Add files using upload-large-folder tool

README.md

@@ -1,3 +1,18 @@
----
-license: cc-by-nc-sa-4.0
----
+# MultiEgoView
+This repository contains the real world data from MultiEgoView a dataset generated with EgoSim:
+
+## EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activity (NeurIPS 2024)
+[Dominik Hollidt](https://dominikvincent.github.io/), [Paul Streli](https://paulstreli.com/), [Jiaxi Jiang](https://jiaxi-jiang.com/), [Yasaman Haghighi](https://ch.linkedin.com/in/yasaman-haghighi), [Changlin Qian](https://ch.linkedin.com/in/changlin-qian-57901616bF), [Xintong Liu](https://ch.linkedin.com/in/xintong-liu-2b022b135), [Christian Holz](https://www.christianholz.net)<br/>
+[Sensing, Interaction & Perception Lab](https://siplab.org), Department of Computer Science, ETH Zürich, Switzerland <br/>
+
+[![arXiv](https://img.shields.io/badge/arXiv-2502.18373-b31b1b.svg)](https://arxiv.org/pdf/2502.18373)
+[![Hugging Face](https://img.shields.io/badge/HuggingFace-Paper-blue.svg)](https://huggingface.co/papers/2502.18373)
+[![Project Page](https://img.shields.io/badge/Project-EgoSim-4b8bbe.svg)](https://siplab.org/projects/EgoSim)
+[![Full Dataset](https://img.shields.io/badge/Dataset-EgoSim-9cf.svg)](https://dataset.egosim.inf.ethz.ch/)
+
+## Dataloading
+
+An example dataloader for the real world data can be found in `code/example_dataloader`. The dataloader uses the huggingface dataset and can be used for action classification. The huggingface dataset can be wrapped by any frameworks dataset. For fast loading additional preprocessing might be required, e.g. smaller video snippets of equal length or padding.
+
+The synthetic data is stored as individual image frames. While this format is ideal for preserving high-fidelity visual information, it is inefficient in terms of memory usage and access speed, especially when used for video classification or regression tasks in machine learning. To address this, we provide a conversion script (`code/dataset_convert.py`) that transforms the EgoSimData into more efficient formats such as MP4 or HDF5.
+

code/dataset_convert.py

@@ -0,0 +1,465 @@
+"""
+DatasetConverter - A utility for processing EgoSim dataset for machine learning.
+Image loading is slow thus a conversion to mp4 or hdf5 is benefitial.
+
+This script provides an example pipeline to convert raw EgoSim simulation
+data into standardized formats suitable for machine learning applications. The 
+converter handles image sequences, SMPL pose parameters, and metadata to generate:
+- MP4 videos with standardized dimensions
+- HDF5 datasets for efficient storage (optional)
+- CSV metadata files for sequences
+- Processed SMPL body model data with joint positions
+"""
+import os
+import pickle
+import signal
+import time
+import traceback
+from pathlib import Path
+from typing import Tuple, List
+
+import cv2
+import h5py
+import numpy as np
+import pandas as pd
+import torch
+from smplx import SMPLX
+from tqdm import trange
+import subprocess
+
+
+class DatasetConverter:
+
+    def __init__(self, label_idx,
+                 image_size: Tuple[int, int],
+                 images_root: Path, # H, W
+                 airsim_rec_root: Path,
+                 video_output_root: Path,
+                 hdf5_output_root: Path,
+                 csv_output_root: Path,
+                 smpl_root: Path,
+                 smpl_out_root: Path,
+                 smplx_model_path: Path,
+                 max_sequence_length: int = 150,
+                 save_as_mp4: bool = True,
+                 save_as_hdf5: bool = False
+                 ):
+        """
+        Initialize the DatasetConverter for transforming raw AirSim data to a format suitable for training models.
+
+        This converter handles various data processing tasks:
+        - Converting raw images to mp4 videos with standardized dimensions
+        - Converting SMPL pose parameters to usable joint positions
+        - Saving sequence data in HDF5 format (optional)
+        - Creating CSV files with sequence metadata
+        
+        Parameters
+        ----------
+        label_idx : dict
+            The Babel action dictionary containing segment information with keys like 'amass_path', 'start_s', 'end_s', 'seg_id', etc.
+        
+        image_size : Tuple[int, int]
+            Desired output size for images/videos as (height, width) in pixels.
+        
+        images_root : Path
+            Root directory containing the raw input images.
+        
+        airsim_rec_root : Path
+            Root directory containing AirSim recording data (airsim_rec.txt files).
+        
+        video_output_root : Path
+            Directory where converted mp4 videos will be saved.
+        
+        hdf5_output_root : Path
+            Directory where HDF5 files of image sequences will be saved.
+        
+        csv_output_root : Path
+            Directory where CSV files with sequence metadata will be saved.
+        
+        smpl_root : Path
+            Root directory containing original SMPL model parameters.
+        
+        smpl_out_root : Path
+            Directory where processed SMPL sequence data will be saved.
+        
+        smplx_model_path : Path
+            Path to the directory containing SMPLX model files.
+        
+        max_sequence_length : int, optional
+            Maximum number of frames to include in a sequence, default is 150.
+
+        save_as_mp4 : bool, optional
+            Whether to save sequences as MP4 videos, default is True.
+        
+        save_as_hdf5 : bool, optional
+            Whether to save sequences as HDF5 files, default is False.
+        """
+        self.label_idx = label_idx
+        self.image_size = image_size
+        self.airsim_rec_root = airsim_rec_root
+        self.images_root = images_root
+        self.video_output_root = video_output_root
+        self.hdf5_output_root = hdf5_output_root
+        self.max_sequence_length = max_sequence_length
+        self.csv_output_root = csv_output_root
+        self.smpl_root = smpl_root
+        self.smpl_out_root = smpl_out_root
+        self.interrupt_caught = False
+        self.airsim_rec_cache = {}
+        self.should_save_as_mp4 = save_as_mp4
+        self.should_save_as_hdf5 = save_as_hdf5
+
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        self.body_model_female = SMPLX(str(smplx_model_path / "SMPLX_FEMALE.npz"),
+                                       batch_size=1,
+                                       gender='female',
+                                       num_betas=16,
+                                       num_expression_coeffs=10,
+                                       ).to(self.device)
+        self.body_model_male = SMPLX(str(smplx_model_path / "SMPLX_MALE.npz"),
+                                     batch_size=1,
+                                     gender='male',
+                                     num_betas=16,
+                                     num_expression_coeffs=10).to(self.device)
+
+    def load_images_from_disk(self, img_path):
+        """Loads image from disk as jpg."""
+        img_path = self.images_root / img_path.parent / "images" / img_path.name
+        try:
+            image = cv2.imread(str(img_path))
+            image = cv2.resize(image, (self.image_size[1], self.image_size[0]))
+        except Exception as e:
+            print(f"Failed to load {img_path} and resize image. Returning empty image", e)
+            return np.zeros((self.image_size[0], self.image_size[1], 3), dtype=np.uint8)
+
+        return image
+
+    def load_images(self, img_paths):
+        """Loads images from disk as jpg files."""
+        images = []
+        for img_path in img_paths:
+            image = self.load_images_from_disk(img_path)
+            images.append(image)
+        return images
+
+    def save_to_hdf5(self, image_paths: List[Path], seg_id: str, view: str):
+        timea = time.time()
+        stacked_images = np.stack(self.load_images(image_paths), axis=0)
+        timeb = time.time()
+        # Save the images as hdf5
+        dataset_name = image_paths[0].parts[0] + ".hdf5"
+        participant_name = image_paths[0].parent.parent.name
+        sequence = image_paths[0].parent.name
+        os.makedirs(self.hdf5_output_root, exist_ok=True)
+        try:
+            with h5py.File(self.hdf5_output_root / dataset_name, 'a', libver='latest') as hdf5_file:
+                seg_group = hdf5_file.require_group(participant_name).require_group(sequence).require_group(seg_id)
+                if view in seg_group:
+                    return
+                    # seg_group[view][...] = stacked_images
+                else:
+                    seg_group.create_dataset(view, data=stacked_images, dtype="uint8")
+        except Exception as e:
+            print(f"Error saving to hdf5: {e}, on {self.hdf5_output_root / dataset_name} with seg_id {seg_id} and view {view}")
+            raise e
+        timec = time.time()
+        print(f"Time to load images: {timeb - timea}, time to save hdf5: {timec - timeb}")
+
+    def valid_image(self, img_path):
+        """checks if the image exists and is not 0 size."""
+        if not img_path.exists():
+            print("Image does not exist", img_path)
+            return False
+        if os.path.getsize(img_path) == 0:
+            print("Image is 0 size", img_path)
+            return False
+        return True
+
+    def save_as_mp4(self, image_paths: List[Path], seg_id: str, view: str):
+        sequence_part = Path(*image_paths[0].parts[-4:-1])
+        output_dir = self.video_output_root / sequence_part / seg_id
+        # Save the images as mp4
+        os.makedirs(output_dir, exist_ok=True)
+        image_file_list_path = output_dir / f"image_file_list_{view}.txt"
+        video_output_path = output_dir / f"{view}.mp4"
+
+        cap = cv2.VideoCapture(str(video_output_path))
+        length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        cap.release()
+        if length == len(image_paths):
+            print("Video (with same number of frames) already exists, skipping")
+            return
+
+        with open(image_file_list_path, 'w') as file:
+            image_paths = [images_root / sequence_part / "images" / image_path.name for image_path in image_paths]
+            image_paths_str = "\n".join([f"file '{self.images_root / image_path}'" for image_path in image_paths if self.valid_image(self.images_root / image_path)])
+            file.write(image_paths_str)
+        
+        # This command uses ffmpeg to create a video from the images. It expects that ffmpeg is compiled with hardware accelerated nvenc support for faster video encoding.
+        # If not available change this line to use the software encoder
+        command =  f"ffmpeg -hide_banner -loglevel error -f concat -r 30 -y -safe 0 -i '{image_file_list_path}' -c:v hevc_nvenc -vf 'scale={self.image_size[1]}:{self.image_size[0]}' -aspect 1:1 -preset p7 -cq:v 1 -pix_fmt yuv420p '{video_output_path}'"
+        # command =  f"ffmpeg -hide_banner -loglevel error -f concat -r 30 -y -safe 0 -i '{image_file_list_path}' -c:v libx265 -vf 'scale={self.image_size[1]}:{self.image_size[0]}' -aspect 1:1 -preset slow -crf 18 -pix_fmt yuv420p '{video_output_path}'"
+
+        try:
+            # Execute the command, capturing stdout and stderr
+            result = subprocess.run(command, shell=True, check=True, text=True, stdout=subprocess.PIPE,
+                                    stderr=subprocess.PIPE)
+        except subprocess.CalledProcessError as e:
+            # Print and write stderr to a file if an error occurs
+            print(f"An error occurred: {e.stderr}")
+            with open("error_files.txt", "a") as error_file:
+                error_file.write(f"Error in ffmpeg: {e.stderr}, on {image_file_list_path} with command: {command}\n")
+
+        os.remove(image_file_list_path)
+
+        # read the video and check the number of frames it has
+        cap = cv2.VideoCapture(str(video_output_path))
+        length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        cap.release()
+        if length != len(image_paths):
+            print(f"Video {video_output_path} has {length} frames, should have {len(image_paths)}")
+
+    def save_smpl_sequence(self, airsim_rec: pd.DataFrame, sequence_path: Path, vehicle_name: str, seg_id: str):
+        # save np arrays as npz file
+        out_file = (smpl_out_root / sequence_path / seg_id).with_suffix(".npz")
+        out_file.parent.mkdir(parents=True, exist_ok=True)
+        # check if output file already exists and contains "joint_positions" key
+        if out_file.exists():
+            try:
+                npz = np.load(out_file)
+                if "joint_positions" in npz:
+                    print(f"Skipping {out_file} as it already exists.")
+                    return
+            except Exception as e:
+                print(f"Error loading {out_file}: {e}")
+
+
+        # load smpl data
+        smpl_path = self.smpl_root / sequence_path
+        smpl_path = smpl_path.with_suffix(".npz")
+        smpl_data = np.load(smpl_path, allow_pickle=True)
+
+        ts = airsim_rec[airsim_rec["VehicleName"] == vehicle_name]["TimeStampAnimationS"]
+
+        def timestamps_to_idx(ts, smpl_data):
+            fps = smpl_data["mocap_frame_rate"]
+            idx = (ts * fps).astype(int)
+            return idx
+
+
+        idx = timestamps_to_idx(ts, smpl_data)
+        idx_len = idx.shape[0]
+        idx = idx[idx < smpl_data["poses"].shape[0]]
+        if idx.shape[0] - idx_len > 2:
+            print(f"Warning: More than 2 frames were removed from the smpl data. {idx_len - idx.shape[0]} frames were removed.")
+
+        poses = smpl_data["poses"][idx]
+        root_orient = smpl_data["root_orient"][idx]
+        trans = smpl_data["trans"][idx]
+        betas = smpl_data["betas"]
+        gender = smpl_data["gender"]
+
+        joint_positions = np.zeros((idx.shape[0], 127, 3))
+        poses_torch = torch.from_numpy(poses[:, 3:66]).float().to(self.device)
+        betas_torch = torch.from_numpy(betas).float().to(self.device)
+        for i in range(idx.shape[0]):
+            if smpl_data["gender"] == "male":
+                bm = self.body_model_male
+            elif smpl_data["gender"] == "female":
+                bm = self.body_model_female
+            else:
+                raise ValueError("Body model can either be male or female.")
+            bp = {
+                "body_pose": poses_torch[i].unsqueeze(0),
+                "betas": betas_torch.unsqueeze(0),
+            }
+            with torch.no_grad():
+                joint_positions[i] = bm.forward(**bp, return_verts=False, use_only_num_joints=-1).joints.cpu().numpy()
+
+        np.savez(out_file,
+                 poses=poses,
+                 root_orient=root_orient,
+                 trans=trans,
+                 betas=betas,
+                 gender=gender,
+                 joint_positions=joint_positions,
+                 )
+
+
+    def save_segment_csv(self, sequence_path, seg_id, airsim_rec, airsim_rec_other_vehicles, vehicle_name):
+        csv_out_path = self.csv_output_root / sequence_path / seg_id
+        csv_out_path.mkdir(parents=True, exist_ok=True)
+        airsim_rec_all = pd.concat([airsim_rec, airsim_rec_other_vehicles])
+        airsim_rec_all = airsim_rec_all.sort_index()
+
+        self.save_smpl_sequence(airsim_rec_all, sequence_path, vehicle_name, seg_id)
+
+        if airsim_rec_all.shape[0] == 0:
+            print("WARNING: airsim_rec_all is empty: saving to empty csv: ", csv_out_path / "airsim_rec.csv")
+        airsim_rec_all.to_csv(csv_out_path / "airsim_rec.csv", index=False)
+
+    def convert_sample(self, idx: int):
+        sequence_path = self.label_idx["amass_path"][idx]
+        start_s = self.label_idx['start_s'][idx]
+        end_s = self.label_idx['end_s'][idx]
+        seg_id = self.label_idx['seg_id'][idx]
+        chunk = self.label_idx['chunk_n'][idx]
+
+
+        airsim_rec_path = self.airsim_rec_root / sequence_path
+
+        # load csv with pandas
+        if not airsim_rec_path.exists():
+            print(f"No airsim_rec.txt found for {sequence_path} in {seg_id}. Skipping Segment of length {start_s} {end_s} ({end_s-start_s})")
+            # append filename to error file
+            with open("error_files.txt", "a") as file:
+                file.write(f"No airsim_rec.txt found for {sequence_path} in {seg_id}. Skipping Segment of length {start_s} {end_s} ({end_s-start_s})\n")
+            return
+
+        airsim_rec_fp = airsim_rec_path / 'airsim_rec.txt'
+        if airsim_rec_fp not in self.airsim_rec_cache:
+            airsim_rec = pd.read_csv(airsim_rec_fp, sep="\t", engine="c", low_memory=False)
+            self.airsim_rec_cache[airsim_rec_fp] = airsim_rec.copy()
+        else:
+            airsim_rec = self.airsim_rec_cache[airsim_rec_fp].copy()
+
+        # vehicle name of character
+        seq_path_split = sequence_path.split("/")
+        vehicle_name = seq_path_split[-3] + "#" + seq_path_split[-2]
+
+        max_time = airsim_rec['TimeStampAnimationS'].max()
+        orig_airsim_rec = airsim_rec #.copy()
+        vehicle_mask = airsim_rec['VehicleName'] == vehicle_name
+        time_mask = (airsim_rec["TimeStampAnimationS"] >= start_s) & (airsim_rec["TimeStampAnimationS"] < end_s)
+        airsim_rec = airsim_rec[vehicle_mask & time_mask]
+
+        frames = len(airsim_rec)
+        if frames == 0:
+            print(f"No frames found for {sequence_path} in {seg_id}. Skipping Segment of length {start_s} {end_s} ({end_s-start_s}. Vehicle max: {max_time})")
+            # append filename to error file
+            with open("error_files.txt", "a") as file:
+                file.write(f"No images in sequence {sequence_path} {seg_id} start: {start_s} end: {end_s}, airsim_rec_path: {airsim_rec_path}\n")
+            return
+
+        end = min(airsim_rec.index[-1] + 1, orig_airsim_rec.shape[0] - 1)
+        orig_airsim_rec_window = orig_airsim_rec.iloc[airsim_rec.index[0]:end]
+        airsim_rec_other_vehicles = orig_airsim_rec_window[orig_airsim_rec_window["VehicleName"] != vehicle_name]
+
+
+
+        subsampling = 1
+        if frames > self.max_sequence_length:
+            if self.label_idx['scale_factor'][idx] > 1.0:
+                # print("Scalling factor > 1.0, subsampling: ")
+                # print("Rows before: ", len(airsim_rec))
+                # take every second row
+                subsampling = int(self.label_idx['scale_factor'][idx])
+                airsim_rec = airsim_rec.iloc[::subsampling]
+                frames = len(airsim_rec)
+                # print("Rows after: ", len(airsim_rec))
+
+            print(
+                f"Warning: More than {self.max_sequence_length} frames found ({frames}). Only using the first {self.max_sequence_length}, airsim_rec_path: {airsim_rec_path}. Start: {start_s} End: {end_s} Vehicle max: {max_time})")
+            with open("error_files.txt", "a") as file:
+                file.write(
+                    f"Warning: More than {self.max_sequence_length} frames found ({frames}). Only using the first {self.max_sequence_length}, airsim_rec_path: {airsim_rec_path}. Start: {start_s} End: {end_s} Vehicle max: {max_time})\n")
+            airsim_rec = airsim_rec.head(self.max_sequence_length)
+            frames = self.max_sequence_length
+
+        # save airsim_rec.csv for current sequence
+        if chunk != 0:
+            seg_id = f"{seg_id}_chunk{chunk:02d}"
+
+        # saving subset airsim_rec to csv
+
+        self.save_segment_csv(sequence_path, seg_id, airsim_rec, airsim_rec_other_vehicles, vehicle_name)
+
+        image_paths = airsim_rec["ImageFile"].dropna().str.split(';').explode().tolist()
+        for view in ["socket1", "socket2", "socket3", "socket4", "socket5", "socket6"]:
+            sequence_paths = [Path(sequence_path) / image_path.replace(".ppm", ".jpg") for image_path in image_paths if
+                                view in image_path]
+
+            if len(sequence_paths) > 150:
+                print(f"Warning: More than 150 images found ({len(sequence_paths)}). Only using the first 150, airsim_rec_path: {airsim_rec_path}. Start: {start_s} End: {end_s} Vehicle max: {max_time})")
+                with open("error_files.txt", "a") as file:
+                    file.write(
+                        f"Warning: More than 150 images found ({len(sequence_paths)}). Only using the first 150, airsim_rec_path: {airsim_rec_path}. Start: {start_s} End: {end_s} Vehicle max: {max_time})\n")
+                sequence_paths = sequence_paths[:150]
+
+            # continue if video path already exists
+            sequence_part = Path(*sequence_paths[0].parts[-4:-1])
+            output_dir = self.video_output_root / sequence_part / seg_id / f"{view}.mp4"
+            if output_dir.exists() and output_dir.stat().st_size != 0:
+                # continue if video already exists
+                continue
+
+            # Save as HDF5 if requested
+            if self.should_save_as_hdf5:
+                self.save_to_hdf5(sequence_paths, seg_id, view)
+
+            # Save as MP4 if requested
+            if self.should_save_as_mp4:
+                self.save_as_mp4(sequence_paths, seg_id, view)
+
+
+    def signal_handler(self, signal, frame):
+        print('Interrupt received, finishing the current operation before exiting...')
+        self.interrupt_caught = True
+
+    def convert_dataset(self):
+        # Set the signal handler for SIGINT (Ctrl+C)
+        signal.signal(signal.SIGINT, self.signal_handler)
+
+        for i in trange(0, len(self.label_idx["amass_path"])):
+            if self.interrupt_caught:
+                print("Interrupt caught, exiting...")
+                break
+            try:
+                self.convert_sample(i)
+            except Exception as e:
+                print(f"An error occurred: {e}, on {self.label_idx['amass_path'][i]} with seg_id {self.label_idx['seg_id'][i]}")
+                traceback.print_exc()
+                with open("error_files.txt", "a") as file:
+                    file.write(f"An error occurred: {e}, on {self.label_idx['amass_path'][i]} with seg_id {self.label_idx['seg_id'][i]}\n")
+
+
+if __name__ == "__main__":
+
+    dataset_paths = [r'babel_v1.0/train_label_60.pkl', r'babel_v1.0/val_label_60.pkl', r'babel_v1.0/test_label_60.pkl']
+    for dataset_path in dataset_paths:
+        with open(dataset_path, 'rb') as f:
+            dataset_split = pickle.load(f)
+
+        # these two paths might be the same
+        images_root = Path("path to images root")
+        airsim_rec_root = Path("paths to airsim rec root")
+        
+        # optional to save videos
+        video_output_root = Path("path to output video root")
+        # optional to save hdf5
+        hdf5_output_root = Path("path to output hdf5 root")
+        
+        # Path where CSV metadata files will be stored
+        csv_output_root = Path("/path/to/output/csv")
+        
+        # Path to directory containing SMPL models
+        smpl_root = Path("/path/to/amass/smpl/models") 
+        
+        # Path where processed SMPL sequence data will be saved
+        smpl_out_root = Path("/path/to/output/smpl/sequences")
+        
+        # Path to directory containing SMPLX model files
+        smplx_model_path = Path("/path/to/smplx/models")
+
+        converter = DatasetConverter(dataset_split,
+                                    (224, 224),
+                                    images_root,
+                                    airsim_rec_root,
+                                    video_output_root,
+                                    hdf5_output_root,
+                                    csv_output_root,
+                                    smpl_root,
+                                    smpl_out_root,
+                                    smplx_model_path)
+
+        converter.convert_dataset()

code/example_dataloader.py

@@ -0,0 +1,141 @@
+from collections import defaultdict
+import json
+from pathlib import Path
+from typing import List, Tuple
+from datasets import Dataset, DatasetDict, Video
+import numpy as np
+from torch.utils.data import DataLoader
+
+from huggingface_hub import HfApi
+
+
+def create_splits(path: Path, split: Tuple[float, float, float]) -> Tuple[List[str], List[str], List[str]]:
+    # find all json files
+    json_files = list(path.glob("**/*.json"))
+    print(f"Found {len(json_files)} json files")
+    
+    participants_length = defaultdict(float)
+    for json_file in json_files:
+        with json_file.open("r") as f:
+            participant = json_file.parts[-3]
+            session = json_file.parts[-2]
+            data = json.load(f)
+            length = data[-1]["end_t"]
+            participants_length[participant] += length
+
+    # split the participants such that their length is approximately as the percentage from the split tuple
+    total_length = sum(participants_length.values())
+    train_target = total_length * split[0]
+    valid_target = total_length * split[1]
+    
+    # Sort participants by length for more balanced distribution
+    sorted_participants = sorted(participants_length.items(), key=lambda x: x[1], reverse=True)
+    
+    # Assign participants to splits
+    train_participants = []
+    valid_participants = []
+    test_participants = []
+    
+    train_length = 0
+    valid_length = 0
+    test_length = 0
+    
+    for participant, length in sorted_participants:
+        # Add to the split with the largest deficit compared to target
+        train_deficit = train_target - train_length if train_length < train_target else -float('inf')
+        valid_deficit = valid_target - valid_length if valid_length < valid_target else -float('inf')
+        
+        # Choose split based on deficit
+        if train_deficit >= valid_deficit and train_deficit > -float('inf'):
+            train_participants.append(participant)
+            train_length += length
+        elif valid_deficit > -float('inf'):
+            valid_participants.append(participant)
+            valid_length += length
+        else:
+            test_participants.append(participant)
+            test_length += length
+
+    print(f"Effective splits: {train_length/total_length:.2f}, {valid_length/total_length:.2f}, {test_length/total_length:.2f}")
+
+    return train_participants, valid_participants, test_participants
+
+def get_smpl_pose(smpl_path: Path, start_t: float, end_t: float, fps: int = 30):
+    smpl_pose = np.load(smpl_path)
+    start_frame = int(start_t * fps)
+    end_frame = int(end_t * fps)
+    pose = {
+        "poses": smpl_pose["poses"][start_frame:end_frame],
+        "trans": smpl_pose["trans"][start_frame:end_frame],
+        "betas": smpl_pose["betas"],
+        "gender": smpl_pose["gender"],
+    }
+
+    return pose
+
+
+def create_dataset_dict(path: Path, split: Tuple[float, float, float] = (0.7, 0.1, 0.2)):
+    assert sum(split) == 1
+
+    splits = create_splits(path, split)
+
+    ds = {"train": defaultdict(list), "val": defaultdict(list), "test": defaultdict(list)}
+    for split, participants in zip(["train", "val", "test"], splits):
+        for participant in participants:
+            # get all the json files for the participant
+            json_files = list(path.glob(f"**/{participant}/**/*.json"))
+            for json_file in json_files:
+                with json_file.open("r") as f:
+                    data = json.load(f)
+                    for action in data:
+                        # skip with 99% probability
+                        if np.random.rand() < 0.95:
+                            continue
+                        session = json_file.parts[-2]
+                        data_folder_relative = json_file.parent.relative_to(path.parent)
+                        entry = {
+                            "participant": participant,
+                            "session": session,
+                            "start_t": action["start_t"],
+                            "end_t": action["end_t"],
+                            "action": action["act_cat"],
+                            "video_head": str(data_folder_relative / "Head_anonymized.mp4"),
+                            "video_pelvis": str(data_folder_relative / "Pelvis_anonymized.mp4"),
+                            "video_left_hand": str(data_folder_relative / "LeftHand_anonymized.mp4"),
+                            "video_right_hand": str(data_folder_relative / "RightHand_anonymized.mp4"),
+                            "video_left_knee": str(data_folder_relative / "LeftKnee_anonymized.mp4"),
+                            "video_right_knee": str(data_folder_relative / "RightKnee_anonymized.mp4"),
+                            **get_smpl_pose(data_folder_relative / "smplx.npz", action["start_t"], action["end_t"])
+                        }
+                        for key in entry:
+                            ds[split][key].append(entry[key])
+
+    return ds                        
+
+def create_huggingface_dataset(ds):
+    huggingface_ds = DatasetDict({
+        "train": Dataset.from_dict(ds["train"]),
+        "val": Dataset.from_dict(ds["val"]),
+        "test": Dataset.from_dict(ds["test"])
+    })
+    print(f"Dataset sizes: Train: {len(huggingface_ds['train'])}, Val: {len(huggingface_ds['val'])}, Test: {len(huggingface_ds['test'])}")
+
+    for split in huggingface_ds:
+        for col in huggingface_ds[split].column_names:
+            if "video" in col:
+                huggingface_ds[split] = huggingface_ds[split].cast_column(col, Video())
+
+    return huggingface_ds
+
+if __name__ == "__main__":
+    ds = create_dataset_dict(Path("path/to/data/of/uncompressed/folders/of/subjects"))
+
+    huggingface_ds = create_huggingface_dataset(ds)
+
+    dataset_sizes = {
+        "train": len(huggingface_ds["train"]),
+        "val": len(huggingface_ds["val"]),
+        "test": len(huggingface_ds["test"])
+    }
+
+    # huggingface_ds can be wrapped with any frameworks dataset, padding might be needed for efficient batching

data/subject001/s1/actions_v2.json

@@ -0,0 +1,542 @@
+[
+    {
+        "start_t": 0.0,
+        "end_t": 3.083333,
+        "raw_label": "jump",
+        "act_cat": [
+            "jump"
+        ]
+    },
+    {
+        "start_t": 3.083333,
+        "end_t": 16.516667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 16.516667,
+        "end_t": 20.933333,
+        "raw_label": "a pose",
+        "act_cat": [
+            "a pose"
+        ]
+    },
+    {
+        "start_t": 20.933333,
+        "end_t": 26.116667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 26.116667,
+        "end_t": 28.566667,
+        "raw_label": "a pose",
+        "act_cat": [
+            "a pose"
+        ]
+    },
+    {
+        "start_t": 28.566667,
+        "end_t": 33.266667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 33.266667,
+        "end_t": 35.266667,
+        "raw_label": "a pose",
+        "act_cat": [
+            "a pose"
+        ]
+    },
+    {
+        "start_t": 35.266667,
+        "end_t": 58.733333,
+        "raw_label": "kick ball",
+        "act_cat": [
+            "kick",
+            "foot movement"
+        ]
+    },
+    {
+        "start_t": 58.733333,
+        "end_t": 77.983333,
+        "raw_label": "throw ball",
+        "act_cat": [
+            "throw"
+        ]
+    },
+    {
+        "start_t": 77.983333,
+        "end_t": 83.233333,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 83.233333,
+        "end_t": 96.183333,
+        "raw_label": "stand",
+        "act_cat": [
+            "stand"
+        ]
+    },
+    {
+        "start_t": 96.183333,
+        "end_t": 130.366667,
+        "raw_label": "dribble ball",
+        "act_cat": [
+            "dribble"
+        ]
+    },
+    {
+        "start_t": 130.366667,
+        "end_t": 138.466667,
+        "raw_label": "side steps back and forth",
+        "act_cat": [
+            "sideways movement"
+        ]
+    },
+    {
+        "start_t": 138.466667,
+        "end_t": 141.933333,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 141.933333,
+        "end_t": 154.583333,
+        "raw_label": "side steps back and forth",
+        "act_cat": [
+            "sideways movement"
+        ]
+    },
+    {
+        "start_t": 154.583333,
+        "end_t": 184.466667,
+        "raw_label": "aim with hand",
+        "act_cat": [
+            "aim",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 184.466667,
+        "end_t": 205.766667,
+        "raw_label": "rotate arms",
+        "act_cat": [
+            "circular movement",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 205.766667,
+        "end_t": 216.716667,
+        "raw_label": "move arms to front",
+        "act_cat": [
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 216.716667,
+        "end_t": 234.966667,
+        "raw_label": "lunge with arms to the side",
+        "act_cat": [
+            "exercise/training",
+            "lunge"
+        ]
+    },
+    {
+        "start_t": 234.966667,
+        "end_t": 254.966667,
+        "raw_label": "lunge",
+        "act_cat": [
+            "exercise/training",
+            "lunge"
+        ]
+    },
+    {
+        "start_t": 254.966667,
+        "end_t": 265.733333,
+        "raw_label": "punch the air in front",
+        "act_cat": [
+            "martial art",
+            "punch"
+        ]
+    },
+    {
+        "start_t": 265.733333,
+        "end_t": 274.516667,
+        "raw_label": "walk with extended arms",
+        "act_cat": [
+            "walk",
+            "t pose"
+        ]
+    },
+    {
+        "start_t": 274.516667,
+        "end_t": 282.866667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 282.866667,
+        "end_t": 310.016667,
+        "raw_label": "swing tennis racket",
+        "act_cat": [
+            "play sport",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 310.016667,
+        "end_t": 315.483333,
+        "raw_label": "arms to face",
+        "act_cat": [
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 315.483333,
+        "end_t": 320.366667,
+        "raw_label": "stand",
+        "act_cat": [
+            "stand"
+        ]
+    },
+    {
+        "start_t": 320.366667,
+        "end_t": 324.516667,
+        "raw_label": "arms to face",
+        "act_cat": [
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 324.516667,
+        "end_t": 336.183333,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 336.183333,
+        "end_t": 350.683333,
+        "raw_label": "stretch arms left and right",
+        "act_cat": [
+            "stretch",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 350.683333,
+        "end_t": 357.316667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 357.316667,
+        "end_t": 368.233333,
+        "raw_label": "stretch body left right",
+        "act_cat": [
+            "stretch",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 368.233333,
+        "end_t": 385.066667,
+        "raw_label": "stand",
+        "act_cat": [
+            "stand"
+        ]
+    },
+    {
+        "start_t": 385.066667,
+        "end_t": 393.616667,
+        "raw_label": "wave arm",
+        "act_cat": [
+            "gesture",
+            "greet"
+        ]
+    },
+    {
+        "start_t": 393.616667,
+        "end_t": 402.116667,
+        "raw_label": "bicep curls",
+        "act_cat": [
+            "exercise/training",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 402.116667,
+        "end_t": 413.233333,
+        "raw_label": "elbow to opposite knee",
+        "act_cat": [
+            "knee movement",
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 413.233333,
+        "end_t": 436.733333,
+        "raw_label": "raise left/right arm",
+        "act_cat": [
+            "arm movement",
+            "raising body part"
+        ]
+    },
+    {
+        "start_t": 436.733333,
+        "end_t": 440.516667,
+        "raw_label": "arms in front of chest",
+        "act_cat": [
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 440.516667,
+        "end_t": 448.433333,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 448.433333,
+        "end_t": 453.616667,
+        "raw_label": "arms in front of chest",
+        "act_cat": [
+            "arm movement"
+        ]
+    },
+    {
+        "start_t": 453.616667,
+        "end_t": 466.816667,
+        "raw_label": "squats",
+        "act_cat": [
+            "squat"
+        ]
+    },
+    {
+        "start_t": 466.816667,
+        "end_t": 476.366667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 476.366667,
+        "end_t": 478.183333,
+        "raw_label": "squats",
+        "act_cat": [
+            "squat"
+        ]
+    },
+    {
+        "start_t": 478.183333,
+        "end_t": 501.116667,
+        "raw_label": "t pose",
+        "act_cat": [
+            "t pose"
+        ]
+    },
+    {
+        "start_t": 501.116667,
+        "end_t": 503.733333,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
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+        "end_t": 504.983333,
+        "raw_label": "t pose",
+        "act_cat": [
+            "t pose"
+        ]
+    },
+    {
+        "start_t": 504.983333,
+        "end_t": 513.916667,
+        "raw_label": "arms over head",
+        "act_cat": [
+            "stretch",
+            "raising body part"
+        ]
+    },
+    {
+        "start_t": 513.916667,
+        "end_t": 527.366667,
+        "raw_label": "walk backward",
+        "act_cat": [
+            "backwards movement"
+        ]
+    },
+    {
+        "start_t": 527.366667,
+        "end_t": 534.516667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 534.516667,
+        "end_t": 544.333333,
+        "raw_label": "balance step feet in one line",
+        "act_cat": [
+            "balance",
+            "foot movements"
+        ]
+    },
+    {
+        "start_t": 544.333333,
+        "end_t": 570.166667,
+        "raw_label": "pick something up one arm",
+        "act_cat": [
+            "take/pick something up",
+            "hand movement"
+        ]
+    },
+    {
+        "start_t": 570.166667,
+        "end_t": 581.766667,
+        "raw_label": "pick something up both arms",
+        "act_cat": [
+            "take/pick something up",
+            "hand movement"
+        ]
+    },
+    {
+        "start_t": 581.766667,
+        "end_t": 587.916667,
+        "raw_label": "stand",
+        "act_cat": [
+            "stand"
+        ]
+    },
+    {
+        "start_t": 587.916667,
+        "end_t": 591.866667,
+        "raw_label": "pick something up both arms",
+        "act_cat": [
+            "take/pick something up",
+            "hand movement"
+        ]
+    },
+    {
+        "start_t": 591.866667,
+        "end_t": 606.016667,
+        "raw_label": "blow kiss",
+        "act_cat": [
+            "hand movements"
+        ]
+    },
+    {
+        "start_t": 606.016667,
+        "end_t": 614.366667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 614.366667,
+        "end_t": 636.083333,
+        "raw_label": "bow",
+        "act_cat": [
+            "bend"
+        ]
+    },
+    {
+        "start_t": 636.083333,
+        "end_t": 645.266667,
+        "raw_label": "jump",
+        "act_cat": [
+            "jump"
+        ]
+    },
+    {
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+        "end_t": 654.116667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
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+        "start_t": 654.116667,
+        "end_t": 657.333333,
+        "raw_label": "jump",
+        "act_cat": [
+            "jump"
+        ]
+    },
+    {
+        "start_t": 657.333333,
+        "end_t": 662.116667,
+        "raw_label": "crouch down",
+        "act_cat": [
+            "crouch"
+        ]
+    },
+    {
+        "start_t": 662.116667,
+        "end_t": 670.233333,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 670.233333,
+        "end_t": 675.233333,
+        "raw_label": "crouch down",
+        "act_cat": [
+            "crouch"
+        ]
+    },
+    {
+        "start_t": 675.233333,
+        "end_t": 682.866667,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 682.866667,
+        "end_t": 697.583333,
+        "raw_label": "jumping jacks",
+        "act_cat": [
+            "jumping jacks",
+            "jump"
+        ]
+    },
+    {
+        "start_t": 697.583333,
+        "end_t": 710.916667,
+        "raw_label": "t pose",
+        "act_cat": [
+            "t pose"
+        ]
+    }
+]

data/subject001/s1/smplx.npz

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+size 28840894

data/subject002/s1/Head_anonymized.mp4

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+size 1169749301

data/subject002/s1/LeftHand_anonymized.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 3863696935

data/subject002/s1/LeftKnee_anonymized.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 1514220489

data/subject002/s1/Pelvis_anonymized.mp4

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+version https://git-lfs.github.com/spec/v1
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+size 1019646275

data/subject002/s1/RightHand_anonymized.mp4

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+size 2002282217

data/subject002/s1/RightKnee_anonymized.mp4

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data/subject002/s1/actions_v2.json

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+[
+    {
+        "start_t": 0.0,
+        "end_t": 4.8,
+        "raw_label": "walk",
+        "act_cat": [
+            "walk"
+        ]
+    },
+    {
+        "start_t": 4.8,
+        "end_t": 23.883333,
+        "raw_label": "a pose",
+        "act_cat": [
+            "a pose"
+        ]
+    },
+    {
+        "start_t": 23.883333,
+        "end_t": 47.8,
+        "raw_label": "kick ball",
+        "act_cat": [
+            "kick",
+            "foot movement"
+        ]
+    },
+    {
+        "start_t": 47.8,
+        "end_t": 66.8,
+        "raw_label": "t pose",
+        "act_cat": [
+            "t pose"
+        ]
+    },
+    {
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