class ClassifyDataAugment():
def __init__(self):
torchvision_process = TorchVisionProcess()
self.augment_transform = torchvision_process.torch_data_augment()
self.dataset_process = ImageDataSetProcess()
self.image_augment = ImageDataAugment()
self.is_torchvision_augment = True
self.is_augment_hsv = True
self.is_augment_affine = True
self.is_lr_flip = True
def augment(self, image_rgb):
image = image_rgb[:]
if self.is_torchvision_augment:
image = self.augment_transform(image)
else:
if self.is_augment_hsv:
image = self.image_augment.augment_hsv(image)
if self.is_augment_affine:
image = self.augment_affine(image)
if self.is_lr_flip:
image = self.augment_lr_flip(image)
return image
def augment_affine(self, src_image):
image_size = (src_image.shape[1], src_image.shape[0])
matrix, degree = self.dataset_process.affine_matrix(image_size,
degrees=(-15, 15),
translate=(0.0, 0.0),
scale=(1.0, 1.0),
shear=(-3, 3))
image = self.dataset_process.image_affine(src_image, matrix,
border_value=(0.0, 0.0, 0.0))
return image
def augment_lr_flip(self, src_image):
image = src_image[:]
if random.random() > 0.5:
image = np.fliplr(image)
return image
class SegmentDataAugment():
def __init__(self):
self.is_augment_hsv = True
self.is_augment_affine = True
self.is_lr_flip = True
self.dataset_process = ImageDataSetProcess()
self.image_augment = ImageDataAugment()
def augment(self, image_rgb, label):
image = image_rgb[:]
target = label[:]
if self.is_augment_hsv:
image = self.image_augment.augment_hsv(image_rgb)
if self.is_augment_affine:
image, target = self.augment_affine(image, target)
if self.is_lr_flip:
image, target = self.augment_lr_flip(image, target)
return image, target
def augment_affine(self, src_image, label):
image_size = (src_image.shape[1], src_image.shape[0])
matrix, degree = self.dataset_process.affine_matrix(image_size,
degrees=(-5, 5),
translate=(0.1, 0.1),
scale=(0.8, 1.1),
shear=(-3, 3))
image = self.dataset_process.image_affine(src_image, matrix,
border_value=(127.5, 127.5, 127.5))
target = self.dataset_process.image_affine(label, matrix,
border_value=250)
return image, target
def augment_lr_flip(self, src_image, label):
image = src_image[:]
target = label[:]
if random.random() > 0.5:
image = np.fliplr(image)
target = np.fliplr(target)
return image, target
class DetectionDataAugment():
def __init__(self):
self.is_augment_hsv = True
self.is_augment_affine = True
self.is_lr_flip = True
self.is_up_flip = False
self.dataset_process = ImageDataSetProcess()
self.image_augment = ImageDataAugment()
def augment(self, image_rgb, labels):
image = image_rgb[:]
targets = labels[:]
if self.is_augment_hsv:
image = self.image_augment.augment_hsv(image_rgb)
if self.is_augment_affine:
image, targets = self.augment_affine(image, targets)
if self.is_lr_flip:
image, targets = self.augment_lr_flip(image, targets)
if self.is_up_flip:
image, targets = self.augment_up_flip(image, targets)
return image, targets
def augment_affine(self, src_image, labels):
image_size = (src_image.shape[1], src_image.shape[0])
matrix, degree = self.dataset_process.affine_matrix(image_size,
degrees=(-5, 5),
translate=(0.1, 0.1),
scale=(0.8, 1.1),
shear=(-3, 3))
image = self.dataset_process.image_affine(src_image, matrix,
border_value=(127.5, 127.5, 127.5))
# Return warped points also
if labels is not None:
targets = []
for object in labels:
points = np.array(object.getVector())
area0 = (points[2] - points[0]) * (points[3] - points[1])
xy = np.ones((4, 3))
# x1y1, x2y2, x1y2, x2y1
xy[:, :2] = points[[0, 1, 2, 3, 0, 3, 2, 1]].reshape(4, 2)
xy = np.squeeze((xy @ matrix.T)[:, :2].reshape(1, 8))
# create new boxes
x = xy[[0, 2, 4, 6]]
y = xy[[1, 3, 5, 7]]
xy = np.array([x.min(), y.min(), x.max(), y.max()])
# apply angle-based reduction
radians = degree * math.pi / 180
reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
x = (xy[2] + xy[0]) / 2
y = (xy[3] + xy[1]) / 2
w = (xy[2] - xy[0]) * reduction
h = (xy[3] - xy[1]) * reduction
xy = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2])
# reject warped points outside of image
np.clip(xy, 0, image_size[0], out=xy)
w = xy[2] - xy[0]
h = xy[3] - xy[1]
area = w * h
ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
i = (w > 4) & (h > 4) & (area / (area0 + 1e-16) > 0.1) & (ar < 10)
if i:
rect = Rect2D()
rect.class_id = object.class_id
rect.min_corner.x = xy[0]
rect.min_corner.y = xy[1]
rect.max_corner.x = xy[2]
rect.max_corner.y = xy[3]
targets.append(rect)
return image, targets
else:
return image, None
def augment_lr_flip(self, src_image, labels):
# random left-right flip
image_size = (src_image.shape[1], src_image.shape[0])
image = src_image[:]
if random.random() > 0.5:
image = np.fliplr(image)
for object in labels:
temp = object.min_corner.x
object.min_corner.x = image_size[0] - object.max_corner.x
object.max_corner.x = image_size[0] - temp
return image, labels
def augment_up_flip(self, src_image, labels):
# random up-down flip
image_size = (src_image.shape[1], src_image.shape[0])
image = src_image[:]
if random.random() > 0.5:
image = np.flipud(image)
for object in labels:
temp = object.min_corner.y
object.min_corner.y = image_size[1] - object.max_corner.y
object.max_corner.y = image_size[1] - temp
return image, labels