Update README.md

README.md

@@ -1,99 +1,99 @@
-# FusionX Multimodal Sample Dataset
-
-This repository contains multimodal dataset capturing synchronized vision and tactile glove data across distinct tasks.
-
-## Dataset Overview
-
-* **RGB Video:** 30 Hz high-definition color stream (`.mp4`).
-* **Depth Data:** Lossless 16-bit depth stream (`.mkv`) and raw 16-bit PNG frames.
-* **Mono Vision:** Left and right monochrome camera views (raw PNG).
-* **Tactile Data:** Per-frame aligned glove sensor data stored in high-performance `.parquet` format.
-* **Calibration:** Intrinsic and extrinsic camera parameters provided in `.json`.
-
----
-
-## Repository Structure
-
-The dataset is organized by task. To optimize performance, raw image frames are compressed into modality-specific ZIP archives.
-
-```text
-dataset/
-├── dataset_info.json            # Global dataset metadata
-├── [task_name]/                 # e.g., box_cut1, drill1, waterpour3
-│   ├── depth.mkv                # Post-processed depth video
-│   ├── rgb.mp4                  # Post-processed RGB video
-│   ├── preview_all.mp4          # Synced visualization (Grid view)
-│   ├── preview_glove.mp4        # Tactile data visualization
-│   ├── frames.parquet           # Main data index (Timestamps + Tactile)
-│   ├── calib.json               # Camera calibration matrices
-│   ├── video_meta.json          # Technical video specifications
-│   ├── depth.zip                # Raw 16-bit PNG frames
-│   ├── mono_left.zip            # Raw mono-left PNG frames
-│   ├── mono_right.zip           # Raw mono-right PNG frames
-│   └── rgb.zip                  # Raw RGB JPG frames
-```
-
-## Tactile Data Schema (`frames.parquet`)
-
-The `frames.parquet` file is the central index for the dataset, providing per-frame synchronization between vision and tactile sensors.
-
-### 1. Vision & Synchronization
-| Column | Description |
-| :--- | :--- |
-| `frame_idx` | Integer index of the frame. |
-| `timestamp` | Global synchronized UNIX timestamp. |
-| `rgb_path`, `depth_path` | Relative paths to the original RGB and Depth images. |
-| `mono_left_path`, `mono_right_path` | Relative paths to the monochrome stereo images. |
-
-### 2. Camera IMU (OAK-D)
-| Column | Description |
-| :--- | :--- |
-| `oak_imu_quaternion` | 4-element orientation vector (W, X, Y, Z). |
-| `oak_imu_gyro` | Angular velocity (rad/s) for X, Y, Z axes. |
-| `oak_imu_accel` | Linear acceleration (m/s²) for X, Y, Z axes. |
-
-### 3. Tactile Glove Data (Left Hand: `lh_`, Right Hand: `rh_`)
-Each hand contains independent timestamping and comprehensive pressure/bend sensing:
-
-| Feature | Columns |
-| :--- | :--- |
-| **Glove Timing** | `_glove_timestamp`, `_glove_dt_ms` |
-| **Raw Pressure** | `_thumb_pressure`, `_index_pressure`, `_middle_pressure`, `_ring_pressure`, `_little_pressure`, `_palm_pressure` |
-| **Calibrated Pressure** | `_thumb_pressure_cal`, `_index_pressure_cal`, `_middle_pressure_cal`, `_ring_pressure_cal`, `_little_pressure_cal`, `_palm_pressure_cal` |
-| **Finger Kinematics** | `_finger_bend` (Raw), `_finger_bend_cal` (Calibrated) |
-| **Glove IMU** | `_imu_quaternion`, `_imu_gyro`, `_imu_accel` |
-
----
-
-## How to Use
-
-### 1. Download the Dataset
-You can clone this repository using Git LFS or use the `huggingface_hub` library:
-
-```python
-from huggingface_hub import snapshot_download
-
-# Download the specific task you need
-snapshot_download(
-    repo_id="touchtronix/FusionX-Multimodal-Sample-Data-V2",
-    repo_type="dataset",
-    allow_patterns="box_cut1/*" 
-)
-```
-### 2. Accessing Raw Frames
-If you require the original image files instead of the video streams, extract the modality zips within the task folder:
-
-```bash
-unzip box_cut1/rgb.zip -d box_cut1/rgb/
-```
----
-
-## License
-
-**Creative Commons Attribution 4.0 (CC BY 4.0)** You are free to:
-* **Use:** Incorporate the data into your own projects.
-* **Share:** Copy and redistribute the material in any medium or format.
-* **Modify:** Remix, transform, and build upon the material.
-* **Commercial Use:** Use the data for commercial purposes.
-
+# FusionX Multimodal Sample Dataset
+
+This repository contains multimodal dataset capturing synchronized vision and tactile glove data across distinct tasks.
+
+## Dataset Overview
+
+* **RGB Video:** 30 Hz 720p color stream (`.mp4`).
+* **Depth Data:** Lossless 16-bit depth stream (`.mkv`) and raw 16-bit PNG frames.
+* **Mono Vision:** Left and right monochrome camera views (raw PNG).
+* **Tactile Data:** Per-frame aligned glove sensor data stored in high-performance `.parquet` format.
+* **Calibration:** Intrinsic and extrinsic parameters provided in `.json`.
+
+---
+
+## Repository Structure
+
+The dataset is organized by task. To optimize performance, raw image frames are compressed into modality-specific ZIP archives.
+
+```text
+dataset/
+├── dataset_info.json            # Global dataset metadata
+├─�� [task_name]/                 # e.g., box_cut1, drill1, waterpour3
+│   ├── depth.mkv                # Post-processed depth video
+│   ├── rgb.mp4                  # Post-processed RGB video
+│   ├── preview_all.mp4          # Synced visualization (Grid view)
+│   ├── preview_glove.mp4        # Tactile data visualization
+│   ├── frames.parquet           # Main data index (Timestamps + Tactile)
+│   ├── calib.json               # Calibration matrices
+│   ├── video_meta.json          # Post-processed video specifications
+│   ├── depth.zip                # Raw 16-bit PNG frames
+│   ├── mono_left.zip            # Raw mono-left PNG frames
+│   ├── mono_right.zip           # Raw mono-right PNG frames
+│   └── rgb.zip                  # Raw RGB JPG frames
+```
+
+## Tactile Data Schema (`frames.parquet`)
+
+The `frames.parquet` file is the central index for the dataset, providing per-frame synchronization between vision and tactile sensors.
+
+### 1. Vision & Synchronization
+| Column | Description |
+| :--- | :--- |
+| `frame_idx` | Integer index of the frame. |
+| `timestamp` | Global synchronized UNIX timestamp. |
+| `rgb_path`, `depth_path` | Relative paths to the original RGB and Depth images. |
+| `mono_left_path`, `mono_right_path` | Relative paths to the monochrome stereo images. |
+
+### 2. Camera IMU (OAK-D)
+| Column | Description |
+| :--- | :--- |
+| `oak_imu_quaternion` | 4-element orientation vector (W, X, Y, Z). |
+| `oak_imu_gyro` | Angular velocity (rad/s) for X, Y, Z axes. |
+| `oak_imu_accel` | Linear acceleration (m/s²) for X, Y, Z axes. |
+
+### 3. Tactile Glove Data (Left Hand: `lh_`, Right Hand: `rh_`)
+Each hand contains independent timestamping and comprehensive pressure/bend sensing:
+
+| Feature | Columns |
+| :--- | :--- |
+| **Glove Timing** | `_glove_timestamp`, `_glove_dt_ms` |
+| **Raw Pressure** | `_thumb_pressure`, `_index_pressure`, `_middle_pressure`, `_ring_pressure`, `_little_pressure`, `_palm_pressure` |
+| **Calibrated Pressure** | `_thumb_pressure_cal`, `_index_pressure_cal`, `_middle_pressure_cal`, `_ring_pressure_cal`, `_little_pressure_cal`, `_palm_pressure_cal` |
+| **Finger Kinematics** | `_finger_bend` (Raw), `_finger_bend_cal` (Calibrated) |
+| **Glove IMU** | `_imu_quaternion`, `_imu_gyro`, `_imu_accel` |
+
+---
+
+## How to Use
+
+### 1. Download the Dataset
+You can clone this repository using Git LFS or use the `huggingface_hub` library:
+
+```python
+from huggingface_hub import snapshot_download
+
+# Download the specific task you need
+snapshot_download(
+    repo_id="touchtronix/FusionX-Multimodal-Sample-Data-V2",
+    repo_type="dataset",
+    allow_patterns="box_cut1/*" 
+)
+```
+### 2. Accessing Raw Frames
+If you require the original image files instead of the video streams, extract the modality zips within the task folder:
+
+```bash
+unzip box_cut1/rgb.zip -d box_cut1/rgb/
+```
+---
+
+## License
+
+**Creative Commons Attribution 4.0 (CC BY 4.0)** You are free to:
+* **Use:** Incorporate the data into your own projects.
+* **Share:** Copy and redistribute the material in any medium or format.
+* **Modify:** Remix, transform, and build upon the material.
+* **Commercial Use:** Use the data for commercial purposes.
+
 *As long as appropriate attribution is provided to **Touchtronix Robotics**.*