# Transform

## ToTensor and 0~1

from torchvision import transforms

to_tensor = transforms.ToTensor()
img_tensor = to_tensor(img)

plt.imshow(img_tensor.squeeze(0), cmap='gray')
plt.title("ToTensor (0~1)")
plt.axis('off')
plt.show()

img_tensor

## Resize

resize = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])
resized_tensor = resize(img)

plt.imshow(resized_tensor.squeeze(0), cmap='gray')
plt.title("Resize to 224x224")
plt.axis('off')
plt.show()

## Normalize

normalize = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5], std=[0.5])
])
norm_tensor = normalize(img)

plt.imshow(norm_tensor.squeeze(0), cmap='gray')
plt.title("Normalize [-1, 1]")
plt.axis('off')
plt.show()

## Random rotaion

rotate = transforms.Compose([
    transforms.RandomRotation(degrees=20),
    transforms.ToTensor()
])
rotated_tensor = rotate(img)

plt.imshow(rotated_tensor.squeeze(0), cmap='gray')
plt.title("Random Rotation ±20°")
plt.axis('off')
plt.show()

## Random horizontal flip

flip = transforms.Compose([
    transforms.RandomHorizontalFlip(p=1.0),
    transforms.ToTensor()
])
flipped_tensor = flip(img)

plt.imshow(flipped_tensor.squeeze(0), cmap='gray')
plt.title("Horizontal Flip")
plt.axis('off')
plt.show()

## Random vertical flip

vertical_flip = transforms.Compose([
    transforms.RandomVerticalFlip(p=1.0),  # 強制垂直翻轉
    transforms.ToTensor()
])

vflipped_tensor = vertical_flip(img)

plt.imshow(vflipped_tensor.squeeze(0), cmap='gray')
plt.title("Vertical Flip")
plt.axis('off')
plt.show()

## ColorJitter

color_jitter = transforms.Compose([
    transforms.ColorJitter(brightness=0.5, contrast=0.5),
    transforms.ToTensor()
])

jittered_tensor = color_jitter(img)

plt.imshow(jittered_tensor.squeeze(0), cmap='gray')
plt.title("ColorJitter: Brightness & Contrast")
plt.axis('off')
plt.show()

## Gaussian blur

gaussian_blur = transforms.Compose([
    transforms.GaussianBlur(kernel_size=5, sigma=(0.1, 2.0)),
    transforms.ToTensor()
])

blurred_tensor = gaussian_blur(img)

plt.imshow(blurred_tensor.squeeze(0), cmap='gray')
plt.title("Gaussian Blur")
plt.axis('off')
plt.show()

## Random crop

random_crop = transforms.Compose([
    transforms.RandomCrop((100, 100)),
    transforms.ToTensor()
])

cropped_tensor = random_crop(img)

plt.imshow(cropped_tensor.squeeze(0), cmap='gray')
plt.title("Random Crop 100x100")
plt.axis('off')
plt.show()

## Elastic transform

from torchvision import transforms
import matplotlib.pyplot as plt
from PIL import Image

img = Image.open("train/XR_FINGER/patient00169/study1_negative/image1.png").convert("L")
img_rgb = img.convert("RGB") 

elastic = transforms.Compose([
    transforms.ElasticTransform(alpha=250.0),
    transforms.ToTensor()
])

elastic_tensor = elastic(img_rgb)


plt.imshow(elastic_tensor.permute(1, 2, 0)) 
plt.title("Elastic Transform")
plt.axis('off')
plt.show()

## Random perspective

perspective = transforms.Compose([
    transforms.RandomPerspective(distortion_scale=0.5, p=1.0),
    transforms.ToTensor()
])

perspective_tensor = perspective(img)

plt.imshow(perspective_tensor.squeeze(0), cmap='gray')
plt.title("Random Perspective")
plt.axis('off')
plt.show()

## Random erasing

erasin = transforms.Compose([
    RandomErasing(p=1.0, scale=(0.05, 0.2), ratio=(0.3, 3.3))
    transforms.ToTensor()
])

erasin_tensor = perspective(img)

plt.imshow(erased_tensor.squeeze(0), cmap='gray')
plt.title("Random Erasing")
plt.axis('off')
plt.show()

## Add gaussian Noise

import torch
class AddGaussianNoise:
    def __init__(self, mean=0., std=0.05):
        self.mean = mean
        self.std = std

    def __call__(self, tensor):
        return tensor + torch.randn_like(tensor) * self.std + self.mean

noise_pipeline = transforms.Compose([
    transforms.ToTensor(),
    AddGaussianNoise(std=0.1)
])

noisy_tensor = noise_pipeline(img)

plt.imshow(noisy_tensor.squeeze(0).clamp(0, 1), cmap='gray')
plt.title("Gaussian Noise")
plt.axis('off')
plt.show()

## Assembled Transform

from torchvision import transforms
import torch
import matplotlib.pyplot as plt
from PIL import Image
import random


img = Image.open("train/XR_FINGER/patient00169/study1_negative/image1.png").convert("L")

transform = transforms.Compose([
    transforms.Resize((256, 256)),
    transforms.RandomHorizontalFlip(p=0.5), 
    transforms.RandomRotation(degrees=10), 
    transforms.ColorJitter(brightness=0.2, contrast=0.2),
    transforms.ToTensor(),
])


augmented_tensor = transform(img)

plt.imshow(augmented_tensor.squeeze(0).clamp(0, 1), cmap='gray')
plt.title("")
plt.axis('off')
plt.show()