| 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") |
|
|
