def spatial_transforms():
'''
空间变换：变形、缩放、旋转
:return:
'''
# 变形+旋转+缩放
spatial_transform = SpatialTransform(camera().shape,
np.array(camera().shape) // 2,
do_elastic_deform=True,
alpha=(0., 1500.),
sigma=(30., 50.),
do_rotation=True,
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.3, 3.),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
my_transforms = []
my_transforms.append(spatial_transform)
all_transforms = Compose(my_transforms)
batchgen = my_data_loader.DataLoader(camera(), 4)
multithreaded_generator = MultiThreadedAugmenter(batchgen, all_transforms,
4, 2)
# 显示转换效果
my_data_loader.plot_batch(multithreaded_generator.__next__())
def random_transform():
'''
随机地对某些批次的数据进行变换
:return:
'''
spatial_transform = SpatialTransform(camera().shape,
np.array(camera().shape) // 2,
do_elastic_deform=True,
alpha=(0., 1500.),
sigma=(30., 50.),
do_rotation=True,
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.3, 3.),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
sometimes_spatial_transform = RndTransform(spatial_transform, prob=0.5)
batchgen = my_data_loader.DataLoader(camera(), 4)
multithreaded_generator = MultiThreadedAugmenter(
batchgen, Compose([sometimes_spatial_transform]), 4, 2)
for _ in range(4):
my_data_loader.plot_batch(multithreaded_generator.__next__())
def crop_transform_random():
'''
从图片的任意位置剪切出一个固定的尺寸
:return:
'''
crop_size = (128, 128)
batchgen = my_data_loader.DataLoader(camera(), 4)
# 随机地从图片上剪切除（128，128）尺寸的图片块
randomCrop = RandomCropTransform(crop_size=crop_size)
spatial_transform = SpatialTransform(crop_size,
np.array(crop_size) // 2,
do_elastic_deform=True,
alpha=(0., 1500.),
sigma=(30., 50.),
do_rotation=True,
angle_z=(0, 2 * np.pi),
do_scale=True,
scale=(0.5, 2),
border_mode_data='constant',
border_cval_data=0,
order_data=1,
random_crop=False)
multithreaded_generator = MultiThreadedAugmenter(
batchgen, Compose([randomCrop, spatial_transform]), 4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def resampling_transform():
'''
对图片进行重采样
:return:
'''
batchgen = my_data_loader.DataLoader(camera(), 4)
resample_transform = ResampleTransform(zoom_range=(0.05, 0.2))
multithreaded_generator = MultiThreadedAugmenter(batchgen,
resample_transform, 4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def noise_transform():
'''
为图片加噪声点
:return:
'''
batchgen = my_data_loader.DataLoader(camera(), 4)
noise_transform = RicianNoiseTransform(noise_variance=(0, 200))
multithreaded_generator = MultiThreadedAugmenter(batchgen, noise_transform,
4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def crop_transform_center_seg():
'''
从图片的正正中心剪切分割
:return:
'''
crop_size = (128, 128)
batchgen = my_data_loader.DataLoader(camera(), 4)
centerCropSeg = CenterCropSegTransform(output_size=crop_size)
multithreaded_generator = MultiThreadedAugmenter(batchgen,
Compose([centerCropSeg]),
4, 2)
my_data_loader.plot_batch(multithreaded_generator.__next__())
def combine_transform():
'''
组合变换：对比度+镜像
:return:
'''
my_transforms = []
# 对比度变换
brightness_transform = ContrastAugmentationTransform((0.3, 3.),
preserve_range=True)
my_transforms.append(brightness_transform)
# 镜像变换
mirror_transform = MirrorTransform(axes=(2, 3))
my_transforms.append(mirror_transform)
all_transform = Compose(my_transforms)
batchgen = my_data_loader.DataLoader(camera(), 4)
multithreaded_generator = MultiThreadedAugmenter(batchgen, all_transform,
4, 2)
# 显示转换效果
my_data_loader.plot_batch(multithreaded_generator.__next__())