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import os
import cv2
import numpy as np
import torch
from einops import rearrange
from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
from .zoedepth.utils.config import get_config
from modules import devices
from annotator.annotator_path import models_path
class ZoeDetector:
model_dir = os.path.join(models_path, "zoedepth")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ZoeD_M12_N.pt"
modelpath = os.path.join(self.model_dir, "ZoeD_M12_N.pt")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
conf = get_config("zoedepth", "infer")
model = ZoeDepth.build_from_config(conf)
model.load_state_dict(torch.load(modelpath, map_location=model.device)['model'])
model.eval()
self.model = model.to(self.device)
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
assert input_image.ndim == 3
image_depth = input_image
with torch.no_grad():
image_depth = torch.from_numpy(image_depth).float().to(self.device)
image_depth = image_depth / 255.0
image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
depth = self.model.infer(image_depth)
depth = depth[0, 0].cpu().numpy()
vmin = np.percentile(depth, 2)
vmax = np.percentile(depth, 85)
depth -= vmin
depth /= vmax - vmin
depth = 1.0 - depth
depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
return depth_image
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