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