File size: 6,424 Bytes
9f85448 | 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 | """Monocular depth estimation."""
from __future__ import annotations
import threading
from pathlib import Path
import cv2
import numpy as np
def _pick_device() -> str:
"""Best available inference device."""
try:
import torch
except ImportError:
return "cpu"
try:
if torch.cuda.is_available():
return "cuda"
if torch.backends.mps.is_available():
return "mps"
except (AttributeError, RuntimeError):
return "cpu"
return "cpu"
class DepthEstimator:
"""Monocular depth from a YOLO depth model."""
def __init__(self, model_path: str | Path, imgsz: int = 384) -> None:
from ultralytics import YOLO
model_path = Path(model_path)
if not model_path.exists():
raise FileNotFoundError(f"Depth model not found: {model_path}")
self.device = _pick_device()
self.model = YOLO(str(model_path))
self.imgsz = imgsz
self._lo: float | None = None
self._hi: float | None = None
self._bounds_alpha = 0.08
def __call__(self, frame: np.ndarray) -> np.ndarray:
"""Raw depth map, same size as frame."""
result = self.model.predict(
frame, imgsz=self.imgsz, device=self.device, verbose=False
)[0]
depth = result.depth.data
if hasattr(depth, "cpu"):
depth = depth.cpu().numpy()
depth = np.asarray(depth, dtype=np.float32)
if depth.ndim == 3:
depth = depth[0]
if depth.shape[:2] != frame.shape[:2]:
depth = cv2.resize(depth, (frame.shape[1], frame.shape[0]),
interpolation=cv2.INTER_LINEAR)
return depth
def normalize(self, depth: np.ndarray) -> np.ndarray:
"""Stable 0..1 map, 0 near, 1 far."""
finite = depth[np.isfinite(depth)]
if finite.size == 0:
return np.full(depth.shape, 0.5, dtype=np.float32)
lo, hi = float(np.percentile(finite, 2.0)), float(np.percentile(finite, 98.0))
if self._lo is None or self._hi is None:
self._lo, self._hi = lo, hi
else:
self._lo += self._bounds_alpha * (lo - self._lo)
self._hi += self._bounds_alpha * (hi - self._hi)
span = max(self._hi - self._lo, 1e-6)
norm = np.clip((depth - self._lo) / span, 0.0, 1.0)
return np.nan_to_num(norm, nan=0.5).astype(np.float32)
@staticmethod
def colorize(depth_norm: np.ndarray) -> np.ndarray:
"""Turbo colormap of a normalized depth map."""
u8 = (np.clip(depth_norm, 0.0, 1.0) * 255.0).astype(np.uint8)
return cv2.applyColorMap(255 - u8, cv2.COLORMAP_TURBO)
@staticmethod
def sample(depth_map: np.ndarray, x: float, y: float, radius: int = 9,
percentile: float = 20.0, default: float = 0.0) -> float:
"""Nearest surface around a point."""
h, w = depth_map.shape[:2]
xi = int(np.clip(round(float(x)), 0, w - 1))
yi = int(np.clip(round(float(y)), 0, h - 1))
x0, x1 = max(0, xi - radius), min(w, xi + radius + 1)
y0, y1 = max(0, yi - radius), min(h, yi + radius + 1)
patch = depth_map[y0:y1, x0:x1]
patch = patch[np.isfinite(patch)]
if patch.size == 0:
return default
return float(np.percentile(patch, percentile))
class DepthWorker:
"""Background depth estimation thread."""
def __init__(self, model_path: str | Path, imgsz: int = 384,
input_width: int = 640) -> None:
self.estimator = DepthEstimator(model_path, imgsz)
self.device = self.estimator.device
self.input_width = input_width
self._pending: np.ndarray | None = None
self._target: tuple[int, int] | None = None
self._metric: np.ndarray | None = None
self._norm: np.ndarray | None = None
self._seq = 0
self._lock = threading.Lock()
self._wake = threading.Event()
self._stop = threading.Event()
self._thread = threading.Thread(target=self._loop, daemon=True)
self._thread.start()
def submit(self, frame: np.ndarray) -> None:
"""Queue the newest frame."""
h, w = frame.shape[:2]
if w > self.input_width:
k = self.input_width / float(w)
small = cv2.resize(frame, (self.input_width, max(1, int(round(h * k)))),
interpolation=cv2.INTER_AREA)
else:
small = frame.copy()
with self._lock:
self._pending = small
self._target = (w, h)
self._wake.set()
def _loop(self) -> None:
"""Consume frames until stopped."""
while not self._stop.is_set():
self._wake.wait(0.1)
self._wake.clear()
with self._lock:
frame, target = self._pending, self._target
self._pending = None
if frame is None or target is None:
continue
try:
metric = self.estimator(frame)
norm = self.estimator.normalize(metric)
except Exception as exc: # keep the app alive on backend errors
print(f"[!] depth failed: {exc}", flush=True)
self._stop.set()
return
if (metric.shape[1], metric.shape[0]) != target:
metric = cv2.resize(metric, target, interpolation=cv2.INTER_LINEAR)
norm = cv2.resize(norm, target, interpolation=cv2.INTER_LINEAR)
with self._lock:
self._metric = metric
self._norm = norm
self._seq += 1
@property
def latest(self) -> np.ndarray | None:
"""Newest metric depth map."""
with self._lock:
return self._metric
@property
def latest_norm(self) -> np.ndarray | None:
"""Newest normalized depth map."""
with self._lock:
return self._norm
@property
def ready(self) -> bool:
"""A depth map is available."""
return self.latest is not None
@property
def frames(self) -> int:
"""Number of finished estimations."""
with self._lock:
return self._seq
def close(self) -> None:
"""Stop the worker thread."""
self._stop.set()
self._wake.set()
self._thread.join(timeout=1.0)
|