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 mmcv
+import numpy as np
+
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class LoadImageFromFile:
+    """Loading image(s) from file.
+
+    Required key: "image_file".
+
+    Added key: "img".
+
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+        color_type (str): Flags specifying the color type of a loaded image,
+          candidates are 'color', 'grayscale' and 'unchanged'.
+        channel_order (str): Order of channel, candidates are 'bgr' and 'rgb'.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 to_float32=False,
+                 color_type='color',
+                 channel_order='rgb',
+                 file_client_args=dict(backend='disk')):
+        self.to_float32 = to_float32
+        self.color_type = color_type
+        self.channel_order = channel_order
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
+
+    def _read_image(self, path):
+        img_bytes = self.file_client.get(path)
+        img = mmcv.imfrombytes(
+            img_bytes, flag=self.color_type, channel_order=self.channel_order)
+        if img is None:
+            raise ValueError(f'Fail to read {path}')
+        if self.to_float32:
+            img = img.astype(np.float32)
+        return img
+
+    def __call__(self, results):
+        """Loading image(s) from file."""
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+
+        image_file = results.get('image_file', None)
+
+        if isinstance(image_file, (list, tuple)):
+            # Load images from a list of paths
+            results['img'] = [self._read_image(path) for path in image_file]
+        elif image_file is not None:
+            # Load single image from path
+            results['img'] = self._read_image(image_file)
+        else:
+            if 'img' not in results:
+                # If `image_file`` is not in results, check the `img` exists
+                # and format the image. This for compatibility when the image
+                # is manually set outside the pipeline.
+                raise KeyError('Either `image_file` or `img` should exist in '
+                               'results.')
+            assert isinstance(results['img'], np.ndarray)
+            if self.color_type == 'color' and self.channel_order == 'rgb':
+                # The original results['img'] is assumed to be image(s) in BGR
+                # order, so we convert the color according to the arguments.
+                if results['img'].ndim == 3:
+                    results['img'] = mmcv.bgr2rgb(results['img'])
+                elif results['img'].ndim == 4:
+                    results['img'] = np.concatenate(
+                        [mmcv.bgr2rgb(img) for img in results['img']], axis=0)
+                else:
+                    raise ValueError('results["img"] has invalid shape '
+                                     f'{results["img"].shape}')
+
+            results['image_file'] = None
+
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'to_float32={self.to_float32}, '
+                    f"color_type='{self.color_type}', "
+                    f'file_client_args={self.file_client_args})')
+        return repr_str