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我正在对 493 个类应用增强，每个类有 1 个或 2 个或 3 个或 4 个图像（其未知的 1 个类可能只有 1 个图像，其他可能有 2 个图像）。当我使用 ImageDataGenerator 应用增强时，我得到增强的图像，但图像的名称是随机生成的，我希望将增强的图像名称作为原始图像名称。我尝试了一些代码：

from keras.preprocessing.image import ImageDataGenerator
from keras.applications.inception_v3 import preprocess_input
import glob,os

path = './newaug'
outpath = './newaug_result5/'

filenames = glob.glob(path + "/**/*.png",recursive=True)
imgnum=50

print (filenames)
for img in filenames:

    if "DS_Store" in img: continue
    src_fname, ext = os.path.splitext(img) 

    train_datagen=ImageDataGenerator(
        preprocessing_function=preprocess_input,
        rotation_range = 10,
        width_shift_range=0.05,
        height_shift_range=0.05,
        fill_mode='constant',cval=0.0)

    jf_datagen=ImageDataGenerator(
        preprocessing_function=preprocess_input
    )

    img_name = src_fname.split('/')[-1]
    new_dir = os.path.join(outpath, src_fname.split('/')[-1].rsplit('-', 1)[0])
    if not os.path.lexists(new_dir):
        os.mkdir(new_dir)
    #save_fname = os.path.join(new_dir, os.path.basename(img_name))
    save_fname = new_dir

    i=0
    train_generator=train_datagen.flow_from_directory(path,target_size=(224,224),
                                                      save_to_dir=save_fname)


    for batch in train_generator:
        i += 1
        if i > imgnum:
            break

    for batch in jf_datagen.flow_from_directory(path,target_size=(224,224),
                                                save_to_dir=save_fname):
        i += 1
        if i > imgnum:
            break

我得到的是图像也属于不同的类别。

classname1/
     |-01_133214.png
     |-02_43434.png (This image actually belongs to class 2)
classname2/
     |-01_13333214.png(This image actually belongs to class 1)
     |-02_4343334.png
     |-03_13333214.png(This image actually belongs to class 3)

我想要的是，生成与类相同的文件夹，并且增强图像应保存在同一类中，名称应与原始图像相同。

classname1/ (Images should belong to same class, for eg 01 signifies classname1)
         |classname1-01_2424424.png
         |classname1-01_2134242.png
         |
         |classname1-01_232424.png
classname2/
         |classname2-02_323212.png
         |classname2-02_321313.png
         |
         |classname2-02_333339.png
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1 回答 1

3

它使用flow而不是flow_from_directory. 代码是：

import numpy as np
import keras,glob,os
import cv2
from keras.preprocessing.image import ImageDataGenerator, array_to_img,img_to_array, load_img

img_path = './newaug'
outpath = './newaug_result7/'

filenames = glob.glob(img_path + "/**/*.png",recursive=True)


for img in filenames:

    if "DS_Store" in img: continue
    src_fname, ext = os.path.splitext(img) 

    datagen = ImageDataGenerator(rotation_range = 10,
            width_shift_range=0.05,
            height_shift_range=0.05,
            fill_mode='constant',cval=0.0)


    img = load_img(img)

    x = img_to_array(img)
    x = x.reshape((1,) + x.shape)

    img_name = src_fname.split('/')[-1]
    new_dir = os.path.join(outpath, src_fname.split('/')[-1].rsplit('-', 1)[0])
    if not os.path.lexists(new_dir):
        os.mkdir(new_dir)
    #save_fname = os.path.join(new_dir, os.path.basename(img_name))
    save_fname = new_dir

    i = 0
    for batch in datagen.flow (x, batch_size=1, save_to_dir = save_fname, 
                               save_prefix = img_name, save_format='jpg'):
        i+=1
        if i>51:
            break
于 2019-08-01T07:57:22.673 回答