorithm
  20. Foreground extraction using GrabCut Algorithm
  21. Template matching
  22. Feature Detection, Description, and Matching
    1. Harris Corner Detection
    2. Shi-Tomasi Detector
    3. Finding Corners with SubPixel Accuracy
    4. Scale-Invariant Feature Transform
      1. Introduction
      2. Scale-Space Extrema Detection

COURSES

Hey, here is the new course about optical character recognition using deep learning and OpenCV-Python. In this course you will learn, what is Optical Character Recognition. You will learn about a general OCR pipeline used by most industries. You will see different Image Pre-processing techniques used in the OCR pipeline. Different Text Detection techniques are used in the OCR pipeline such as EAST and CTPN. Next, we will learn the different text recognition techniques used in OCR pipeline such as CRNN (CNN+RNN+CTC). And finally, we will see the end-to-end implementation of a real-life OCR use case using Pytesseract. If you are interested go through the course link provided by the following banner:

PROJECTS

  1. Creating a Snake Game using OpenCV-Python
  2. Creating a Bouncing Ball Screensaver using OpenCV-Python
  3. Extracting and Saving Video Frames using OpenCV-Python
  4. Creating Video from Images using OpenCV-Python
  5. Creating gif from video using OpenCV and imageio
  6. Creating a window recorder using OpenCV-Python
  7. Create own image using Numpy and OpenCV
  8. Changing Video Resolution using OpenCV-Python
  9. Object Tracking Using Color Models OpenCV-Python
  10. Removing Text highlighter using Colorspace OpenCV-Python
  11. Creating Subplots in OpenCV-Python
  12. Add borders to the image
  13. How to write rotated text using OpenCV-Python?
  14. Geometric Transformation of images using OpenCV-Python
  15. Write Text on images in real-time using OpenCV-Python
  16. Write Text on images at mouse click position using OpenCV-Python
  17. Show current DateTime on live video using OpenCV-Python
  18. Set Camera Timer using OpenCV-Python
  19. Add image to a live camera feed using OpenCV-Python
  20. Image Overlays using Bitwise Operations OpenCV-Python