for (img,mask) in train_generator: img,mask = adjustData(img,mask,flag_multi_class,num_class) yield (img,mask)
时间: 2024-05-19 17:14:08 浏览: 14
This code snippet defines a generator function that yields batches of training data. The function takes two arguments: the training data generator (img) and the corresponding mask generator (mask).
The adjustData() function is called on each batch of data to preprocess and augment the images and masks as necessary. The flag_multi_class parameter specifies whether the problem is a multi-class segmentation task, and the num_class parameter specifies the number of classes in the segmentation task.
Finally, the processed batches of data are yielded to the calling function. This generator function can be used to feed batches of training data to a deep learning model for training.
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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:
``` python
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。
train_generator.class_indices.items
train_generator.class_indices.items 是一个函数,它会返回一个由训练集图像的类别和对应索引值组成的字典项列表。
在机器学习中,一般的任务是将输入的图像分到不同的类别中。为了进行训练和预测,每个类别需要一个唯一的标识符。train_generator.class_indices 属性提供了类别和对应的索引值之间的映射。例如,假设我们有三个类别:"猫","狗"和"鸟",对应的索引值分别为0,1和2。那么 train_generator.class_indices 的结果就是一个字典项列表,其中包含三个键值对:{"猫": 0, "狗": 1, "鸟": 2}。
这个字典项列表可以用于多种用途。例如,可以将其用作训练集图像的标签,将图像文件名与其对应的索引值匹配起来。还可以用于对测试集图像进行预测,将模型输出的概率分布对应到相应的类别。通过 train_generator.class_indices.items 可以获取到这个字典项列表,以便在需要时进行索引和转换。
总之,train_generator.class_indices.items 返回一个包含训练集图像类别和对应索引值的字典项列表,可以在分类任务中用于标签匹配和预测结果的索引转换。