feat: 🎸 Enhancement, Update snippets, test cases, docs of cv

✅ Closes: #125

This pull request introduces a comprehensive guide to various computer vision techniques along with code implementations for each technique. The changes include updates to the README.md file and the addition of new Python scripts for different computer vision tasks.

Documentation and Examples:

pysnippets/Computer_vision/README.md: Added a detailed guide covering ten computer vision techniques, including depth estimation, edge detection, face recognition, feature matching, image classification, image enhancement, image segmentation, object detection, optical flow, and video tracking. Each section includes a brief explanation, relevant libraries, and example code.

New Python Scripts for Computer Vision Techniques:

pysnippets/Computer_vision/depth_estimation.py: Added a script to estimate depth maps from stereo images using Semi-Global Block Matching (SGBM).
pysnippets/Computer_vision/edge_detection.py: Added a script to detect edges in an image using multiple methods, including Canny, Sobel, and Laplacian.
pysnippets/Computer_vision/face_recognition.py: Added a script for face detection and recognition using Haar cascades and Local Binary Patterns Histograms (LBPH).
pysnippets/Computer_vision/feature_matching.py: Added a script for feature matching between two images using SIFT and FLANN-based matcher.
pysnippets/Computer_vision/image_classification.py: Added a script to create, train, and evaluate a CNN model for image classification using TensorFlow and Keras.
pysnippets/Computer_vision/image_enhancement.py: Added a script to enhance images by adjusting luminance, reducing noise, and applying sharpening.
pysnippets/Computer_vision/image_segmentation.py: Added a script for image segmentation using Otsu's thresholding and the watershed algorithm.

@UTSAVS26 kindly review this PR, and I request level 3. please kindly allow this

UTSAVS26 / PySnippets

feat: 🎸 Enhancement, Update snippets, test cases, docs of cv #444