Update README.md

README.md

@@ -1,3 +1,59 @@
----
-license: c-uda
----
+---
+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")
+```