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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 PIL import Image | |
| import cv2 | |
| def convert_to_numpy(image): | |
| if isinstance(image, Image.Image): | |
| image = np.array(image) | |
| elif isinstance(image, torch.Tensor): | |
| image = image.detach().cpu().numpy() | |
| elif isinstance(image, np.ndarray): | |
| image = image.copy() | |
| else: | |
| raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.' | |
| return image | |
| def resize_image(input_image, resolution): | |
| H, W, C = input_image.shape | |
| H = float(H) | |
| W = float(W) | |
| k = float(resolution) / min(H, W) | |
| H *= k | |
| W *= k | |
| H = int(np.round(H / 64.0)) * 64 | |
| W = int(np.round(W / 64.0)) * 64 | |
| img = cv2.resize( | |
| input_image, (W, H), | |
| interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) | |
| return img, k | |
| def resize_image_ori(h, w, image, k): | |
| img = cv2.resize( | |
| image, (w, h), | |
| interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA) | |
| return img | |
| class DepthAnnotator: | |
| def __init__(self, cfg, device=None): | |
| from .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) | |
| 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 |