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