def _augmentation_fn(self, image, boxes):
# there are a lot of hyperparameters here,
# you will need to tune them all, haha
image, boxes = random_image_crop(image,
boxes,
probability=0.9,
min_object_covered=0.9,
aspect_ratio_range=(0.93, 1.07),
area_range=(0.4, 0.9),
overlap_thresh=0.4)
image = tf.image.resize_images(
image, [self.image_height, self.image_width],
method=RESIZE_METHOD) if self.resize else image
# if you do color augmentations before resizing, it will be very slow!
image = random_color_manipulations(image,
probability=0.45,
grayscale_probability=0.05)
image = random_pixel_value_scale(image,
minval=0.85,
maxval=1.15,
probability=0.2)
boxes = random_jitter_boxes(boxes, ratio=0.01)
image, boxes = random_flip_left_right(image, boxes)
return image, boxes
def _augmentation_fn(self, image, boxes, labels):
# there are a lot of hyperparameters here,
# you will need to tune them all, haha.
image, boxes, labels = random_image_crop(image,
boxes,
labels,
probability=0.8,
min_object_covered=0.0,
aspect_ratio_range=(0.85,
1.15),
area_range=(0.333, 0.8),
overlap_thresh=0.3)
image = tf.image.resize_images(image,
[self.image_height, self.image_width],
method=RESIZE_METHOD)
# if you do color augmentations before resizing, it will be very slow!
image = random_color_manipulations(image,
probability=0.7,
grayscale_probability=0.07)
image = random_pixel_value_scale(image,
minval=0.85,
maxval=1.15,
probability=0.7)
boxes = random_jitter_boxes(boxes, ratio=0.05)
image = random_colored_patches(image,
max_patches=20,
probability=0.5,
size_to_image_ratio=0.1)
image, boxes = random_flip_left_right(image, boxes)
return image, boxes, labels