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from typing import Dict, Optional, Tuple
import numpy as np
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL.Image import Image
from torch import Tensor
from transformers.image_processing_utils import BaseImageProcessor
# from transformers import VideoMAEImageProcessor, ViTImageProcessor
INPUT_IMAGE_SIZE = (352, 352)
rgb_transform = transforms.Compose(
[
transforms.Resize(INPUT_IMAGE_SIZE),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
gt_transform = transforms.ToTensor()
depth_transform = transforms.Compose(
[transforms.Resize(INPUT_IMAGE_SIZE), transforms.ToTensor()]
)
# See VideoMAEImageProcessor, ViTImageProcessor for more examples
class BBSNetImageProcessor(BaseImageProcessor):
model_input_names = ["bbsnet_preprocessor"]
def __init__(self, testsize: Optional[int] = 352, **kwargs) -> None:
super().__init__(**kwargs)
self.testsize = testsize
def preprocess(
self,
inputs: Dict[str, Image], # {'rgb': ..., 'gt': ..., 'depth': ...}
**kwargs
) -> Dict[str, Tensor]:
rs = dict()
if "rgb" in inputs:
rs["rgb"] = rgb_transform(inputs["rgb"]).unsqueeze(0)
if "gt" in inputs:
rs["gt"] = gt_transform(inputs["gt"]).unsqueeze(0)
if "depth" in inputs:
rs["depth"] = depth_transform(inputs["depth"]).unsqueeze(0)
return rs
def postprocess(
self, logits: Tensor, size: Tuple[int, int], **kwargs
) -> np.ndarray:
logits: Tensor = F.upsample(
logits, size=size, mode="bilinear", align_corners=False
)
res: np.ndarray = logits.sigmoid().squeeze().data.cpu().numpy()
res = (res - res.min()) / (res.max() - res.min() + 1e-8)
return res
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