def verify_no_negative_regr():
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
visual_effect_generator = random_visual_effect_generator(
contrast_range=(0.9, 1.1),
brightness_range=(-.1, .1),
hue_range=(-0.05, 0.05),
saturation_range=(0.95, 1.05)
)
common_args = {
'batch_size': 1,
'image_min_side': 800,
'image_max_side': 1333,
'preprocess_image': preprocess_image,
}
generator = PascalVocGenerator(
'datasets/voc_trainval/VOC0712',
'trainval',
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
skip_difficult=True,
**common_args
)
i = 0
for image_group, targets in generator:
i += 1
if i > 20000:
break
def create_generators(args, preprocess_image):
"""
Create generators for training and validation.
Args
args: parseargs object containing configuration for generators.
preprocess_image: Function that preprocesses an image for the network.
"""
common_args = {
'batch_size': args.batch_size,
'config': args.config,
'image_min_side': args.image_min_side,
'image_max_side': args.image_max_side,
'preprocess_image': preprocess_image,
}
# create random transform generator for augmenting training data
if args.random_transform:
transform_generator = random_transform_generator(
min_rotation=-0.1,
max_rotation=0.1,
min_translation=(-0.1, -0.1),
max_translation=(0.1, 0.1),
min_shear=-0.1,
max_shear=0.1,
min_scaling=(0.9, 0.9),
max_scaling=(1.1, 1.1),
flip_x_chance=0.5,
flip_y_chance=0.5,
)
visual_effect_generator = random_visual_effect_generator(
contrast_range=(0.9, 1.1),
brightness_range=(-.1, .1),
hue_range=(-0.05, 0.05),
saturation_range=(0.95, 1.05)
)
else:
transform_generator = random_transform_generator(flip_x_chance=0.5)
visual_effect_generator = None
if args.dataset_type == 'pascal':
train_generator = PascalVocGenerator(
args.pascal_path,
'trainval',
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
skip_difficult=True,
**common_args
)
validation_generator = PascalVocGenerator(
args.pascal_path,
'val',
shuffle_groups=False,
skip_difficult=True,
**common_args
)
elif args.dataset_type == 'csv':
train_generator = CSVGenerator(
args.annotations_path,
args.classes_path,
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args
)
if args.val_annotations_path:
validation_generator = CSVGenerator(
args.val_annotations_path,
args.classes_path,
shuffle_groups=False,
**common_args
)
else:
validation_generator = None
elif args.dataset_type == 'coco':
# import here to prevent unnecessary dependency on cocoapi
from generators.coco_generator import CocoGenerator
train_generator = CocoGenerator(
args.coco_path,
'train2017',
transform_generator=transform_generator,
visual_effect_generator=visual_effect_generator,
**common_args
)
validation_generator = CocoGenerator(
args.coco_path,
'val2017',
shuffle_groups=False,
**common_args
)
else:
raise ValueError('Invalid data type received: {}'.format(args.dataset_type))
return train_generator, validation_generator
from utils.image import random_visual_effect_generator
import cv2
img = cv2.imread('test/demo.jpg')
aug = random_visual_effect_generator()
while True:
gen = next(aug)(img)
print(type(gen))
cv2.imshow("trest", gen)
if cv2.waitKey() == 27:
break
cv2.destroyAllWindows()