Added

OpenHumnoidActuatedFaceData.py

@@ -0,0 +1,119 @@
+import json, random, math, os
+from pathlib import Path
+from datasets import (
+    BuilderConfig, DatasetInfo, DownloadManager, GeneratorBasedBuilder,
+    SplitGenerator, Split, Features, Image, Value
+)
+from huggingface_hub import hf_hub_url
+
+_REPO_ID = "infosys/OpenHumnoidActuatedFaceData"
+_IMAGES_PER_SHARD = 10_000            # how many files you put in each tar
+_TAR_TPL = "images-{start:05d}-{end:05d}.tar"  # file-name pattern
+
+
+class ImageSubsetConfig(BuilderConfig):
+    def __init__(self, name, sample_size=None, **kw):
+        super().__init__(name=name, version="1.0.2",
+                         description=kw.get("description", ""))
+        self.sample_size = sample_size
+
+
+class MyImageDataset(GeneratorBasedBuilder):
+    BUILDER_CONFIGS = [
+        ImageSubsetConfig("full",  sample_size=None,
+                          description="Entire dataset (≈100 GB)"),
+        ImageSubsetConfig("small", sample_size=20_000,
+                          description="20 K random images"),
+    ]
+    DEFAULT_CONFIG_NAME = "small"
+
+    # ------------------------------------------------------------------ #
+    # 1. Schema                                                          #
+    # ------------------------------------------------------------------ #
+    def _info(self):
+        return DatasetInfo(
+            description="Humanoid face images + 16 servo angles.",
+            features=Features(
+                {
+                    "image": Image(),              # PIL.Image is fine
+                    "actuated_angle":
+                        {str(i): Value("int32") for i in range(16)},
+                }
+            ),
+        )
+
+    # ------------------------------------------------------------------ #
+    # 2. Download                                                         #
+    # ------------------------------------------------------------------ #
+    def _split_generators(self, dl_manager: DownloadManager):
+        # ---- 2-a: load metadata -------------------------------------- #
+        meta_url = hf_hub_url(_REPO_ID, "metadata.json", repo_type="dataset")
+        meta_path = dl_manager.download(meta_url)
+        with open(meta_path, encoding="utf-8") as f:
+            metadata = json.load(f)
+
+        all_names = sorted(metadata)
+        selected = (                           # sampling logic
+            sorted(random.sample(all_names, self.config.sample_size))
+            if self.config.sample_size else all_names
+        )
+        selected_set = set(selected)
+
+        # ---- 2-b: figure out which shards we need -------------------- #
+        max_idx = len(all_names) - 1
+        n_shards = math.floor(max_idx / _IMAGES_PER_SHARD) + 1
+        shard_files = [
+            _TAR_TPL.format(start=s*_IMAGES_PER_SHARD,
+                            end=min((s+1)*_IMAGES_PER_SHARD-1, max_idx))
+            for s in range(n_shards)
+        ]
+
+        # ---- 2-c: download (and extract) each tar -------------------- #
+        tar_urls   = [hf_hub_url(_REPO_ID, f, repo_type="dataset")
+                      for f in shard_files]
+        local_tars = dl_manager.download(tar_urls)          # .tar paths
+
+        #   we’ll stream from the tar, so no extract() needed
+
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={
+                    "tar_paths": local_tars,
+                    "metadata": metadata,
+                    "want": selected_set,
+                },
+            )
+        ]
+
+    # ------------------------------------------------------------------ #
+    # 3. Generate                                                         #
+    # ------------------------------------------------------------------ #
+    def _generate_examples(self, tar_paths, metadata, want):
+        """Stream over each tar and yield only requested files."""
+        idx = 0
+        for tar_path in tar_paths:
+            # iterate without extraction
+            for inner_path, fobj in \
+                    self._iter_archive_fast(tar_path):      # helper below
+                fname = Path(inner_path).name               # strip tar prefix
+                if fname not in want:
+                    continue
+
+                angles = metadata[fname]
+                yield idx, {
+                    "image": {"bytes": fobj.read(), "path": fname},
+                    "actuated_angle":
+                        {str(i): int(angles.get(str(i), 0)) for i in range(16)}
+                }
+                idx += 1
+
+    # Small wrapper so we don’t import datasets.utils.file_utils directly
+    @staticmethod
+    def _iter_archive_fast(tar_path):
+        import tarfile
+        with tarfile.open(tar_path) as tar:
+            for member in tar:
+                if member.isfile():
+                    f = tar.extractfile(member)
+                    yield member.name, f

README.md

@@ -1,6 +1,6 @@
 ---
 dataset_info:
-- config_name: full
+  config_name: small
   features:
   - name: image
     dtype: image
@@ -40,193 +40,14 @@ dataset_info:
       dtype: int32
   splits:
   - name: train
-    num_bytes: 153291676475
-    num_examples: 135236
-  download_size: 153306274200
-  dataset_size: 153291676475
-- config_name: small
-  features:
-  - name: image
-    dtype: image
-  - name: actuated_angle
-    struct:
-    - name: '0'
-      dtype: int32
-    - name: '1'
-      dtype: int32
-    - name: '2'
-      dtype: int32
-    - name: '3'
-      dtype: int32
-    - name: '4'
-      dtype: int32
-    - name: '5'
-      dtype: int32
-    - name: '6'
-      dtype: int32
-    - name: '7'
-      dtype: int32
-    - name: '8'
-      dtype: int32
-    - name: '9'
-      dtype: int32
-    - name: '10'
-      dtype: int32
-    - name: '11'
-      dtype: int32
-    - name: '12'
-      dtype: int32
-    - name: '13'
-      dtype: int32
-    - name: '14'
-      dtype: int32
-    - name: '15'
-      dtype: int32
-  splits:
-  - name: train
-    num_bytes: 22669945934
+    num_bytes: 22669945934.0
     num_examples: 20000
   download_size: 22672135072
-  dataset_size: 22669945934
+  dataset_size: 22669945934.0
 configs:
-- config_name: full
-  data_files:
-  - split: train
-    path: full/train-*
 - config_name: small
   data_files:
   - split: train
     path: small/train-*
   default: true
-license: mit
-task_categories:
-- robotics
-- reinforcement-learning
-tags:
-- robotics
-- humanoid
-- reinforcement
-- learning
-size_categories:
-- 100K<n<1M
----
-
-# 🤖 Open Humanoid Actuated Face Dataset
-
-<p align="center">
-  <img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/imgesFace.png" alt="Sample Face Image" width="400"/>
-</p>
-
-## Dataset Summary
-The **Open Humanoid Actuated Face Dataset** is designed for researchers working on  
-facial‑actuation control, robotics, reinforcement learning, and human–computer interaction.
-
-* **Origin** – collected during a reinforcement‑learning (RL) training loop whose objective was to reproduce human facial expressions.
-* **Platform** – a modified **i2Head InMoov** humanoid head with a silicone skin.
-* **Control** – **16 actuators** driving facial features and eyeballs.
-* **Pairing** – each example contains the raw RGB image **and** the exact actuator angles that produced it.
-
----
-
-## Dataset Structure
-
-| Field           | Type          | Description                                                   |
-|-----------------|---------------|---------------------------------------------------------------|
-| `image`         | `Image`       | RGB capture of the humanoid face (resolution **1280x720**). |
-| `actuated_angle`| `struct`      | 16 integer key - values (`"0"`, `"1"` .. so on)  |
-
-
-### Actuator Index Reference
-
-| Idx | Actuator                       | Idx | Actuator                |
-|:---:|--------------------------------|:---:|-------------------------|
-| 00  | Cheek – Left                   | 08  | Eyelid Upper – Right    |
-| 01  | Cheek – Right                  | 09  | Eyelid Lower – Right    |
-| 02  | Eyeball Sideways – Left        | 10  | Forehead – Right        |
-| 03  | Eyeball Up/Down – Left         | 11  | Forehead – Left         |
-| 04  | Eyelid Upper – Left            | 12  | Upper Nose              |
-| 05  | Eyelid Lower – Left            | 13  | Eyebrow – Right         |
-| 06  | Eyeball Up/Down – Right        | 14  | Jaw                     |
-| 07  | Eyeball Sideways – Right       | 15  | Eyebrow – Left          |
-
----
-
-### Actuator Mapping Images
-
-| Full‑Face Map | Eye‑Only Map |
-|:-------------:|:-----------:|
-| <br><img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/Screenshot%202025-05-02%20at%204.04.28%E2%80%AFPM.png" width="50%"/> | <br><img src="https://huggingface.co/datasets/iamirulofficial/Test2/resolve/main/Screenshot%202025-05-02%20at%204.03.56%E2%80%AFPM.png" width="50%"/> |
-
-
----
-
-## Dataset Statistics
-
-| Split | Samples | Size |
-|-------|---------|------|
-| **Train (full)**  | **[135k]** | ≈ 153 GB |
-| **Train (small)** | 20k | ≈ 22 GB |
-
-
----
-
-## Usage Example
-
-```python
-from datasets import load_dataset, Image
-
-# load the small subset
-ds = load_dataset("infosys/OpenHumnoidActuatedFaceData", name="small", split="train")
-ds = ds.cast_column("image", Image())   # decode image bytes ➜ PIL.Image
-
-img = ds[0]["image"]
-angles = ds[0]["actuated_angle"]        # {'0': 90, '1': 20, ...}
-img.show()
-print(angles)
-````
-
-> **Tip** 
-> For the full corpus use `name="full"` (may require `streaming=True` once the dataset grows).
-
----
-
-## Data Collection & RL Setup
-
-A detailed description of the RL pipeline, reward design, and actuator hardware will appear in our upcoming paper (in preparation, 2025). Briefly:
-
-1. **Vision module** extracts target expression keypoints from live human video.
-2. **Policy network** predicts 16 actuator set‑points.
-3. **Real‑time reward** computes expression similarity + smoothness penalties.
-4. Images & angle vectors are logged every *N* steps, forming this dataset.
-
----
-
-## License
-
-Released under the **MIT License** – free for commercial and non‑commercial use.
-
----
-
-## Citation
-
-```bibtex
-@misc{amirul2025openhumanoidface,
-  title   = {Open Humanoid Actuated Face Dataset},
-  author  = {Amirul et al.},
-  year    = {2025},
-  url     = {https://huggingface.co/datasets/infosys/OpenHumnoidActuatedFaceData}
-}
-```
-
 ---
-
-## Contribution
-1. Amirul Islam (amirul.islam@infosys.com)
-2. Anant Pande (anant.pande@infosys.com)
-3. Allahbaksh Asadullah (allabaksh_asadullah@infosys.com) - Mentor
-
-
-## Acknowledgements
-
-Big thanks to the Infosys and Mohammed Rafee Tarafdar (CTO, Infosys) for providing us resources to work on this research project and thanks to all the community and everyone who helped us making this possible.
-

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