前言：
这里主要针对图像数据进行预处理.定义了一个 class Pokemon(Dataset) 类，实现
图像数据集加载,划分的基本方法.
目录：
- 整体框架
- init
- load_images
- save_csv
- divide_data
- len
- denormalize
- getitem
- main
- ImageFolder
一 整体框架
我们需要创建一个自定义的数据集类，该类必须继承自Dataset类，
重点实现以下三个方法：
init
__len__()
__getitem__()
二 init
实现了图像数据集的加载
根据mode 进行划分
def __init__(self, root, resize, mode,fileName):
#初始化函数
super(Pokemon, self).__init__()
self.root = root
self.resize = resize
self.name2label ={}
#遍历目录
path = os.path.join(root)
#用子目录文件夹名字作为分类key
for name in sorted(os.listdir(path)):
subDir = os.path.join(root, name)
if not os.path.isdir(subDir):
continue
else:
self.name2label[name] = len(self.name2label.keys())
csv_path = os.path.join(self.root, fileName)
print("\n csv_path: ",csv_path)
if not os.path.exists(csv_path):
images = self.load_images()
self.save_csv(fileName, images)
self.images, self.labels = self.load_csv(fileName)
self.divide_data(mode)三 load_images
加载指定目录下面的图片，
把图片路径保存到列表里面
def load_images(self):
images =[]
for name in self.name2label.keys():
#pokeon\\newtwoo\\00001.png
#返回所有匹配的文件路径列表。它只有一个参数pathname，定义了文件路径匹配规则，这里可以是绝对路径，也可以是相对路径。下面是使用glob.glob的例子：
pngPath = os.path.join(self.root, name,'*.png')
jpgPath = os.path.join(self.root, name,'*.jpg')
jpegPath = os.path.join(self.root, name,'*.jpeg')
png = glob.glob(pngPath)
jpg =glob.glob(jpgPath)
jpeg = glob.glob(jpegPath)
images +=jpg
images +=jpeg
images +=png
print("\n images ",len(images))
random.shuffle(images)
return images四 save_csv
图片路径,标签保存到csv 文件里面
#image, label
def save_csv(self, fileName, images):
path = os.path.join(self.root, fileName)
csvfile = open(path,mode='w',newline='')
writer = csv.writer(csvfile)
for img in images:
name = img.split(os.sep)[-2]
label = self.name2label[name]
writer.writerow([img, label])
csvfile.close()|---|
| |
四 load_csv
加载 csv 文件
def load_csv(self, fileName):
path = os.path.join(self.root, fileName)
csvfile = open(path,mode='r',newline='')
reader = csv.reader(csvfile)
images =[]
labels =[]
for row in reader:
img, label = row
label = int(label)
images.append(img)
labels.append(label)
m = len(images)
n = len(labels)
print("\n number images: %d number labels: %d"%(m,n))
return images,labels五 divide_data
数据集划分
训练集： 60%
验证集： 20%
测试机：20%
def divide_data(self,mode):
N = len(self.images)
if 'train' == mode: #0->60%
start = 0
end = int(0.6*N)
elif 'val' == mode:#60%->80%
start = int(0.6*N)
end = int(0.8*N)
else:#80%->100%
start = int(0.8*N)
end = N
self.images = self.images[start:end]
self.labels = self.labels[start:end]
m = len(self.images )
print("\n number divide images: %d "%(m))六 len
返回数据集大小
def __len__(self):
#总的数据
N = len(self.images)
return N七 denormalize
图像数据 标准后，当需要显示原图片的时候，需要反标准化
def denormalize(self,x_hat):
#x_hat =(x-mean)/std
#x = x_hat*std+mean
#x: [c,h,w]
#mean: [3]=>[3,1,1]
mean=[0.485, 0.456, 0.406]
std=[0.229, 0.224, 0.225]
mean = torch.tensor(mean).unsqueeze(1).unsqueeze(1)
std = torch.tensor(std).unsqueeze(1).unsqueeze(1)
x =x_hat*std+mean
return x八 getitem
根据指定的索引获取对应的图片，以及标签值
def __getitem__(self, index):
#返回当前index 对应的图片数据
#self.images, self.labels
#idx ~[0,N]
img_path = self.images[index] #图片路径
label = self.labels[index] #图片标签
#print("\n img_path",img_path)
tf = transforms.Compose([
lambda x:Image.open(x).convert('RGB'),
transforms.Resize((int(self.resize*1.25) , int(self.resize*1.25))),
transforms.RandomRotation(15),
transforms.ToTensor(),
transforms.CenterCrop(self.resize),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
img = tf(img_path)
label = torch.tensor(label)
#print("\n index ",index, "\t img ",img.shape,"\t label ",label)
return img, label九 main
1 先定义一个class Pokemon(Dataset): 类，并实现上面的方法
2 数据集的迭代加载，以及通过visdom 工具加载显示
def main():
root ='pokemon'
resize =224
mode = 'test' #数据集分为三种 tain,val,test
csvfile ='data.csv'
db = Pokemon(root, resize, mode,csvfile)
viz = visdom.Visdom()
# datetime转字符串
time.time() #显示当前的时间戳
curtime = time.strftime('%H:%M:%S') #结构化输出当前的时间
BATCH_SIZE = 32
loader = DataLoader(dataset = db, batch_size = BATCH_SIZE,shuffle = True)
for step, (batchX, batchY) in enumerate(loader):
print( '| Step: ', step, '| batch x: ',batchX.shape, '| batch y: ', batchY.shape)
viz.images(db.denormalize(batchX),nrow=8, win='batchX',opts=dict(title=curtime))
viz.text(str(batchY.numpy()),win='batchY',opts=dict(title='label'))
time.sleep(10)
if __name__ == "__main__" :
main()十 ImageFolder
自己的图像数据集如果有规律的话，可以直接用PyTorch API 函数实现 Pokemon
类的功能
from torchvision.datasets import ImageFolder
from torchvision import transforms
imgMean =[0.485, 0.456, 0.406]
imgStd = [0.229, 0.224, 0.225]
normalize=transforms.Normalize(mean=imgMean,std=imgStd)
transform=transforms.Compose([
transforms.RandomCrop(180),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), #将图片转换为Tensor,归一化至[0,1]
normalize
])
dataset=ImageFolder('./data/train',transform=transform)参考：