| import os
|
| import torch
|
| import cv2
|
| import numpy as np
|
| import torch.nn.functional as F
|
| from torchvision.transforms import Compose
|
| from safetensors.torch import load_file
|
|
|
| from depth_anything_v2.dpt import DepthAnythingV2
|
| from depth_anything_v2.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 DepthAnythingV2Detector:
|
| """https://github.com/MackinationsAi/Upgraded-Depth-Anything-V2"""
|
|
|
| model_dir = os.path.join(models_path, "depth_anything_v2")
|
|
|
| def __init__(self, device: torch.device):
|
| self.device = device
|
| self.model = (
|
| DepthAnythingV2(
|
| encoder="vitl",
|
| features=256,
|
| out_channels=[256, 512, 1024, 1024],
|
| )
|
| .to(device)
|
| .eval()
|
| )
|
| remote_url = os.environ.get(
|
| "CONTROLNET_DEPTH_ANYTHING_V2_MODEL_URL",
|
| "https://huggingface.co/MackinationsAi/Depth-Anything-V2_Safetensors/resolve/main/depth_anything_v2_vitl.safetensors",
|
| )
|
| model_path = load_model(
|
| "depth_anything_v2_vitl.safetensors", remote_url=remote_url, model_dir=self.model_dir
|
| )
|
| self.model.load_state_dict(load_file(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:
|
| depth_color = cv2.applyColorMap(depth, cv2.COLORMAP_INFERNO)[:, :, ::-1]
|
| return depth_color
|
| else:
|
| return depth
|
|
|
| def unload_model(self):
|
| self.model.to("cpu") |
