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