我正在通过Python和Keras中的深度学习进行图像色彩校正的监督方法。我完成了数据的训练，并预测了测试数据上的值。现在，我想展示数据集中的一些颜色校正图像，以便我可以直观地将它们与原始图像进行比较。我陷入了一个循环，我不知道如何解决这个问题。有人可以帮助我提供代码或一些提示吗？
我已经比较了预测光照的数量和基本事实的数量，但我想用预测的光照绘制它们的外观图片。我正在使用 3 倍交叉验证，这使得它更加困难。 我有 1000 多张图片，但为了数字简单起见，假设我只有 12 张。我将训练其中的 8 个并测试其中的 4 个。
#this is the part where the training and testing is happening, images are loaded
#in xs variable and ground truth illumination is loaded in ys variable
for i in range (3):
print('nFold ',i)
X_train = xs [folds[i]==0, :]
X_test = xs [folds[i]==1, :]
Y_train = ys [folds[i]==0, :]
Y_test = np.zeros((4,3), dtype=np.uint8)
model = None
model = create_model()
history = model.fit(X_train, Y_train, epochs=10, batch_size=8)
Y_test = model.predict(X_test, batch_size=4)
print("Predicted values for fold %d:" % i, Y_test)
for y in Y_test[:]:
predicted.append(y)
这部分代码运行良好，我不知道的是如何在使用预测的照明进行颜色校正后绘制甚至仅保存这 12 张图像中的每一张。
编辑：我已经提取了每张照片的预测值。如何将它们应用于图像？
如果我理解正确，您希望使用模型预测的光源对色偏图像进行白平衡。您的预测由 3 个值组成(假设 [alpha、beta、ceta])，这些值是将应用于彩色投射图像的每个通道(蓝色、绿色、红色)的校正增益。
但是，在应用校正增益之前，您需要对图像执行伽马线性化(更多信息在这里)。
以下是一些示例代码来帮助您：
import cv2
import numpy as np
def gamma_decode(B_gamma, G_gamma, R_gamma):
B_gamma = B_gamma/255
G_gamma = G_gamma/255
R_gamma = R_gamma/255
gamma = 1/2.2
B_gamma_decode = 255*(B_gamma**(1/gamma))
G_gamma_decode = 255*(G_gamma**(1/gamma))
R_gamma_decode = 255*(R_gamma**(1/gamma))
return (B_gamma_decode, G_gamma_decode, R_gamma_decode)
def gamma_encode(B_channel, G_channel, R_channel):
B_channel = B_channel/255
G_channel = G_channel/255
R_channel = R_channel/255
gamma = 1/2.2
if np.all(B_channel <= 0):
B_gamma_cor = (B_channel**(gamma + 0j))
B_gamma_cor = 255*(abs(B_gamma_cor))
else:
B_gamma_cor = 255*(B_channel**gamma)
if np.all(G_channel <= 0):
G_gamma_cor = (G_channel**(gamma + 0j))
G_gamma_cor = 255*(abs(G_gamma_cor))
else:
G_gamma_cor = 255*(G_channel**gamma)
if np.all(R_channel <= 0):
R_gamma_cor = (R_channel**(gamma + 0j))
R_gamma_cor = 255*(abs(R_gamma_cor))
else:
R_gamma_cor = 255*(R_channel**gamma)
return (B_gamma_cor, G_gamma_cor, R_gamma_cor)
def white_balance(img, pred_illum)
B_channel, G_channel, R_channel = cv2.split(img)
alpha, beta, ceta = pred_illum
#Gamma_decoding
B_channel, G_channel, R_channel = gamma_decode(B_channel, G_channel, R_channel)
#Correction
B_cor = (alpha*B_channel)
G_cor = (beta*G_channel)
R_cor = (ceta*R_channel)
#Gamma encoding
B_cor, G_cor, R_cor = gamma_encode(B_cor, G_cor, R_cor)
#Convert to uint8 to display
B_cor = B_cor.astype(np.uint8)
G_cor = G_cor.astype(np.uint8)
R_cor = R_cor.astype(np.uint8)
img_white_balanced = cv2.merge((B_cor, G_cor, R_cor))
return img_white_balanced