Update conversion script to skip missing files

requirements.txt

@@ -17,6 +17,9 @@ filterpy
 h5py
 
 # verified versions
-onnx==1.8.1
-onnxruntime==1.8.0
-onnx-simplifier==0.3.5
+# onnx==1.8.1
+# onnxruntime==1.8.0
+# onnx-simplifier==0.3.5
+onnx>=1.14.0
+onnxruntime>=1.14.0
+onnx-simplifier>=0.4.10

tools/convert_crowdhuman_to_coco.py

@@ -4,54 +4,71 @@
 from PIL import Image
 
 DATA_PATH = 'datasets/crowdhuman/'
-OUT_PATH = DATA_PATH + 'annotations/'
+OUT_PATH = os.path.join(DATA_PATH, 'annotations')
 SPLITS = ['val', 'train']
 DEBUG = False
 
 def load_func(fpath):
-    print('fpath', fpath)
-    assert os.path.exists(fpath)
-    with open(fpath,'r') as fid:
+    print('Loading annotation file:', fpath)
+    assert os.path.exists(fpath), f"Annotation file not found: {fpath}"
+    with open(fpath, 'r') as fid:
         lines = fid.readlines()
-    records =[json.loads(line.strip('\n')) for line in lines]
+    records = [json.loads(line.strip('\n')) for line in lines]
     return records
 
 if __name__ == '__main__':
     if not os.path.exists(OUT_PATH):
         os.mkdir(OUT_PATH)
     for split in SPLITS:
-        data_path = DATA_PATH + split
-        out_path = OUT_PATH + '{}.json'.format(split)
-        out = {'images': [], 'annotations': [], 'categories': [{'id': 1, 'name': 'person'}]}
-        ann_path = DATA_PATH + 'annotation_{}.odgt'.format(split)
+        data_path = os.path.join(DATA_PATH, split)
+        out_path = os.path.join(OUT_PATH, '{}.json'.format(split))
+        out = {
+            'images': [],
+            'annotations': [],
+            'categories': [{'id': 1, 'name': 'person'}]
+        }
+        ann_path = os.path.join(DATA_PATH, 'annotation_{}.odgt'.format(split))
         anns_data = load_func(ann_path)
         image_cnt = 0
         ann_cnt = 0
-        video_cnt = 0
+
         for ann_data in anns_data:
+            file_path = os.path.join(DATA_PATH, 'CrowdHuman_{}'.format(split), '{}.jpg'.format(ann_data['ID']))
+            if not os.path.exists(file_path):
+                print('Skipping missing file:', file_path)
+                continue  # Skip this annotation if the file is not found
+            try:
+                im = Image.open(file_path)
+            except Exception as e:
+                print("Error opening file {}: {}".format(file_path, e))
+                continue
+
             image_cnt += 1
-            file_path = DATA_PATH + 'CrowdHuman_{}/'.format(split) + '{}.jpg'.format(ann_data['ID'])
-            im = Image.open(file_path)
-            image_info = {'file_name': '{}.jpg'.format(ann_data['ID']), 
-                          'id': image_cnt,
-                          'height': im.size[1], 
-                          'width': im.size[0]}
+            image_info = {
+                'file_name': '{}.jpg'.format(ann_data['ID']),
+                'id': image_cnt,
+                'height': im.size[1],
+                'width': im.size[0]
+            }
             out['images'].append(image_info)
+
             if split != 'test':
-                anns = ann_data['gtboxes']
-                for i in range(len(anns)):
+                anns = ann_data.get('gtboxes', [])
+                for ann in anns:
                     ann_cnt += 1
-                    fbox = anns[i]['fbox']
-                    ann = {'id': ann_cnt,
-                         'category_id': 1,
-                         'image_id': image_cnt,
-                         'track_id': -1,
-                         'bbox_vis': anns[i]['vbox'],
-                         'bbox': fbox,
-                         'area': fbox[2] * fbox[3],
-                         'iscrowd': 1 if 'extra' in anns[i] and \
-                                         'ignore' in anns[i]['extra'] and \
-                                         anns[i]['extra']['ignore'] == 1 else 0}
-                    out['annotations'].append(ann)
-        print('loaded {} for {} images and {} samples'.format(split, len(out['images']), len(out['annotations'])))
-        json.dump(out, open(out_path, 'w'))
+                    fbox = ann['fbox']
+                    annotation = {
+                        'id': ann_cnt,
+                        'category_id': 1,
+                        'image_id': image_cnt,
+                        'track_id': -1,
+                        'bbox_vis': ann['vbox'],
+                        'bbox': fbox,
+                        'area': fbox[2] * fbox[3],
+                        'iscrowd': 1 if ('extra' in ann and 'ignore' in ann['extra'] and ann['extra']['ignore'] == 1) else 0
+                    }
+                    out['annotations'].append(annotation)
+
+        print('Loaded {}: {} images and {} annotations'.format(split, len(out['images']), len(out['annotations'])))
+        with open(out_path, 'w') as fout:
+            json.dump(out, fout)