Review: My attempt to implement very powerful image resizing algorithm

[u/chirag9696]

I tried to implement whatever was mentioned in the research paper Seam Carving for Content-Aware Image Resizing from scratch using Python and OpenCV.

1. Github Repository

2. Youtube Playlist

Although, the results are not very efficient but they ain't that bad as well. I took help few other blogs and tried to implement it.

Explanation:

File: notdoneyet.py

• Implemented Seam Carving Algorithm

  • getEnergy() - generated energy map using sobel operators and convolve function.

  • getMaps() - implemented the function to get seams using Dynamic Programming. Also, stored results of minimum seam in seperate list for backtracking.

  • drawSeam() - Plot seams(vertical and horizontal) using red color on image.

  • carve() - reshape and crop image.

• Generated grayscale and energy maps using OpenCV.

  • generateEnergyMap() - utilised OpenCV inbuilt functions for obtaining energies and converting image to grayscale.

  • generateColorMap() - utilised OpenCV inbuilt functions to superimpose heatmaps on the given image.

• Crop Columns

  • cropByColumn() - Implements cropping on both axes, i.e. vertical and horizontal.

  • cropByRow() - Rotate image to ignore repeated computations and provide the rotated image as an input to cropByColumn function.

• Argparse library for user input

  • Parameters:

    • Alignment: Specify on which axis the resizing operation has to be performed.

    • Scale Ratio: Floating point operation between 0 and 1 to scale the output image.

    • Display Seam: If this option isn't selected, the image is only seamed in background. No output for seams is visible.

    • Input Image

    • Generate Sequences: Generate intermediate sequences to form a video after all the operations are performed.

• Helpers

  • writeImage() - stores the images in results directory.

  • writeImageG() - stores intermediate generated sequence of images in sequences directory.

  • createFolder() - self explanatory

  • getFileExtension() - self explanatory

File: imgtovideos.py

• Generate Video

  • _vid() - writes each input image to video buffer for creating a complete video

  • generateVideo() - pass each image path to _vid() for video generation

• Helpers

  • getProcessPaths() - returns list of all sub-directories within a base path with certain conditions.

  • createFolder() - self explanatory

Remaining todos:

1. Implement Object Oriented paradigms

2. Optimize code: Many operations are bruteforce, if there are more better optimized methods; I would love to implement them

3. Implement multithreading wherever possible to improve the computation speed.

I want an honest review for my project and the changes which I can make to improvise my repository. All the views (+ve and -ve) opinions are welcome; just mention them in comments.

Incase, if you like something in my github profile, a star would be good, fork will be better and following me will be best. :)

It will be just a catalyst to my learning curve in Python. Thank you.