Update README.md

README.md

@@ -19,7 +19,117 @@ This dataset contains 1,500 episodes of cloth folding, collected using Agilex's
 - Performance: Near 100% success rate in completing the folding task
 
 # Usage
-To be updated🏗️
+You can find `.hdf5` and `.mp4` files in each directory. The `.mp4` files are just used for visulization and are not used for training. The `.hdf5` files contains all necessary keys and data, including:
+
+## HDF5 file hierarchy
+```
+├── action # nx14 absolute bimanual joints, not used in our paper
+├── base_action # nx2 chassis actions, not used in our paper
+├── language_instruction # 🌟"fold the cloth"
+├── observations
+│   ├── eef # nx14 absolute eef pos using euler angles to represent the rotation, not used in our paper
+│   │   eef_quaternion # nx16 absolute eef pos using quaternion to represent the robtation, not used in our paper
+│   │   eef_6d # 🌟nx20 absolute eef pos using rotate6d to represent the robtation
+│   │   eef_left_time # 🌟nx1 the time stamp for left arm eef pos, can be used for resample or interpolation
+│   │   eef_right_time # 🌟nx1 the time stamp for right arm eef pos, can be used for resample or interpolation
+│   ├── qpos # nx14 absolute bimanual joints, not used in our paper
+│   ├── qpos_left_time # nx1 the time stamp for left arm joint pos, can be used for resample or interpolation, not used in our paper
+│   ├── qpos_right_time # nx1 the time stamp for right arm joint pos, can be used for resample or interpolation, not used in our paper
+│   ├── qvel # nx14 bimanual joint velocity, not used in our paper
+│   ├── effort # nx14 bimanual joint effort, not used in our paper
+│   ├── images
+│   │   ├── cam_high  # 🌟the encoded head cam view, should be decoded using cv2
+│   │   ├── cam_left_wrist  # 🌟the encoded left wrist view, should be decoded using cv2
+│   │   ├── cam_right_wrist  # 🌟the encoded right wrist view, should be decoded using cv2
+├── time_stamp # the time stamp for each sample, not used in our paper
+```
+
+How to read the hdf5 file:
+
+```
+import h5py
+import cv2
+import io
+from mmengine import fileio
+
+
+path = "REPLACE TO YOUR HDF5 FILE PATH HERE"
+
+# load the hdf5 file
+value = fileio.get(path)
+f = io.BytesIO(value)
+h = h5py.File(f,'r')
+
+# you can monitor the hdf5 hierarchy by print out its keys
+print(h.keys())
+
+# this is one example to read out the data, for example, the 'cam_high' data
+head_view_bytes = h['observations/images/cam_high'][()]  # 🌟 NOTE: we compress all images to bytes using cv2.imencode
+head_view = cv2.imdecode(head_view_bytes, cv2.IMREAD_COLOR)  # 🌟 NOTE: we should decode it back to RGB images for furture usage
+
+#Then you can go free to use our data :)
+# ...
+# ...
+```
+
+## Visualize the data
+You can find some dictionary have `.mp4` file for visulization. If you want to visualize all the `.hdf5` file, you can run the following data:
+
+```
+from mmengine import fileio
+import io
+import h5py
+import cv2
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from PIL import Image
+from IPython.display import display, Image as IPImage
+from IPython.display import Video
+import os
+import imageio
+import numpy as np
+
+# 🌟 Just replace the path here, can run this script. This script will generate all the .mp4 files for the .hdf5 file
+top_path = 'REPLACE TO YOUR XVLA-SOFT-FOLD PATH'
+hdf5_files = fileio.list_dir_or_file(top_path, suffix='.hdf5', recursive=True, list_dir=False)
+
+for hdf5_name in hdf5_files:
+    path = os.path.join(top_path, hdf5_name)
+    # Prepare OpenCV VideoWriter to save as MP4
+    video_path = path.replace('.hdf5', '.mp4')
+    fps = 30  # Adjust the FPS if needed
+    image_list = []
+    print(video_path)
+    if os.path.exists(video_path):
+        print(f"pass {video_path}, it already exists")
+        continue
+    
+    
+    value = fileio.get(path)
+    f = io.BytesIO(value)
+    h = h5py.File(f,'r')
+
+    images = h['/observations/images/cam_high'][()]
+    images_left = h['/observations/images/cam_left_wrist'][()]
+    images_right = h['/observations/images/cam_right_wrist'][()]
+    ep_len = images.shape[0]
+    
+    for i in tqdm(range(ep_len)):
+        img = images[i]
+        img_left = images_left[i]
+        img_right = images_right[i]
+        
+        img = cv2.imdecode(img, cv2.IMREAD_COLOR)  # Decode image from bytes
+        img_left = cv2.imdecode(img_left, cv2.IMREAD_COLOR)  # Decode image from bytes
+        img_right = cv2.imdecode(img_right, cv2.IMREAD_COLOR)  # Decode image from bytes
+        
+        img = np.concatenate([img, img_left, img_right], axis = 1)
+        image_list.append(img)
+
+    # Release the VideoWriter and show output
+    imageio.mimsave(video_path, image_list, fps=fps)
+```
+
 
 # Citation
 If you use this dataset in your research or for any related work, please cite the X-VLA Paper: