Reduces arbitary images' dimensions using the content-aware seam carving algorithm.
Depends on numpy and scipy, which can be installed with PIP.
The script was tested with Python 2.7.14 on Windows 10.
seamcarving.py <inputfilename> <reduceheightby> <reducewidthby> <outputfilename>
All parameters are mandatory.
Example call:
seamcarving.py tower.png 6 94 tower-seamcarved.png
Images must have 3 channels per pixel and be in a format that scipy.misc.imread understands.
I used this image for testing. It's 274 x 186 pixels (width x height). The example call above reduces it to a square 180 x 180 image.
Limitations of the implementation:
- The image is always converted to greyscale.
- The energy function is rather simple.
- First, all vertical seams are removed, then all horizontal seams. I.e. there is no minimizing of total seam cost.
- When using 'large' images (i.e. anything with more than 400 pixels in any dimension), the process is painfully slow.
Written as a programming excercise for the Computer Vision course of the winter semester 2017/18 at the University of Münster.