controlnet_aux/zoe/__init__.py

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

from ..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, local_files_only=False):
 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, local_files_only=local_files_only)
 
 conf = get_config(model_type, "infer")
 model_cls = ZoeDepth if model_type == "zoedepth" else ZoeDepthNK
 model = model_cls.build_from_config(conf)
 model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))['model'])
 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):
 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
 with torch.no_grad():
 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)

 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