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| # -*- coding: utf-8 -*- | |
| # Copyright (c) Alibaba, Inc. and its affiliates. | |
| # Midas Depth Estimation | |
| # From https://github.com/isl-org/MiDaS | |
| # MIT LICENSE | |
| from abc import ABCMeta | |
| import numpy as np | |
| import torch | |
| from einops import rearrange | |
| from PIL import Image | |
| from scepter.modules.annotator.base_annotator import BaseAnnotator | |
| from scepter.modules.annotator.midas.api import MiDaSInference | |
| from scepter.modules.annotator.registry import ANNOTATORS | |
| from scepter.modules.annotator.utils import resize_image, resize_image_ori | |
| from scepter.modules.utils.config import dict_to_yaml | |
| from scepter.modules.utils.distribute import we | |
| from scepter.modules.utils.file_system import FS | |
| class MidasDetector(BaseAnnotator, metaclass=ABCMeta): | |
| def __init__(self, cfg, logger=None): | |
| super().__init__(cfg, logger=logger) | |
| pretrained_model = cfg.get('PRETRAINED_MODEL', None) | |
| if pretrained_model: | |
| with FS.get_from(pretrained_model, wait_finish=True) as local_path: | |
| self.model = MiDaSInference(model_type='dpt_hybrid', | |
| model_path=local_path) | |
| self.a = cfg.get('A', np.pi * 2.0) | |
| self.bg_th = cfg.get('BG_TH', 0.1) | |
| def forward(self, 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.' | |
| 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(we.device_id) | |
| 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_np = depth.cpu().numpy() # float16 error | |
| # x = cv2.Sobel(depth_np, cv2.CV_32F, 1, 0, ksize=3) | |
| # y = cv2.Sobel(depth_np, cv2.CV_32F, 0, 1, ksize=3) | |
| # z = np.ones_like(x) * self.a | |
| # x[depth_pt < self.bg_th] = 0 | |
| # y[depth_pt < self.bg_th] = 0 | |
| # normal = np.stack([x, y, z], axis=2) | |
| # normal /= np.sum(normal**2.0, axis=2, keepdims=True)**0.5 | |
| # normal_image = (normal * 127.5 + 127.5).clip(0, 255).astype(np.uint8) | |
| depth_image = resize_image_ori(h, w, depth_image, k) | |
| return depth_image | |
| def get_config_template(): | |
| return dict_to_yaml('ANNOTATORS', | |
| __class__.__name__, | |
| MidasDetector.para_dict, | |
| set_name=True) | |