Update readme & remove example 1 (#108)

Co-authored-by: Remi <re.cadene@gmail.com>
- Update instructions for installing the library
- Remove deprecated example 1 (as we are now only using `LeRobotDataset` since #91)

examples/1_load_hugging_face_dataset.py

@@ -1,69 +0,0 @@
-"""
-This script demonstrates the visualization of various robotic datasets from Hugging Face hub.
-It covers the steps from loading the datasets, filtering specific episodes, and converting the frame data to MP4 videos.
-Importantly, the dataset format is agnostic to any deep learning library and doesn't require using `lerobot` functions.
-It is compatible with pytorch, jax, numpy, etc.
-
-As an example, this script saves frames of episode number 5 of the PushT dataset to a mp4 video and saves the result here:
-`outputs/examples/1_visualize_hugging_face_datasets/episode_5.mp4`
-
-This script supports several Hugging Face datasets, among which:
-1. [Pusht](https://huggingface.co/datasets/lerobot/pusht)
-2. [Xarm Lift Medium](https://huggingface.co/datasets/lerobot/xarm_lift_medium)
-3. [Xarm Lift Medium Replay](https://huggingface.co/datasets/lerobot/xarm_lift_medium_replay)
-4. [Xarm Push Medium](https://huggingface.co/datasets/lerobot/xarm_push_medium)
-5. [Xarm Push Medium Replay](https://huggingface.co/datasets/lerobot/xarm_push_medium_replay)
-6. [Aloha Sim Insertion Human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human)
-7. [Aloha Sim Insertion Scripted](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_scripted)
-8. [Aloha Sim Transfer Cube Human](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_human)
-9. [Aloha Sim Transfer Cube Scripted](https://huggingface.co/datasets/lerobot/aloha_sim_transfer_cube_scripted)
-
-To try a different Hugging Face dataset, you can replace this line:
-```python
-hf_dataset, fps = load_dataset("lerobot/pusht", split="train"), 10
-```
-by one of these:
-```python
-hf_dataset, fps = load_dataset("lerobot/xarm_lift_medium", split="train"), 15
-hf_dataset, fps = load_dataset("lerobot/xarm_lift_medium_replay", split="train"), 15
-hf_dataset, fps = load_dataset("lerobot/xarm_push_medium", split="train"), 15
-hf_dataset, fps = load_dataset("lerobot/xarm_push_medium_replay", split="train"), 15
-hf_dataset, fps = load_dataset("lerobot/aloha_sim_insertion_human", split="train"), 50
-hf_dataset, fps = load_dataset("lerobot/aloha_sim_insertion_scripted", split="train"), 50
-hf_dataset, fps = load_dataset("lerobot/aloha_sim_transfer_cube_human", split="train"), 50
-hf_dataset, fps = load_dataset("lerobot/aloha_sim_transfer_cube_scripted", split="train"), 50
-```
-"""
-# TODO(rcadene): remove this example file of using hf_dataset
-
-from pathlib import Path
-
-import imageio
-from datasets import load_dataset
-
-# TODO(rcadene): list available datasets on lerobot page using `datasets`
-
-# download/load hugging face dataset in pyarrow format
-hf_dataset, fps = load_dataset("lerobot/pusht", split="train", revision="v1.1"), 10
-
-# display name of dataset and its features
-# TODO(rcadene): update to make the print pretty
-print(f"{hf_dataset=}")
-print(f"{hf_dataset.features=}")
-
-# display useful statistics about frames and episodes, which are sequences of frames from the same video
-print(f"number of frames: {len(hf_dataset)=}")
-print(f"number of episodes: {len(hf_dataset.unique('episode_index'))=}")
-print(
-    f"average number of frames per episode: {len(hf_dataset) / len(hf_dataset.unique('episode_index')):.3f}"
-)
-
-# select the frames belonging to episode number 5
-hf_dataset = hf_dataset.filter(lambda frame: frame["episode_index"] == 5)
-
-# load all frames of episode 5 in RAM in PIL format
-frames = hf_dataset["observation.image"]
-
-# save episode frames to a mp4 video
-Path("outputs/examples/1_load_hugging_face_dataset").mkdir(parents=True, exist_ok=True)
-imageio.mimsave("outputs/examples/1_load_hugging_face_dataset/episode_5.mp4", frames, fps=fps)

examples/1_load_lerobot_dataset.py

@@ -58,8 +58,8 @@ frames = [(frame * 255).type(torch.uint8) for frame in frames]
 frames = [frame.permute((1, 2, 0)).numpy() for frame in frames]
 
 # and finally save them to a mp4 video
-Path("outputs/examples/2_load_lerobot_dataset").mkdir(parents=True, exist_ok=True)
-imageio.mimsave("outputs/examples/2_load_lerobot_dataset/episode_5.mp4", frames, fps=dataset.fps)
+Path("outputs/examples/1_load_lerobot_dataset").mkdir(parents=True, exist_ok=True)
+imageio.mimsave("outputs/examples/1_load_lerobot_dataset/episode_5.mp4", frames, fps=dataset.fps)
 
 # For many machine learning applications we need to load histories of past observations, or trajectorys of future actions. Our datasets can load previous and future frames for each key/modality,
 # using timestamps differences with the current loaded frame. For instance: