gocheck 0.1.3

A Python library for barrel distortion correction in wide-angle images

GoCheck

A Python library for barrel distortion correction in wide-angle images using field of view (FOV) calculations.

Features

• Theoretical Distortion Calculation: Calculate distortion coefficients (k1, k2) based on field of view
• Barrel Distortion Correction: Remove barrel distortion from wide-angle images
• OpenCV Integration: Built on top of OpenCV for efficient image processing
• Simple API: Easy-to-use functions for quick distortion correction

Installation

pip install gocheck

Quick Start

import gocheck

# Correct barrel distortion using field of view
corrected_image = gocheck.undistortion_fov('input_image.jpg', fov=120)

# Calculate theoretical distortion coefficients
k1, k2 = gocheck.theoretical_k1_k2_calculation(fov_degrees=120, image_width=500)
print(f"Distortion coefficients: k1={k1}, k2={k2}")

API Reference

undistortion_fov(image_path, fov)

Corrects barrel distortion in an image based on the field of view.

Parameters:

• image_path (str): Path to the input image
• fov (float): Field of view in degrees

Returns:

• numpy.ndarray: Corrected image as NumPy array

theoretical_k1_k2_calculation(fov_degrees, image_width=500)

Calculates theoretical distortion coefficients based on field of view.

Parameters:

• fov_degrees (float): Field of view in degrees
• image_width (int, optional): Image width in pixels (default: 500)

Returns:

• tuple: (k1, k2) distortion coefficients

Dependencies

• OpenCV (opencv-python >= 4.5.0)
• NumPy (numpy >= 1.19.0)

Use Cases

• Action camera footage correction
• Wide-angle lens distortion removal
• Drone camera image processing
• Security camera footage enhancement
• VR/AR content preprocessing

Example

import cv2
import matplotlib.pyplot as plt
import gocheck

# Load and correct an image with 150° FOV
corrected = gocheck.undistortion_fov('wide_angle_photo.jpg', fov=150)

# Save the corrected image (BGR format)
cv2.imwrite('corrected_photo.jpg', corrected)

# For matplotlib display, convert to RGB
corrected_rgb = cv2.cvtColor(corrected, cv2.COLOR_BGR2RGB)
plt.imshow(corrected_rgb)
plt.show()

# For OpenCV display (BGR format)
cv2.imshow('Corrected', corrected)
cv2.waitKey(0)
cv2.destroyAllWindows()

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

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

Changelog

• v0.1.3: Reverted to BGR output format to maintain OpenCV ecosystem consistency - use cv2.cvtColor(result, cv2.COLOR_BGR2RGB) for matplotlib display
• v0.1.2: Fixed color display issue - function now returns RGB format compatible with matplotlib (no more cv2.cvtColor needed)
• v0.1.1: Added direct import support - now you can use gocheck.undistortion_fov() instead of gocheck.image_distortion.undistortion_fov()
• v0.1.0: Initial release with barrel distortion correction functionality

Author

jujuspace