Cv features

This guide covers the basic usage of the ShallowLearn.ComputerVisionFeatures module. These functions are designed for tasks such as edge detection, texture analysis, and feature extraction. Follow the steps below to understand and utilize these tools effectively.

Installation

Ensure you have the required dependencies installed. Use the following commands to install them:

pip install numpy opencv-python scikit-image

Importing Modules

To use the functions, import the ShallowLearn.ComputerVisionFeatures module:

import numpy as np
import cv2
from ShallowLearn.ComputerVisionFeatures import (
    edge_density,
    texture_features,
    color_histogram,
    sobel_edge_detection,
    gabor_features,
    histogram_of_oriented_gradients
)

Edge Detection

Edge Density

Computes the edge density of an image using the Canny Edge Detector.

# Example usage
edge_density_map = edge_density(image)

Texture Analysis

Local Binary Pattern (LBP)

Computes texture features using the Local Binary Pattern method.

# Example usage
lbp_texture = texture_features(image, P=8, R=1)

Color Analysis

Color Histogram

Computes the color histogram for each channel in the image.

# Example usage
hist = color_histogram(image, bins=32)

Feature Extraction

Histogram of Oriented Gradients (HOG)

Computes the HOG feature descriptor for an image.

# Example usage
hog_features = histogram_of_oriented_gradients(image, pixels_per_cell=(16, 16), cells_per_block=(4, 4), orientations=9)

Advanced Edge Detection

Sobel Edge Detection

Applies Sobel edge detection to an image.

# Example usage
sobel_edges = sobel_edge_detection(image)

Gabor Filtering

Gabor Features

Applies a Gabor filter to an image.

# Example usage
gabor_response = gabor_features(image, frequency=0.6)

Conclusion

This guide introduces the core functions in ShallowLearn.ComputerVisionFeatures to help analyze and process image data effectively. Explore each function and adapt them to your specific needs. For more detailed documentation, check the reference guide