Requirement: Python 3.8.

Folder 1 contains 1.py and 2.py that correspond to exercise 1.{1-4} and exercise 2.

• just press Space

• Key 1 - HSV
• Key 2 - LAB
• Key 3 - YUV
• Key 4 - Adaptive Gausian Thresholding
• Key 5 - OTSU' Thresholding
• Key 6 - Canny Edge Detection
• Key 7 - SIFT Detection
• Key 9 - Gaussian Blur
• Key 0 - Nothing
• Key q - Quit

• Key q - Quit

• Key 1 - OpenCV based Sobel edge detection
• Key 2 - Fourier based Sobel edge detection (My smart approach)
• Key 3 - For loop based Sobel edge detection (Stupid approach)
• Key q - Quit

• Key Space - Quit from viewing a clustered image
• Key q - Quit
• Keys 1-6 - to choose

General problems of k-means clustering:

1. Choosing k manually
2. Dependence on the initial values (Main in current assignment)
3. Lack of robustness (sensitivity to outliers)
4. Distance is sensitive to the number of dimensions

Probable solutions:

1. Scree plot
2. Kmeans++ approach (Which was implicitly implemented)
3. Use medians (even better PAM algorithm)
4. Dimension reduction by using PCA (PCA is here as simple ans well-known approach)

• Press Any Key

• NO OUTPUT, the results will be saved in 2/2_result folder

• just press Space