|
|
|
|
@ -0,0 +1,147 @@
|
|
|
|
|
# 输入图片处理
|
|
|
|
|
|
|
|
|
|
import os
|
|
|
|
|
import math
|
|
|
|
|
import numpy as np
|
|
|
|
|
import tensorflow as tf
|
|
|
|
|
import matplotlib.pyplot as plt
|
|
|
|
|
|
|
|
|
|
# -----------------生成图片路径和标签的List------------------------------------
|
|
|
|
|
# 生成图片的存储位置 /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
|
|
|
|
|
train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
|
|
|
|
|
|
|
|
|
|
one = []
|
|
|
|
|
label_one = []
|
|
|
|
|
two = []
|
|
|
|
|
label_two = []
|
|
|
|
|
three = []
|
|
|
|
|
label_three = []
|
|
|
|
|
four = []
|
|
|
|
|
label_four = []
|
|
|
|
|
five = []
|
|
|
|
|
label_five = []
|
|
|
|
|
six = []
|
|
|
|
|
label_six = []
|
|
|
|
|
seven = []
|
|
|
|
|
label_seven = []
|
|
|
|
|
eight = []
|
|
|
|
|
label_eight = []
|
|
|
|
|
nine = []
|
|
|
|
|
label_nine = []
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# step1：获取'/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train'下所有的图片路径名，存放到
|
|
|
|
|
# 对应的列表中，同时贴上标签，存放到label列表中。
|
|
|
|
|
# ratio是测试集的比例
|
|
|
|
|
def get_files(file_dir, ratio):
|
|
|
|
|
for file in os.listdir(file_dir + '/one'):
|
|
|
|
|
one.append(file_dir + '/one' + '/' + file)
|
|
|
|
|
label_one.append(0)
|
|
|
|
|
for file in os.listdir(file_dir + '/two'):
|
|
|
|
|
two.append(file_dir + '/two' + '/' + file)
|
|
|
|
|
label_two.append(1)
|
|
|
|
|
for file in os.listdir(file_dir + '/three'):
|
|
|
|
|
three.append(file_dir + '/three' + '/' + file)
|
|
|
|
|
label_three.append(2)
|
|
|
|
|
for file in os.listdir(file_dir + '/four'):
|
|
|
|
|
four.append(file_dir + '/four' + '/' + file)
|
|
|
|
|
label_four.append(3)
|
|
|
|
|
for file in os.listdir(file_dir + '/five'):
|
|
|
|
|
five.append(file_dir + '/five' + '/' + file)
|
|
|
|
|
label_five.append(5)
|
|
|
|
|
for file in os.listdir(file_dir + '/six'):
|
|
|
|
|
six.append(file_dir + '/six' + '/' + file)
|
|
|
|
|
label_six.append(6)
|
|
|
|
|
for file in os.listdir(file_dir + '/seven'):
|
|
|
|
|
seven.append(file_dir + '/seven' + '/' + file)
|
|
|
|
|
label_seven.append(7)
|
|
|
|
|
for file in os.listdir(file_dir + '/eight'):
|
|
|
|
|
eight.append(file_dir + '/eight' + '/' + file)
|
|
|
|
|
label_eight.append(8)
|
|
|
|
|
for file in os.listdir(file_dir + '/nine'):
|
|
|
|
|
nine.append(file_dir + '/nine' + '/' + file)
|
|
|
|
|
label_nine.append(9)
|
|
|
|
|
|
|
|
|
|
# step2：对生成的图片路径和标签List做打乱处理把所有的数据合起来组成一个list（img和lab）
|
|
|
|
|
# np.hstack水平（按列）按顺序堆叠数组。
|
|
|
|
|
# >>> a = np.array((1,2,3))
|
|
|
|
|
# >>> b = np.array((2,3,4))
|
|
|
|
|
# >>> np.hstack((a,b))
|
|
|
|
|
# array([1, 2, 3, 2, 3, 4])
|
|
|
|
|
image_list = np.hstack((one, two, three, four, five, six, seven, eight, nine))
|
|
|
|
|
label_list = np.hstack((label_one, label_two, label_three, label_four, label_five, label_six, label_seven, label_eight, label_nine))
|
|
|
|
|
|
|
|
|
|
# 利用shuffle打乱顺序
|
|
|
|
|
temp = np.array([image_list, label_list])
|
|
|
|
|
temp = temp.transpose()
|
|
|
|
|
np.random.shuffle(temp)
|
|
|
|
|
|
|
|
|
|
# 从打乱的temp中再取出list（img和lab）
|
|
|
|
|
# image_list = list(temp[:, 0])
|
|
|
|
|
# label_list = list(temp[:, 1])
|
|
|
|
|
# label_list = [int(i) for i in label_list]
|
|
|
|
|
# return image_list, label_list
|
|
|
|
|
|
|
|
|
|
# 将所有的img和lab转换成list
|
|
|
|
|
all_image_list = list(temp[:, 0])
|
|
|
|
|
all_label_list = list(temp[:, 1])
|
|
|
|
|
|
|
|
|
|
# 将所得List分为两部分，一部分用来训练tra，一部分用来测试val
|
|
|
|
|
# ratio是测试集的比例
|
|
|
|
|
# n_sample全部样本数
|
|
|
|
|
n_sample = len(all_label_list)
|
|
|
|
|
n_val = int(math.ceil(n_sample * ratio)) # 测试样本数
|
|
|
|
|
n_train = n_sample - n_val # 训练样本数
|
|
|
|
|
|
|
|
|
|
# 训练的图片和标签
|
|
|
|
|
tra_images = all_image_list[0:n_train]
|
|
|
|
|
tra_labels = all_label_list[0:n_train]
|
|
|
|
|
tra_labels = [int(float(i)) for i in tra_labels]
|
|
|
|
|
|
|
|
|
|
# 测试图片和标签
|
|
|
|
|
val_images = all_image_list[n_train:-1]
|
|
|
|
|
val_labels = all_label_list[n_train:-1]
|
|
|
|
|
val_labels = [int(float(i)) for i in val_labels]
|
|
|
|
|
|
|
|
|
|
return tra_images, tra_labels, val_images, val_labels
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
# --------------------生成Batch----------------------------------------------
|
|
|
|
|
|
|
|
|
|
# step1：将上面生成的List传入get_batch() ，转换类型，产生一个输入队列queue，因为img和lab
|
|
|
|
|
# 是分开的，所以使用tf.train.slice_input_producer()，然后用tf.read_file()从队列中读取图像
|
|
|
|
|
# image_W, image_H, ：设置好固定的图像高度和宽度
|
|
|
|
|
# 设置batch_size：每个batch要放多少张图片
|
|
|
|
|
# capacity：一个队列最大多少
|
|
|
|
|
def get_batch(image, label, image_W, image_H, batch_size, capacity):
|
|
|
|
|
# 转换类型
|
|
|
|
|
image = tf.cast(image, tf.string)
|
|
|
|
|
label = tf.cast(label, tf.int32)
|
|
|
|
|
|
|
|
|
|
# make an input queue
|
|
|
|
|
# tf.train.slice_input_producer是一个tensor生成器，作用是按照设定，
|
|
|
|
|
# 每次从一个tensor列表中按顺序或者随机抽取出一个tensor放入文件名队列。
|
|
|
|
|
input_queue = tf.compat.v1.train.slice_input_producer([image, label])
|
|
|
|
|
|
|
|
|
|
label = input_queue[1]
|
|
|
|
|
image_contents = tf.io.read_file(input_queue[0]) # read img from a queue
|
|
|
|
|
|
|
|
|
|
# step2：将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等。
|
|
|
|
|
image = tf.image.decode_jpeg(image_contents, channels=3)
|
|
|
|
|
|
|
|
|
|
# step3：数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮。
|
|
|
|
|
image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
|
|
|
|
|
image = tf.image.per_image_standardization(image)
|
|
|
|
|
|
|
|
|
|
# step4：生成batch
|
|
|
|
|
# image_batch: 4D tensor [batch_size, width, height, 3],dtype=tf.float32
|
|
|
|
|
# label_batch: 1D tensor [batch_size], dtype=tf.int32
|
|
|
|
|
image_batch, label_batch = tf.compat.v1.train.batch([image, label],
|
|
|
|
|
batch_size=batch_size,
|
|
|
|
|
num_threads=32,
|
|
|
|
|
capacity=capacity)
|
|
|
|
|
# 重新排列label，行数为[batch_size]
|
|
|
|
|
label_batch = tf.reshape(label_batch, [batch_size])
|
|
|
|
|
image_batch = tf.cast(image_batch, tf.float32)
|
|
|
|
|
return image_batch, label_batch
|
|
|
|
|
|
