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f348660 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | import torch
import torchvision.transforms.functional as TF
from torchvision.transforms import Resize, InterpolationMode
from typing import Union
import numpy as np
AZIMUTH_RESOLUTION = 128
ELEVATION_RESOLUTION = 64
# Depth output resolution: height=64, width=128
DEPTH_TARGET_HEIGHT = 64
DEPTH_TARGET_WIDTH = 128
resize_transform = Resize(
size=[ELEVATION_RESOLUTION, AZIMUTH_RESOLUTION],
interpolation=InterpolationMode.BILINEAR,
antialias=True,
)
depth_resize_transform = Resize(
size=(DEPTH_TARGET_HEIGHT, DEPTH_TARGET_WIDTH),
interpolation=InterpolationMode.BILINEAR,
antialias=True,
)
def translate_radar(radar_data):
"""
Applies normalization to radar data after batching from dataloader.
Called before passing data into the model.
Args:
radar_data: Batched radar tensor from dataloader
Shape: [B, 64, 8, 2, 256, 2] (batch, doppler, azimuth, elevation, range, channels)
- Channel 0: raw amplitude values
- Channel 1: phase normalized to [-1, 1] (divided by π)
Returns:
Processed radar tensor with same shape [B, 64, 8, 2, 256, 2]
- Channel 0: sqrt(amplitude * 1e-3) for magnitude normalization
- Channel 1: phase * π (converted back to radians [-π, π])
"""
radar_mag = radar_data[..., 0] # [B, 64, 8, 2, 256] - Extract raw amplitude
radar_phase = radar_data[..., 1] # [B, 64, 8, 2, 256] - Extract normalized phase
# Normalize amplitude: scale then sqrt
radar_mag_processed = torch.sqrt(radar_mag * 1e-6)
# Convert phase back to radians: [-1, 1] -> [-π, π]
radar_phase_processed = radar_phase * torch.pi
# Stack channels back together: [B, 64, 8, 2, 256, 2]
radar_data_translated = torch.stack(
[radar_mag_processed, radar_phase_processed], dim=-1
)
return radar_data_translated
def resize_depth(
depth_map: Union[torch.Tensor, np.ndarray],
) -> Union[torch.Tensor, np.ndarray]:
"""
Process depth map from dataloader (same pipeline as denoiser/control crop_depth):
mm -> meters, clamp [0, 11.2] m, normalize to [0, 1], resize to (64, 128) (h, w).
Args:
depth_map: Depth in millimeters. Torch or numpy.
Shapes: (H, W), (B, H, W), or (B, 1, H, W).
Returns:
Depth in [0, 1], spatial size (64, 128). Shape [B, 64, 128] for batched input.
"""
is_numpy = isinstance(depth_map, np.ndarray)
if is_numpy:
depth_map = torch.from_numpy(depth_map)
depth_map = depth_map.float()
original_shape = depth_map.shape
if depth_map.dim() == 2:
depth_map = depth_map.unsqueeze(0) # (H, W) -> (1, H, W)
elif depth_map.dim() == 3:
depth_map = depth_map.unsqueeze(1) # (B, H, W) -> (B, 1, H, W)
elif depth_map.dim() != 4:
raise ValueError(f"Unexpected depth shape: {original_shape}")
invalid_mask = ~(torch.isfinite(depth_map) & (depth_map >= 0))
depth_map[invalid_mask] = 0.0
depth_map = depth_map / 1000.0 # mm -> meters
max_depth_m = 11.2
depth_map = torch.clamp(depth_map, min=0.0, max=max_depth_m)
depth_map = depth_map / max_depth_m # [0, 1]
invalid_mask = ~torch.isfinite(depth_map)
depth_map[invalid_mask] = 0.0
depth_map = depth_resize_transform(depth_map) # (..., 64, 128)
depth_values = depth_map.squeeze(1) # [B, 64, 128] or [1, 64, 128]
if len(original_shape) == 2:
depth_values = depth_values.squeeze(0) # (64, 128)
if is_numpy:
depth_values = depth_values.numpy()
return depth_values
def quantize_depth_to_occupancy(depth_values, num_range_bins=64):
"""
Quantizes 2D depth values into 3D binary occupancy grid.
Args:
depth_values: Resized depth tensor
Shape: [B, elevation, azimuth]
Values: normalized to [0, 1] range
num_range_bins: Number of range bins for quantization (default: 64)
Returns:
Binary 3D occupancy grid
Shape: [B, elevation, azimuth, num_range_bins]
Values: binary (0 or 1) indicating occupied bins
"""
B, elevation, azimuth = depth_values.shape
# Quantize normalized depth [0, 1] directly to range bins [0, num_range_bins-1]
# Each bin represents 1/num_range_bins of the normalized depth range
bin_indices = torch.floor(
depth_values / (1.0 / num_range_bins)
).long() # [B, elevation, azimuth]
bin_indices = torch.clamp(
bin_indices, 0, num_range_bins - 1
) # Handle edge case where depth_values = 1.0
# Create binary 3D occupancy grid
occupancy_grid = torch.zeros(
B,
elevation,
azimuth,
num_range_bins,
dtype=torch.float32,
device=depth_values.device,
) # [B, elevation, azimuth, num_range_bins]
# Set occupied bins to 1
# Use advanced indexing to mark the appropriate range bin for each (elevation, azimuth) cell
batch_idx = torch.arange(B, device=depth_values.device)[:, None, None].expand(
B, elevation, azimuth
)
elevation_idx = torch.arange(elevation, device=depth_values.device)[
None, :, None
].expand(B, elevation, azimuth)
azimuth_idx = torch.arange(azimuth, device=depth_values.device)[
None, None, :
].expand(B, elevation, azimuth)
occupancy_grid[batch_idx, elevation_idx, azimuth_idx, bin_indices] = 1.0
return occupancy_grid # [B, elevation, azimuth, num_range_bins]
def dequantize_depth(occupancy_grid):
"""
Converts 3D binary occupancy grid back to 2D depth map.
This is the inverse operation of quantize_depth_to_occupancy.
Args:
occupancy_grid: Binary 3D occupancy grid
Shape: [B, 64, 128, 64] (batch, elevation, azimuth, range)
Values: binary (0 or 1) or continuous (predicted probabilities)
Returns:
Reconstructed depth map
Shape: [B, 1, 64, 128] (batch, channel, elevation, azimuth)
Values: normalized to [0, 1] range
"""
num_range_bins = occupancy_grid.shape[3]
# Find the range bin with maximum value for each (elevation, azimuth) cell
# For binary: finds the occupied bin
# For continuous: finds the most likely bin
bin_indices = torch.argmax(occupancy_grid, dim=3) # [B, 64, 128]
# Convert bin indices back to normalized depth values [0, 1]
# Use bin center: (bin_idx + 0.5) / num_bins
depth_values = (bin_indices.float() + 1) / num_range_bins # [B, 64, 128]
# Add channel dimension: [B, 64, 128] -> [B, 1, 64, 128]
depth_map = depth_values.unsqueeze(1) # [B, 1, 64, 128]
return depth_map
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