Spaces:
Sleeping
Sleeping
File size: 2,257 Bytes
0cdb4e8 | 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 | """Monocular depth estimation (MiDaS) with a synthetic fallback.
Real depth (when the ML stack is present) is used as inpaint/ControlNet
conditioning and to help recover the floor plane. Without ML we synthesize a
plausible vertical gradient (far near the top/horizon, near at the bottom),
which is enough for conditioning and a UI preview and is consistent with the
heuristic floor model.
"""
from __future__ import annotations
import functools
from pathlib import Path
import cv2
import numpy as np
from ..config import settings
from .ml_runtime import ml_available
@functools.lru_cache(maxsize=1)
def _load_midas():
import torch
model = torch.hub.load("intel-isl/MiDaS", settings.MIDAS_MODEL)
transforms = torch.hub.load("intel-isl/MiDaS", "transforms")
tf = (
transforms.small_transform
if "small" in settings.MIDAS_MODEL.lower()
else transforms.dpt_transform
)
model.eval()
return model, tf
def estimate_depth(image_path: Path) -> np.ndarray:
"""Return a HxW float32 depth map normalized to [0, 1] (1 = nearest)."""
img = cv2.imread(str(image_path))
if img is None:
raise ValueError(f"Could not read image for depth: {image_path}")
h, w = img.shape[:2]
if not ml_available():
return _synthetic_depth(h, w)
try:
import torch
model, tf = _load_midas()
rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
batch = tf(rgb)
with torch.no_grad():
pred = model(batch)
pred = torch.nn.functional.interpolate(
pred.unsqueeze(1), size=(h, w), mode="bicubic", align_corners=False
).squeeze().cpu().numpy()
d = pred.astype("float32")
d = (d - d.min()) / (float(np.ptp(d)) + 1e-6)
return d
except Exception:
return _synthetic_depth(h, w)
def _synthetic_depth(h: int, w: int) -> np.ndarray:
col = np.linspace(0.0, 1.0, h, dtype="float32").reshape(h, 1)
return np.repeat(col, w, axis=1)
def depth_to_image(depth: np.ndarray) -> np.ndarray:
"""Convert a [0, 1] depth map to an 8-bit BGR image for saving/preview."""
vis = (np.clip(depth, 0.0, 1.0) * 255).astype("uint8")
return cv2.applyColorMap(vis, cv2.COLORMAP_INFERNO)
|