51 lines
1.6 KiB
Python
Executable File
51 lines
1.6 KiB
Python
Executable File
# ZoeDepth
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# https://github.com/isl-org/ZoeDepth
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import os
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import cv2
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import numpy as np
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import torch
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from einops import rearrange
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from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
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from .zoedepth.utils.config import get_config
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from model_lib.ControlNet.annotator.util import annotator_ckpts_path
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import pdb
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class ZoeDetector:
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def __init__(self):
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# remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ZoeD_M12_N.pt"
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modelpath = os.path.join(annotator_ckpts_path, "ZoeD_M12_N.pt")
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if not os.path.exists(modelpath):
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raise FileNotFoundError
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conf = get_config("zoedepth", "infer")
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model = ZoeDepth.build_from_config(conf)
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model.load_state_dict(torch.load(modelpath)['model'])
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model = model.cuda()
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model.device = 'cuda'
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model.eval()
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self.model = model
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def __call__(self, input_image):
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assert input_image.ndim == 3
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image_depth = input_image
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with torch.no_grad():
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image_depth = image_depth / 255.0
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print(image_depth.shape)
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image_depth = image_depth.unsqueeze(0)
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# image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
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print(image_depth.shape)
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depth = self.model.infer(image_depth)
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print(depth.shape)
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depth = depth[0, 0].cpu().numpy()
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vmin = np.percentile(depth, 2)
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vmax = np.percentile(depth, 85)
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depth -= vmin
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depth /= vmax - vmin
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depth = 1.0 - depth
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depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
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return depth_image
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