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 json
+import os
+from argparse import ArgumentParser
+
+from mmcv import track_iter_progress
+from PIL import Image
+from xtcocotools.coco import COCO
+
+from mmpose.apis import inference_top_down_pose_model, init_pose_model
+
+
+def main():
+    """Visualize the demo images.
+
+    pose_keypoints 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 person bboxes in COCO format.')
+    parser.add_argument(
+        '--out-json-file',
+        type=str,
+        default='',
+        help='Output json contains pseudolabeled annotation')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        default=False,
+        help='whether to show img')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--kpt-thr', type=float, default=0.3, help='Keypoint score threshold')
+
+    args = parser.parse_args()
+
+    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())
+
+    # optional
+    return_heatmap = False
+
+    # e.g. use ('backbone', ) to return backbone feature
+    output_layer_names = None
+
+    categories = [{'id': 1, 'name': 'person'}]
+    img_anno_dict = {'images': [], 'annotations': [], 'categories': categories}
+
+    # process each image
+    ann_uniq_id = int(0)
+    for i in track_iter_progress(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'])
+
+        width, height = Image.open(image_name).size
+        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)
+
+        pose_results, returned_outputs = inference_top_down_pose_model(
+            pose_model,
+            image_name,
+            person_results,
+            bbox_thr=None,
+            format='xywh',
+            dataset=dataset,
+            return_heatmap=return_heatmap,
+            outputs=output_layer_names)
+
+        # add output of model and bboxes to dict
+        for indx, i in enumerate(pose_results):
+            pose_results[indx]['keypoints'][
+                pose_results[indx]['keypoints'][:, 2] < args.kpt_thr, :3] = 0
+            pose_results[indx]['keypoints'][
+                pose_results[indx]['keypoints'][:, 2] >= args.kpt_thr, 2] = 2
+            x = int(pose_results[indx]['bbox'][0])
+            y = int(pose_results[indx]['bbox'][1])
+            w = int(pose_results[indx]['bbox'][2] -
+                    pose_results[indx]['bbox'][0])
+            h = int(pose_results[indx]['bbox'][3] -
+                    pose_results[indx]['bbox'][1])
+            bbox = [x, y, w, h]
+            area = round((w * h), 0)
+
+            images = {
+                'file_name': image_name.split('/')[-1],
+                'height': height,
+                'width': width,
+                'id': int(image_id)
+            }
+
+            annotations = {
+                'keypoints': [
+                    int(i) for i in pose_results[indx]['keypoints'].reshape(
+                        -1).tolist()
+                ],
+                'num_keypoints':
+                len(pose_results[indx]['keypoints']),
+                'area':
+                area,
+                'iscrowd':
+                0,
+                'image_id':
+                int(image_id),
+                'bbox':
+                bbox,
+                'category_id':
+                1,
+                'id':
+                ann_uniq_id,
+            }
+
+            img_anno_dict['annotations'].append(annotations)
+            ann_uniq_id += 1
+
+        img_anno_dict['images'].append(images)
+
+    # create json
+    with open(args.out_json_file, 'w') as outfile:
+        json.dump(img_anno_dict, outfile, indent=2)
+
+
+if __name__ == '__main__':
+    main()
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+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import subprocess
+from datetime import date
+
+import torch
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Process a checkpoint to be published')
+    parser.add_argument('in_file', help='input checkpoint filename')
+    parser.add_argument('out_file', help='output checkpoint filename')
+    args = parser.parse_args()
+    return args
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    if out_file.endswith('.pth'):
+        out_file_name = out_file[:-4]
+    else:
+        out_file_name = out_file
+
+    date_now = date.today().strftime('%Y%m%d')
+    final_file = out_file_name + f'-{sha[:8]}_{date_now}.pth'
+    subprocess.Popen(['mv', out_file, final_file])
+
+
+def main():
+    args = parse_args()
+    process_checkpoint(args.in_file, args.out_file)
+
+
+if __name__ == '__main__':
+    main()