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MBARI

semantic-release Supported Platforms license-GPL

About

voc-imagecropper is a simple utility to crop from PNG or JPEG images bounding box annotations in PASCAL VOC formatted annotations. This is used to crop (and optionally resize) images from full frames for classification testing.

Arguments

  • run as you: -u $(id -u):$(id -g)
  • remove after running: --rm
  • run interactively: -it
  • --resize the cropped image to the specified size, e.g. --resize 128x128 crop then resizes to 128x128
  • -d root directory to the raw data
  • -o path to output image crops to
  • --minsize minimum size pixel width or height dimension - useful to remove images too small for classification
  • --machine_friednly output class names with underscores instead of spaces, or special characters, e.g. dots, e.g. sp_A instead of sp. A
  • (optional) --labels list of space separated labels to load - defaults to everything.
  • (optional) --image_dir path where raw images associated with the annotations are located - overrides those defined in the PASCAL formatted annotations

Note

An exception to the standard PASCAL VOC format is that the uuid is used. If that exists in the annotation, it is used to name the prefix of the output file.
If not, the filename is used with an appended integer prefix. This is to ensure unique filenames for the cropped images.

Build

You can skip this step if you are using the pre-built docker image.

docker build -t mbari/voc-imagecropper .

Run example

docker run -it \
--rm -u $(id -u):$(id -g) \
-v $PWD/data:/data mbari/voc-imagecropper \
-d /data/annotations \
--image_dir /data/imgs \
-o /data/out

Should see output similar to:

2023-07-17 22:06:09,885 INFO Searching in data/annotations for .xml files
2023-07-17 22:06:09,886 INFO Found 1 files
2023-07-17 22:06:09,886 INFO Using 1 processes to convert 1 annotations ...
2023-07-17 22:06:10,341 INFO Calculating mean and std ...
2023-07-17 22:06:10,341 INFO Computing statistics for data
Computing statistics for data : 100% (1 of 1) || Elapsed Time: 0:00:00 Time:  0:00:00
2023-07-17 22:06:10,491 INFO Writing data/stats.json
2023-07-17 22:06:10,492 INFO Number of labels: 1
2023-07-17 22:06:10,492 INFO Number of frames: 1
2023-07-17 22:06:10,492 INFO Mean: [0.33466873 0.40399225 0.39004853]
2023-07-17 22:06:10,492 INFO Std: [0. 0. 0.]
2023-07-17 22:06:10,492 INFO Writing data/labels.txt