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Configuration error
Configuration error
| #https://github.com/SkyTNT/anime-segmentation/tree/main | |
| #Only adapt isnet_is (https://huggingface.co/skytnt/anime-seg/blob/main/isnetis.ckpt) | |
| import torch.nn as nn | |
| import torch | |
| from .isnet import ISNetDIS | |
| import numpy as np | |
| import cv2 | |
| from comfy.model_management import get_torch_device | |
| DEVICE = get_torch_device() | |
| class AnimeSegmentation: | |
| def __init__(self, ckpt_path): | |
| super(AnimeSegmentation).__init__() | |
| sd = torch.load(ckpt_path, map_location="cpu") | |
| self.net = ISNetDIS() | |
| #gt_encoder isn't used during inference | |
| self.net.load_state_dict({k.replace("net.", ''):v for k, v in sd.items() if k.startswith("net.")}) | |
| self.net = self.net.to(DEVICE) | |
| self.net.eval() | |
| def get_mask(self, input_img, s=640): | |
| input_img = (input_img / 255).astype(np.float32) | |
| if s == 0: | |
| img_input = np.transpose(input_img, (2, 0, 1)) | |
| img_input = img_input[np.newaxis, :] | |
| tmpImg = torch.from_numpy(img_input).float().to(DEVICE) | |
| with torch.no_grad(): | |
| pred = self.net(tmpImg)[0][0].sigmoid() #https://github.com/SkyTNT/anime-segmentation/blob/main/train.py#L92C20-L92C47 | |
| pred = pred.cpu().numpy()[0] | |
| pred = np.transpose(pred, (1, 2, 0)) | |
| #pred = pred[:, :, np.newaxis] | |
| return pred | |
| h, w = h0, w0 = input_img.shape[:-1] | |
| h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s) | |
| ph, pw = s - h, s - w | |
| img_input = np.zeros([s, s, 3], dtype=np.float32) | |
| img_input[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] = cv2.resize(input_img, (w, h)) | |
| img_input = np.transpose(img_input, (2, 0, 1)) | |
| img_input = img_input[np.newaxis, :] | |
| tmpImg = torch.from_numpy(img_input).float().to(DEVICE) | |
| with torch.no_grad(): | |
| pred = self.net(tmpImg)[0][0].sigmoid() #https://github.com/SkyTNT/anime-segmentation/blob/main/train.py#L92C20-L92C47 | |
| pred = pred.cpu().numpy()[0] | |
| pred = np.transpose(pred, (1, 2, 0)) | |
| pred = pred[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w] | |
| #pred = cv2.resize(pred, (w0, h0))[:, :, np.newaxis] | |
| pred = cv2.resize(pred, (w0, h0)) | |
| return pred | |
| def __call__(self, np_img, img_size): | |
| mask = self.get_mask(np_img, int(img_size)) | |
| np_img = (mask * np_img + 255 * (1 - mask)).astype(np.uint8) | |
| mask = (mask * 255).astype(np.uint8) | |
| #np_img = np.concatenate([np_img, mask], axis=2, dtype=np.uint8) | |
| #mask = mask.repeat(3, axis=2) | |
| return mask, np_img | |