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input_data.py
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import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np
import os
import glob
from itertools import groupby
import os
# os.environ["CUDA_VISIBLE_DEVICES"]="-1" #
from collections import defaultdict
# 适用于所有类别图片在同一个文件夹中的情况，本例不使用此函数
def get_files(file_dir):
"""
:param file_dir: file directory
:return:list of images and labels
"""
airport=[]
port = []
beach = []
label_airport = [] # 0
label_port = [] # 1
label_beach = [] # 2
for file in os.listdir(file_dir):
name = file.split(sep='_')
name = name.split(sep='-') # 部分文件名以'-'相隔
if name[0]=='airport':
airport.append(file_dir+file)
label_airport.append(0)
if name[0]=='port':
port.append(file_dir+file)
label_port.append(1)
if name[0]=='beach':
beach.append(file_dir+file)
label_beach.append(2)
# ...
# ...
print('There are %d airports\nThere are %d ports\nThere are %d beaches' % (len(airport), len(port),
len(beach)))
image_list = np.hstack((airport, port,))
label_list = np.hstack((label_airport, label_port))
temp = np.array([image_list, label_list])
temp = temp.transpose() # 转置操作
np.random.shuffle(temp)
image_list = list(temp[:, 0])
label_list = list(temp[:, 1])
label_list = [int(i) for i in label_list]
return image_list, label_list
def get_batch(image, label, image_W, image_H, batch_size, capacity):
"""
获得批次
:param image:list type
:param label:list type
:param image_W:image width
:param image_H:image height
:param batch_size:batch size
:param capacity:the maximum elements in queue
:return:
image_batch: 4D tensor [batch_size, width, height, 3], dtype=tf.float32
label_batch: 1D tensor [batch_size], dtype=tf.int32
"""
image = tf.cast(image, tf.string) # list形式，要转换为tensorflow可识别的
label = tf.cast(label, tf.int32)
# make an input queue
input_queue = tf.train.slice_input_producer([image, label],shuffle=True) # 因为image 和 label 是分开的
label = input_queue[1]
image_contents = tf.read_file(input_queue[0]) # 读取队列中的图像
image = tf.image.decode_jpeg(image_contents, channels=3) # 解码这些图像
"""
data argumentation should go to here
"""
# image = tf.image.resize_images(image, [600,600], method=tf.image.ResizeMethod.BICUBIC)
# #效果非常的差原因找到了，是因为resize之后返回的图像是float格式
# 需要转化为unit8才能正常显示
#image = tf.image.resize_images(image, [400, 400], method=tf.image.ResizeMethod.BICUBIC)
#image = tf.cast(image, tf.uint8)
# 如果需要填充、裁剪可以选择下面的函数
# image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
image = tf.image.resize_images(image,[image_W,image_H]) # 缩放图片， 以加快训练速度
# if you want to test the generated batches of images, you might want to comment the following line.
# 如果想看到正常的图片，请注释掉111行（标准化）和 126行（image_batch = tf.cast(image_batch, tf.float32)）
# 训练时不要注释掉！
image = tf.image.per_image_standardization(image) #数据标准化
# 生成batch
image_batch, label_batch = tf.train.batch([image,label],
batch_size=batch_size,
num_threads=2,
capacity=capacity)
# you can also use shuffle_batch
# image_batch, label_batch = tf.train.shuffle_batch([image,label],
# batch_size=BATCH_SIZE,
# num_threads=64,
# capacity=CAPACITY,
# min_after_dequeue=CAPACITY-1)
label_batch = tf.reshape(label_batch, [batch_size]) # 讲label_batch reshape成 [batch_size]这么多行的的tensor
image_batch = tf.cast(image_batch, tf.float32)
return image_batch, label_batch
def new_getfiles(train_dir):
training_image_list=[]
training_label_list=[]
testing_image_list=[]
testing_label_list=[]
image_filenames = glob.glob(train_dir)
label_with_filedir = map(lambda filename:(filename.split('\\')[5],filename), image_filenames )
for label, filedirs in groupby(label_with_filedir, lambda x :x[0]):
for i, filedir in enumerate(filedirs):
if i % 5 == 0:
testing_label_list.append(label)
testing_image_list.append(filedir[1])
else:
training_label_list.append(label)
training_image_list.append(filedir[1])
print("There are %d %s images " % (i,label))
print(' %d images in testing dataset and %d images in training dataset' % (len(testing_label_list),
len(training_label_list)))
# convert the label from string to int
testing_numlabel=tf.to_int32(tf.argmax(tf.to_int32(tf.stack([tf.equal(testing_label_list, ['Airport']),
tf.equal(testing_label_list, ['Beach']),
tf.equal(testing_label_list, ['Bridge']),
tf.equal(testing_label_list, ['Commercial']),
tf.equal(testing_label_list, ['Desert']),
tf.equal(testing_label_list, ['Farmland']),
tf.equal(testing_label_list, ['footballField']),
tf.equal(testing_label_list, ['Forest']),
tf.equal(testing_label_list, ['Industrial']),
tf.equal(testing_label_list, ['Meadow']),
tf.equal(testing_label_list, ['Mountain']),
tf.equal(testing_label_list, ['Park']),
tf.equal(testing_label_list, ['Parking']),
tf.equal(testing_label_list, ['Pond']),
tf.equal(testing_label_list, ['Port']),
tf.equal(testing_label_list, ['railwayStation']),
tf.equal(testing_label_list, ['Residential']),
tf.equal(testing_label_list, ['River']),
tf.equal(testing_label_list, ['Viaduct'])]))))
training_numlabel=tf.to_int32(tf.argmax(tf.to_int32(tf.stack([tf.equal(training_label_list, ['Airport']),
tf.equal(training_label_list, ['Beach']),
tf.equal(training_label_list, ['Bridge']),
tf.equal(training_label_list, ['Commercial']),
tf.equal(training_label_list, ['Desert']),
tf.equal(training_label_list, ['Farmland']),
tf.equal(training_label_list, ['footballField']),
tf.equal(training_label_list, ['Forest']),
tf.equal(training_label_list, ['Industrial']),
tf.equal(training_label_list, ['Meadow']),
tf.equal(training_label_list, ['Mountain']),
tf.equal(training_label_list, ['Park']),
tf.equal(training_label_list, ['Parking']),
tf.equal(training_label_list, ['Pond']),
tf.equal(training_label_list, ['Port']),
tf.equal(training_label_list, ['railwayStation']),
tf.equal(training_label_list, ['Residential']),
tf.equal(training_label_list, ['River']),
tf.equal(training_label_list, ['Viaduct'])]))))
return training_image_list,training_numlabel,testing_image_list,testing_numlabel
def get_freeTest_images(image_files_dir):
'''
the freeTest images fold shoul have only image documents
:param image_files_dir:
:return:
'''
freeTest_list=[]
for file in os.listdir(image_files_dir):
freeTest_list.append(image_files_dir+file)
print(freeTest_list)
return freeTest_list
# train_dir = 'D:\\DL\\RS_data\\WH-RSDataset\\RSDataset\\*\\*.jpg'
# train, train_label, test, test_label=new_getfiles(train_dir)
# print(train,train_label)
# sess=tf.Session()
# print(sess.run(train_label))
# TEST
# To test the generated batches of images
# When training the model, DO comment the following codes
# BATCH_SIZE = 10
# CAPACITY = 256
# IMG_W = 500
# IMG_H = 500
#
#
# train_dir='D:\\DL\\RS_data\\WH-RSDataset\\RSDataset\\*\\*.jpg'
#
# testing_numlabel, testing_image_list, training_numlabel, training_image_list = new_getfiles(train_dir)
# image_batch, label_batch = get_batch(training_image_list, training_numlabel, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
#
# with tf.Session() as sess:
# i = 0
# coord = tf.train.Coordinator()
#
# threads = tf.train.start_queue_runners(sess=sess, coord=coord)
# test= coord.should_stop()
#
# try:
# while not test and i < 1:
#
# img, label = sess.run([image_batch, label_batch])
#
# # just test one batch
# for j in np.arange(BATCH_SIZE):
# print('label: %d' %label[j])
# plt.figure(j)
# plt.imshow(img[j,:,:,:])
# plt.show()
# i += 1
#
# except tf.errors.OutOfRangeError:
# print('done!')
# finally:
# coord.request_stop()
#
# coord.join(threads)
# sess.close()