[人工智能]目标检测学习笔记——TTA

transforms.ToTensor()源码

TTA用法示例：

class BaseWheatTTA:
    """ author: @shonenkov """
    image_size = 512

    def augment(self, image):
        raise NotImplementedError
    
    def batch_augment(self, images):
        raise NotImplementedError
    
    def deaugment_boxes(self, boxes):
        raise NotImplementedError

class TTAHorizontalFlip(BaseWheatTTA):
    """ author: @shonenkov """

    def augment(self, image):
        return image.flip(1)
    
    def batch_augment(self, images):
        return images.flip(2)
    
    def deaugment_boxes(self, boxes):
        boxes[:, [1,3]] = self.image_size - boxes[:, [3,1]]
        return boxes

class TTAVerticalFlip(BaseWheatTTA):
    """ author: @shonenkov """
    
    def augment(self, image):
        return image.flip(2)
    
    def batch_augment(self, images):
        return images.flip(3)
    
    def deaugment_boxes(self, boxes):
        boxes[:, [0,2]] = self.image_size - boxes[:, [2,0]]
        return boxes
    
class TTARotate90(BaseWheatTTA):
    """ author: @shonenkov """
    
    def augment(self, image):
        return torch.rot90(image, 1, (1, 2))

    def batch_augment(self, images):
        return torch.rot90(images, 1, (2, 3))
    
    def deaugment_boxes(self, boxes):
        res_boxes = boxes.copy()
        res_boxes[:, [0,2]] = self.image_size - boxes[:, [1,3]]
        res_boxes[:, [1,3]] = boxes[:, [2,0]]
        return res_boxes

class TTACompose(BaseWheatTTA):
    """ author: @shonenkov """
    def __init__(self, transforms):
        self.transforms = transforms
        
    def augment(self, image):
        for transform in self.transforms:
            image = transform.augment(image)
        return image
    
    def batch_augment(self, images):
        for transform in self.transforms:
            images = transform.batch_augment(images)
        return images
    
    def prepare_boxes(self, boxes):
        result_boxes = boxes.copy()
        result_boxes[:,0] = np.min(boxes[:, [0,2]], axis=1)
        result_boxes[:,2] = np.max(boxes[:, [0,2]], axis=1)
        result_boxes[:,1] = np.min(boxes[:, [1,3]], axis=1)
        result_boxes[:,3] = np.max(boxes[:, [1,3]], axis=1)
        return result_boxes
    
    def deaugment_boxes(self, boxes):
        for transform in self.transforms[::-1]:
            boxes = transform.deaugment_boxes(boxes)
        return self.prepare_boxes(boxes)

transform = TTACompose([
    TTARotate90(),
    TTAVerticalFlip(),
])


numpy_image = cv2.imread("D:\smart_hedian\lab.png")# 读取图片返回（h,w,c)的ndarray
cv2.imshow("origin",numpy_image)
trans = torchvision.transforms.ToTensor()# 将ndarray转化成tensor
image = trans(numpy_image)
tta_image = transform.augment(image)
tta_image_numpy = tta_image.permute(1,2,0).cpu().numpy().copy()# 将（n,h,w)tensor转化成（h,w,n)再转成ndarray才能用cv2.imshow()显示
cv2.imshow("tta",tta_image_numpy)
cv2.waitKey(0)