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Configuration error
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import numpy as np
import torch
from einops import repeat
from PIL import Image
from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, DEPTH_ANYTHING_MODEL_NAME
from custom_controlnet_aux.depth_anything.depth_anything.dpt import DPT_DINOv2
from custom_controlnet_aux.depth_anything.depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from torchvision.transforms import Compose
import cv2
import torch.nn.functional as F
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(),
])
#https://huggingface.co/LiheYoung/depth_anything_vitl14/raw/main/config.json
DPT_CONFIGS = {
"depth_anything_vitl14.pth": {"encoder": "vitl", "features": 256, "out_channels": [256, 512, 1024, 1024], "use_bn": False, "use_clstoken": False},
"depth_anything_vitb14.pth": {"encoder": "vitb", "features": 128, "out_channels": [96, 192, 384, 768], "use_bn": False, "use_clstoken": False},
"depth_anything_vits14.pth": {"encoder": "vits", "features": 64, "out_channels": [48, 96, 192, 384], "use_bn": False, "use_clstoken": False}
}
class DepthAnythingDetector:
def __init__(self, model):
self.model = model
self.device = "cpu"
@classmethod
def from_pretrained(cls, pretrained_model_or_path=DEPTH_ANYTHING_MODEL_NAME, filename="depth_anything_vitl14.pth"):
model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder="checkpoints", repo_type="space")
model = DPT_DINOv2(**DPT_CONFIGS[filename], localhub=True)
model.load_state_dict(torch.load(model_path, map_location="cpu"))
model.eval()
return cls(model)
def to(self, device):
self.model.to(device)
self.device = device
return self
def __call__(self, input_image, detect_resolution=512, output_type=None, upscale_method="INTER_CUBIC", **kwargs):
input_image, output_type = common_input_validate(input_image, output_type, **kwargs)
t, remove_pad = resize_image_with_pad(np.zeros_like(input_image), detect_resolution, upscale_method)
t = remove_pad(t)
h, w = t.shape[:2]
h, w = int(h), int(w)
image = transform({'image': input_image / 255.})['image']
image = torch.from_numpy(image).unsqueeze(0).to(self.device)
with torch.no_grad():
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
detected_map = repeat(depth, "h w -> h w 3").cpu().numpy().astype(np.uint8)
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map |