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| import os | |
| import torch | |
| import torch.nn.functional as F | |
| import numpy as np | |
| from einops import rearrange | |
| from modules import devices | |
| from annotator.annotator_path import models_path | |
| import torchvision.transforms as transforms | |
| import dsine.utils.utils as utils | |
| from dsine.models.dsine import DSINE | |
| from scripts.utils import resize_image_with_pad | |
| class NormalDsineDetector: | |
| model_dir = os.path.join(models_path, "normal_dsine") | |
| def __init__(self): | |
| self.model = None | |
| self.device = devices.get_device_for("controlnet") | |
| def load_model(self): | |
| remote_model_path = "https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/Annotators/dsine.pt" | |
| modelpath = os.path.join(self.model_dir, "dsine.pt") | |
| if not os.path.exists(modelpath): | |
| from scripts.utils import load_file_from_url | |
| load_file_from_url(remote_model_path, model_dir=self.model_dir) | |
| model = DSINE() | |
| model.pixel_coords = model.pixel_coords.to(self.device) | |
| model = utils.load_checkpoint(modelpath, model) | |
| model.eval() | |
| self.model = model.to(self.device) | |
| self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| def unload_model(self): | |
| if self.model is not None: | |
| self.model.cpu() | |
| def __call__(self, input_image, new_fov=60.0, iterations=5, resulotion=512): | |
| if self.model is None: | |
| self.load_model() | |
| self.model.to(self.device) | |
| self.model.num_iter = iterations | |
| orig_H, orig_W = input_image.shape[:2] | |
| l, r, t, b = utils.pad_input(orig_H, orig_W) | |
| input_image, remove_pad = resize_image_with_pad(input_image, resulotion) | |
| assert input_image.ndim == 3 | |
| image_normal = input_image | |
| with torch.no_grad(): | |
| image_normal = torch.from_numpy(image_normal).float().to(self.device) | |
| image_normal = image_normal / 255.0 | |
| image_normal = rearrange(image_normal, 'h w c -> 1 c h w') | |
| image_normal = self.norm(image_normal) | |
| intrins = utils.get_intrins_from_fov(new_fov=new_fov, H=orig_H, W=orig_W, device=self.device).unsqueeze(0) | |
| intrins[:, 0, 2] += l | |
| intrins[:, 1, 2] += t | |
| normal = self.model(image_normal, intrins=intrins)[-1] | |
| normal = normal[:, :, t:t+orig_H, l:l+orig_W] | |
| normal = ((normal + 1) * 0.5).clip(0, 1) | |
| normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy() | |
| normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8) | |
| return remove_pad(normal_image) | |