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import os
import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
import safetensors
from modules import devices
from modules.shared import opts
from modules.control.util import HWC3, resize_image
from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
from .zoedepth.models.zoedepth_nk.zoedepth_nk_v1 import ZoeDepthNK
from .zoedepth.utils.config import get_config
class ZoeDetector:
def __init__(self, model):
self.model = model
@classmethod
def from_pretrained(cls, pretrained_model_or_path, model_type="zoedepth", filename=None, cache_dir=None):
filename = filename or "ZoeD_M12_N.pt"
if os.path.isdir(pretrained_model_or_path):
model_path = os.path.join(pretrained_model_or_path, filename)
else:
model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir)
if model_type == "zoedepth":
model_cls = ZoeDepth
elif model_type == "zoedepth_nk":
model_cls = ZoeDepthNK
else:
raise ValueError(f"ZoeDepth unknown model type {model_type}")
conf = get_config(model_type, "infer")
model = model_cls.build_from_config(conf)
# model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))['model'])
if model_path.lower().endswith('.safetensors'):
model_dict = safetensors.torch.load_file(model_path, device='cpu')
else:
model_dict = torch.load(model_path, map_location=torch.device('cpu'))
if hasattr(model_dict, 'model'):
model_dict = model_dict['model']
model.load_state_dict(model_dict, strict=False)
# timm compatibility issue <https://github.com/isl-org/ZoeDepth/issues/82>
for b in model.core.core.pretrained.model.blocks:
b.drop_path = torch.nn.Identity()
model.eval()
return cls(model)
def to(self, device):
self.model.to(device)
return self
def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type=None, gamma_corrected=False):
self.model.to(devices.device)
device = next(iter(self.model.parameters())).device
if not isinstance(input_image, np.ndarray):
input_image = np.array(input_image, dtype=np.uint8)
output_type = output_type or "pil"
else:
output_type = output_type or "np"
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
assert input_image.ndim == 3
image_depth = input_image
image_depth = torch.from_numpy(image_depth).float().to(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)
if opts.control_move_processor:
self.model.to('cpu')
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
if gamma_corrected:
depth = np.power(depth, 2.2)
depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
detected_map = depth_image
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, _C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
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