LeRobotDataset v2.1 (#711)
Co-authored-by: Remi <remi.cadene@huggingface.co> Co-authored-by: Remi Cadene <re.cadene@gmail.com>
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@@ -38,22 +38,40 @@ def safe_stop_image_writer(func):
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return wrapper
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def image_array_to_image(image_array: np.ndarray) -> PIL.Image.Image:
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def image_array_to_pil_image(image_array: np.ndarray, range_check: bool = True) -> PIL.Image.Image:
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# TODO(aliberts): handle 1 channel and 4 for depth images
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if image_array.ndim == 3 and image_array.shape[0] in [1, 3]:
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if image_array.ndim != 3:
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raise ValueError(f"The array has {image_array.ndim} dimensions, but 3 is expected for an image.")
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if image_array.shape[0] == 3:
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# Transpose from pytorch convention (C, H, W) to (H, W, C)
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image_array = image_array.transpose(1, 2, 0)
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elif image_array.shape[-1] != 3:
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raise NotImplementedError(
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f"The image has {image_array.shape[-1]} channels, but 3 is required for now."
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)
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if image_array.dtype != np.uint8:
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# Assume the image is in [0, 1] range for floating-point data
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image_array = np.clip(image_array, 0, 1)
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if range_check:
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max_ = image_array.max().item()
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min_ = image_array.min().item()
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if max_ > 1.0 or min_ < 0.0:
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raise ValueError(
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"The image data type is float, which requires values in the range [0.0, 1.0]. "
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f"However, the provided range is [{min_}, {max_}]. Please adjust the range or "
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"provide a uint8 image with values in the range [0, 255]."
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)
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image_array = (image_array * 255).astype(np.uint8)
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return PIL.Image.fromarray(image_array)
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def write_image(image: np.ndarray | PIL.Image.Image, fpath: Path):
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try:
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if isinstance(image, np.ndarray):
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img = image_array_to_image(image)
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img = image_array_to_pil_image(image)
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elif isinstance(image, PIL.Image.Image):
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img = image
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else:
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