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--- |
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license: c-uda |
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--- |
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# ObjectPose9D Dataset |
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## Data Structure |
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Each data sample contains 4 attributes: |
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- **`source`**: Source dataset name (e.g., "cityscapes") |
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- **`prompt`**: Text description or prompt |
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- **`image`**: Image data (stored as binary) |
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- **`map`**: CNOCS Map (stored as binary in EXR format) |
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### Cityscapes Subset |
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Due to license restrictions, images from the **Cityscapes** source cannot be redistributed directly. |
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- For Cityscapes samples, the `image` field contains only the **relative path** within `leftImg8bit` |
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- You must download the original Cityscapes dataset from: [https://www.cityscapes-dataset.com/downloads/](https://www.cityscapes-dataset.com/downloads/) |
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- For other sources, `image` contains the actual binary image data |
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### CNOCS Map |
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The `map` field stores the CNOCS Map from the paper in EXR format as binary data. |
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## Usage |
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### Loading the Dataset |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("FudanCVL/ObjectPose9D", streaming=True) |
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for i, item in enumerate(dataset["train"]): |
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if item["source"] == "cityscapes": |
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# For cityscapes: image field is a path string |
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image_path = item["image"].decode("utf-8") |
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else: |
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with open(f"{i}_{item['source']}.jpg", "wb") as f: |
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f.write(item["image"]) |
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with open(f"{i}_{item['source']}.exr", "wb") as f: |
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f.write(item["map"]) |
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``` |
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### Visualizing CNOCS Maps |
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```python |
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import numpy as np |
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from openexr_numpy import imread |
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from PIL import Image |
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# Read EXR map |
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cnocs_map = imread("map.exr") |
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cnocs_map_uint8 = (cnocs_map * 255).clip(0, 255).astype(np.uint8) |
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img = Image.fromarray(cnocs_map_uint8) |
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img.save("map.png") |
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``` |
