voc2coco

This is script for converting VOC format XMLs to COCO format json(ex. coco_eval.json).

Why we need to convert VOC xmls to COCO format json ?

We can use COCO API, this is very useful(ex. calculating mAP).

How to use

1. Make labels.txt

labels.txt if need for making dictionary for converting label to id.

Sample labels.txt

Label1
Label2
...

In order to get all labels from your *.xml files, you can use this command in shell:

grep -REoh '<name>.*</name>' /Path_to_Folder | sort | uniq

This will search for all name tags in VOC.xml files, then show unique ones. You can also go further and create labels.txt file.

grep -ERoh '<name>(.*)</name>' /Path_to_folder | sort | uniq | sed 's/<name>//g' | sed 's/<\/name>//g' > labels.txt

2. Run script

2.1 Usage 1(Use ids list)

$ python voc2coco.py \
    --ann_dir /path/to/annotation/dir \
    --ann_ids /path/to/annotations/ids/list.txt \
    --labels /path/to/labels.txt \
    --output /path/to/output.json \
    <option> --ext xml

2.2 Usage 2(Use annotation paths list)

Sample paths.txt

/path/to/annotation/file.xml
/path/to/annotation/file2.xml
...

$ python voc2coco.py \
    --ann_paths_list /path/to/annotation/paths.txt \
    --labels /path/to/labels.txt \
    --output /path/to/output.json \
    <option> --ext xml

2.3 Usage 3(Annotation paths list with ordered ids)

$ python voc2coco.py \
    --ann_paths_list /path/to/annotation/paths.txt \
    --labels /path/to/labels.txt \
    --output /path/to/output.json \
    <option> --ext xml \
    --ordered_ids

3. Example of usage

In this case, you can convert Shenggan/BCCD_Dataset: BCCD Dataset is a small-scale dataset for blood cells detection. by this script.

$ python voc2coco.py
    --ann_dir sample/Annotations \
    --ann_ids sample/dataset_ids/test.txt \
    --labels sample/labels.txt \
    --output sample/bccd_test_cocoformat.json \
    --ext xml

# Check output
$ ls sample/ | grep bccd_test_cocoformat.json
bccd_test_cocoformat.json

# Check output
cut -f -4 -d , sample/bccd_test_cocoformat.json
{"images": [{"file_name": "BloodImage_00007.jpg", "height": 480, "width": 640, "id": "BloodImage_00007"}