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Running
on
Zero
Running
on
Zero
| import os | |
| import warnings | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from ..util import HWC3, nms, resize_image, safe_step | |
| from .model import pidinet | |
| class PidiNetDetector: | |
| def __init__(self, netNetwork): | |
| self.netNetwork = netNetwork | |
| def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False): | |
| filename = filename or "table5_pidinet.pth" | |
| if os.path.isdir(pretrained_model_or_path): | |
| model_path = os.path.join(pretrained_model_or_path, filename) | |
| else: | |
| model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only) | |
| netNetwork = pidinet() | |
| netNetwork.load_state_dict({k.replace('module.', ''): v for k, v in torch.load(model_path)['state_dict'].items()}) | |
| netNetwork.eval() | |
| return cls(netNetwork) | |
| def to(self, device): | |
| self.netNetwork.to(device) | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, image_resolution=512, safe=False, output_type="pil", scribble=False, apply_filter=False, **kwargs): | |
| if "return_pil" in kwargs: | |
| warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) | |
| output_type = "pil" if kwargs["return_pil"] else "np" | |
| if type(output_type) is bool: | |
| warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") | |
| if output_type: | |
| output_type = "pil" | |
| device = next(iter(self.netNetwork.parameters())).device | |
| if not isinstance(input_image, np.ndarray): | |
| input_image = np.array(input_image, dtype=np.uint8) | |
| input_image = HWC3(input_image) | |
| input_image = resize_image(input_image, detect_resolution) | |
| assert input_image.ndim == 3 | |
| input_image = input_image[:, :, ::-1].copy() | |
| with torch.no_grad(): | |
| image_pidi = torch.from_numpy(input_image).float().to(device) | |
| image_pidi = image_pidi / 255.0 | |
| image_pidi = rearrange(image_pidi, 'h w c -> 1 c h w') | |
| edge = self.netNetwork(image_pidi)[-1] | |
| edge = edge.cpu().numpy() | |
| if apply_filter: | |
| edge = edge > 0.5 | |
| if safe: | |
| edge = safe_step(edge) | |
| edge = (edge * 255.0).clip(0, 255).astype(np.uint8) | |
| detected_map = edge[0, 0] | |
| detected_map = HWC3(detected_map) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR) | |
| if scribble: | |
| detected_map = nms(detected_map, 127, 3.0) | |
| detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0) | |
| detected_map[detected_map > 4] = 255 | |
| detected_map[detected_map < 255] = 0 | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |