def adjustData(img,mask,flag_multi_class,num_class): if(flag_multi_class): img = img / 255 mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0] new_mask = np.zeros(mask.shape + (num_class,)) for i in range(num_class): #for one pixel in the image, find the class in mask and convert it into one-hot vector #index = np.where(mask == i) #index_mask = (index[0],index[1],index[2],np.zeros(len(index[0]),dtype = np.int64) + i) if (len(mask.shape) == 4) else (index[0],index[1],np.zeros(len(index[0]),dtype = np.int64) + i) #new_mask[index_mask] = 1 new_mask[mask == i,i] = 1 new_mask = np.reshape(new_mask,(new_mask.shape[0],new_mask.shape[1]*new_mask.shape[2],new_mask.shape[3])) if flag_multi_class else np.reshape(new_mask,(new_mask.shape[0]*new_mask.shape[1],new_mask.shape[2])) mask = new_mask elif(np.max(img) > 1): img = img / 255 mask = mask /255 mask[mask > 0.5] = 1 mask[mask <= 0.5] = 0 return (img,mask)

这是一个用于对图像数据和标签数据进行预处理的函数，其中参数img和mask分别表示图像数据和标签数据，flag_multi_class表示是否为多分类问题，num_class表示分类数目。函数主要实现了以下功能：

1. 如果flag_multi_class为True，则将标签数据转换为one-hot编码形式，方便在多分类模型中使用。

2. 如果图像数据和标签数据的像素值都大于1，则将它们都归一化到0到1之间。

3. 将标签数据中像素值大于0.5的部分设置为1，小于等于0.5的部分设置为0，以得到二分类标签数据。

最后，函数将处理后的图像数据和标签数据作为元组返回。

相关问题

def trainGenerator(batch_size,train_path,image_folder,mask_folder,aug_dict,image_color_mode = "grayscale", mask_color_mode = "grayscale",image_save_prefix = "image",mask_save_prefix = "mask", flag_multi_class = False,num_class = 2,save_to_dir = None,target_size = (256,256),seed = 1): ''' can generate image and mask at the same time use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same if you want to visualize the results of generator, set save_to_dir = "your path" ''' image_datagen = ImageDataGenerator(**aug_dict) mask_datagen = ImageDataGenerator(**aug_dict) #image_datagen中flow函数接收numpy数组和标签为参数,生成经过数据提升或标准化后的batch数据,并在一个无限循环中不断的返回batch数据。flow_from_directory函数以文件夹路径为参数,生成经过数据提升/归一化后的数据,在一个无限循环中无限产生batch数据 image_generator = image_datagen.flow_from_directory( train_path, classes = [image_folder], class_mode = None, color_mode = image_color_mode, target_size = target_size, batch_size = batch_size, save_to_dir = save_to_dir, save_prefix = image_save_prefix, seed = seed) mask_generator = mask_datagen.flow_from_directory( train_path, classes = [mask_folder], class_mode = None, color_mode = mask_color_mode, target_size = target_size, batch_size = batch_size, save_to_dir = save_to_dir, save_prefix = mask_save_prefix, seed = seed) train_generator = zip(image_generator, mask_generator) for (img,mask) in train_generator: img,mask = adjustData(img,mask,flag_multi_class,num_class) yield (img,mask)

这是一个生成器函数，用于生成训练集的数据。其中参数batch_size表示每次生成的数据量，train_path表示训练集路径，image_folder表示保存图像数据的文件夹，mask_folder表示保存标签数据的文件夹，aug_dict表示数据增强的参数字典，image_color_mode表示图像数据的颜色模式，mask_color_mode表示标签数据的颜色模式，image_save_prefix和mask_save_prefix分别表示保存图像和标签数据的前缀，flag_multi_class表示是否为多分类问题，num_class表示分类数目，save_to_dir表示保存增强后的图像和标签数据的路径，target_size表示图像和标签数据的尺寸大小，seed表示随机数种子。

该函数首先使用ImageDataGenerator生成图像和标签数据的生成器，然后调用flow_from_directory函数生成经过数据增强或归一化后的图像和标签数据。最后，该函数使用zip函数将图像数据生成器和标签数据生成器打包成一个可迭代的train_generator，并通过调用adjustData函数对图像和标签数据进行预处理，最终返回预处理后的图像和标签数据。整个过程是在一个无限循环中不断地生成数据。

def trainGenerator(batch_size,train_path,image_folder,mask_folder,aug_dict,image_color_mode = "grayscale", mask_color_mode = "grayscale",image_save_prefix = "image",mask_save_prefix = "mask", flag_multi_class = False,num_class = 2,save_to_dir = None,target_size = (256,256),seed = 1): ''' can generate image and mask at the same time use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same if you want to visualize the results of generator, set save_to_dir = "your path" ''' image_datagen = ImageDataGenerator(**aug_dict) mask_datagen = ImageDataGenerator(**aug_dict) #image_datagen中flow函数接收numpy数组和标签为参数,生成经过数据提升或标准化后的batch数据,并在一个无限循环中不断的返回batch数据。flow_from_directory函数以文件夹路径为参数,生成经过数据提升/归一化后的数据,在一个无限循环中无限产生batch数据 image_generator = image_datagen.flow_from_directory( train_path, classes = [image_folder], class_mode = None, color_mode = image_color_mode, target_size = target_size, batch_size = batch_size, save_to_dir = save_to_dir, save_prefix = image_save_prefix, seed = seed) mask_generator = mask_datagen.flow_from_directory( train_path, classes = [mask_folder], class_mode = None, color_mode = mask_color_mode, target_size = target_size, batch_size = batch_size, save_to_dir = save_to_dir, save_prefix = mask_save_prefix, seed = seed) train_generator = zip(image_generator, mask_generator) for (img,mask) in train_generator: img,mask = adjustData(img,mask,flag_multi_class,num_class) yield (img,mask)把这段代码中读取训练集img改成读取文件中的四张灰度图并合并为四通道图作为训练集

好的，我可以帮您修改这段代码来读取文件中的四张灰度图并将它们合并为一个四通道图像作为训练集。请参考下面的代码：

import cv2
import numpy as np
from keras.preprocessing.image import ImageDataGenerator

def trainGenerator(batch_size, train_path, img_folder, mask_folder, aug_dict, img_color_mode="rgba",
                   mask_color_mode="grayscale", img_save_prefix="image", mask_save_prefix="mask",
                   flag_multi_class=False, num_class=2, save_to_dir=None, target_size=(256, 256), seed=1):
    '''
    can generate image and mask at the same time
    use the same seed for image_datagen and mask_datagen to ensure the transformation for image and mask is the same
    if you want to visualize the results of generator, set save_to_dir = "your path"
    '''
    
    # 读取四个灰度图像并将它们合并为一个四通道图像
    img1 = cv2.imread('img1.png', cv2.IMREAD_GRAYSCALE)
    img2 = cv2.imread('img2.png', cv2.IMREAD_GRAYSCALE)
    img3 = cv2.imread('img3.png', cv2.IMREAD_GRAYSCALE)
    img4 = cv2.imread('img4.png', cv2.IMREAD_GRAYSCALE)
    img = cv2.merge((img1, img2, img3, img4))
    
    # 将图像转换为四通道 rgba 格式
    img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGBA)
    
    # 构造 image_datagen 和 mask_datagen
    image_datagen = ImageDataGenerator(**aug_dict)
    mask_datagen = ImageDataGenerator(**aug_dict)
    
    # 使用 image_datagen 和 mask_datagen 生成数据
    image_generator = image_datagen.flow(np.array([img]), batch_size=batch_size, save_to_dir=save_to_dir,
                                         save_prefix=img_save_prefix, seed=seed)
    mask_generator = mask_datagen.flow_from_directory(train_path, classes=[mask_folder], class_mode=None,
                                                       color_mode=mask_color_mode, target_size=target_size,
                                                       batch_size=batch_size, save_to_dir=save_to_dir,
                                                       save_prefix=mask_save_prefix, seed=seed)
    
    # 将 image_generator 和 mask_generator 合并为一个 train_generator
    train_generator = zip(image_generator, mask_generator)
    
    # 循环遍历 train_generator 并返回调整后的数据
    for (img, mask) in train_generator:
        img, mask = adjustData(img, mask, flag_multi_class, num_class)
        yield (img, mask)

在上面的代码中，我们首先读取四个灰度图像并将它们合并为一个四通道图像。然后，我们将四通道图像转换为 RGBA 格式，以便与掩膜图像一起使用。接下来，我们使用 `ImageDataGenerator` 类构造了 `image_datagen` 和 `mask_datagen`，并使用它们分别生成四通道图像和掩膜图像的数据。最后，我们将 `image_generator` 和 `mask_generator` 合并为一个 `train_generator` 并循环遍历它，返回调整后的数据。