The purpose of this program is to deduplicate images! The program gives the option of deduplicating in two styles:
- Near duplicates
- Exact duplicates
Just clone this repository and run the setup.py install. NOTE: This library was written for Python 2.
git clone https://github.com/nmante/image_deduplication.git
cd image_deduplication
# You may need to run this setup.py install with sudo
python setup.py install
Once you run that script, try running this command:
dedup
You should see a help menu on how to use the Command Line Interface!
To try out the program on a few images, you can use the images/ directory. Just run these commands in the dedup directory:
chmod a+x test.sh
./test.sh
This will create two output directories:
test_output_exact_deduplicated_images/
test_output_near_deduplicated_images/
The directories will contain a few things
- Unique images
- And a folder
_duplicateswith the duplicate images
usage: dedup [-h] [-e | -n] [-i DUMP_DIR | -l JSON_METADATA]
[-o OUTPUT_JSON] [-d OUTPUT_DIR] [-s] [-j NUM_JOBS]
[-k BIT_DISTANCE]
This program takes a set of N images, finds duplicate images in the set, and
returns a set of deduplicated images.
optional arguments:
-h, --help show this help message and exit
-e, --exact_duplicates
Use this flag to deduplicate images via an "exact"
deduplication methodology. Default behavior is to use
exact duplicates.
-n, --near_duplicates
Use this flag to deduplicate images via a "near"
deduplication methodology
-i DUMP_DIR, --dump_dir DUMP_DIR
The input directory containing your unprocessed images
-l JSON_METADATA, --json_metadata JSON_METADATA
A jsonlines file containing the filename and tika
metadata files
-o OUTPUT_JSON, --output_json OUTPUT_JSON
Write the locations and hashes of each deduplicated
image to a JSON file. Defaults to
'image_locations.json'
-d OUTPUT_DIR, --output_dir OUTPUT_DIR
Output deduplicated images to directory.
-s, --show_duplicates
Use this flag to generate a directory which contains
duplicates. Defaults behavior doesn't show duplicates.
-j NUM_JOBS, --num_jobs NUM_JOBS
Number of worker threads to divide the deduplication.
Defaults to 2. The more images the more jobs you
should create
-k BIT_DISTANCE, --bit_distance BIT_DISTANCE
Difference k between simhash fingerprints
The program requires a directory of images. You don't need to worry about the structure of the folder (i.e. subdirectories). If there are images in the directory, the program will find them.
If you're using Apatche Nutch, generate a dump directory
# Merge segments from crawl
bin/nutch mergesegs <MERGED_SEG_DIR_TO_CREATE> -dir <CRAWL_SEGMENTS_DIR>
# Create a dump directory from that merged segment
bin/nutch dump -segment <PREVIOUSLY_CREATED_MERGED_SEG_DIR> -outputDir <OUTPUT_DUMP_DIR_TO_CREATE>
This dump directory would be what you pass to the deduplication script.
# Use the -s flag to also show duplicate images
# Also split this among 8 jobs with the -j flag
dedup -i <INPUT_IMAGE_DIR> -d <OUTPUT_IMAGE_DIR_TO_CREATE> -s -j 8
# Use the -n flag to do near deduplication
# Use the -j flag to split this among 4 jobs
dedup -i <INPUT_IMAGE_DIR> -d <OUTPUT_IMAGE_DIR_TO_CREATE> -s -n -j 4
The program outputs a few things:
- JSON - JSON file which shows the file locations of deduplicated images, as well as the locations of the duplicates
- Initial_Image_Count - The number of images before the algorithm runs
- Final_Image_Count - The final number of images after deduplication
- Images (OPTIONAL) - If you choose, the program can conveniently put the deduplicated (and duplicate) images into an output folder
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