Spaces:
Runtime error
Runtime error
File size: 2,550 Bytes
c19ca42 |
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 |
import os
import torch
import cv2
import numpy as np
import torch.nn.functional as F
from torchvision.transforms import Compose
from depth_anything.dpt import DPT_DINOv2
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from .util import load_model
from .annotator_path import models_path
transform = Compose(
[
Resize(
width=518,
height=518,
resize_target=False,
keep_aspect_ratio=True,
ensure_multiple_of=14,
resize_method="lower_bound",
image_interpolation_method=cv2.INTER_CUBIC,
),
NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
PrepareForNet(),
]
)
class DepthAnythingDetector:
"""https://github.com/LiheYoung/Depth-Anything"""
model_dir = os.path.join(models_path, "depth_anything")
def __init__(self, device: torch.device):
self.device = device
self.model = (
DPT_DINOv2(
encoder="vitl",
features=256,
out_channels=[256, 512, 1024, 1024],
localhub=False,
)
.to(device)
.eval()
)
remote_url = os.environ.get(
"CONTROLNET_DEPTH_ANYTHING_MODEL_URL",
"https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth",
)
model_path = load_model(
"depth_anything_vitl14.pth", remote_url=remote_url, model_dir=self.model_dir
)
self.model.load_state_dict(torch.load(model_path))
def __call__(self, image: np.ndarray, colored: bool = True) -> np.ndarray:
self.model.to(self.device)
h, w = image.shape[:2]
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) / 255.0
image = transform({"image": image})["image"]
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
@torch.no_grad()
def predict_depth(model, image):
return model(image)
depth = predict_depth(self.model, image)
depth = F.interpolate(
depth[None], (h, w), mode="bilinear", align_corners=False
)[0, 0]
depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
depth = depth.cpu().numpy().astype(np.uint8)
if colored:
return cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
else:
return depth
def unload_model(self):
self.model.to("cpu")
|