89 lines
3.2 KiB
Python
89 lines
3.2 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 .utils import convert_to_numpy, resize_image, resize_image_ori
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class DepthAnnotator:
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def __init__(self, cfg, device=None):
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from .midas.api import MiDaSInference
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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 = MiDaSInference(model_type='dpt_hybrid', model_path=pretrained_model).to(self.device)
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self.a = cfg.get('A', np.pi * 2.0)
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self.bg_th = cfg.get('BG_TH', 0.1)
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@torch.no_grad()
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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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image_depth = image
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h, w, c = image.shape
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image_depth, k = resize_image(image_depth,
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1024 if min(h, w) > 1024 else min(h, w))
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image_depth = torch.from_numpy(image_depth).float().to(self.device)
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image_depth = image_depth / 127.5 - 1.0
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image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
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depth = self.model(image_depth)[0]
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depth_pt = depth.clone()
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depth_pt -= torch.min(depth_pt)
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depth_pt /= torch.max(depth_pt)
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depth_pt = depth_pt.cpu().numpy()
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depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
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depth_image = depth_image[..., None].repeat(3, 2)
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depth_image = resize_image_ori(h, w, depth_image, k)
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return depth_image
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class DepthVideoAnnotator(DepthAnnotator):
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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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class DepthV2Annotator:
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def __init__(self, cfg, device=None):
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from .depth_anything_v2.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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