mirror of
https://github.com/vladmandic/sdnext.git
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90 lines
3.7 KiB
Python
90 lines
3.7 KiB
Python
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 huggingface_hub import hf_hub_download
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from PIL import Image
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import safetensors
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from modules import devices
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from modules.shared import opts
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from modules.control.util import HWC3, resize_image
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from .zoedepth.models.zoedepth.zoedepth_v1 import ZoeDepth
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from .zoedepth.models.zoedepth_nk.zoedepth_nk_v1 import ZoeDepthNK
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from .zoedepth.utils.config import get_config
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class ZoeDetector:
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def __init__(self, model):
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self.model = model
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@classmethod
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def from_pretrained(cls, pretrained_model_or_path, model_type="zoedepth", filename=None, cache_dir=None, local_files_only=False):
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filename = filename or "ZoeD_M12_N.pt"
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if os.path.isdir(pretrained_model_or_path):
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model_path = os.path.join(pretrained_model_or_path, filename)
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else:
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model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
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if model_type == "zoedepth":
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model_cls = ZoeDepth
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elif model_type == "zoedepth_nk":
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model_cls = ZoeDepthNK
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else:
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raise ValueError(f"ZoeDepth unknown model type {model_type}")
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conf = get_config(model_type, "infer")
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model = model_cls.build_from_config(conf)
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# model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu'))['model'])
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if model_path.lower().endswith('.safetensors'):
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model_dict = safetensors.torch.load_file(model_path, device='cpu')
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else:
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model_dict = torch.load(model_path, map_location=torch.device('cpu'))
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if hasattr(model_dict, 'model'):
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model_dict = model_dict['model']
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model.load_state_dict(model_dict, strict=False)
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# timm compatibility issue <https://github.com/isl-org/ZoeDepth/issues/82>
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for b in model.core.core.pretrained.model.blocks:
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b.drop_path = torch.nn.Identity()
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model.eval()
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return cls(model)
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def to(self, device):
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self.model.to(device)
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return self
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def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type=None, gamma_corrected=False):
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self.model.to(devices.device)
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device = next(iter(self.model.parameters())).device
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if not isinstance(input_image, np.ndarray):
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input_image = np.array(input_image, dtype=np.uint8)
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output_type = output_type or "pil"
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else:
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output_type = output_type or "np"
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input_image = HWC3(input_image)
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input_image = resize_image(input_image, detect_resolution)
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assert input_image.ndim == 3
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image_depth = input_image
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image_depth = torch.from_numpy(image_depth).float().to(device)
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image_depth = image_depth / 255.0
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image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
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depth = self.model.infer(image_depth)
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if opts.control_move_processor:
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self.model.to('cpu')
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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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if gamma_corrected:
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depth = np.power(depth, 2.2)
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depth_image = (depth * 255.0).clip(0, 255).astype(np.uint8)
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detected_map = depth_image
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detected_map = HWC3(detected_map)
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img = resize_image(input_image, image_resolution)
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H, W, _C = img.shape
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detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
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if output_type == "pil":
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detected_map = Image.fromarray(detected_map)
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return detected_map
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