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from typing import Dict, Tuple, Union
import cv2
import numpy as np
import torch
from PIL import Image
from PIL.Image import Image as PilImage
from torchvision import transforms
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_utils import ImageInput
class RescaleT(object):
def __init__(self, output_size: Union[int, Tuple[int, int]]) -> None:
super().__init__()
assert isinstance(output_size, (int, tuple))
self.output_size = output_size
def __call__(self, sample) -> Dict[str, np.ndarray]:
image, label = sample["image"], sample["label"]
h, w = image.shape[:2]
if isinstance(self.output_size, int):
if h > w:
new_h, new_w = self.output_size * h / w, self.output_size
else:
new_h, new_w = self.output_size, self.output_size * w / h
else:
new_h, new_w = self.output_size
new_h, new_w = int(new_h), int(new_w)
# resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
# img = transform.resize(image,(new_h,new_w),mode='constant')
# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)
# img = transform.resize(image, (self.output_size, self.output_size), mode='constant')
img = (
cv2.resize(
image,
(self.output_size, self.output_size),
interpolation=cv2.INTER_AREA,
)
/ 255.0
)
# lbl = transform.resize(label, (self.output_size, self.output_size),
# mode='constant',
# order=0,
# preserve_range=True)
lbl = cv2.resize(
label, (self.output_size, self.output_size), interpolation=cv2.INTER_NEAREST
)
lbl = np.expand_dims(lbl, axis=-1)
lbl = np.clip(lbl, np.min(label), np.max(label))
return {"image": img, "label": lbl}
class ToTensorLab(object):
"""Convert ndarrays in sample to Tensors."""
def __init__(self, flag: int = 0) -> None:
self.flag = flag
def __call__(self, sample):
image, label = sample["image"], sample["label"]
tmpLbl = np.zeros(label.shape)
if np.max(label) < 1e-6:
label = label
else:
label = label / np.max(label)
# change the color space
if self.flag == 2: # with rgb and Lab colors
tmpImg = np.zeros((image.shape[0], image.shape[1], 6))
tmpImgt = np.zeros((image.shape[0], image.shape[1], 3))
if image.shape[2] == 1:
tmpImgt[:, :, 0] = image[:, :, 0]
tmpImgt[:, :, 1] = image[:, :, 0]
tmpImgt[:, :, 2] = image[:, :, 0]
else:
tmpImgt = image
# tmpImgtl = color.rgb2lab(tmpImgt)
tmpImgtl = cv2.cvtColor(tmpImgt, cv2.COLOR_RGB2LAB)
# nomalize image to range [0,1]
tmpImg[:, :, 0] = (tmpImgt[:, :, 0] - np.min(tmpImgt[:, :, 0])) / (
np.max(tmpImgt[:, :, 0]) - np.min(tmpImgt[:, :, 0])
)
tmpImg[:, :, 1] = (tmpImgt[:, :, 1] - np.min(tmpImgt[:, :, 1])) / (
np.max(tmpImgt[:, :, 1]) - np.min(tmpImgt[:, :, 1])
)
tmpImg[:, :, 2] = (tmpImgt[:, :, 2] - np.min(tmpImgt[:, :, 2])) / (
np.max(tmpImgt[:, :, 2]) - np.min(tmpImgt[:, :, 2])
)
tmpImg[:, :, 3] = (tmpImgtl[:, :, 0] - np.min(tmpImgtl[:, :, 0])) / (
np.max(tmpImgtl[:, :, 0]) - np.min(tmpImgtl[:, :, 0])
)
tmpImg[:, :, 4] = (tmpImgtl[:, :, 1] - np.min(tmpImgtl[:, :, 1])) / (
np.max(tmpImgtl[:, :, 1]) - np.min(tmpImgtl[:, :, 1])
)
tmpImg[:, :, 5] = (tmpImgtl[:, :, 2] - np.min(tmpImgtl[:, :, 2])) / (
np.max(tmpImgtl[:, :, 2]) - np.min(tmpImgtl[:, :, 2])
)
# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
tmpImg[:, :, 0] = (tmpImg[:, :, 0] - np.mean(tmpImg[:, :, 0])) / np.std(
tmpImg[:, :, 0]
)
tmpImg[:, :, 1] = (tmpImg[:, :, 1] - np.mean(tmpImg[:, :, 1])) / np.std(
tmpImg[:, :, 1]
)
tmpImg[:, :, 2] = (tmpImg[:, :, 2] - np.mean(tmpImg[:, :, 2])) / np.std(
tmpImg[:, :, 2]
)
tmpImg[:, :, 3] = (tmpImg[:, :, 3] - np.mean(tmpImg[:, :, 3])) / np.std(
tmpImg[:, :, 3]
)
tmpImg[:, :, 4] = (tmpImg[:, :, 4] - np.mean(tmpImg[:, :, 4])) / np.std(
tmpImg[:, :, 4]
)
tmpImg[:, :, 5] = (tmpImg[:, :, 5] - np.mean(tmpImg[:, :, 5])) / np.std(
tmpImg[:, :, 5]
)
elif self.flag == 1: # with Lab color
tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
if image.shape[2] == 1:
tmpImg[:, :, 0] = image[:, :, 0]
tmpImg[:, :, 1] = image[:, :, 0]
tmpImg[:, :, 2] = image[:, :, 0]
else:
tmpImg = image
# tmpImg = color.rgb2lab(tmpImg)
print("tmpImg:", tmpImg.min(), tmpImg.max())
exit()
tmpImg = cv2.cvtColor(tmpImg, cv2.COLOR_RGB2LAB)
# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))
tmpImg[:, :, 0] = (tmpImg[:, :, 0] - np.min(tmpImg[:, :, 0])) / (
np.max(tmpImg[:, :, 0]) - np.min(tmpImg[:, :, 0])
)
tmpImg[:, :, 1] = (tmpImg[:, :, 1] - np.min(tmpImg[:, :, 1])) / (
np.max(tmpImg[:, :, 1]) - np.min(tmpImg[:, :, 1])
)
tmpImg[:, :, 2] = (tmpImg[:, :, 2] - np.min(tmpImg[:, :, 2])) / (
np.max(tmpImg[:, :, 2]) - np.min(tmpImg[:, :, 2])
)
tmpImg[:, :, 0] = (tmpImg[:, :, 0] - np.mean(tmpImg[:, :, 0])) / np.std(
tmpImg[:, :, 0]
)
tmpImg[:, :, 1] = (tmpImg[:, :, 1] - np.mean(tmpImg[:, :, 1])) / np.std(
tmpImg[:, :, 1]
)
tmpImg[:, :, 2] = (tmpImg[:, :, 2] - np.mean(tmpImg[:, :, 2])) / np.std(
tmpImg[:, :, 2]
)
else: # with rgb color
tmpImg = np.zeros((image.shape[0], image.shape[1], 3))
image = image / np.max(image)
if image.shape[2] == 1:
tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 0.229
tmpImg[:, :, 1] = (image[:, :, 0] - 0.485) / 0.229
tmpImg[:, :, 2] = (image[:, :, 0] - 0.485) / 0.229
else:
tmpImg[:, :, 0] = (image[:, :, 0] - 0.485) / 0.229
tmpImg[:, :, 1] = (image[:, :, 1] - 0.456) / 0.224
tmpImg[:, :, 2] = (image[:, :, 2] - 0.406) / 0.225
tmpLbl[:, :, 0] = label[:, :, 0]
# change the r,g,b to b,r,g from [0,255] to [0,1]
# transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225))
tmpImg = tmpImg.transpose((2, 0, 1))
tmpLbl = label.transpose((2, 0, 1))
return {"image": torch.from_numpy(tmpImg), "label": torch.from_numpy(tmpLbl)}
def apply_transform(
data: Dict[str, np.ndarray], rescale_size: int, to_tensor_lab_flag: int
) -> Dict[str, torch.Tensor]:
transform = transforms.Compose(
[RescaleT(output_size=rescale_size), ToTensorLab(flag=to_tensor_lab_flag)]
)
return transform(data) # type: ignore
class BASNetImageProcessor(BaseImageProcessor):
model_input_names = ["pixel_values"]
def __init__(
self, rescale_size: int = 256, to_tensor_lab_flag: int = 0, **kwargs
) -> None:
super().__init__(**kwargs)
self.rescale_size = rescale_size
self.to_tensor_lab_flag = to_tensor_lab_flag
def preprocess(self, images: ImageInput, **kwargs) -> BatchFeature:
if not isinstance(images, PilImage):
raise ValueError(f"Expected PIL.Image, got {type(images)}")
image_pil = images
image_npy = np.array(image_pil, dtype=np.uint8)
width, height = image_pil.size
label_npy = np.zeros((height, width), dtype=np.uint8)
assert image_npy.shape[-1] == 3
output = apply_transform(
{"image": image_npy, "label": label_npy},
rescale_size=self.rescale_size,
to_tensor_lab_flag=self.to_tensor_lab_flag,
)
image = output["image"]
assert isinstance(image, torch.Tensor)
return BatchFeature(
data={"pixel_values": image.float().unsqueeze(dim=0)}, tensor_type="pt"
)
def postprocess(
self, prediction: torch.Tensor, width: int, height: int
) -> PilImage:
def _norm_prediction(d: torch.Tensor) -> torch.Tensor:
ma, mi = torch.max(d), torch.min(d)
# division while avoiding zero division
dn = (d - mi) / ((ma - mi) + torch.finfo(torch.float32).eps)
return dn
prediction = _norm_prediction(prediction)
prediction = prediction.squeeze()
prediction = prediction * 255 + 0.5
prediction = prediction.clamp(0, 255)
prediction_np = prediction.cpu().numpy()
image = Image.fromarray(prediction_np).convert("RGB")
image = image.resize((width, height), resample=Image.Resampling.BILINEAR)
return image
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