imagecrop 0.0.1

Use OpenCV to extract image crops using homography and feature matching

Image extraction using a template. Uses homography and feature matching, and stores results in a SQLite database for faster reprocessing. Usage:

from image_extract.extract import Extracter
ex = Extracter()
ex.crop_images(image_directory, crop_template, file_extension)

Successful crops are extracted to a directory called successful_crops, directly underneath image_directory. Each template used creates a subdirectory, named after its MD5:

image_directory
    - img1.jpg
    - …
    - imgn.jpg
    - successful_crops
        - 2a1bdab44c5e81af34f47f3395a3da7e
            - img1_cropped.jpg

Call ex.summary(path) to see information on extracted crops for a given directory.

Call ex.delete(path[, template_md5]) to delete extracted crops for a given template. If no template value is given, all extracted crops in that directory are removed.

For best results, the template image should be of the same (or similar) resolution as the image from which the crop is to be extracted.