数据增强之仿射变换

原创

用户4363240

修改于 2019-11-26 18:18:16

1.7K0

修改于 2019-11-26 18:18:16

文章被收录于专栏：Vison

一般情况下，不同的图像任务和模型有不同的数据增强方法。然而比较通用的有图像的仿射变换，颜色抖动，水平/垂直翻转，随机crop。

其中，仿射变换包括旋转，平移，错切（shear)，尺度变化（scale)。仿射变换特点：直线经过仿射变换仍然是直线。

在pytorch中，一般每个变换写一个类，然后多个变换compose在一起。

Class Compose(object):
	def __init__(self, transforms):
		self.transforms = transforms
	def __call__(self, *items):
		for t in self.transforms:
			item = t(*item)
		return item

# example
Class ColorJitter(torchvision.transforms.ColorJitter):
	def __call__(self, image, data):
		img = Image.fromarray(image)
		# data are annotations, like points, bboxes
		# do corresponding transforms to annotations if need, such as in Flip, Rotation, etc 
		return np.array(super().__call__(img)), data

Class Dataset(object):
	def gen_transforms(self):
		transforms = [
		you_define_transform1(),
		you_define_transform2()]
		self.transforms = Compose(transforms)

	def __getitem__(self, index):
		# ...
		image, data = self.transforms(image, data)
		# ...

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Shear:

theta = 30 # degree

# method1: directly calculate the affine matrix
shear = np.array([
                    [1, np.tan(theta), 0],
                    [0, 1, 0]], dtype=np.float32)

# method2: use 3 points to define a transform
# for example, choose red, blue, green points in above wiki image
point1 = np.float32([[0,0], [img.shape[1],0], [0,img.shape[0]]])
point2 = np.float32([[0,0], [img.shape[1],0], [img.shape[0]*np.tan(theta), img.shape[0]]])
M_shear = cv2.getAffineTransform(point1, point2)

# shear equals to M_shear

sheared_point = M_shear.dot(point)
# choose borderValue according to MEAN in your preprocess
img = cv2.warpAffine(img, M_shear, (int(sheared_point[0]), int(sheared_point[1])),borderMode=cv2.BORDER_CONSTANT, borderValue=(128, 128, 128))

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pytorch