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# import the necessary packages

from keras.preprocessing.image import ImageDataGenerator

from keras.preprocessing.image import img_to_array

from keras.preprocessing.image import load_img

import numpy as np

import argparse

from keras_util import *

# construct the argument parse and parse the arguments

ap = argparse.ArgumentParser()

ap.add_argument("-i", "--image", required=True,

    help="path to the input image")

ap.add_argument("-o", "--output", required=True,

    help="path to output directory to store augmentation examples")

ap.add_argument("-p", "--prefix", type=str, default="image",

    help="output filename prefix")

args = vars(ap.parse_args())

# load the input image, convert it to a NumPy array, and then

# reshape it to have an extra dimension

print("[INFO] loading example image...")

target_size = None

#target_size=(224,224)

image = load_img(args["image"], target_size=target_size)

image = img_to_array(image)

image = np.expand_dims(image, axis=0) # 1,h,w,c

# construct the image generator for data augmentation then

# initialize the total number of images generated thus far

# preprocessing_function: The function will run after the image is resized and augmented.

# The function should take one argument:

#    one image (Numpy tensor with rank 3),

#     and should output a Numpy tensor with the same shape.

# for 1 image --->(424,640,3)--->aug---(424,640,3)--->preprocess_input--->(424,640,3)

# for 1 image --->resize--->(224,224,3)--->aug---(224,224,3)--->preprocess_input--->(224,224,3)

aug = ImageDataGenerator(preprocessing_function=resnet.preprocess_input,

                         rotation_range=30,

                         width_shift_range=0.1,

                         height_shift_range=0.1,

                         shear_range=0.2,

                         zoom_range=0.2,

                         horizontal_flip=True,

                         fill_mode="nearest")

total = 0

# construct the actual Python generator

print("[INFO] generating images...")

imageGen = aug.flow(image,

                    batch_size=1,

                    save_to_dir=args["output"],

                    save_prefix=args["prefix"],

                    save_format="png")

next_image = next(imageGen)

print(next_image.shape)

print(next_image[0, :5,:5,0])

# loop over examples from our image data augmentation generator

for image in imageGen:

    # increment our counter

    total += 1

    # if we have reached 10 examples, break from the loop

    if total == 10:

        break

output

target_size = None:

1 image --->(424,640,3)--->aug--->(424,640,3)--->preprocess_input--->(424,640,3)

target_size = (224,224):

1 image --->resize--->(224,224,3)--->aug--->(224,224,3)--->preprocess_input--->(224,224,3)

Reference

History

20190910: created.

Copyright

Post author: kezunlin
Post link: https://kezunlin.me/post/8db507ff/
Copyright Notice: All articles in this blog are licensed under CC BY-NC-SA 3.0 unless stating additionally.

巴特西

keras对图像数据进行增强 | keras data augmentation

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