Add Label Factory dataset 0000

README.md

@@ -49,20 +49,75 @@ Every configuration provides deterministic `train`, `validation`, and `test`
 splits. Adding another numbered capture appends rows through the wildcard paths;
 it does not replace earlier datasets.
 
+## Loading all or several numbered datasets
+
+The configuration automatically combines every uploaded directory (`0000`,
+`0001`, `0002`, ...). Filter `dataset_id` to use selected captures:
+
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("KarolyArmin/Hand_tools", "object_pose_estimation")
-train = dataset["train"]
-validation = dataset["validation"]
-test = dataset["test"]
+poses = load_dataset("KarolyArmin/Hand_tools", "object_pose_estimation")
+selected_ids = set(["0000", "0001", "0004"])
+for split_name in poses:
+    poses[split_name] = poses[split_name].filter(
+        lambda row: row["dataset_id"] in selected_ids
+    )
+```
+
+## Depth-estimation example
 
-# PNG payloads are stored directly in Parquet.
+```python
+from datasets import load_dataset
 from io import BytesIO
 from PIL import Image
-rgb = Image.open(BytesIO(train[0]["image_png"]))
-depth = Image.open(BytesIO(train[0]["depth_png"]))
-mask = Image.open(BytesIO(train[0]["instance_mask_png"]))
+import numpy as np
+
+depth_dataset = load_dataset("KarolyArmin/Hand_tools", "depth_estimation")
+sample = depth_dataset["train"][0]
+rgb = Image.open(BytesIO(sample["image_png"])).convert("RGB")
+depth_native = np.asarray(Image.open(BytesIO(sample["depth_png"])))
+depth_mm = depth_native.astype(np.float32) * sample["depth_scale"]
+depth_m = depth_mm / 1000.0
+intrinsics_3x3 = np.asarray(sample["camera_intrinsics"]).reshape(3, 3)
+```
+
+## Instance-segmentation example
+
+```python
+from datasets import load_dataset
+from io import BytesIO
+from PIL import Image
+import json
+import numpy as np
+
+instances = load_dataset("KarolyArmin/Hand_tools", "instance_segmentation")
+sample = instances["validation"][0]
+rgb = Image.open(BytesIO(sample["image_png"])).convert("RGB")
+instance_mask = np.asarray(Image.open(BytesIO(sample["instance_mask_png"])))
+annotations = json.loads(sample["annotations_json"])
+boxes_xywh = np.asarray(sample["boxes_xywh"])
+instance_names = sample["instance_names"]
+```
+
+## Object-pose-estimation example
+
+```python
+from datasets import load_dataset
+from io import BytesIO
+from PIL import Image
+import numpy as np
+
+poses = load_dataset("KarolyArmin/Hand_tools", "object_pose_estimation")
+sample = poses["test"][0]
+rgb = Image.open(BytesIO(sample["image_png"])).convert("RGB")
+depth = np.asarray(Image.open(BytesIO(sample["depth_png"])))
+instance_mask = np.asarray(Image.open(BytesIO(sample["instance_mask_png"])))
+world_from_camera = np.asarray(sample["world_from_camera"]).reshape(4, 4)
+camera_from_world = np.asarray(sample["camera_from_world"]).reshape(4, 4)
+world_from_objects = np.asarray(sample["world_from_objects"]).reshape(-1, 4, 4)
+camera_from_objects = np.asarray(sample["camera_from_objects"]).reshape(-1, 4, 4)
+object_names = sample["object_names"]
 ```
 
 ## Contents