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import os |
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import warnings |
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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, nms, resize_image, safe_step |
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from .model import pidinet |
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class PidiNetDetector: |
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def __init__(self, netNetwork): |
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self.netNetwork = netNetwork |
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@classmethod |
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def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False): |
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filename = filename or "table5_pidinet.pth" |
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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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netNetwork = pidinet() |
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netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()}) |
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netNetwork.eval() |
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return cls(netNetwork) |
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def to(self, device): |
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self.netNetwork.to(device) |
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return self |
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def __call__(self, input_image, detect_resolution=512, image_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=False, **kwargs): |
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if "return_pil" in kwargs: |
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warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) |
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output_type = "pil" if kwargs["return_pil"] else "np" |
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if type(output_type) is bool: |
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warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") |
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if output_type: |
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output_type = "pil" |
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device = next(iter(self.netNetwork.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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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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input_image = input_image[:, :, ::-1].copy() |
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with torch.no_grad(): |
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image_pidi = torch.from_numpy(input_image).float().to(device) |
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image_pidi = image_pidi / 255.0 |
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image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w') |
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edge = self.netNetwork(image_pidi)[-1] |
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edge = edge.cpu().numpy() |
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if apply_filter: |
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edge = edge > 0.5 |
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if safe: |
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edge = safe_step(edge) |
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edge = (edge * 255.0).clip(0, 255).astype(np.uint8) |
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detected_map = edge[0, 0] |
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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 scribble: |
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detected_map = nms(detected_map, 127, 3.0) |
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detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0) |
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detected_map[detected_map > 4] = 255 |
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detected_map[detected_map < 255] = 0 |
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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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