cv-toolkit-new 0.1.3

A comprehensive computer vision toolkit for image processing and feature detection

CV Toolkit

A comprehensive computer vision toolkit providing functionalities for image processing, geometric transformations, feature detection, and more.

Features

• Image Processing: Load, resize, convert, and filter images
• Geometric Transformations: Translation, rotation, scaling, reflection, shearing
• Homography Computation: Compute and apply homography matrices
• Perspective Transformation: Perspective warping of images
• Camera Calibration: Calibrate cameras and undistort images
• Fundamental Matrix: Compute fundamental matrix from stereo images
• Edge, Line & Corner Detection: Canny, Hough, Harris, Shi-Tomasi
• SIFT Features: Scale-Invariant Feature Transform
• SURF & HOG: Feature descriptors for object detection

Installation

From PyPI (when published)

pip install cv-toolkit

From Source (Local Development)

# Navigate to the directory containing setup.py
cd path/to/CV

# Install in editable mode
pip install -e .

# Or install normally
pip install .

Quick Start

Example 1: Image Processing

from cv_toolkit import image_processing

# Run demo with sample image
image_processing.demo()

# Or use individual functions
img = image_processing.load_sample_image()
img_gray = image_processing.convert_to_grayscale(img)
img_blur = image_processing.apply_gaussian_blur(img, sigma=2)

Example 2: Geometric Transformations

from cv_toolkit import geometric_transforms

# Run demo
geometric_transforms.demo()

# Or use specific transformations
img = geometric_transforms.data.astronaut()
img_rotated = geometric_transforms.rotate_image(img, angle_deg=45)
img_scaled = geometric_transforms.scale_image(img, scale_x=0.5, scale_y=0.5)

Example 3: Feature Detection (SIFT)

from cv_toolkit import sift_descriptor

# Run demo
sift_descriptor.demo()

# Or use the pipeline
keypoints, descriptors, img_kp = sift_descriptor.sift_pipeline(
    img=your_image, 
    save_results=True
)

Example 4: Edge and Corner Detection

from cv_toolkit import edge_line_corner_detection

# Run full detection pipeline
results = edge_line_corner_detection.detection_pipeline(save_results=True)

# Or use individual functions
edges = edge_line_corner_detection.detect_edges(img)
lines_img = edge_line_corner_detection.detect_lines(img)
corners = edge_line_corner_detection.detect_shi_tomasi_corners(img)

Example 5: Fundamental Matrix

from cv_toolkit import fundamental_matrix

# Run demo with sample stereo images
F, matches, epi1, epi2 = fundamental_matrix.fundamental_matrix_pipeline(
    save_results=True
)

Module Overview

Module	Description
image_processing	Basic image operations (load, resize, filter)
geometric_transforms	Geometric transformations (rotate, scale, etc.)
homography	Homography matrix computation
perspective_transform	Perspective transformations
camera_calibration	Camera calibration and undistortion
fundamental_matrix	Fundamental matrix from stereo pairs
edge_line_corner_detection	Edge, line, and corner detection
sift_descriptor	SIFT feature detection
surf_hog_descriptor	SURF and HOG descriptors

Requirements

• Python >= 3.7
• NumPy >= 1.19.0
• OpenCV >= 4.5.0
• scikit-image >= 0.18.0
• matplotlib >= 3.3.0

Usage Examples

Import All Modules

from cv_toolkit import (
    image_processing,
    geometric_transforms,
    homography,
    perspective_transform,
    camera_calibration,
    fundamental_matrix,
    edge_line_corner_detection,
    sift_descriptor,
    surf_hog_descriptor
)

Run All Demos

import cv_toolkit.image_processing as ip
import cv_toolkit.geometric_transforms as gt
import cv_toolkit.sift_descriptor as sift

ip.demo()
gt.demo()
sift.demo()

Development

Install in Development Mode

pip install -e .[dev]

Run Tests (when implemented)

pytest

License

MIT License - see LICENSE file for details

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Author

Your Name - [e-mail]

Acknowledgments

• Built using OpenCV, scikit-image, and NumPy
• Based on classical computer vision algorithms and techniques