Delete v0.0.1

v0.0.1/README.md

@@ -1 +0,0 @@
-First Draft of a dataset using dora-arms

v0.0.1/aloha-client/Cargo.toml

@@ -1,14 +0,0 @@
-[package]
-name = "aloha-client"
-version = "0.1.0"
-edition = "2021"
-
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
-
-[dependencies]
-
-serialport = "4.2.0"
-rustypot = { git = "https://github.com/haixuanTao/rustypot" }
-tokio = { version = "1.37.0", features = ["full"] }
-eyre = "0.6.12"
-dora-node-api = "0.3.3"

v0.0.1/aloha-client/src/main.rs

@@ -1,59 +0,0 @@
-use dora_node_api::{
-    arrow::array::UInt32Array, dora_core::config::DataId, DoraNode, Event, IntoArrow,
-};
-use eyre::Result;
-use rustypot::{device::xm, DynamixelSerialIO};
-use std::time::Duration;
-
-fn main() -> Result<()> {
-    let (mut node, mut events) = DoraNode::init_from_env()?;
-    let mut puppet_serial_port = serialport::new("/dev/ttyDXL_puppet_right", 1_000_000)
-        .timeout(Duration::from_millis(20))
-        .open()
-        .expect("Failed to open port");
-    let io = DynamixelSerialIO::v2();
-    xm::sync_write_torque_enable(
-        &io,
-        puppet_serial_port.as_mut(),
-        &[1, 2, 3, 4, 5, 6, 7, 8, 9],
-        &[1; 9],
-    )
-    .expect("Communication error");
-
-    while let Some(Event::Input {
-        id,
-        metadata: _,
-        data,
-    }) = events.recv()
-    {
-        match id.as_str() {
-            "puppet_goal_position" => {
-                let buffer: UInt32Array = data.to_data().into();
-                let target: &[u32] = buffer.values();
-                xm::sync_write_goal_position(
-                    &io,
-                    puppet_serial_port.as_mut(),
-                    &[1, 2, 3, 4, 5, 6, 7, 8, 9],
-                    &target,
-                )
-                .expect("Communication error");
-            }
-            "tick" => {
-                let pos = xm::sync_read_present_position(
-                    &io,
-                    puppet_serial_port.as_mut(),
-                    &[1, 2, 3, 4, 5, 6, 7, 8, 9],
-                )
-                .expect("Communication error");
-                node.send_output(
-                    DataId::from("puppet_position".to_owned()),
-                    Default::default(),
-                    pos.into_arrow(),
-                )?;
-            }
-            _ => todo!(),
-        };
-    }
-
-    Ok(())
-}

v0.0.1/aloha-teleop/Cargo.toml

@@ -1,15 +0,0 @@
-[package]
-name = "aloha-teleop"
-version = "0.1.0"
-edition = "2021"
-
-# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
-
-[dependencies]
-
-serialport = "4.2.0"
-rustypot = { git = "https://github.com/haixuanTao/rustypot" }
-tokio = { version = "1.37.0", features = ["full"] }
-eyre = "0.6.12"
-clap = { version = "4.5.4", features = ["derive"] }
-dora-node-api = "0.3.3"

v0.0.1/aloha-teleop/src/main.rs

@@ -1,126 +0,0 @@
-use dora_node_api::{dora_core::config::DataId, DoraNode, IntoArrow, MetadataParameters};
-use eyre::{Context, Result};
-use rustypot::{device::xm, DynamixelSerialIO};
-use serialport::SerialPort;
-use std::{
-    sync::mpsc,
-    time::{Duration, Instant},
-};
-
-static MAX_MASTER_GRIPER: u32 = 2554;
-static MAX_PUPPET_GRIPER: u32 = 3145;
-
-static MIN_MASTER_GRIPER: u32 = 1965;
-static MIN_PUPPET_GRIPER: u32 = 2500;
-use clap::Parser;
-
-/// Simple aloha teleop program for recording data
-#[derive(Parser, Debug)]
-#[command(version, about, long_about = None)]
-struct Args {
-    #[arg(short, long, default_value = "/dev/ttyDXL_master_right")]
-    master_path: String,
-
-    #[arg(short, long, default_value = "/dev/ttyDXL_puppet_right")]
-    puppet_path: String,
-
-    #[arg(long, default_value = "1000000")]
-    master_baudrate: u32,
-
-    #[arg(long, default_value = "1000000")]
-    puppet_baudrate: u32,
-}
-
-enum State {
-    Position(Vec<u32>),
-    GoalPosition(Vec<u32>),
-}
-
-fn main_multithreaded(
-    io: DynamixelSerialIO,
-    mut master_serial_port: Box<dyn SerialPort>,
-    mut puppet_serial_port: Box<dyn SerialPort>,
-) -> Result<()> {
-    let (tx, rx) = mpsc::channel();
-    let (tx_dora, rx_dora) = mpsc::channel();
-    let tx_dora_read = tx_dora.clone();
-    std::thread::spawn(move || loop {
-        let now = Instant::now();
-        let pos = xm::sync_read_present_position(
-            &io,
-            master_serial_port.as_mut(),
-            &[1, 2, 3, 4, 5, 6, 7, 8, 9],
-        )
-        .expect("Read Communication error");
-        tx.send((now, pos.clone())).unwrap();
-        tx_dora_read.send(State::Position(pos)).unwrap();
-    });
-
-    let io = DynamixelSerialIO::v2();
-    let join = std::thread::spawn(move || {
-        while let Ok((_now, pos)) = rx.recv() {
-            // Compute linear interpolation for gripper as input and output range missmatch
-            let gripper = (pos[8] - MIN_MASTER_GRIPER) * (MAX_PUPPET_GRIPER - MIN_PUPPET_GRIPER)
-                / (MAX_MASTER_GRIPER - MIN_MASTER_GRIPER)
-                + MIN_PUPPET_GRIPER;
-            let mut target = pos;
-            target[8] = gripper;
-            xm::sync_write_goal_position(
-                &io,
-                puppet_serial_port.as_mut(),
-                &[1, 2, 3, 4, 5, 6, 7, 8, 9],
-                &target,
-            )
-            .expect("Write Communication error");
-            // println!("elapsed time: {:?}", now.elapsed());
-            tx_dora.send(State::GoalPosition(target)).unwrap();
-        }
-    });
-
-    if std::env::var("DORA_NODE_CONFIG").is_ok() {
-        let (mut node, mut events) = DoraNode::init_from_env()?;
-        while let Ok(target) = rx_dora.recv() {
-            let parameters = MetadataParameters::default();
-            match target {
-                State::Position(pos) => {
-                    let output = DataId::from("puppet_goal_position".to_owned());
-                    node.send_output(output.clone(), parameters, pos.into_arrow())?;
-                }
-                State::GoalPosition(pos) => {
-                    let output = DataId::from("puppet_state".to_owned());
-                    node.send_output(output.clone(), parameters, pos.into_arrow())?;
-                }
-            }
-            if events.recv_timeout(Duration::from_nanos(100)).is_none() {
-                println!("Events channel finished");
-                break;
-            }
-        }
-    } else {
-        join.join().unwrap();
-    };
-    Ok(())
-}
-
-fn main() -> Result<()> {
-    let args = Args::parse();
-    let master_serial_port = serialport::new(args.master_path, args.master_baudrate)
-        .timeout(Duration::from_millis(2))
-        .open()
-        .context("Failed to open port")?;
-    let mut puppet_serial_port = serialport::new(args.puppet_path, args.puppet_baudrate)
-        .timeout(Duration::from_millis(2))
-        .open()
-        .context("Failed to open port")?;
-    let io = DynamixelSerialIO::v2();
-    xm::sync_write_torque_enable(
-        &io,
-        puppet_serial_port.as_mut(),
-        &[1, 2, 3, 4, 5, 6, 7, 8, 9],
-        &[1; 9],
-    )
-    .expect("Communication error");
-
-    main_multithreaded(io, master_serial_port, puppet_serial_port)?;
-    Ok(())
-}

v0.0.1/compress_image.py

@@ -1,28 +0,0 @@
-from dora import Node
-import shutil
-
-FPS = 50
-
-frame_id = 0
-
-node = Node()
-
-for event in node:
-    if event["TYPE"] != "INPUT":
-        break
-
-    result = {"path": f"videos/{fname}", "timestamp": frame_id / FPS}
-
-    frame_id += 1
-
-
-tmp_imgs_dir = out_dir / "tmp_images"
-save_images_concurrently(imgs_array, tmp_imgs_dir)
-
-# encode images to a mp4 video
-fname = f"{img_key}_episode_{ep_idx:06d}.mp4"
-video_path = "out" / "videos" / fname
-encode_video_frames(tmp_imgs_dir, video_path, FPS)
-
-# clean temporary images directory
-shutil.rmtree(tmp_imgs_dir)

v0.0.1/data/episode_1.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fb0cb717b54bda0a3460241096e9b5e974e9da44a89722959693c5a347af69d2
-size 42285

v0.0.1/data/episode_2.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0350bb61ff20b4c9d4b67e1293b1c45ec82942fadbefaf51a8d29b3998cdbe66
-size 36604

v0.0.1/data/episode_3.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:7c37d6ac89906fd6f313641ad371923c0f9044e45463deeb274b9003ad46eab5
-size 33102

v0.0.1/data/episode_4.parquet

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:61ce815a1d8a9907f6b0935884f25a8c1359835669e8598424f50610433884e4
-size 70380

v0.0.1/keyboard_node.py

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-from pynput import keyboard
-from pynput.keyboard import Key, Events
-import pyarrow as pa
-from dora import Node
-
-
-node = Node()
-buffer_text = ""
-space = False
-submitted_text = []
-cursor = 1
-with keyboard.Events() as events:
-    while True:
-        event = events.get(0.1)
-        if event is not None and isinstance(event, Events.Press):
-            if event.key == Key.space and space == False:
-                node.send_output("space", pa.array([cursor]))
-                space = True
-
-        elif event is not None and isinstance(event, Events.Release):
-            if event.key == Key.space:
-                node.send_output("space", pa.array([0]))
-                cursor += 1
-                space = False
-
-        if node.next(0.001) is None:
-            break
-# 1

v0.0.1/lerobot_webcam_saver.py

@@ -1,74 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import os
-import time
-import numpy as np
-import cv2
-import pyarrow as pa
-from pathlib import Path
-from dora import Node
-import subprocess
-
-node = Node()
-
-CAMERA_NAME = os.getenv("CAMERA_NAME", "camera" )
-CAMERA_WIDTH = 640
-CAMERA_HEIGHT = 480
-FPS = 50
-
-i = 0
-episode = 0
-out_dir = "videos" / Path(f"cam_{CAMERA_NAME}_episode_{episode}")
-
-for event in node:
-    event_type = event["type"]
-    if event_type == "INPUT":
-        if event["id"] == "record_episode":
-            record_episode = event["value"].to_numpy()[0]
-            print(f"Recording episode {record_episode}", flush=True)
-            # Save Episode Video
-            if episode != 0 and record_episode == 0:
-                out_dir = "videos" / Path(f"{CAMERA_NAME}_episode_{episode}")
-                fname = f"{CAMERA_NAME}_episode_{episode}.mp4"
-                video_path = Path("videos") / fname
-                # Save video
-                ffmpeg_cmd = (
-                    f"ffmpeg -r {FPS} "
-                    "-f image2 "
-                    "-loglevel error "
-                    f"-i {str(out_dir / 'frame_%06d.png')} "
-                    "-vcodec libx264 "
-                    "-g 2 "
-                    "-pix_fmt yuv444p "
-                    f"{str(video_path)}"
-                )
-                print(ffmpeg_cmd, flush=True)
-                subprocess.Popen(ffmpeg_cmd.split(" "), start_new_session=True)
-                episode = record_episode
-                
-            # Make new directory and start saving images
-            elif episode == 0 and record_episode != 0:
-                episode = record_episode
-                out_dir = "videos" / Path(f"{CAMERA_NAME}_episode_{episode}")
-                out_dir.mkdir(parents=True, exist_ok=True)
-                i = 0
-            else:
-                continue
-                
-        elif event["id"] == "image":
-            # Only record image when in episode.
-            # Episode 0 is for not recording periods.
-            if episode == 0:
-                continue
-
-            fname = f"{CAMERA_NAME}_episode_{episode}.mp4"
-            node.send_output(
-                "saved_image",
-                pa.array([{"path": f"videos/{fname}", "timestamp": i / FPS}]),
-                event["metadata"],
-            )
-            image = event["value"].to_numpy().reshape((CAMERA_HEIGHT, CAMERA_WIDTH, 3))
-            path = str(out_dir / f"frame_{i:06d}.png")
-            cv2.imwrite(path, image)
-            i += 1

v0.0.1/llm_op.py

@@ -1,234 +0,0 @@
-from dora import DoraStatus
-import pylcs
-import os
-import pyarrow as pa
-from transformers import AutoModelForCausalLM, AutoTokenizer
-import torch
-
-import gc  # garbage collect library
-import re
-import time
-
-CHATGPT = False
-MODEL_NAME_OR_PATH = "TheBloke/deepseek-coder-6.7B-instruct-GPTQ"
-
-CODE_MODIFIER_TEMPLATE = """
-### Instruction
-Respond with one block of modified code only in ```python block. No explaination.
-
-```python
-{code}
-```
-
-{user_message}
-
-### Response:
-"""
-
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME_OR_PATH,
-    device_map="auto",
-    trust_remote_code=True,
-    revision="main",
-    max_length=1024,
-).to("cuda:0")
-
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME_OR_PATH, use_fast=True)
-
-
-def extract_python_code_blocks(text):
-    """
-    Extracts Python code blocks from the given text that are enclosed in triple backticks with a python language identifier.
-
-    Parameters:
-    - text: A string that may contain one or more Python code blocks.
-
-    Returns:
-    - A list of strings, where each string is a block of Python code extracted from the text.
-    """
-    pattern = r"```python\n(.*?)\n```"
-    matches = re.findall(pattern, text, re.DOTALL)
-    if len(matches) == 0:
-        pattern = r"```python\n(.*?)(?:\n```|$)"
-        matches = re.findall(pattern, text, re.DOTALL)
-        if len(matches) == 0:
-            return [text]
-        else:
-            matches = [remove_last_line(matches[0])]
-
-    return matches
-
-
-def remove_last_line(python_code):
-    """
-    Removes the last line from a given string of Python code.
-
-    Parameters:
-    - python_code: A string representing Python source code.
-
-    Returns:
-    - A string with the last line removed.
-    """
-    lines = python_code.split("\n")  # Split the string into lines
-    if lines:  # Check if there are any lines to remove
-        lines.pop()  # Remove the last line
-    return "\n".join(lines)  # Join the remaining lines back into a string
-
-
-def calculate_similarity(source, target):
-    """
-    Calculate a similarity score between the source and target strings.
-    This uses the edit distance relative to the length of the strings.
-    """
-    edit_distance = pylcs.edit_distance(source, target)
-    max_length = max(len(source), len(target))
-    # Normalize the score by the maximum possible edit distance (the length of the longer string)
-    similarity = 1 - (edit_distance / max_length)
-    return similarity
-
-
-def find_best_match_location(source_code, target_block):
-    """
-    Find the best match for the target_block within the source_code by searching line by line,
-    considering blocks of varying lengths.
-    """
-    source_lines = source_code.split("\n")
-    target_lines = target_block.split("\n")
-
-    best_similarity = 0
-    best_start_index = 0
-    best_end_index = -1
-
-    # Iterate over the source lines to find the best matching range for all lines in target_block
-    for start_index in range(len(source_lines) - len(target_lines) + 1):
-        for end_index in range(start_index + len(target_lines), len(source_lines) + 1):
-            current_window = "\n".join(source_lines[start_index:end_index])
-            current_similarity = calculate_similarity(current_window, target_block)
-            if current_similarity > best_similarity:
-                best_similarity = current_similarity
-                best_start_index = start_index
-                best_end_index = end_index
-
-    # Convert line indices back to character indices for replacement
-    char_start_index = len("\n".join(source_lines[:best_start_index])) + (
-        1 if best_start_index > 0 else 0
-    )
-    char_end_index = len("\n".join(source_lines[:best_end_index]))
-
-    return char_start_index, char_end_index
-
-
-def replace_code_in_source(source_code, replacement_block: str):
-    """
-    Replace the best matching block in the source_code with the replacement_block, considering variable block lengths.
-    """
-    replacement_block = extract_python_code_blocks(replacement_block)[0]
-    start_index, end_index = find_best_match_location(source_code, replacement_block)
-    if start_index != -1 and end_index != -1:
-        # Replace the best matching part with the replacement block
-        new_source = (
-            source_code[:start_index] + replacement_block + source_code[end_index:]
-        )
-        return new_source
-    else:
-        return source_code
-
-
-class Operator:
-    def __init__(self) -> None:
-        self.policy_init = False
-
-    def on_event(
-        self,
-        dora_event,
-        send_output,
-    ) -> DoraStatus:
-        global model, tokenizer
-        if dora_event["type"] == "INPUT" and dora_event["id"] == "text":
-            input = dora_event["value"][0].as_py()
-            # Path to the current file
-            current_file_path = __file__
-
-            # Directory of the current file
-            current_directory = os.path.dirname(current_file_path)
-            path = current_directory + "/policy.py"
-
-            with open(path, "r", encoding="utf8") as f:
-                code = f.read()
-
-            user_message = input
-            start_llm = time.time()
-
-            output = self.ask_llm(
-                CODE_MODIFIER_TEMPLATE.format(code=code, user_message=user_message)
-            )
-
-            source_code = replace_code_in_source(code, output)
-            print("response time:", time.time() - start_llm, flush=True)
-
-            print("response: ", output, flush=True)
-            with open(path, "w") as file:
-                file.write(source_code)
-
-            gc.collect()
-            torch.cuda.empty_cache()
-
-        return DoraStatus.CONTINUE
-
-    def ask_llm(self, prompt):
-
-        # Generate output
-        # prompt = PROMPT_TEMPLATE.format(system_message=system_message, prompt=prompt))
-        input = tokenizer(prompt, return_tensors="pt")
-        input_ids = input.input_ids.cuda()
-
-        # add attention mask here
-        attention_mask = input.attention_mask.cuda()
-
-        output = model.generate(
-            inputs=input_ids,
-            temperature=0.7,
-            do_sample=True,
-            top_p=0.95,
-            top_k=40,
-            max_new_tokens=512,
-            attention_mask=attention_mask,
-            eos_token_id=tokenizer.eos_token_id,
-        )
-        # Get the tokens from the output, decode them, print them
-
-        # Get text between im_start and im_end
-        return tokenizer.decode(output[0], skip_special_tokens=True)[len(prompt) :]
-
-
-if __name__ == "__main__":
-    op = Operator()
-
-    # Path to the current file
-    current_file_path = __file__
-
-    # Directory of the current file
-    current_directory = os.path.dirname(current_file_path)
-
-    path = current_directory + "/policy.py"
-    with open(path, "r", encoding="utf8") as f:
-        raw = f.read()
-
-    op.on_event(
-        {
-            "type": "INPUT",
-            "id": "text",
-            "value": pa.array(
-                [
-                    {
-                        "path": path,
-                        "user_message": "When I say suit up, get the hat and the get the food.",
-                    },
-                ]
-            ),
-            "metadata": [],
-        },
-        print,
-    )

v0.0.1/plot_node.py

@@ -1,53 +0,0 @@
-import os
-import cv2
-from dora import Node
-
-
-IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "1280"))
-IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "720"))
-FONT = cv2.FONT_HERSHEY_SIMPLEX
-
-
-node = Node()
-
-joint = None
-text = None
-
-for event in node:
-    if event["type"] == "INPUT":
-        dora_id = event["id"]
-
-        if dora_id == "position":
-            joint = event["value"].to_numpy()
-        if "text" in dora_id:
-            text = event["value"][0].as_py()
-
-        if dora_id == "image":
-            image = (
-                event["value"].to_numpy().reshape((IMAGE_HEIGHT, IMAGE_WIDTH, 3)).copy()
-            )
-            if text is not None:
-                cv2.putText(
-                    image,
-                    f"Speech: {text}",
-                    (20, 40),
-                    FONT,
-                    0.5,
-                    (190, 250, 0),
-                    2,
-                )
-
-            if joint is not None:
-                cv2.putText(
-                    image,
-                    f"pos: {joint}",
-                    (20, 20),
-                    FONT,
-                    0.5,
-                    (190, 250, 100),
-                    2,
-                )
-
-            cv2.imshow("frame", image)
-            if cv2.waitKey(1) & 0xFF == ord("q"):
-                break

v0.0.1/policy.py

@@ -1,17 +0,0 @@
-import pyarrow as pa
-from dora import DoraStatus
-
-
-class Operator:
-    def __init__(self):
-        self.actions = ["get_food", "get_hat"]
-
-    def on_event(self, event: dict, send_output) -> DoraStatus:
-        if event["type"] == "INPUT":
-            id = event["id"]
-            # On initialization
-            if id == "speech":
-                text: str = event["value"][0].as_py().lower()
-                # send_output("action", pa.array([""]))
-
-        return DoraStatus.CONTINUE

v0.0.1/realsense_node.py

@@ -1,26 +0,0 @@
-import pyrealsense2 as rs
-import numpy as np
-from dora import Node
-import pyarrow as pa
-import os
-import cv2
-
-IMAGE_WIDTH = int(os.getenv("IMAGE_WIDTH", "1280"))
-IMAGE_HEIGHT = int(os.getenv("IMAGE_HEIGHT", "720"))
-CAMERA_ID = os.getenv("CAMERA_ID")
-pipe = rs.pipeline()
-config = rs.config()
-config.enable_device(CAMERA_ID)
-config.enable_stream(rs.stream.color, IMAGE_WIDTH, IMAGE_HEIGHT, rs.format.bgr8, 15)
-profile = pipe.start(config)
-
-node = Node()
-
-for event in node:
-    frames = pipe.wait_for_frames()
-    color_frame = frames.get_color_frame()
-    color_images = np.asanyarray(color_frame.get_data())
-    node.send_output("image", pa.array(color_images.ravel()))
-    cv2.imshow(CAMERA_ID, color_images)
-    if cv2.waitKey(1) & 0xFF == ord("q"):
-        break

v0.0.1/record_2arms_teleop.yml

@@ -1,125 +0,0 @@
-nodes:
-  # - id: teleop_left
-    # custom:
-      # source: cargo
-      # args: run --release -p aloha-teleop  -- --master-path /dev/ttyDXL_master_left --puppet-path /dev/ttyDXL_puppet_left
-      # outputs:
-        # - puppet_goal_position
-        # - puppet_state
-  
-  - id: teleop_right
-    custom:
-      source: cargo
-      args: run --release -p aloha-teleop  -- --master-path /dev/ttyDXL_master_right --puppet-path /dev/ttyDXL_puppet_right
-      inputs:
-        heartbeat: dora/timer/millis/20
-      outputs:
-        - puppet_goal_position
-        - puppet_state
-
-  - id: dora-record
-    custom:
-      build: cargo install --git https://github.com/dora-rs/dora dora-record
-      source: dora-record
-      inputs:
-        # puppet_left_goal_position: teleop_left/puppet_goal_position
-        action: teleop_right/puppet_goal_position
-        # puppet_left_state: teleop_left/puppet_state
-        state: teleop_right/puppet_state
-        record_episode: keyboard/space
-        cam_left_wrist: cam_saver_left_wrist/saved_image
-        cam_right_wrist: cam_saver_right_wrist/saved_image
-        cam_bottom: cam_saver_bottom/saved_image
-        cam_high: cam_saver_high/saved_image
-
-  - id: cam_left_wrist
-    custom:
-      source: ../nodes/webcam.py
-      inputs:
-        tick: dora/timer/millis/20
-      outputs:
-        - image
-      envs:
-        CAMERA_INDEX: 2
-
-  - id: cam_right_wrist
-    custom:
-      source: ../nodes/webcam.py
-      inputs:
-        tick: dora/timer/millis/20
-      outputs:
-        - image
-      envs:
-        CAMERA_INDEX: 22
-  
-  - id: cam_bottom
-    custom:
-      source: ../nodes/webcam.py
-      inputs:
-        tick: dora/timer/millis/20
-      outputs:
-        - image
-      envs:
-        CAMERA_INDEX: 8
-  
-  - id: cam_high
-    custom:
-      source: ../nodes/webcam.py
-      inputs:
-        tick: dora/timer/millis/20
-      outputs:
-        - image
-      envs:
-        CAMERA_INDEX: 14
-  
-  - id: keyboard
-    custom: 
-      source: ../nodes/keyboard_node.py
-      inputs:
-        heartbeat: dora/timer/millis/20
-      outputs:
-        - space
-
-  - id: cam_saver_left_wrist
-    custom:
-      source: ../nodes/lerobot_webcam_saver.py
-      inputs:
-        image: cam_left_wrist/image
-        record_episode: keyboard/space
-      outputs:
-        - saved_image
-      envs:
-        CAMERA_NAME: cam_left_wrist
-
-  - id: cam_saver_right_wrist
-    custom:
-      source: ../nodes/lerobot_webcam_saver.py
-      inputs:
-        image: cam_right_wrist/image
-        record_episode: keyboard/space
-      outputs:
-        - saved_image
-      envs:
-        CAMERA_NAME: cam_right_wrist
-
-  - id: cam_saver_bottom
-    custom:
-      source: ../nodes/lerobot_webcam_saver.py
-      inputs:
-        image: cam_bottom/image
-        record_episode: keyboard/space
-      outputs:
-        - saved_image
-      envs:
-        CAMERA_NAME: cam_bottom
-
-  - id: cam_saver_high
-    custom:
-      source: ../nodes/lerobot_webcam_saver.py
-      inputs:
-        image: cam_up/image
-        record_episode: keyboard/space
-      outputs:
-        - saved_image
-      envs:
-        CAMERA_NAME: cam_high

v0.0.1/replay.py

@@ -1,27 +0,0 @@
-from dora import Node
-import pandas as pd
-import pyarrow as pa
-import time
-
-
-TOPIC = "puppet_goal_position"
-
-
-if __name__ == "__main__":
-    node = Node()
-
-    for event in node:
-        if event["type"] == "INPUT":
-            for action in event["value"]:
-                print(action, flush=True)
-                action = action.as_py()
-
-                df = pd.read_parquet(action + ".parquet")
-
-                initial_time = df["timestamp_utc"].iloc[0]
-                current_time = initial_time
-                for index, row in df.iterrows():
-                    delta_time = (row["timestamp_utc"] - current_time).microseconds
-                    current_time = row["timestamp_utc"]
-                    time.sleep(delta_time / 1_000_000)
-                    node.send_output(TOPIC, pa.array(row[TOPIC], type=pa.uint32()))

v0.0.1/videos/cam_bottom_episode_1.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6e4f0bcfd515557c5f2bab4c3ab9017259e0bee53472219707a226007194cf30
-size 3158732

v0.0.1/videos/cam_bottom_episode_2.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e10c84b3ef013fbff8e9c57ab11f0a70caf32be719aacc7c1cd5cf9968b79521
-size 2678292

v0.0.1/videos/cam_bottom_episode_3.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:be545dfb0cc4e44e3b0ff1d5332afb55dc7db71331f0a7feac289ab2802af877
-size 2198902

v0.0.1/videos/cam_bottom_episode_4.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:cf203cf3af951a0750c000dcacfe5e5e9b60df0d35bc9c3a08e97ccfd42051ab
-size 6130834

v0.0.1/videos/cam_left_wrist_episode_1.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:fc61cb434dd1e11ce5e9a4c620d847c1d15a02119ec358731989193ce2d180f6
-size 2300015

v0.0.1/videos/cam_left_wrist_episode_2.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:e804bc3ae0802ff2e6bc8664f595e39cb7f14806fde7a0e81ea7fc1d0752cb29
-size 1133727

v0.0.1/videos/cam_left_wrist_episode_4.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d4cb5314c5cf9ba0c99d44fb23d0dc7703eb153962c94666fb797f9806940199
-size 2517780

v0.0.1/videos/cam_right_wrist_episode_1.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:44f7a972481d14c291425d06edb6adcd58604a3f06aa7adeb0e1d48881b04a04
-size 3803200

v0.0.1/videos/cam_right_wrist_episode_2.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:6b8c56eb557a192e5ca892e156102a9f5de80b52bd0281e34dbc105a9a8666c7
-size 2467078

v0.0.1/videos/cam_right_wrist_episode_3.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:62a442a29ed41d0c94a815c79f4b89b580dd2e7c2344072f1894fe318e7a35c9
-size 2297313

v0.0.1/videos/cam_right_wrist_episode_4.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:33d95fb5203acc5a4b65896717db822c0836510998fa3f81d6fce72b491a081b
-size 4973378

v0.0.1/videos/cam_up_episode_1.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:5ea20f9e369f5f26380403504d1701e3b2f1fface1ce8af88b7b8e950e4985be
-size 3389350

v0.0.1/videos/cam_up_episode_2.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:d2807059c207e75704445a58f70a22278f6398d6fd1b7ef2f59455cff531f57f
-size 2830621

v0.0.1/videos/cam_up_episode_3.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3b8e2672570044735cf32a28e9ad786701dca327118062002f7021290e3f32a5
-size 2379028

v0.0.1/videos/cam_up_episode_4.mp4

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bf09eaee125f7c244fdbd3a238d27e608ab5d87133186c3ab07876080460361b
-size 6435591

v0.0.1/webcam.py

@@ -1,45 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import os
-import time
-import numpy as np
-import cv2
-import pyarrow as pa
-
-from dora import Node
-
-node = Node()
-
-CAMERA_INDEX = int(os.getenv("CAMERA_INDEX", 0))
-CAMERA_WIDTH = 640
-CAMERA_HEIGHT = 480
-video_capture = cv2.VideoCapture(CAMERA_INDEX)
-font = cv2.FONT_HERSHEY_SIMPLEX
-
-
-for event in node:
-    event_type = event["type"]
-    if event_type == "INPUT":
-        ret, frame = video_capture.read()
-        if not ret:
-            frame = np.zeros((CAMERA_HEIGHT, CAMERA_WIDTH, 3), dtype=np.uint8)
-            cv2.putText(
-                frame,
-                "No Webcam was found at index %d" % (CAMERA_INDEX),
-                (int(30), int(30)),
-                font,
-                0.75,
-                (255, 255, 255),
-                2,
-                1,
-            )
-        node.send_output(
-            "image",
-            pa.array(frame.ravel()),
-            event["metadata"],
-        )
-        cv2.imshow(str(CAMERA_INDEX), frame)
-        if cv2.waitKey(1) & 0xFF == ord("q"):
-            break
-video_capture.release()

v0.0.1/whisper_node.py

@@ -1,59 +0,0 @@
-import pyarrow as pa
-import whisper
-from pynput import keyboard
-from pynput.keyboard import Key, Events
-from dora import Node
-
-import torch
-import numpy as np
-import pyarrow as pa
-import sounddevice as sd
-import gc  # garbage collect library
-
-model = whisper.load_model("base")
-
-SAMPLE_RATE = 16000
-
-node = Node()
-
-
-def get_text(duration) -> str:
-
-    ## Microphone
-    audio_data = sd.rec(
-        int(SAMPLE_RATE * duration),
-        samplerate=SAMPLE_RATE,
-        channels=1,
-        dtype=np.int16,
-        blocking=True,
-    )
-
-    audio = audio_data.ravel().astype(np.float32) / 32768.0
-
-    ## Speech to text
-    audio = whisper.pad_or_trim(audio)
-    return model.transcribe(audio, language="en")
-
-
-## Check for keyboard event
-with keyboard.Events() as events:
-    for dora_event in node:
-        if dora_event["type"] == "INPUT":
-            event = events.get(0.1)
-            if (
-                event is not None
-                and (event.key == Key.alt_r or event.key == Key.ctrl_r)
-                and isinstance(event, Events.Press)
-            ):
-                if event.key == Key.alt_r:
-                    result = get_text(5)
-                    node.send_output(
-                        "text_llm", pa.array([result["text"]]), dora_event["metadata"]
-                    )
-                elif event.key == Key.ctrl_r:
-                    result = get_text(3)
-                    node.send_output(
-                        "text_policy",
-                        pa.array([result["text"]]),
-                        dora_event["metadata"],
-                    )