本文整理匯總了Python中tensorpack.dataflow.imgaug.RandomOrderAug方法的典型用法代碼示例。如果您正苦於以下問題：Python imgaug.RandomOrderAug方法的具體用法？Python imgaug.RandomOrderAug怎麽用？Python imgaug.RandomOrderAug使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類tensorpack.dataflow.imgaug的用法示例。
在下文中一共展示了imgaug.RandomOrderAug方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_data
# 需要導入模塊: from tensorpack.dataflow import imgaug [as 別名]
# 或者: from tensorpack.dataflow.imgaug import RandomOrderAug [as 別名]
def get_data(name, batch):
isTrain = name == 'train'
image_shape = 224
if isTrain:
augmentors = [
# use lighter augs if model is too small
GoogleNetResize(crop_area_fraction=0.49 if args.width_ratio < 1 else 0.08,
target_shape=image_shape),
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(int(image_shape*256/224), cv2.INTER_CUBIC),
imgaug.CenterCrop((image_shape, image_shape)),
]
return get_imagenet_dataflow(args.data_dir, name, batch, augmentors,
meta_dir = args.meta_dir) 示例2: fbresnet_augmentor
# 需要導入模塊: from tensorpack.dataflow import imgaug [as 別名]
# 或者: from tensorpack.dataflow.imgaug import RandomOrderAug [as 別名]
def fbresnet_augmentor():
# assme BGR input
augmentors = [
imgaug.GoogleNetRandomCropAndResize(),
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb->bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(0.1,
eigval=np.asarray(
[0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]
)]),
imgaug.Flip(horiz=True),
]
return augmentors 示例3: fbresnet_augmentor
# 需要導入模塊: from tensorpack.dataflow import imgaug [as 別名]
# 或者: from tensorpack.dataflow.imgaug import RandomOrderAug [as 別名]
def fbresnet_augmentor():
# assme BGR input
augmentors = [
GoogleNetResize(),
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb->bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(0.1,
eigval=np.asarray(
[0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]
)]),
imgaug.Flip(horiz=True),
]
return augmentors 示例4: get_data
# 需要導入模塊: from tensorpack.dataflow import imgaug [as 別名]
# 或者: from tensorpack.dataflow.imgaug import RandomOrderAug [as 別名]
def get_data(is_train,
batch_size,
data_dir_path,
input_image_size=224,
resize_inv_factor=0.875):
assert (resize_inv_factor > 0.0)
resize_value = int(math.ceil(float(input_image_size) / resize_inv_factor))
if is_train:
augmentors = [
GoogleNetResize(
crop_area_fraction=0.08,
target_shape=input_image_size),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([
[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]], dtype="float32")[::-1, ::-1])]),
imgaug.Flip(horiz=True)]
else:
augmentors = [
# imgaug.ResizeShortestEdge(resize_value, cv2.INTER_CUBIC),
imgaug.ResizeShortestEdge(resize_value, cv2.INTER_LINEAR),
imgaug.CenterCrop((input_image_size, input_image_size))
]
return get_imagenet_dataflow(
datadir=data_dir_path,
is_train=is_train,
batch_size=batch_size,
augmentors=augmentors) 示例5: get_data
# 需要導入模塊: from tensorpack.dataflow import imgaug [as 別名]
# 或者: from tensorpack.dataflow.imgaug import RandomOrderAug [as 別名]
def get_data(name, batch):
isTrain = name == 'train'
if isTrain:
augmentors = [
# use lighter augs if model is too small
imgaug.GoogleNetRandomCropAndResize(crop_area_fraction=(0.49 if args.ratio < 1 else 0.08, 1.)),
imgaug.RandomOrderAug(
[imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(0.1,
eigval=np.asarray(
[0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array(
[[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1]
)]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
]
return get_imagenet_dataflow(
args.data, name, batch, augmentors)