Tensorflow分类器项目自定义数据读入的实现

import os
import keras
import matplotlib.pyplot as plt
from PIL import Image
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split

image_path = r'D:\Projects\ImageClassifier\data\set'
path = ".\data"
# 你也可以使用相对路径的方式
# image_path =os.path.join(path, "set")

label_name = "labels.txt"
label_path = os.path.join(path, label_name)
class_names = np.loadtxt(label_path, type(""))

re_load = False
re_build = False
# re_load = True
re_build = True

data_name = "data.npz"
data_path = os.path.join(path, data_name)
model_name = "model.h5"
model_path = os.path.join(path, model_name)

count = 0

# 这里判断是否存在序列化之后的数据，re_load是一个开关，是否强制重新处理，测试用，可以去除。
if not os.path.exists(data_path) or re_load:
  labels = []
  images = []
  print('Handle images')
  # 由于label.txt是和图片防止目录的分类目录一一对应的，即每个子目录的目录名就是labels.txt里的一个label，所以这里可以通过读取class_names的每一项去拼接path后读取
  for index, name in enumerate(class_names):
    # 这里是拼接后的子目录path
    classpath = os.path.join(image_path, name)
    # 先判断一下是否是目录
    if not os.path.isdir(classpath):
      continue
    # limit是测试时候用的这里可以去除
    limit = 0
    for image_name in os.listdir(classpath):
      if limit >= max_size:
        break
      # 这里是拼接后的待处理的图片path
      imagepath = os.path.join(classpath, image_name)
      count = count + 1
      limit = limit + 1
      # 利用Image打开图片
      img = Image.open(imagepath)
      # 缩放到你最初确定要处理的图片分辨率大小
      img = img.resize((IMG_SIZE_X, IMG_SIZE_Y))
      # 转为灰度图片，这里彩色通道会干扰结果，并且会加大计算量
      img = img.convert("L")
      # 转为numpy数组
      img = np.array(img)
      # 由（30，30）转为（1，30，30）（即`channels_first`），当然你也可以转换为（30，30，1）（即`channels_last`）但为了之后预览处理后的图片方便这里采用了（1，30，30）的格式存放
      img = np.reshape(img, (1, IMG_SIZE_X, IMG_SIZE_Y))
      # 这里利用循环生成labels数据，其中存放的实际是class_names中对应元素的索引
      labels.append([index])
      # 添加到images中，最后统一处理
      images.append(img)
      # 循环中一些状态的输出，可以去除
      print("{} class: {} {} limit: {} {}"
         .format(count, index + 1, class_names[index], limit, imagepath))
  # 最后一次性将images和labels都转换成numpy数组
  npy_data = np.array(images)
  npy_labels = np.array(labels)
  # 处理数据只需要一次，所以我们选择在这里利用numpy自带的方法将处理之后的数据序列化存储
  np.savez(data_path, x=npy_data, y=npy_labels)
  print("Save images by npz")
else:
  # 如果存在序列化号的数据，便直接读取，提高速度
  npy_data = np.load(data_path)["x"]
  npy_labels = np.load(data_path)["y"]
  print("Load images by npz")
image_data = npy_data
labels_data = npy_labels

# 最后一步就是将原始数据分成训练数据和测试数据
train_images, test_images, train_labels, test_labels = \
  train_test_split(image_data, labels_data, test_size=0.2, random_state=6)

print("_________________________________________________________________")
print("%-28s %-s" % ("Name", "Shape"))
print("=================================================================")
print("%-28s %-s" % ("Image Data", image_data.shape))
print("%-28s %-s" % ("Labels Data", labels_data.shape))
print("=================================================================")

print('Split train and test data,p=%')
print("_________________________________________________________________")
print("%-28s %-s" % ("Name", "Shape"))
print("=================================================================")
print("%-28s %-s" % ("Train Images", train_images.shape))
print("%-28s %-s" % ("Test Images", test_images.shape))
print("%-28s %-s" % ("Train Labels", train_labels.shape))
print("%-28s %-s" % ("Test Labels", test_labels.shape))
print("=================================================================")

print("Normalize images")
train_images = train_images / 255.0
test_images = test_images / 255.0

import os

import keras
import matplotlib.pyplot as plt
from PIL import Image
from keras.layers import *
from keras.models import *
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from sklearn.model_selection import train_test_split

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# 支持中文
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
re_load = False
re_build = False
# re_load = True
re_build = True
epochs = 50
batch_size = 5
count = 0
max_size = 2000000000