【Record】Summary and implementation of data processing methods

【Record】Summary and implementation of data processing methods

background

As a key step in pre-processing, data enhancement plays an important role in the entire computer vision;

Data augmentation is often the key to determining the quality of datasets, and is mainly used for data augmentation. In deep learning-based tasks, the diversity and quantity of data can often determine the upper limit of the model;

This record is mainly the source code implementation of some methods in data enhancement;

Common Data Augmentation Methods

First of all, if you use the Pytorch framework, its internal torchvision has already packaged many methods of data enhancement;

from torchvision import transforms

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

Next, implement some main methods yourself;

Common data enhancement methods are: Compose, RandomHflip, RandomVflip, Reszie, RandomCrop, Normalize, Rotate, RandomRotate

1、Compose

Function: sort and integrate multiple methods, and call them sequentially;

# 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

Function: random horizontal flip;

# 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

Through the random number 0 or 1, the image may be reversed or not reversed;

3、RandomVflip

Function: random vertical flip

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

4、RandomCrop

Function: random cropping;

# 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

Function: Regularize the image data, that is, subtract the mean and divide the variance;

# 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

Function: rotate the image;

# 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

Function: random rotation, widely used in image enhancement;

# 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

Role: to achieve scaling;

# 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)

Other Data Augmentation Methods

Some other data enhancement methods are mostly special clipping;

1. Center cropping

# 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. Random brightness enhancement

# 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. Random contrast enhancement

# 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. Random saturation enhancement

# 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. Boundary Expansion

# 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

Of course, there are many other ways of data enhancement, so I won’t continue to explain them here;

expand

In addition to using the data enhancement package that comes with Pytorch, you can also use the imgaug package (a package based on data processing, including a large number of data processing methods, and the code is completely open source)

Code address: https://github.com/aleju/imgaug

Documentation: https://imgaug.readthedocs.io/en/latest/index.html

It is strongly recommended that you take a look at this documentation. Many data processing methods in it can be quickly applied to actual projects, and can also deepen your understanding of image processing;