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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 huggingface_hub import hf_hub_download |
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from PIL import Image |
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from ..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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conf = get_config(model_type, "infer") |
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model_cls = ZoeDepth if model_type == "zoedepth" else ZoeDepthNK |
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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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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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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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with torch.no_grad(): |
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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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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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