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Configuration error
Configuration error
| from .network import UNet | |
| from .util import seg2img | |
| import torch | |
| import os | |
| import cv2 | |
| from custom_controlnet_aux.util import HWC3, resize_image_with_pad, common_input_validate, custom_hf_download, BDS_MODEL_NAME | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from einops import rearrange | |
| from .anime_segmentation import AnimeSegmentation | |
| import numpy as np | |
| class AnimeFaceSegmentor: | |
| def __init__(self, model, seg_model): | |
| self.model = model | |
| self.seg_model = seg_model | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=BDS_MODEL_NAME, filename="UNet.pth", seg_filename="isnetis.ckpt"): | |
| model_path = custom_hf_download(pretrained_model_or_path, filename, subfolder="Annotators") | |
| seg_model_path = custom_hf_download("skytnt/anime-seg", seg_filename) | |
| model = UNet() | |
| ckpt = torch.load(model_path, map_location="cpu") | |
| model.load_state_dict(ckpt) | |
| model.eval() | |
| seg_model = AnimeSegmentation(seg_model_path) | |
| seg_model.net.eval() | |
| return cls(model, seg_model) | |
| def to(self, device): | |
| self.model.to(device) | |
| self.seg_model.net.to(device) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", remove_background=True, **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| input_image, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| with torch.no_grad(): | |
| if remove_background: | |
| print(input_image.shape) | |
| mask, input_image = self.seg_model(input_image, 0) #Don't resize image as it is resized | |
| image_feed = torch.from_numpy(input_image).float().to(self.device) | |
| image_feed = rearrange(image_feed, 'h w c -> 1 c h w') | |
| image_feed = image_feed / 255 | |
| seg = self.model(image_feed).squeeze(dim=0) | |
| result = seg2img(seg.cpu().detach().numpy()) | |
| detected_map = HWC3(result) | |
| detected_map = remove_pad(detected_map) | |
| if remove_background: | |
| mask = remove_pad(mask) | |
| H, W, C = detected_map.shape | |
| tmp = np.zeros([H, W, C + 1]) | |
| tmp[:,:,:C] = detected_map | |
| tmp[:,:,3:] = mask | |
| detected_map = tmp | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map[..., :3]) | |
| return detected_map | |