Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-09-15 14:13:49 +08:00
commit 7e4ef6f98b
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+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+from argparse import ArgumentParser
+
+from xtcocotools.coco import COCO
+
+from mmpose.apis import (inference_mesh_model, init_pose_model,
+                         vis_3d_mesh_result)
+
+
+def main():
+    """Visualize the demo images.
+
+    Require the json_file containing boxes.
+    """
+    parser = ArgumentParser()
+    parser.add_argument('pose_config', help='Config file for detection')
+    parser.add_argument('pose_checkpoint', help='Checkpoint file')
+    parser.add_argument('--img-root', type=str, default='', help='Image root')
+    parser.add_argument(
+        '--json-file',
+        type=str,
+        default='',
+        help='Json file containing image info.')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        default=False,
+        help='whether to show img')
+    parser.add_argument(
+        '--out-img-root',
+        type=str,
+        default='',
+        help='Root of the output img file. '
+        'Default not saving the visualization images.')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+
+    args = parser.parse_args()
+
+    assert args.show or (args.out_img_root != '')
+
+    coco = COCO(args.json_file)
+    # build the pose model from a config file and a checkpoint file
+    pose_model = init_pose_model(
+        args.pose_config, args.pose_checkpoint, device=args.device.lower())
+
+    dataset = pose_model.cfg.data['test']['type']
+
+    img_keys = list(coco.imgs.keys())
+
+    # process each image
+    for i in range(len(img_keys)):
+        # get bounding box annotations
+        image_id = img_keys[i]
+        image = coco.loadImgs(image_id)[0]
+        image_name = os.path.join(args.img_root, image['file_name'])
+        ann_ids = coco.getAnnIds(image_id)
+
+        # make person bounding boxes
+        person_results = []
+        for ann_id in ann_ids:
+            person = {}
+            ann = coco.anns[ann_id]
+            # bbox format is 'xywh'
+            person['bbox'] = ann['bbox']
+            person_results.append(person)
+
+        # test a single image, with a list of bboxes
+        pose_results = inference_mesh_model(
+            pose_model,
+            image_name,
+            person_results,
+            bbox_thr=None,
+            format='xywh',
+            dataset=dataset)
+
+        if args.out_img_root == '':
+            out_file = None
+        else:
+            os.makedirs(args.out_img_root, exist_ok=True)
+            out_file = os.path.join(args.out_img_root, f'vis_{i}.jpg')
+
+        vis_3d_mesh_result(
+            pose_model,
+            pose_results,
+            image_name,
+            show=args.show,
+            out_file=out_file)
+
+
+if __name__ == '__main__':
+    main()