def get_transform(train=True,
resize=(224, 224),
useImgaug=True,
advanced=False):
if train:
if useImgaug:
transforms = bboxAug.Compose([
bboxAug.Augment(advanced=advanced),
bboxAug.Pad(),
bboxAug.Resize(resize, False),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
bboxAug.Normalize(image_std=[0.229, 0.224, 0.225])
])
else:
transforms = bboxAug.Compose([
bboxAugv2.RandomHorizontalFlip(),
bboxAugv2.ResizeFixMinAndRandomCrop(int(resize[0] * 1.1),
resize), # 用于resize到固定大小
# bboxAugv2.RandomDropAndResizeMaxMin(0.2, 600, 1000), # 用于 fasterrecnn
bboxAugv2.RandomLight(),
bboxAugv2.RandomColor(),
bboxAugv2.RandomChanels(),
bboxAugv2.RandomNoise(),
bboxAugv2.RandomBlur(),
bboxAugv2.RandomRotate(),
bboxAugv2.RandomAffine(),
bboxAugv2.RandomDropPixelV2(),
bboxAugv2.RandomCutMixV2(),
bboxAugv2.RandomMosaic(),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
bboxAug.Normalize(image_std=[0.229, 0.224, 0.225])
])
"""
transforms = bboxAug.Compose([
# bboxAug.RandomChoice(),
bboxAug.RandomHorizontalFlip(),
bboxAug.RandomBrightness(),
bboxAug.RandomBlur(),
bboxAug.RandomSaturation(),
bboxAug.RandomHue(),
# bboxAug.RandomRotate(angle=5),
# bboxAug.RandomTranslate(),
bboxAug.Pad(), bboxAug.Resize(resize, False),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
bboxAug.Normalize(image_std=[0.229, 0.224, 0.225])
])
"""
else:
transforms = bboxAug.Compose([
bboxAug.Pad(),
bboxAug.Resize(resize, False),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
bboxAug.Normalize(image_std=[0.229, 0.224, 0.225])
])
return transforms
def get_transform_keypoints(train=True,
resize=(224, 224),
useImgaug=True,
advanced=False):
if train:
transforms = bboxAug.Compose([
bboxAug.RandomHorizontalFlip(),
bboxAug.RandomBrightness(),
bboxAug.RandomBlur(),
bboxAug.RandomSaturation(),
bboxAug.RandomHue(),
bboxAug.Pad(),
bboxAug.Resize(resize, False),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
bboxAug.Normalize(image_std=[0.229, 0.224, 0.225])
])
else:
transforms = bboxAug.Compose([
bboxAug.Pad(),
bboxAug.Resize(resize, False),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
bboxAug.Normalize(image_std=[0.229, 0.224, 0.225])
])
return transforms
def get_transforms(mode, advanced=False):
if mode == 0: # 推荐
return bboxAug.Compose([
bboxAug.Augment(advanced),
bboxAugv2.ResizeFixMinAndRandomCrop(448,
(416, 416)), # 用于resize到固定大小
# bboxAugv2.RandomDropAndResizeMaxMin(0.2,600,1000), # 用于 fasterrecnn
bboxAugv2.RandomRotate(),
bboxAugv2.RandomAffine(),
bboxAugv2.RandomDropPixelV2(),
# # bboxAugv2.RandomCutMixV2(),
bboxAugv2.RandomMosaic(),
# random.choice([bboxAugv2.RandomCutMixV2(),bboxAugv2.RandomMosaic()]),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
])
elif mode == 1: # 推荐
return bboxAug.Compose([
bboxAugv2.RandomHorizontalFlip(),
bboxAugv2.ResizeFixMinAndRandomCrop(448,
(416, 416)), # 用于resize到固定大小
# bboxAugv2.RandomDropAndResizeMaxMin(0.2,600,1000), # 用于 fasterrecnn
bboxAugv2.RandomLight(),
bboxAugv2.RandomColor(),
bboxAugv2.RandomChanels(),
bboxAugv2.RandomNoise(),
bboxAugv2.RandomBlur(),
bboxAugv2.RandomRotate(),
bboxAugv2.RandomAffine(),
bboxAugv2.RandomDropPixelV2(),
# bboxAugv2.RandomCutMixV2(),
bboxAugv2.RandomMosaic(),
# random.choice([bboxAugv2.RandomCutMixV2(),bboxAugv2.RandomMosaic()]),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
])
elif mode == 2: # 不推荐
return bboxAug.Compose([
# bboxAug.RandomChoice(),
bboxAug.RandomHorizontalFlip(),
bboxAug.RandomBrightness(),
bboxAug.RandomBlur(),
bboxAug.RandomSaturation(),
bboxAug.RandomHue(),
bboxAug.RandomRotate(angle=5),
# bboxAug.RandomTranslate(), # 有问题
# bboxAug.Augment(False),
bboxAug.Pad(),
bboxAug.Resize((416, 416), False),
# bboxAug.ResizeMinMax(600,1000),
# bboxAug.ResizeFixAndPad(),
# bboxAug.RandomHSV(),
bboxAug.RandomCutout(),
bboxAug.ToTensor(), # PIL --> tensor
# bboxAug.Normalize() # tensor --> tensor
])