【记录】数据处理方法总结及实现

【记录】数据处理方法总结及实现

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

4、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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