.gitignore

@@ -1,5 +0,0 @@
-data/
-exp/
-detector/
-out/
-pretrained-models/

loop_alphapose_training.py

@@ -1,70 +0,0 @@
-"""
-TODO this script runs Alphapose's train.py, the created model is used to re-annotate the training-images, which is then fed back into the system
-
-For now the only thing it does is that it merges alphapose-results.json with the coco input dataset.
-"""
-
-import argparse
-import datetime
-from io import TextIOWrapper
-import json
-import logging
-
-logging.basicConfig()
-logger = logging.getLogger('loop_alphapose_training')
-logger.setLevel(logging.INFO)
-
-def coco_alphapose_merge_results(annotations_file: TextIOWrapper, results_file:TextIOWrapper, out_file: TextIOWrapper):
-    today = datetime.datetime.now().strftime("%Y/%m/%d")
-    info = {"description": "COCO 2017 Dataset, modified by Ruben van de Ven","url": "http://cocodataset.org","version": "0.1","year": 2023,"contributor": "COCO Consortium, Ruben van de Ven","date_created": today}
-    annotations = json.loads(annotations_file.read())
-    results = json.loads(results_file.read())
-    annotations_ann:list = annotations['annotations']
-
-    id_counts = {}
-
-
-    for i, result in enumerate(results):
-        if type(result['image_id']) == str:
-            result['image_id'] = int(result['image_id'][:-4])
-        
-        result['id'] = i
-        result['iscrowd'] = 0 # TODO make sure this is a right terminology/assumption (what is this crowd here anyway individaul/crowd?)
-        result['bbox'] = result['box'] # TODO result.pop('box') to rename instead of copy
-        result['area'] = 1 # TODO : for now to bypass ignore in alphapose/datasets/mscoco.py:87
-        result['num_keypoints'] = 17 # TODO : verify that this is indeed always all points
-        
-        # There can be multiple annotations per image. Try to match the originals by keeping track
-        # of their order of occurence
-        # if result['image_id'] not in id_counts:
-        #     id_counts[result['image_id']] = 0
-
-        # # find matching annotations in original
-        # origs = list(filter(lambda ann: ann['image_id'] == result['image_id'], annotations_ann))
-        # assert len(origs) > id_counts[result['image_id']], f"Len should be one, found {len(origs)} for {result['image_id']}: {origs=}"
-        # orig = origs[id_counts[result['image_id']]]
-        # id_counts[result['image_id']] += 1
-
-        # result['id'] = orig['id'] # we keep track of the original id
-        
-    annotations['annotations'] = results
-    annotations['info'] = info
-
-    json.dump(annotations,out_file)
-    logger.info(f'wrote to {out_file.name}')
-
-
-if __name__ == "__main__":
-
-    parser = argparse.ArgumentParser(description='Merge alphapose-results.json with an input dataset')
-    parser.add_argument('--annotations-file', required=True, type=argparse.FileType('r'),
-                        help='an annotations file from the COCO dataset (eg. person_keypoints_train2017.json)')
-    parser.add_argument('--results-file', required=True, type=argparse.FileType('r'),
-                        help='path to the alphapose-results.json')
-    parser.add_argument('--out-file', required=True, type=argparse.FileType('w'),
-                        help='the filename of the merged result')
-    
-
-    args = parser.parse_args()
-    
-    coco_alphapose_merge_results(args.annotations_file, args.results_file, args.out_file)