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* Add depth_anything_v2.py * Update preprocessor_compiled.py Added depth_anything_v2 preprocessor * Update processor.py Added depth_anything_v2 preprocessor * Update depth_test.py Added depth_anything_v2 to general_depth_modules * Update modules_test.py Added depth_anything_v2 to expected_module_names & expected_module_alias * Update install.py Added depth_anything_v2 to install.py * Update README.md * Fix model URL --------- Co-authored-by: Mackinations.Ai <[email protected]>
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Original file line number | Diff line number | Diff line change |
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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""" | ||
|
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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") |
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