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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 |
