Introduction to Data Science project about automaticaly cropping archival images, so there is no photo border in the image. Team: Alvin Meltsov, Hain Zuppur.
Necessary dependencies for running the code:
-
cv2
pip install opencv-python -
cv2
pip install scikit-image -
argh
pip install argh
... and ofcourse:
pip install numpy pandas
## Running the script
To run the algorithm on a single image, run the edge_kernel_single.py and pass the image file name in as a argument.
python edge_kernel_single.py [your file]
There are optional parameters for the kernel passed on with argh:
-
--rotation [even int]an even number specifing the rotational range trialed with this algorithm. When using large pictures and you know the angle, set it to smaller number. Default 20 degrees (-10 .. 10). -
--minlen [float]float between 1 .. 0 which marks how long lines in relation to the length of the axis of the picture should be detected from the picture. Default 0.59. -
--thresh [float]float value which specifies the thresholding z-score where image is cropped. The higher the score the less the chance of cropping. Default 1.98. -
--sizediff [int]integer above 1. How much of size of the image must be preserved to crop. If the frame is on average 1/4 of the image, you don't want to set it to 8, which means that only 1/8 of the picture can remain.
To run the algorithm on a directory of images, run the run_batch.sh and pass the directory in as a argument.
./run_batch.sh [directory of your pictures]