Keras Image 数据生成器抛出未找到文件错误？

Posted 2023-02-23

【中文标题】Keras Image 数据生成器抛出未找到文件错误？

【英文标题】：Keras Image data generator throwing no files found error?

【发布时间】：2017-09-05 04:45:23

【问题描述】：

我无法从 keras 运行简单的数据生成器代码

import os
import keras as K
from keras.preprocessing.image import ImageDataGenerator

def save_images_from_generator(maximal_nb_of_images, generator):
    nb_of_images_processed = 0
    for x, _ in generator:
        nb_of_images += x.shape[0]
        if nb_of_images <= maximal_nb_of_images:
            for image_nb in range(x.shape[0]):
                your_custom_save(x[image_nb]) # your custom function for saving images
        else:
            break

Gen=ImageDataGenerator(featurewise_center=True,
    samplewise_center=False,
    featurewise_std_normalization=False,
    samplewise_std_normalization=False,
    zca_whitening=True,
    rotation_range=90,
    width_shift_range=0.2,
    height_shift_range=0.1,
    shear_range=0.5,
    zoom_range=0.2,
    channel_shift_range=0.1,
    fill_mode='nearest',
    cval=0.,
    horizontal_flip=True,
    vertical_flip=True,
    rescale=None,
    preprocessing_function=None)


if __name__ == '__main__':
    save_images_from_generator(40,Gen.flow_from_directory('C:\\Users\\aanilil\\PycharmProjects\\untitled\\images_input', target_size=(150, 150),class_mode=None,save_prefix='augm',save_to_dir='C:\\Users\\aanilil\\PycharmProjects\\untitled\\im_output\\'))

输出

Using TensorFlow backend.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Found 0 images belonging to 0 classes.
Traceback (most recent call last):
  File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\pydevd.py", line 1578, in <module>
    globals = debugger.run(setup['file'], None, None, is_module)
  File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\pydevd.py", line 1015, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "C:\Program Files (x86)\JetBrains\PyCharm Community Edition 2016.3.2\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/Users/aanilil/PycharmProjects/untitled/generate_data_from_folder.py", line 35, in <module>
    save_images_from_generator(40,Gen.flow_from_directory('C:\\Users\\aanilil\\PycharmProjects\\untitled\\images_input', target_size=(150, 150),class_mode=None,save_prefix='augm',save_to_dir='C:\\Users\\aanilil\\PycharmProjects\\untitled\\im_output\\'))
  File "C:/Users/aanilil/PycharmProjects/untitled/generate_data_from_folder.py", line 7, in save_images_from_generator
    for x, _ in generator:
  File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\keras\preprocessing\image.py", line 727, in __next__
    return self.next(*args, **kwargs)
  File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\keras\preprocessing\image.py", line 950, in next
    index_array, current_index, current_batch_size = next(self.index_generator)
  File "C:\ProgramData\Anaconda3\envs\tensorflow\lib\site-packages\keras\preprocessing\image.py", line 710, in _flow_index
    current_index = (self.batch_index * batch_size) % n
ZeroDivisionError: integer division or modulo by zero

当我做一个操作系统时。 listdir 我得到这样的输出

os.listdir('C:\\Users\\aanilil\\PycharmProjects\\untitled\\images_input') 
['download (1).png', 'download.jpg', 'download.png', 'images.jpg']

所以输入文件夹中有图像，它仍然会抛出与找不到文件相关的错误

【参考方案1】：

Keras 假设图像存储在一个文件夹树中，每个类都有一个单独的子文件夹，如下所示：

一些/路径/ class1/ image1.jpg image2.jpg class2/ image3.jpg 等 等

因此，在您的情况下，解决方案是在“C:\Users\aanilil\PycharmProjects\untitled\images_input”下创建一个子文件夹并将图像移动到那里。当然，如果这是您的目标，您将需要多个类子文件夹来训练分类器。

【参考方案2】：

如果您没有预定义类，另一种可能性是将所有图像放在图像文件夹中的子文件夹中，例如：

flow_from_directory(directory = "/path/images/",…)

您在图像/数据中的实际数据

【参考方案3】：

错误是因为路径有子目录'category'，例如猫和狗。您应该创建一个包含所有图像的新目录。 示例数据集包含：

../输入.../train/ 自闭症/

image1.jpg image2.jpg

../输入.../train/ 非自闭症/

image1.jpg image2.jpg

将所有图片复制到一个目录/文件夹：

from distutils.dir_util import copy_tree
toDir = "AllTrain"
fromdir = "../input/autistic-children-data-set/train/autistic"
copy_tree(fromdir ,toDir)
fromdirNon = "../input/autistic-children-data-set/train/non_autistic"
copy_tree(fromdirNon ,toDir)

为每个类别添加标签：

filenames = []
categories = []
Train_autistic = os.listdir("../input/autistic-children-data-set/train/autistic/")
for filename in Train_autistic :
        categories.append(1)
filenames.extend(Train_autistic )

Train_non_autistic = os.listdir("../input/autistic-children-data-set/train/non_autistic/")
for filename in Train_non_autistic :
        categories.append(0)
filenames.extend(Train_non_autistic )

train_df = pd.DataFrame(
    'filename': filenames,
    'category': categories
)

train_df["category"] = train_df["category"].replace(0: 'non_autistic', 1: 'autistic') 

然后使用：

train_generator = train_datagen.flow_from_dataframe(
    train_df, "AllTrain/", 
    x_col='filename',
    y_col='category',
    target_size=IMAGE_SIZE,
    class_mode='categorical',
    batch_size=batch_size
)

不知道：

train_generator = train_datagen.flow_from_dataframe(
    train_df, "../input/autistic-children-data-set/train", 
    target_size=IMAGE_SIZE,
    class_mode='binary',
    batch_size=batch_size
)

【参考方案4】：

它只是关于你的文件路径 看 这是我用于训练图像的文件 =

C:/Users/Admin/python/Dataset/training_set/data

这是我的测试图像文件 =

C:/Users/Admin/python/Dataset/test_set/data 并在每个路径的data 文件夹中放置我的图像。

但是现在，如果你在命令中给出这个，你需要给它：

test_set = train_datagen.flow_from_directory('C:/Users/Admin/python/Dataset/test_set',target_size=(435,116),batch_size=4,class_mode='binary')

和

test_set = train_datagen.flow_from_directory('C:/Users/Admin/python/Dataset/test_set',target_size=(435,116),batch_size=4,class_mode='binary')

不要在此路径中提及“数据”文件夹。 这将解决问题