| | |
| | |
| |
|
| | 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) |
| |
|
| | @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 |
