Python实现一键改变raw格式照片风格

pip install rawpy
pip install torch torchvision

import rawpy
import cv2
import torch
import torchvision.transforms as transforms
import torchvision.models as models
from PIL import Image

def neural_style_transfer(input_image, style_image, iterations=300, content_weight=1, style_weight=1e5):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = models.vgg19(pretrained=True).features.to(device).eval()

    content_image = input_image.clone().detach().requires_grad_(True).to(device)
    style_image = style_image.clone().detach().to(device)

    optimizer = torch.optim.LBFGS([content_image.requires_grad_()])
    
    for i in range(iterations):
        def closure():
            content_image.data.clamp_(0, 1)
            optimizer.zero_grad()
            features_content = model(content_image)
            features_style = model(style_image)
            
            # ... (省略了详细的风格迁移实现代码)
            
            return loss

        optimizer.step(closure)
    
    return content_image.clamp_(0, 1)

raw_image_path = 'your_raw_image_path.raw'
with rawpy.imread(raw_image_path) as raw:
    rgb_image = raw.postprocess()
    input_image = Image.fromarray(rgb_image)

style_image_path = 'your_style_image_path.jpg'
style_image = Image.open(style_image_path)

transform = transforms.Compose([
    transforms.Resize(512),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

input_image_tensor = transform(input_image).unsqueeze(0)
style_image_tensor = transform(style_image).unsqueeze(0)

output_image_tensor = neural_style_transfer(input_image_tensor, style_image_tensor)
output_image = transforms.ToPILImage()(output_image_tensor.squeeze(0))

output_image_path = 'output_image.jpg'
output_image.save(output_image_path)

pip install rawpy
pip install imageio
pip install opencv-python

import rawpy
import imageio
import cv2
import numpy as np

raw_image_path = 'your_raw_image_path.raw'
with rawpy.imread(raw_image_path) as raw:
    rgb_image = raw.postprocess()

bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)

denoised_image = cv2.fastNlMeansDenoisingColored(bgr_image, None, 10, 10, 7, 21)

denoised_rgb_image = cv2.cvtColor(denoised_image, cv2.COLOR_BGR2RGB)

output_image_path = 'output_image.png'
imageio.imwrite(output_image_path, denoised_rgb_image)