| |
| |
| 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 |
|
|
|
|
| class DepthV2Annotator: |
| def __init__(self, cfg, device=None): |
| from .depth_anything_v2.dpt import DepthAnythingV2 |
| 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 = DepthAnythingV2(encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024]).to(self.device) |
| self.model.load_state_dict( |
| torch.load( |
| pretrained_model, |
| map_location=self.device |
| ) |
| ) |
| self.model.eval() |
|
|
| @torch.inference_mode() |
| @torch.autocast('cuda', enabled=False) |
| def forward(self, image): |
| image = convert_to_numpy(image) |
| depth = self.model.infer_image(image) |
|
|
| depth_pt = depth.copy() |
| depth_pt -= np.min(depth_pt) |
| depth_pt /= np.max(depth_pt) |
| depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8) |
|
|
| depth_image = depth_image[..., np.newaxis] |
| depth_image = np.repeat(depth_image, 3, axis=2) |
| return depth_image |
|
|
|
|
| class DepthV2VideoAnnotator(DepthV2Annotator): |
| 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 |
|
|
