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110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
import pathlib
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from typing import Literal, Union
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from einops import repeat
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from PIL import Image
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from torchvision.transforms import Compose
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from invokeai.app.services.config.config_default import InvokeAIAppConfig
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from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
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from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
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from invokeai.backend.util.devices import choose_torch_device
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from invokeai.backend.util.util import download_with_progress_bar
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config = InvokeAIAppConfig.get_config()
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DEPTH_ANYTHING_MODELS = {
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"large": {
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"url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitl14.pth?download=true",
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"local": "any/annotators/depth_anything/depth_anything_vitl14.pth",
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},
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"base": {
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"url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vitb14.pth?download=true",
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"local": "any/annotators/depth_anything/depth_anything_vitb14.pth",
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},
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"small": {
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"url": "https://huggingface.co/spaces/LiheYoung/Depth-Anything/resolve/main/checkpoints/depth_anything_vits14.pth?download=true",
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"local": "any/annotators/depth_anything/depth_anything_vits14.pth",
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},
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}
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transform = Compose(
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[
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Resize(
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width=518,
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height=518,
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resize_target=False,
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keep_aspect_ratio=True,
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ensure_multiple_of=14,
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resize_method="lower_bound",
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image_interpolation_method=cv2.INTER_CUBIC,
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),
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NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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PrepareForNet(),
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]
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)
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class DepthAnythingDetector:
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def __init__(self) -> None:
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self.model = None
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self.model_size: Union[Literal["large", "base", "small"], None] = None
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def load_model(self, model_size=Literal["large", "base", "small"]):
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DEPTH_ANYTHING_MODEL_PATH = pathlib.Path(config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"])
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if not DEPTH_ANYTHING_MODEL_PATH.exists():
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download_with_progress_bar(DEPTH_ANYTHING_MODELS[model_size]["url"], DEPTH_ANYTHING_MODEL_PATH)
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if not self.model or model_size != self.model_size:
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del self.model
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self.model_size = model_size
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match self.model_size:
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case "small":
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self.model = DPT_DINOv2(encoder="vits", features=64, out_channels=[48, 96, 192, 384])
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case "base":
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self.model = DPT_DINOv2(encoder="vitb", features=128, out_channels=[96, 192, 384, 768])
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case "large":
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self.model = DPT_DINOv2(encoder="vitl", features=256, out_channels=[256, 512, 1024, 1024])
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case _:
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raise TypeError("Not a supported model")
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self.model.load_state_dict(torch.load(DEPTH_ANYTHING_MODEL_PATH.as_posix(), map_location="cpu"))
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self.model.eval()
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self.model.to(choose_torch_device())
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return self.model
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def to(self, device):
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self.model.to(device)
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return self
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def __call__(self, image, resolution=512, offload=False):
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image = np.array(image, dtype=np.uint8)
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image = image[:, :, ::-1] / 255.0
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image_height, image_width = image.shape[:2]
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image = transform({"image": image})["image"]
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image = torch.from_numpy(image).unsqueeze(0).to(choose_torch_device())
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with torch.no_grad():
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depth = self.model(image)
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depth = F.interpolate(depth[None], (image_height, image_width), mode="bilinear", align_corners=False)[0, 0]
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depth = (depth - depth.min()) / (depth.max() - depth.min()) * 255.0
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depth_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
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depth_map = Image.fromarray(depth_map)
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new_height = int(image_height * (resolution / image_width))
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depth_map = depth_map.resize((resolution, new_height))
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if offload:
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del self.model
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return depth_map
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