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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 | |
| 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 | |