| |
|
| |
|
| |
|
| | 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 |
