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Dataloader for MobiCom processed dataset (output of processor.py).
Uses the optimized format produced by processor.py:
- radar.npy: (N, doppler, elevation, azimuth, range) complex64
- dji_rgb.avi: DJI RGB video (FFV1), (N, H, W, 3) uint8
- zed_depth.npy: (N, H, W) uint16, depth in millimeters
This module provides:
- `RiceDataset`: frame-level dataset returning radar amplitude/phase, DJI RGB,
and ZED depth (ground truth).
- `create_rice_dataloader`: generic dataloader for an arbitrary set of sequences.
- `create_split_dataloaders`: reads train/val/test split from split.json
(default: radar_model/split.json) and returns train/val/test dataloaders.
"""
import json
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import cv2
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, random_split
class RiceDataset(Dataset):
"""
Dataset for processor.py output: radar, DJI RGB, and ZED depth per frame.
Args:
root_dir: Root directory containing sequence subdirs (e.g. processed/),
each with radar.npy, dji_rgb.avi, zed_depth.npy.
sequences: Optional list of sequence names to load. If None, loads all
subdirs that contain the three required files.
frame_skip: Sample every frame_skip frames (1 = all frames).
return_radar_complex: If True, return radar as complex tensor; if False,
return radar_amplitude and radar_phase as separate float tensors.
depth_in_meters: If True, convert depth from mm to meters.
rgb_normalize: If True, return RGB in [0, 1] float; else uint8 [0, 255].
"""
# GRT inference consumes radar and depth only. Smoke-Eval packages RGB
# frames as ``dji_rgb.npy`` rather than the original training video, so
# requiring the unused video would incorrectly discard every sequence.
REQUIRED_FILES = ("radar.npy", "zed_depth.npy")
def __init__(
self,
root_dir: str,
sequences: Optional[List[str]] = None,
frame_skip: int = 1,
return_radar_complex: bool = False,
depth_in_meters: bool = True,
rgb_normalize: bool = True,
):
self.root_dir = Path(root_dir)
self.frame_skip = max(1, frame_skip)
self.return_radar_complex = return_radar_complex
self.depth_in_meters = depth_in_meters
self.rgb_normalize = rgb_normalize
self.sequences = self._discover_sequences(sequences)
self.index_map: List[Tuple[str, int]] = [] # (seq_name, frame_idx)
self._seq_arrays: Dict[str, Dict] = {} # seq -> {radar, depth, dji_rgb}
self._build_index()
def _discover_sequences(self, sequences: Optional[List[str]] = None) -> List[str]:
"""Return list of sequence names that have all required files."""
if not self.root_dir.is_dir():
raise FileNotFoundError(f"Root directory not found: {self.root_dir}")
all_seqs = sorted(
d.name
for d in self.root_dir.iterdir()
if d.is_dir() and not d.name.startswith(".")
)
valid = []
for name in all_seqs:
seq_dir = self.root_dir / name
if all((seq_dir / f).exists() for f in self.REQUIRED_FILES):
valid.append(name)
if sequences is not None:
valid = [s for s in valid if s in sequences]
return valid
def _build_index(self) -> None:
"""Build (seq_name, frame_idx) index, using radar.npy for frame count."""
self.index_map.clear()
for seq_name in self.sequences:
seq_dir = self.root_dir / seq_name
radar_path = seq_dir / "radar.npy"
radar = np.load(radar_path, mmap_mode="r")
n_frames = radar.shape[0]
for i in range(0, n_frames, self.frame_skip):
self.index_map.append((seq_name, i))
# def _load_video_rgb(self, path: Path) -> np.ndarray:
# """Load RGB AVI (e.g. FFV1) as (N, H, W, 3) uint8 RGB."""
# cap = cv2.VideoCapture(str(path))
# if not cap.isOpened():
# raise RuntimeError(f"Failed to open video: {path}")
# frames = []
# while True:
# ret, frame = cap.read()
# if not ret:
# break
# rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# frames.append(rgb)
# cap.release()
# if not frames:
# return np.empty((0, 0, 0, 3), dtype=np.uint8)
# return np.stack(frames, axis=0)
def _load_sequence_arrays(self, seq_name: str) -> Dict:
"""Lazy-load or return cached arrays for a sequence."""
if seq_name not in self._seq_arrays:
seq_dir = self.root_dir / seq_name
# dji_rgb = self._load_video_rgb(seq_dir / "dji_rgb.avi")
self._seq_arrays[seq_name] = {
"radar": np.load(seq_dir / "radar.npy", mmap_mode="r"),
"depth": np.load(seq_dir / "zed_depth.npy", mmap_mode="r"),
# "dji_rgb": dji_rgb,
}
return self._seq_arrays[seq_name]
def __len__(self) -> int:
return len(self.index_map)
def __getitem__(self, idx: int) -> Dict[str, torch.Tensor]:
seq_name, frame_idx = self.index_map[idx]
arrs = self._load_sequence_arrays(seq_name)
# (H, W, 3) uint8
# rgb = np.asarray(arrs["dji_rgb"][frame_idx])
# (H, W) uint16 mm (processor saves as uint16)
depth = np.asarray(arrs["depth"][frame_idx]).astype(np.float32)
# (doppler, elevation, azimuth, range) complex64
radar = np.asarray(arrs["radar"][frame_idx]).copy()
# Depth: uint16 mm -> float; optional mm -> m; handle invalid
if self.depth_in_meters:
depth = depth / 1000.0
invalid = ~(np.isfinite(depth) & (depth > 0))
depth[invalid] = 0.0
depth = depth[np.newaxis, ...] # (1, H, W)
# RGB: (H, W, 3) -> (3, H, W)
# rgb = np.transpose(rgb, (2, 0, 1))
# if self.rgb_normalize:
# rgb = rgb.astype(np.float32) / 255.0
# Radar: amplitude and phase
radar_amplitude = np.abs(radar).astype(np.float32)
radar_phase = np.angle(radar).astype(np.float32) / np.pi
out = {
"radar_amplitude": torch.from_numpy(radar_amplitude),
"radar_phase": torch.from_numpy(radar_phase),
# "rgb": torch.from_numpy(rgb),
"depth": torch.from_numpy(depth),
"sequence": seq_name,
"frame_idx": frame_idx,
}
if self.return_radar_complex:
out["radar_cube"] = torch.from_numpy(radar.copy())
# Depth in mm for optional use (1, H, W) float32
depth_mm = np.asarray(arrs["depth"][frame_idx]).astype(np.float32)
out["depth_mm"] = torch.from_numpy(depth_mm[np.newaxis, ...])
return out
def create_rice_dataloader(
root_dir: str,
batch_size: int = 8,
num_workers: int = 0,
frame_skip: int = 1,
sequences: Optional[List[str]] = None,
return_radar_complex: bool = False,
depth_in_meters: bool = True,
rgb_normalize: bool = True,
shuffle: bool = True,
) -> DataLoader:
"""Create a DataLoader for the Rice (processor output) dataset."""
dataset = RiceDataset(
root_dir=root_dir,
sequences=sequences,
frame_skip=frame_skip,
return_radar_complex=return_radar_complex,
depth_in_meters=depth_in_meters,
rgb_normalize=rgb_normalize,
)
return DataLoader(
dataset,
batch_size=batch_size,
shuffle=shuffle,
num_workers=num_workers,
pin_memory=True,
)
def create_split_dataloaders(
root_dir: str,
split_json_path: Optional[str] = None,
batch_size: int = 8,
num_workers: int = 0,
frame_skip: int = 1,
return_radar_complex: bool = False,
depth_in_meters: bool = True,
rgb_normalize: bool = True,
val_ratio: float = 0.2,
seed: Optional[int] = 42,
) -> Tuple[DataLoader, DataLoader, DataLoader]:
"""
Create train/val/test dataloaders using split.json.
Reads the dataset split from split.json. If split_json_path is None,
uses radar_model/split.json (same directory as this module).
Split JSON format:
{ "test": ["seq_x", ...], "train": ["seq_a", ...] } // "train" optional
If "train" is present and non-empty, only those sequences are used for train/val.
Otherwise, all sequences under root_dir with required files that are not in "test" are used for training.
Validation is a random fraction (val_ratio) of the training samples.
Returns:
train_loader, val_loader, test_loader
"""
if split_json_path is None:
split_path = Path(__file__).resolve().parent / "split.json"
else:
split_path = Path(split_json_path)
if not split_path.exists() and not split_path.is_absolute():
# Resolve relative path from this module's directory (e.g. radar_model/)
fallback = Path(__file__).resolve().parent / split_path.name
if fallback.exists():
split_path = fallback
with split_path.open("r") as f:
split = json.load(f)
test_sequences = split.get("test", [])
train_sequences_json = split.get("train", None)
# Discover all valid sequences in root_dir
_discover = RiceDataset(
root_dir=root_dir,
sequences=None,
frame_skip=frame_skip,
return_radar_complex=return_radar_complex,
depth_in_meters=depth_in_meters,
rgb_normalize=rgb_normalize,
)
test_set = set(test_sequences)
if train_sequences_json is not None and len(train_sequences_json) > 0:
# Use explicit train list (intersect with discovered so only valid seqs are used)
train_sequences = [s for s in train_sequences_json if s in _discover.sequences]
else:
# No "train" key: use all discovered sequences not in test
train_sequences = [s for s in _discover.sequences if s not in test_set]
full_train_dataset = RiceDataset(
root_dir=root_dir,
sequences=train_sequences,
frame_skip=frame_skip,
return_radar_complex=return_radar_complex,
depth_in_meters=depth_in_meters,
rgb_normalize=rgb_normalize,
)
# Random split of training data for validation
n_total = len(full_train_dataset)
n_val = int(n_total * val_ratio)
if n_val == 0 and n_total > 0:
n_val = 1
n_train = n_total - n_val
if n_total == 0:
train_dataset = full_train_dataset
val_dataset = RiceDataset(
root_dir=root_dir,
sequences=[],
frame_skip=frame_skip,
return_radar_complex=return_radar_complex,
depth_in_meters=depth_in_meters,
rgb_normalize=rgb_normalize,
)
elif seed is None:
train_dataset, val_dataset = random_split(
full_train_dataset, [n_train, n_val]
)
else:
generator = torch.Generator()
generator.manual_seed(seed)
train_dataset, val_dataset = random_split(
full_train_dataset, [n_train, n_val], generator=generator
)
test_dataset = RiceDataset(
root_dir=root_dir,
sequences=test_sequences,
frame_skip=frame_skip,
return_radar_complex=return_radar_complex,
depth_in_meters=depth_in_meters,
rgb_normalize=rgb_normalize,
)
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers,
pin_memory=True,
)
val_loader = DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
)
test_loader = DataLoader(
test_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True,
)
return train_loader, val_loader, test_loader
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