Because sometimes, you have folders full of badly named pictures, and you want to be able to understand what you have in your hard drive.
Prerequisites & installation
You need DeepDetect installed, the easiest way is using docker:
docker pull beniz/deepdetect_cpu docker run -d -p 8080:8080 beniz/deepdetect_cpu
Right now, the only supported installation of DeepDetect that works with DeepSort is the deepdetect_cpu container,
because it contain the good path for the pre-installed
Then, download the latest DeepSort release from https://github.com/CorentinB/DeepSort/releases
Unzip your release, rename it
DeepSort and make it executable with:
chmod +x DeepSort
DeepSort support few different parameters, you're obliged to fill two of them:
-u that correspond to the URL of your DeepDetect server.
-i that correspond to your local folder full of images.
For more informations, refeer to the helper:
./DeepSort --help [-u|--url] is required usage: deepsort [-h|--help] -u|--url "<value>" -i|--input "<value>" [-o|--output "<value>"] [-n|--network (resnet-50|googlenet)] [-R|--recursive] [-j|--jobs <integer>] [-d|--dry-run] AI powered image tagger backed by DeepDetect Arguments: -h --help Print help information -u --url URL of your DeepDetect instance (i.e: http://localhost:8080) -i --input Your input folder. -o --output Your output folder, if output is set, original files will not be renamed, but the renamed version will be copied in the output folder. -n --network The pre-trained deep neural network you want to use, can be resnet-50 or googlenet. Default: resnet-50 -R --recursive Process files recursively. -j --jobs Number of parallel jobs. Default: 1 -d --dry-run Just classify images and return results, do not apply.
- Getting docker out of the loop (each user install his own DeepDetect)
- ResNet 50 integration
- Output folder (copy and not rename)
- NSFW tagging (Yahoo open_nsfw)
- XMP metadata writing
- GPU support