1. 精灵宝可梦数据介绍
精灵数据放置在pokeman的根目录下，包含了5个子文件夹，每个文件夹存放了当前类别的精灵图片。
2. 代码
import tensorflow as tf
import glob
import os
import random, csv
def load_csv(root, filename, name2label):
# 从csv文件返回images,labels列表
# root:数据集根目录，filename:csv文件名， name2label:类别名编码表
if not os.path.exists(os.path.join(root, filename)):
# 如果csv文件不存在，则创建
images = []
for name in name2label.keys(): # 遍历所有子目录，获得所有的图片
# 只考虑后缀为png,jpg,jpeg的图片：'pokemon\\mewtwo\\00001.png
images += glob.glob(os.path.join(root, name, '*.png'))
images += glob.glob(os.path.join(root, name, '*.jpg'))
images += glob.glob(os.path.join(root, name, '*.jpeg'))
# 打印数据集信息：1167, 'pokemon\\bulbasaur\\00000000.png'
print(len(images), images)
random.shuffle(images) # 随机打散顺序
# 创建csv文件，并存储图片路径及其label信息
with open(os.path.join(root, filename), mode='w', newline='') as f:
writer = csv.writer(f)
for img in images: # 'pokemon\\bulbasaur\\00000000.png'
name = img.split(os.sep)[-2]
label = name2label[name]
# 'pokemon\\bulbasaur\\00000000.png', 0
writer.writerow([img, label])
print('written into csv file:', filename)
# 此时已经有csv文件，直接读取
images, labels = [], []
with open(os.path.join(root, filename)) as f:
reader = csv.reader(f)
for row in reader:
# 'pokemon\\bulbasaur\\00000000.png', 0
img, label = row
label = int(label)
images.append(img)
labels.append(label)
# 返回图片路径list和标签list
return images, labels
def load_pokemon(root, mode='train'):
# 创建数字编码表
name2label = {} # "sq...":0
# 遍历根目录下的子文件夹，并排序，保证映射关系固定
for name in sorted(os.listdir(os.path.join(root))):
# 跳过非文件夹
if not os.path.isdir(os.path.join(root, name)):
continue
# 给每个类别编码一个数字
name2label[name] = len(name2label.keys())
# 读取Label信息
# [file1,file2,], [3,1]
images, labels = load_csv(root, 'images.csv', name2label)
if mode == 'train': # 60%
images = images[:int(0.6 * len(images))]
labels = labels[:int(0.6 * len(labels))]
elif mode == 'val': # 20% = 60%->80%
images = images[int(0.6 * len(images)):int(0.8 * len(images))]
labels = labels[int(0.6 * len(labels)):int(0.8 * len(labels))]
else: # 20% = 80%->100%
images = images[int(0.8 * len(images)):]
labels = labels[int(0.8 * len(labels)):]
return images, labels, name2label
# 这里的mean和std根据真实的数据计算获得，比如ImageNet
img_mean = tf.constant([0.485, 0.456, 0.406])
img_std = tf.constant([0.229, 0.224, 0.225])
def normalize(x, mean=img_mean, std=img_std):
# 标准化
# x: [224, 224, 3]
# mean: [224, 224, 3], std: [3]
x = (x - mean)/std
return x
def denormalize(x, mean=img_mean, std=img_std):
# 标准化的逆过程
x = x * std + mean
return x
def preprocess(x,y):
# x: 图片的路径List，y：图片的数字编码List
x = tf.io.read_file(x) # 根据路径读取图片
x = tf.image.decode_jpeg(x, channels=3) # 图片解码
x = tf.image.resize(x, [244, 244]) # 图片缩放
# 数据增强
# x = tf.image.random_flip_up_down(x)
x= tf.image.random_flip_left_right(x) # 左右镜像
x = tf.image.random_crop(x, [224, 224, 3]) # 随机裁剪
# 转换成张量
# x: [0,255]=> 0~1
x = tf.cast(x, dtype=tf.float32) / 255.
# 0~1 => D(0,1)
x = normalize(x) # 标准化
y = tf.convert_to_tensor(y) # 转换成张量
return x, y
def main():
import time
# 加载pokemon数据集，指定加载训练集
images, labels, table = load_pokemon('pokeman', 'train')
print('images:', len(images))
print('labels:', len(labels))
print('table:', table)
# images: string path
# labels: number
db = tf.data.Dataset.from_tensor_slices((images, labels))
db = db.shuffle(1000).map(preprocess).batch(32)
# 创建TensorBoard对象
writter = tf.summary.create_file_writer('logs/ch15')
for step, (x,y) in enumerate(db):
# x: [32, 224, 224, 3]
# y: [32]
with writter.as_default():
x = denormalize(x) # 反向normalize，方便可视化
# 写入图片数据
tf.summary.image('img',x,step=step,max_outputs=9)
time.sleep(5)
if __name__ == '__main__':
main()