写一个利用神经网络训练相机响应函数的代码

以下是一个基于PyTorch实现的相机响应函数训练代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import cv2

class ResponseNet(nn.Module):
    def __init__(self):
        super(ResponseNet, self).__init__()
        self.fc1 = nn.Linear(256, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 32)
        self.fc4 = nn.Linear(32, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.relu(self.fc3(x))
        x = self.fc4(x)
        return x

def train_response_net(train_data, train_labels, num_epochs=100, lr=0.001):
    model = ResponseNet()
    optimizer = optim.Adam(model.parameters(), lr=lr)
    criterion = nn.MSELoss()

    train_data = torch.from_numpy(train_data).float()
    train_labels = torch.from_numpy(train_labels).float()

    for epoch in range(num_epochs):
        optimizer.zero_grad()
        outputs = model(train_data)
        loss = criterion(outputs, train_labels)
        loss.backward()
        optimizer.step()
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

    return model

def estimate_camera_response(images, exposures, model):
    num_images = len(images)
    num_samples = images[0].shape[0] * images[0].shape[1]
    B = np.zeros((num_images, num_samples))
    for i in range(num_images):
        img = images[i]
        img = img.reshape((num_samples, 3))
        B[i, :] = np.log(exposures[i])[:, np.newaxis]
        B[i, :] = np.tile(B[i, :], (1, 3))
        img = np.log(img)
        img = torch.from_numpy(img).float()
        with torch.no_grad():
            output = model(img)
        g = torch.exp(output)
        g = g.numpy()
        B[i, :] = B[i, :] * g

    lE = np.zeros(num_samples)
    w = np.zeros(num_samples)

    for i in range(num_samples):
        w[i] = np.sum(B[:, i] > 0)
        lE[i] = np.sum(B[:, i]) / np.sum(B[:, i] > 0)

    return np.exp(lE.reshape(images[0].shape[:2])), w

# 读取多张曝光不同的图片和曝光时间
img1 = cv2.imread('img1.jpg')
img2 = cv2.imread('img2.jpg')
img3 = cv2.imread('img3.jpg')
exposures = np.array([1.0 / 30, 1.0 / 15, 1.0 / 8])

# 构造训练数据和标签
train_data = np.vstack((img1.reshape((-1, 3)), img2.reshape((-1, 3)), img3.reshape((-1, 3))))
train_labels = np.tile(exposures, img1.shape[0] * img1.shape[1])

# 训练相机响应函数
model = train_response_net(train_data, train_labels)

# 估计曝光不同的图片的相机响应函数
images = [img1, img2, img3]
response, weight = estimate_camera_response(images, exposures, model)

# 将响应函数应用到待处理图片
img = cv2.imread('img.jpg')
img = np.float32(img) / 255.0
img = np.clip(img, 1e-6, 1 - 1e-6)
lW = weight * np.log(img)
lW = np.sum(lW, axis=1) / np.sum(weight)
lW = np.reshape(lW, img.shape[:2])
imgHDR = np.exp(lW - response)
imgHDR = imgHDR / np.max(imgHDR)
cv2.imwrite('imgHDR.jpg', np.uint8(imgHDR * 255))

这段代码实现了一个简单的全连接神经网络，用于训练相机响应函数。训练数据由多张曝光不同的图片组成，每张图片的像素值作为一个样本，曝光时间作为标签。训练完成后，可以使用训练好的模型估计新图片的相机响应函数，进而生成HDR图像。