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| # ZoeDepth | |
| # https://github.com/isl-org/ZoeDepth | |
| 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 ...annotator.util import annotator_ckpts_path | |
| from huggingface_hub import hf_hub_download | |
| class ZoeDetector: | |
| def __init__(self): | |
| model_path = os.path.join(annotator_ckpts_path, "ZoeD_M12_N.pt") | |
| if not os.path.exists(model_path): | |
| model_path = hf_hub_download("lllyasviel/Annotators", "ZoeD_M12_N.pt") | |
| conf = get_config("zoedepth", "infer") | |
| model = ZoeDepth.build_from_config(conf) | |
| model.load_state_dict(torch.load(model_path)['model'], strict=False) | |
| model = model.cuda() | |
| model.device = 'cuda' | |
| model.eval() | |
| self.model = model | |
| def __call__(self, input_image): | |
| assert input_image.ndim == 3 | |
| image_depth = input_image | |
| with torch.no_grad(): | |
| image_depth = torch.from_numpy(image_depth).float().cuda() | |
| 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 | |