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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np
import torch
from einops import rearrange
from .utils import convert_to_numpy, resize_image, resize_image_ori
class DepthAnnotator:
def __init__(self, cfg, device=None):
from .midas.api import MiDaSInference
pretrained_model = cfg['PRETRAINED_MODEL']
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
self.model = MiDaSInference(model_type='dpt_hybrid', model_path=pretrained_model).to(self.device)
self.a = cfg.get('A', np.pi * 2.0)
self.bg_th = cfg.get('BG_TH', 0.1)
@torch.no_grad()
@torch.inference_mode()
@torch.autocast('cuda', enabled=False)
def forward(self, image):
image = convert_to_numpy(image)
image_depth = image
h, w, c = image.shape
image_depth, k = resize_image(image_depth,
1024 if min(h, w) > 1024 else min(h, w))
image_depth = torch.from_numpy(image_depth).float().to(self.device)
image_depth = image_depth / 127.5 - 1.0
image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
depth = self.model(image_depth)[0]
depth_pt = depth.clone()
depth_pt -= torch.min(depth_pt)
depth_pt /= torch.max(depth_pt)
depth_pt = depth_pt.cpu().numpy()
depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
depth_image = depth_image[..., None].repeat(3, 2)
depth_image = resize_image_ori(h, w, depth_image, k)
return depth_image
class DepthVideoAnnotator(DepthAnnotator):
def forward(self, frames):
ret_frames = []
for frame in frames:
anno_frame = super().forward(np.array(frame))
ret_frames.append(anno_frame)
return ret_frames |