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# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np
import torch
from einops import rearrange
from PIL import Image
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
class DepthV2Annotator:
def __init__(self, cfg, device=None):
from .dpt import DepthAnythingV2
# Model configurations for different variants
self.model_configs = {
'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
'vitg': {'encoder': 'vitg', 'features': 384, 'out_channels': [1536, 1536, 1536, 1536]}
}
# Get model variant from config, default to 'vitl' if not specified
model_variant = cfg.get('MODEL_VARIANT', 'vitl')
if model_variant not in self.model_configs:
raise ValueError(f"Invalid model variant '{model_variant}'. Must be one of: {list(self.model_configs.keys())}")
pretrained_model = cfg['PRETRAINED_MODEL']
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") if device is None else device
# Get configuration for the selected model variant
config = self.model_configs[model_variant]
# Initialize model with the appropriate configuration
self.model = DepthAnythingV2(
encoder=config['encoder'],
features=config['features'],
out_channels=config['out_channels']
).to(self.device)
self.model.load_state_dict(
torch.load(
pretrained_model,
map_location=self.device,
weights_only=True
)
)
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 |