sharpedge 1.1.6

Collection of image processing tools and techniques, including padding, flipping, colorscale conversion, seam carving, and image shrinking. Designed for efficient manipulation and transformation of images.

SharpEdge

Collection of image processing tools and techniques, including padding, flipping, colorscale conversion, seam carving, and image shrinking. Designed for efficient manipulation and transformation of images.

Summary

This package provides a comprehensive set of image processing utilities tailored for seamless integration of image manipulation projects. It includes essential tools for image transformations, seam carving, and processing. This package enables users to efficiently prepare, manipulate and modify image based on the needs of the user. The sharpedge package is valuable for users seeking tools for resizing and compressing images while maintaining visual content integrity.

Functions

• reposition_image
  This function allows you to manipulate the position and orientation of an image by flipping, rotating, or shifting it. You can customize the actions based on your needs, such as horizontal flips, rotation to the left or right, or shifting the image along the X and Y axes.

• frame_image
  Enhance the aesthetic of your image by adding a decorative frame around it. The frame can be customized with adjustable border sizes, placement inside or outside the image, and a specified color for the border.

• modulate_image
  Modify the color channels of an image to achieve effects like grayscale conversion or specific channel isolation. This function is useful for color manipulation tasks, including transforming RGB images to simpler formats for analysis or artistic purposes.

• pooling_image
  Apply pooling techniques to an image using a defined window size and a specified function, such as mean or max pooling. Pooling is commonly used to reduce image dimensions while preserving key features, making it especially relevant for preprocessing in computer vision tasks.

• pca_compression
  Compress an image using Principal Component Analysis (PCA) by retaining only the most significant features while discarding less important data. This method is ideal for reducing file size while preserving a specified proportion of the original variance in the image.

• seam_carve
  Resize an image intelligently using seam carving to preserve important content while adjusting dimensions. This technique minimizes distortion by removing or inserting paths of least importance, making it effective for content-aware resizing.

Where This Fits in the Python Ecosystem

This package fits into the broader Python image ecosystem, along with packages like OpenCV, Pillow, and scikit-learn. While OpenCV and Pillow provide general-purpose image processing tools, and scikit-learn includes PCA for dimensionality reduction, this package stands out for its simplicity and streamlined functionality. It specializes in content-aware resizing and transformations, focusing on practical utilities for advanced image manipulations. It offers unique capabilities, allowing users to accomplish tasks quickly without needing to manage complex parameters.

Installation

$ pip install sharpedge

Usage

To harness the image processing magic of SharpEdge, follow these steps:

1. Import the required functions from the package:

   from sharpedge import reposition_image
   from sharpedge import frame_image
   from sharpedge import modulate_image
   from sharpedge import pooling_image
   from sharpedge import pca_compression
   from sharpedge import seam_carve
   

2. (Optional) Load your image as a NumPy array.

   # Ensure that you have matplotlib and numpy installed in your environment
   import matplotlib.pyplot as plt
   import numpy as np
   
   # Load the image from the given path
   img = plt.imread(PATH_TO_IMAGE)
   
   # Only execute this conversion for functions other than seam_carve
   # Convert the loaded image to the range [0, 255] and cast it to np.uint8 
   # This is necessary for processing images with most functions, but 'seam_carve' handles float inputs directly
   img = np.round(np.clip(img * 255, 0, 255)).astype(np.uint8)
   

3. Process your images using the available functions:

   • Flip, rotate, and shift an image based on the specified requested action：

     # Flip horizontally, rotate left, and shift the image
     repositioned_img = reposition_image(img, flip='horizontal', rotate='left', shift_x=10, shift_y=20)
     

   • Add a decorative frame around the image with customizable color:

     # Add a frame around your image
     framed_img = frame_image(img, h_border=30, w_border=30, inside=True, color=255)
     

   • Convert or manipulate image color channels:

     # Convert an RGB image to grayscale
     grayscale_image = modulate_image(rgb_image, mode='gray')
     

   • Perform pooling on an image using a specified window size and pooling function:

     # Perform pooling on an image with mean pooling function
     pooled_img = pooling_image(img, window_size=10, func=np.mean)
     

   • Compress the input image using Principal Component Analysis (PCA):

     # Compress a grayscale image by retaining 80% of the variance
     compressed_img = pca_compression(grayscale_img, preservation_rate=0.8)
     

   • Resize the image using seam carving to the target dimensions:

     # Seam carve an image to resize it to the target dimensions
     resized_img = seam_carve(img, target_height=300, target_width=400)
     

Contributors

Archer Liu, Hankun Xiao, Inder Khera, Jenny Zhang (ordered alphabetically)

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

sharpedge was created by Jenny Zhang, Archer Liu, Inder Khera, Hankun Xiao. It is licensed under the terms of the MIT license.

Credits

sharpedge was created with cookiecutter and the py-pkgs-cookiecutter template.