背景
上一篇博客介绍了 pytorch 的 transforms 模块，有需要的移步 研究一下 pytorch 的 transforms 模块。现在，我想像 T.Normalise() 一样，对每个图片做一下对比度增强，而其他的转换方法保持不变，依旧采用随机处理。
实现方式是 采用 __call__ 机制，具体如下：
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from PIL import ImageEnhance
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class Contrast(object):
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def __init__(self,degree):
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self.degree = degree
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def __call__(self,img):
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return contrast(img,self.degree)
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def contrast(img,degree):
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enh_contrast = ImageEnhance.Contrast(img)
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enh_contrast.enhance(degree)
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return img
上面定义了新的数据转换方法：Contrast，使用方法如下，以开源的代码为例 class ChaojieDataset(Dataset):
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#2.define dataset
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class ChaojieDataset(Dataset):
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def __init__(self,label_list,transforms=None,train=True,test=False):
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self.test = test
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self.train = train
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imgs = []
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if self.test:
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for index,row in label_list.iterrows():
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imgs.append((row[ "filename"]))
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self.imgs = imgs
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else:
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for index,row in label_list.iterrows():
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imgs.append((row[ "filename"],row["label"]))
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self.imgs = imgs
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if transforms is None:
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if self.test or not train:
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self.transforms = T.Compose([
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T.Resize((config.img_weight,config.img_height)),
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T.ToTensor(),
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T.Normalize(mean = [ 0.485,0.456,0.406],
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std = [ 0.229,0.224,0.225])])
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else:
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self.transforms = T.Compose([
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T.Resize((config.img_weight,config.img_height)),
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T.RandomRotation( 30),
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T.RandomHorizontalFlip(),
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T.RandomVerticalFlip(),
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T.RandomAffine( 45),
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Contrast( 1.8), ## 在此添加 ##
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T.ToTensor(),
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T.Normalize(mean = [ 0.485,0.456,0.406],
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std = [ 0.229,0.224,0.225])])
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else:
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self.transforms = transforms
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def __getitem__(self,index):
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if self.test:
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filename = self.imgs[index]
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img = Image.open(filename)
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img = self.transforms(img)
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return img,filename
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else:
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filename,label = self.imgs[index]
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img = Image.open(filename)
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img = self.transforms(img)
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return img,label
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def __len__(self):
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return len(self.imgs)
这样，每一张图片除了进行归一化和转变成张量外，都做了对比度增强，当然也可以设置一个随机数，每次随机选择要增强的对比度。