本文主要是介绍同时增强多个目标：masks, bounding boxes, keypoints，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
参考链接：
Simultaneous augmentation of multiple targets: masks, bounding boxes, keypoints - Albumentations Documentation
Albumentations 可以将相同的一组变换应用于 the input image 和 all the targets 传递到transform：masks, bounding boxes, keypoints。
Step 1. 使用指定边界框和关键点格式的参数定义 Compose 。
transform = A.Compose([A.RandomCrop(width=330, height=330), A.RandomBrightnessContrast(p=0.2)],bbox_params=A.BboxParams(format="coco", label_fields=["bbox_classes"]),keypoint_params=A.KeypointParams(format="xy", label_fields=["keypoints_classes"]),
)Step 2. 从磁盘加载所有必需的数据。
For example, here is an image from the COCO dataset. that has one associated mask, one bounding box with the class label person, and five keypoints that define body parts.
An example image with mask, bounding boxes and keypoints
Step 3. 传递所有目标进行转换并接收其增强版本¶
transformed = transform(image=img,mask=mask,bboxes=bboxes,bbox_classes=bbox_classes,keypoints=keypoints,keypoints_classes=keypoints_classes,
)
transformed_image = transformed["image"]
transformed_mask = transformed["mask"]
transformed_bboxes = transformed["bboxes"]
transformed_bbox_classes = transformed["bbox_classes"]
transformed_keypoints = transformed["keypoints"]
transformed_keypoints_classes = transformed["keypoints_classes"]
The augmented version of the image and its targets
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