def __init__(self, resize_height, resize_width, mean, std, use_coordinates,
model, n_workers):
self.normalizer = ImageUtilities.image_normalizer(mean, std)
self.use_coordinates = use_coordinates
self.resize_height = resize_height
self.resize_width = resize_width
self.model = model
self.n_workers = n_workers
self.img_resizer = ImageUtilities.image_resizer(
self.resize_height, self.resize_width)
if self.use_coordinates:
self.coordinate_adder = AddCoordinates(with_r=True, usegpu=False)
def __init__(self, resize_height, resize_width, mean,
std, use_coordinates, model, n_workers):
self.normalizer = ImageUtilities.image_normalizer(mean, std)
self.use_coordinates = use_coordinates
self.resize_height = resize_height
self.resize_width = resize_width
self.model = model
self.n_workers = n_workers
self.img_resizer = ImageUtilities.image_resizer(
self.resize_height, self.resize_width)
if self.use_coordinates:
self.coordinate_adder = ImageUtilities.coordinate_adder(
self.resize_height, self.resize_width)
def __init__(self, mode, labels, mean, std, image_size_height, image_size_width, annotation_size_height, annotation_size_width,
crop_scale, crop_ar, random_cropping=True, horizontal_flipping=True, random_jitter=True):
self._mode = mode
assert self._mode in ['training', 'test']
self.n_classes = len(labels)
self.mean = mean
self.std = std
self.image_size_height = image_size_height
self.image_size_width = image_size_width
self.random_cropping = random_cropping
self.crop_scale = crop_scale
self.crop_ar = crop_ar
self.annotation_size_height = annotation_size_height
self.annotation_size_width = annotation_size_width
self.horizontal_flipping = horizontal_flipping
self.random_jitter = random_jitter
if self._mode == 'training':
if self.random_cropping:
self.image_random_cropper = ImageUtilities.image_random_cropper_and_resizer(self.image_size_height, self.image_size_width)
self.annotation_random_cropper = ImageUtilities.image_random_cropper_and_resizer(self.annotation_size_height,
self.annotation_size_width,
interpolation=Image.NEAREST)
else:
self.image_resizer = ImageUtilities.image_resizer(self.image_size_height, self.image_size_width)
self.annotation_resizer = ImageUtilities.image_resizer(self.annotation_size_height, self.annotation_size_width,
interpolation=Image.NEAREST)
if self.horizontal_flipping:
self.horizontal_flipper = ImageUtilities.image_random_horizontal_flipper()
if self.random_jitter:
self.random_jitterer = transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1)
else:
self.image_resizer = ImageUtilities.image_resizer(self.image_size_height, self.image_size_width)
self.annotation_resizer = ImageUtilities.image_resizer(self.annotation_size_height, self.annotation_size_width,
interpolation=Image.NEAREST)
self.image_normalizer = ImageUtilities.image_normalizer(self.mean, self.std)
def __init__(self, resize_height, resize_width, mean, std, model):
self.normalizer = ImageUtilities.image_normalizer(mean, std)
self.resize_height = resize_height
self.resize_width = resize_width
self.model = model
def __init__(self,
mode,
n_classes,
max_n_objects,
mean,
std,
image_height,
image_width,
random_hor_flipping=True,
random_ver_flipping=True,
random_transposing=True,
random_90x_rotation=True,
random_rotation=True,
random_color_jittering=True,
random_grayscaling=True,
random_channel_swapping=True,
random_gamma=True,
random_resolution=True):
self._mode = mode
self.n_classes = n_classes
self.max_n_objects = max_n_objects
assert self._mode in ['training', 'test']
self.mean = mean
self.std = std
self.image_height = image_height
self.image_width = image_width
self.random_horizontal_flipping = random_hor_flipping
self.random_vertical_flipping = random_ver_flipping
self.random_transposing = random_transposing
self.random_90x_rotation = random_90x_rotation
self.random_rotation = random_rotation
self.random_color_jittering = random_color_jittering
self.random_grayscaling = random_grayscaling
self.random_channel_swapping = random_channel_swapping
self.random_gamma = random_gamma
self.random_resolution = random_resolution
if self._mode == 'training':
if self.random_horizontal_flipping:
self.horizontal_flipper = IU.image_random_horizontal_flipper()
if self.random_vertical_flipping:
self.vertical_flipper = IU.image_random_vertical_flipper()
if self.random_transposing:
self.transposer = IU.image_random_transposer()
if self.random_rotation:
self.image_rotator = IU.image_random_rotator(random_bg=True)
self.annotation_rotator = IU.image_random_rotator(
Image.NEAREST, random_bg=False)
if self.random_90x_rotation:
self.image_rotator_90x = IU.image_random_90x_rotator()
self.annotation_rotator_90x = IU.image_random_90x_rotator(
Image.NEAREST)
if self.random_color_jittering:
self.color_jitter = IU.image_random_color_jitter(
brightness=0.4, contrast=0.4, saturation=0.4, hue=0.2)
if self.random_grayscaling:
self.grayscaler = IU.image_random_grayscaler(p=0.3)
if self.random_channel_swapping:
self.channel_swapper = IU.image_random_channel_swapper(p=0.5)
if self.random_gamma:
self.gamma_adjuster = IU.image_random_gamma([0.7, 1.3], gain=1)
if self.random_resolution:
self.resolution_degrader = IU.image_random_resolution(
[0.7, 1.3])
self.img_resizer = IU.image_resizer(self.image_height,
self.image_width)
self.ann_resizer = IU.image_resizer(self.image_height,
self.image_width,
interpolation=Image.NEAREST)
else:
self.img_resizer = IU.image_resizer(self.image_height,
self.image_width)
self.ann_resizer = IU.image_resizer(self.image_height,
self.image_width,
interpolation=Image.NEAREST)
self.image_normalizer = IU.image_normalizer(self.mean, self.std)
def __init__(self,
mode,
n_classes,
max_n_objects,
mean,
std,
image_height,
image_width,
random_hor_flipping=True,
random_ver_flipping=True,
random_90x_rotation=True,
random_rotation=True,
random_color_jittering=True,
use_coordinates=False):
self._mode = mode
self.n_classes = n_classes
self.max_n_objects = max_n_objects
assert self._mode in ['training', 'test']
self.mean = mean
self.std = std
self.image_height = image_height
self.image_width = image_width
self.random_horizontal_flipping = random_hor_flipping
self.random_vertical_flipping = random_ver_flipping
self.random_90x_rotation = random_90x_rotation
self.random_rotation = random_rotation
self.random_color_jittering = random_color_jittering
self.use_coordinates = use_coordinates
if self._mode == 'training':
if self.random_horizontal_flipping:
self.horizontal_flipper = IU.image_random_horizontal_flipper()
if self.random_vertical_flipping:
self.vertical_flipper = IU.image_random_vertical_flipper()
if self.random_90x_rotation:
self.rotator_90x = IU.image_random_90x_rotator()
if self.random_rotation:
self.rotator = IU.image_random_rotator(expand=True)
if self.random_color_jittering:
self.color_jitter = IU.image_random_color_jitter(
brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1)
self.img_resizer = IU.image_resizer(self.image_height,
self.image_width)
self.ann_resizer = IU.image_resizer(self.image_height,
self.image_width,
interpolation=Image.NEAREST)
else:
self.img_resizer = IU.image_resizer(self.image_height,
self.image_width)
self.ann_resizer = IU.image_resizer(self.image_height,
self.image_width,
interpolation=Image.NEAREST)
self.image_normalizer = IU.image_normalizer(self.mean, self.std)
if self.use_coordinates:
self.coordinate_adder = IU.coordinate_adder(
self.image_height, self.image_width)
