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import numpy as np
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import torch
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from einops import rearrange
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from PIL import Image
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import cv2
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def convert_to_numpy(image):
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if isinstance(image, Image.Image):
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image = np.array(image)
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elif isinstance(image, torch.Tensor):
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image = image.detach().cpu().numpy()
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elif isinstance(image, np.ndarray):
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image = image.copy()
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else:
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raise f'Unsurpport datatype{type(image)}, only surpport np.ndarray, torch.Tensor, Pillow Image.'
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return image
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def resize_image(input_image, resolution):
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H, W, C = input_image.shape
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H = float(H)
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W = float(W)
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k = float(resolution) / min(H, W)
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H *= k
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W *= k
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H = int(np.round(H / 64.0)) * 64
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W = int(np.round(W / 64.0)) * 64
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img = cv2.resize(
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input_image, (W, H),
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interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
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return img, k
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def resize_image_ori(h, w, image, k):
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img = cv2.resize(
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image, (w, h),
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interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
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return img
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class DepthAnnotator:
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def __init__(self, cfg, device=None):
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from .api import MiDaSInference
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pretrained_model = cfg['PRETRAINED_MODEL']
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self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
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self.model = MiDaSInference(model_type='dpt_hybrid', model_path=pretrained_model).to(self.device)
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self.a = cfg.get('A', np.pi * 2.0)
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self.bg_th = cfg.get('BG_TH', 0.1)
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@torch.no_grad()
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@torch.inference_mode()
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@torch.autocast('cuda', enabled=False)
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def forward(self, image):
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image = convert_to_numpy(image)
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image_depth = image
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h, w, c = image.shape
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image_depth, k = resize_image(image_depth,
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1024 if min(h, w) > 1024 else min(h, w))
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image_depth = torch.from_numpy(image_depth).float().to(self.device)
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image_depth = image_depth / 127.5 - 1.0
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image_depth = rearrange(image_depth, 'h w c -> 1 c h w')
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depth = self.model(image_depth)[0]
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depth_pt = depth.clone()
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depth_pt -= torch.min(depth_pt)
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depth_pt /= torch.max(depth_pt)
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depth_pt = depth_pt.cpu().numpy()
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depth_image = (depth_pt * 255.0).clip(0, 255).astype(np.uint8)
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depth_image = depth_image[..., None].repeat(3, 2)
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depth_image = resize_image_ori(h, w, depth_image, k)
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return depth_image
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class DepthVideoAnnotator(DepthAnnotator):
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def forward(self, frames):
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ret_frames = []
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for frame in frames:
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anno_frame = super().forward(np.array(frame))
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ret_frames.append(anno_frame)
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return ret_frames |
