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详解Python中数据处理的方法总结及实现

发布时间:2024-01-23 23:04:20来源:周小白软件园编辑:本站整理

背景

数据增强作为前处理的关键步骤,在整个计算机视觉中有着具足轻重的地位;

数据增强往往是决定数据集质量的关键,主要用于数据增广,在基于深度学习的任务中,数据的多样性和数量往往能够决定模型的上限;

本次记录主要是对数据增强中一些方法的源码实现;

常用数据增强方法

首先如果是使用Pytorch框架,其内部的torchvision已经包装好了数据增强的很多方法;

from torchvision import transforms  data_aug = transforms.Compose[ transforms.Resize(size=240), transforms.RandomHorizontalFlip(0.5), transforms.ToTensor() ] 

接下来自己实现一些主要的方法;

常见的数据增强方法有:Compose、RandomHflip、RandomVflip、Reszie、RandomCrop、Normalize、Rotate、RandomRotate

1、Compose

作用:对多个方法的排序整合,并且依次调用;

# 排序(compose) class Compose(object): def __init__(self, transforms): self.transforms = transforms def __call__(self, img): for t in self.transforms: img = t(img)# 通过循环不断调用列表中的方法 return img 

2、RandomHflip

作用:随机水平翻转;

# 随机水平翻转(random h flip) class RandomHflip(object): def __call__(self, image): if random.randint(2): return cv2.flip(image, 1) else: return image 

通过随机数0或1,实现对图像可能反转或不翻转;

3、RandomVflip

作用:随机垂直翻转

class RandomVflip(object): def __call__(self, image): if random.randint(2): return cv2.flip(image, 0) else: return image 

4、RandomCrop

作用:随机裁剪;

# 缩放(scale) def scale_down(src_size, size): w, h = size sw, sh = src_size if sh < h: w, h = float(w * sh) / h, sh if sw < w: w, h = sw, float(h * sw) / w return int(w), int(h)  # 固定裁剪(fixed crop) def fixed_crop(src, x0, y0, w, h, size=None): out = src[y0:y0 + h, x0:x0 + w] if size is not None and (w, h) != size: out = cv2.resize(out, (size[0], size[1]), interpolation=cv2.INTER_CUBIC) return out  # 随机裁剪(random crop) class RandomCrop(object): def __init__(self, size): self.size = size def __call__(self, image): h, w, _ = image.shape new_w, new_h = scale_down((w, h), self.size) if w == new_w: x0 = 0 else: x0 = random.randint(0, w - new_w) if h == new_h: y0 = 0 else: y0 = random.randint(0, h - new_h)  ​​​​​​​out = fixed_crop(image, x0, y0, new_w, new_h, self.size) return out

5、Normalize

作用:对图像数据进行正则化,也就是减均值除方差的作用;

# 正则化(normalize) class Normalize(object): def __init__(self,mean, std): ''' :param mean: RGB order :param std:  RGB order ''' self.mean = np.array(mean).reshape(3,1,1) self.std = np.array(std).reshape(3,1,1) def __call__(self, image): ''' :param image:  (H,W,3)  RGB :return: ''' return (image.transpose((2, 0, 1)) / 255. - self.mean) / self.std 

6、Rotate

作用:对图像进行旋转;

# 旋转(rotate) def rotate_nobound(image, angle, center=None, scale=1.): (h, w) = image.shape[:2] # if the center is None, initialize it as the center of the image if center is None: center = (w // 2, h // 2)# perform the rotation M = cv2.getRotationMatrix2D(center, angle, scale)# 这里是实现得到旋转矩阵 rotated = cv2.warpAffine(image, M, (w, h))# 通过矩阵进行仿射变换 return rotated 

7、RandomRotate

作用:随机旋转,广泛适用于图像增强;

# 随机旋转(random rotate) class FixRandomRotate(object): 	# 这里的随机旋转是指在0、90、180、270四个角度下的 def __init__(self, angles=[0,90,180,270], bound=False): self.angles = angles self.bound = bound  def __call__(self,img): do_rotate = random.randint(0, 4) angle=self.angles[do_rotate] if self.bound: img = rotate_bound(img, angle) else: img = rotate_nobound(img, angle) return img 

8、Resize

作用:实现缩放;

# 大小重置(resize) class Resize(object): def __init__(self, size, inter=cv2.INTER_CUBIC): self.size = size self.inter = inter  def __call__(self, image): return cv2.resize(image, (self.size[0], self.size[0]), interpolation=self.inter) 

其他数据增强方法

其他一些数据增强的方法大部分是特殊的裁剪;

1、中心裁剪

# 中心裁剪(center crop) def center_crop(src, size): h, w = src.shape[0:2] new_w, new_h = scale_down((w, h), size)  x0 = int((w - new_w) / 2) y0 = int((h - new_h) / 2)  out = fixed_crop(src, x0, y0, new_w, new_h, size) return out 

2、随机亮度增强

# 随机亮度增强(random brightness) class RandomBrightness(object): def __init__(self, delta=10): assert delta >= 0 assert delta <= 255 self.delta = delta  def __call__(self, image): if random.randint(2): delta = random.uniform(-self.delta, self.delta) image = (image + delta).clip(0.0, 255.0) # print('RandomBrightness,delta ',delta) return image 

3、随机对比度增强

# 随机对比度增强(random contrast) class RandomContrast(object): def __init__(self, lower=0.9, upper=1.05): self.lower = lower self.upper = upper assert self.upper >= self.lower, "contrast upper must be >= lower." assert self.lower >= 0, "contrast lower must be non-negative."  # expects float image def __call__(self, image): if random.randint(2): alpha = random.uniform(self.lower, self.upper) # print('contrast:', alpha) image = (image * alpha).clip(0.0,255.0) return image 

4、随机饱和度增强

# 随机饱和度增强(random saturation) class RandomSaturation(object): def __init__(self, lower=0.8, upper=1.2): self.lower = lower self.upper = upper assert self.upper >= self.lower, "contrast upper must be >= lower." assert self.lower >= 0, "contrast lower must be non-negative."  def __call__(self, image): if random.randint(2): alpha = random.uniform(self.lower, self.upper) image[:, :, 1] *= alpha # print('RandomSaturation,alpha',alpha) return image 

5、边界扩充

# 边界扩充(expand border) class ExpandBorder(object): def __init__(self,  mode='constant', value=255, size=(336,336), resize=False): self.mode = mode self.value = value self.resize = resize self.size = size  def __call__(self, image): h, w, _ = image.shape if h > w: pad1 = (h-w)//2 pad2 = h - w - pad1 if self.mode == 'constant': image = np.pad(image, ((0, 0), (pad1, pad2), (0, 0)),    self.mode, constant_values=self.value) else: image = np.pad(image,((0,0), (pad1, pad2),(0,0)), self.mode) elif h < w: pad1 = (w-h)//2 pad2 = w-h - pad1 if self.mode == 'constant': image = np.pad(image, ((pad1, pad2),(0, 0), (0, 0)),    self.mode,constant_values=self.value) else: image = np.pad(image, ((pad1, pad2), (0, 0), (0, 0)),self.mode) if self.resize: image = cv2.resize(image, (self.size[0], self.size[0]),interpolation=cv2.INTER_LINEAR) return image 

当然还有很多其他数据增强的方式,在这里就不继续做说明了;

拓展

除了可以使用Pytorch中自带的数据增强包之外,也可以使用imgaug这个包(一个基于数据处理的包、包含大量的数据处理方法,并且代码完全开源)

代码地址:https://github.com/aleju/imgaug

说明文档:https://imgaug.readthedocs.io/en/latest/index.html

强烈建议大家看看这个说明文档,其中的很多数据处理方法可以快速的应用到实际项目中,也可以加深对图像处理的理解;

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