A.Perspective(p=0.8, scale=(0.05,0.3)), ], p=0.9, additional_targets={'image0':'image'})#训练时进行数据增广ifself.train: transformed = self.transform(image=data['ir'], image0=datax['speckle'], mask=data['gt']) data['ir'] = transformed['image'] data['speckle'] = transformed[...
additional_targets参数允许在同一个Compose中处理多张图像或图像及其对应的掩码: transform = A.Compose([ A.HorizontalFlip(p=0.5), A.Rotate(limit=40, p=0.7), ], additional_targets={'mask':'mask'})# 示例掩码mask = cv2.imread('path_to_mask.png', cv2.IMREAD_GRAYSCALE)# 应用增强augmented = ...
HorizontalFlip 类的初始化方法(HorizontalFlip(p=1))在BasicTransform中: def__init__(self,always_apply=False,p=0.5):self.p=pself.always_apply=always_applyself._additional_targets={}# replay mode paramsself.deterministic=Falseself.save_key="replay"self.params={}self.replay_mode=Falseself.applied...
),additional_targets={"keypoints2":"keypoints"}, ) Now you can also add them usingadd_targets: transform=A.Compose(transforms=[A.Rotate(limit=(90.0,90.0),p=1.0)],keypoint_params=A.KeypointParams(angle_in_degrees=True,check_each_transform=True,format="xyas",label_fields=None,remove_invi...
Summary by Sourcery Enhance transformation classes with improved docstrings, refactorings for better code organization, and expanded support for additional targets. Update tests to align with these...
Pixel-level transforms will change just an input image and will leave any additional targets such as masks, bounding boxes, and keypoints unchanged. The list of pixel-level transforms: AdvancedBlur Blur CLAHE ChannelDropout ChannelShuffle ColorJitter ...
在 Torchvision 中有很多经典数据集可以下载使用，在官方文档中可以看到具体有哪些数据集可以使用：...
Custom taskssuch as autoencoders, more then three channel images - refer toComposeclassdocumentationto useadditional_targets. You can use thisGoogle Colaboratory notebookto adjust image augmentation parameters and see the resulting images. Authors ...
You can apply the same transform to any number of images, masks, bounding boxes, and sets of keypoints using the additional_targets functionalitynotebook with examples Contributors@ternaus,@ayasyrev get_params_dependent_on data Relevant for those who build custom transforms. ...
740 次提交 提交 .github boosted versions for checks (#1245) 3年前 albumentations AddRandomCropFromBordersand support for bbox in ElasticTransform f… 3年前 benchmark Version 1.0.0 (#917) 4年前 conda.recipe Update linters (#831) 4年前 ...