mirror of
https://github.com/Wan-Video/Wan2.1.git
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57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
# -*- coding: utf-8 -*-
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import numpy as np
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import torch
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from einops import rearrange
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from PIL import Image
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def convert_to_numpy(image):
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if isinstance(image, Image.Image):
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image = np.array(image)
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elif isinstance(image, torch.Tensor):
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image = image.detach().cpu().numpy()
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elif isinstance(image, np.ndarray):
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image = image.copy()
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else:
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raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
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return image
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class DepthV2Annotator:
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def __init__(self, cfg, device=None):
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from .dpt import DepthAnythingV2
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pretrained_model = cfg['PRETRAINED_MODEL']
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
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self.model = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024]).to(self.device)
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self.model.load_state_dict(
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torch.load(
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pretrained_model,
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map_location=self.device
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)
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)
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self.model.eval()
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@torch.inference_mode()
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@torch.autocast('cuda', enabled=False)
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def forward(self, image):
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image = convert_to_numpy(image)
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depth = self.model.infer_image(image)
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depth_pt = depth.copy()
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depth_pt -= np.min(depth_pt)
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depth_pt /= np.max(depth_pt)
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depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
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depth_image = depth_image[..., np.newaxis]
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depth_image = np.repeat(depth_image, 3, axis=2)
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return depth_image
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class DepthV2VideoAnnotator(DepthV2Annotator):
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def forward(self, frames):
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ret_frames = []
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for frame in frames:
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anno_frame = super().forward(np.array(frame))
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ret_frames.append(anno_frame)
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return ret_frames
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