def logic(self, image):
for param in self.augmentation_params:
self.augmentation_data.append([
str(param.augmentation_value),
iaa.Salt(p=param.augmentation_value).to_deterministic().
augment_image(image), param.detection_tag
])
def __init__(self,
batch_size,
input_shape,
anchors,
num_classes,
real_annotation_lines,
is_training=True):
self.batch_size = batch_size
self.input_shape = input_shape
self.anchors = anchors
self.num_classes = num_classes
self.real_annotation_lines = real_annotation_lines
self.is_training = is_training
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.aug_pipe = iaa.Sequential([
iaa.SomeOf((0, 5), [
iaa.Sharpen(alpha=1.0, lightness=(0.75, 1.5)),
iaa.EdgeDetect(alpha=(0, 0.5)),
iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, 0.05 * 255)),
iaa.OneOf([
iaa.Dropout((0.01, 0.05)),
iaa.Salt((0.03, 0.15)),
]),
iaa.Add((-10, 10)),
iaa.Multiply((0.5, 1.5)),
iaa.ContrastNormalization((0.5, 2.0)),
sometimes(
iaa.ElasticTransformation(alpha=(0.1, 2.0), sigma=0.25)),
],
random_order=True)
],
random_order=True)
def __init__(self):
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.seq = iaa.Sequential([
iaa.OneOf([
iaa.Multiply((0.6, 1.0), per_channel=0.5),
iaa.Multiply((1.0, 2.0), per_channel=0.5),
]),
sometimes(
iaa.OneOf([
iaa.Dropout((0.02, 0.03)),
iaa.Salt((0.02, 0.03))
])
),
sometimes(iaa.ChannelShuffle(1.0)),
#sometimes(iaa.Invert(1.0, per_channel=True)),
#sometimes(iaa.Invert(1.0)),
#sometimes(iaa.CropAndPad(
# percent=(-0.1, 0.1), pad_cval=(0,255)
#)),
iaa.Affine(
#scale={"x": (0.8, 1.1), "y": (0.8, 1.1)}, # scale images to 80-120% of their size, individually per axis
rotate=(-10, 10), # rotate by -45 to +45 degrees
mode=ia.ALL # use any of scikit-image's warping modes (see 2nd image from the top for examples)
),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.04))),
sometimes(iaa.AdditiveGaussianNoise((0.02, 0.1))),
sometimes(iaa.AdditivePoissonNoise((0.02,0.1))),
#sometimes(iaa.Pad(
# percent=(0, 0.15), pad_mode=["edge"]
#))
])
def augment(image, bbox):
x = random.randint(-60, 60)
y = random.randint(-60, 60)
aug = iaa.Sequential([iaa.AdditiveGaussianNoise(scale=random.uniform(.001, .01) * 255), # gaussian noise
iaa.Multiply(random.uniform(0.5, 1.5)), # brightness
iaa.Affine(translate_px={"x": x, "y": y}, # translation
scale=random.uniform(0.5, 1.5), # zoom in and out
rotate=random.uniform(-25, 25), # rotation
shear=random.uniform(-5, 5), # shear transformation
cval=(0, 255))]) # fill the empty space with color
aug.add(iaa.Salt(.001))
bbs = ia.BoundingBoxesOnImage([ia.BoundingBox(x1=bbox[0], y1=bbox[1], x2=bbox[2], y2=bbox[3])], shape=image.shape)
aug = aug.to_deterministic()
image_aug = aug.augment_image(image)
bbs_aug = aug.augment_bounding_boxes([bbs])[0]
b = bbs_aug.bounding_boxes
bbs_aug = [b[0].x1, b[0].y1, b[0].x2, b[0].y2]
bbs_aug = np.asarray(bbs_aug)
bbs_aug[0] = bbs_aug[0] if bbs_aug[0] > 0 else 0
bbs_aug[1] = bbs_aug[1] if bbs_aug[1] > 0 else 0
bbs_aug[2] = bbs_aug[2] if bbs_aug[2] < size else size
bbs_aug[3] = bbs_aug[3] if bbs_aug[3] < size else size
return image_aug, bbs_aug
def image_aug(image):
"""
@param image:
@return:
"""
seq = iaa.SomeOf(
(1, 3),
[
iaa.Crop(px=(0, 16)), # 裁剪
iaa.Multiply((0.7, 1.3)), # 改变色调
iaa.Affine(scale=(0.5, 0.7)), # 放射变换
iaa.GaussianBlur(sigma=(0, 1.5)), # 高斯模糊
iaa.AddToHueAndSaturation(value=(25, -25)),
iaa.ChannelShuffle(1), # RGB三通道随机交换
iaa.ElasticTransformation(alpha=0.1),
iaa.Grayscale(alpha=(0.2, 0.5)),
iaa.Pepper(p=0.03),
iaa.AdditiveGaussianNoise(scale=(0.03 * 255, 0.05 * 255)),
iaa.Dropout(p=(0.03, 0.05)),
iaa.Salt(p=(0.03, 0.05)),
iaa.AverageBlur(k=(1, 3)),
iaa.Add((-10, 10)),
iaa.CoarseSalt(size_percent=0.01)
])
seq_det = seq.to_deterministic()
image_aug = seq_det.augment_images([image])[0]
return image_aug
def generator(image_list):
for name in image_list:
fileName = name
name = os.path.join(PATH, name)
images = cv2.imread(name)
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
seq = iaa.Sequential([
iaa.Flipud(p=0.5),
iaa.Fliplr(p=0.5),
sometimes(iaa.Pepper(p=0.10)),
sometimes(iaa.Salt(p=0.03)),
sometimes(iaa.AdditivePoissonNoise(lam=8.0)),
sometimes(iaa.JpegCompression(compression=50)),
sometimes(iaa.PiecewiseAffine(scale=0.015)),
sometimes(iaa.MotionBlur(k=7, angle=0)),
sometimes(iaa.MotionBlur(k=5, angle=144))
],
random_order=False)
for i in range(10):
images_aug = seq.augment_image(images)
name = 'aug_' + fileName.split('.')[0] + "-" + str(i) + '.jpg'
name = os.path.join(PATH, name)
cv2.imwrite(name, images_aug)
print(name + " is saved.")
def imgaugRGB(img):
print(img.shape)
seq = iaa.Sequential(
[
# blur
iaa.SomeOf((0, 2), [
iaa.GaussianBlur((0.0, 2.0)),
iaa.AverageBlur(k=(3, 7)),
iaa.MedianBlur(k=(3, 7)),
iaa.BilateralBlur(d=(1, 7)),
iaa.MotionBlur(k=(3, 7))
]),
#color
iaa.SomeOf(
(0, 2),
[
#iaa.WithColorspace(),
iaa.AddToHueAndSaturation((-20, 20)),
#iaa.ChangeColorspace(to_colorspace[], alpha=0.5),
iaa.Grayscale(alpha=(0.0, 0.2))
]),
#brightness
iaa.OneOf([
iaa.Sequential([
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5)
]),
iaa.Add((-10, 10), per_channel=0.5),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.FrequencyNoiseAlpha(exponent=(-4, 0),
first=iaa.Multiply(
(0.5, 1.5), per_channel=0.5),
second=iaa.ContrastNormalization(
(0.5, 2.0), per_channel=0.5))
]),
#contrast
iaa.SomeOf((0, 2), [
iaa.GammaContrast((0.5, 1.5), per_channel=0.5),
iaa.SigmoidContrast(
gain=(0, 10), cutoff=(0.25, 0.75), per_channel=0.5),
iaa.LogContrast(gain=(0.75, 1), per_channel=0.5),
iaa.LinearContrast(alpha=(0.7, 1.3), per_channel=0.5)
]),
#arithmetic
iaa.SomeOf((0, 3), [
iaa.AdditiveGaussianNoise(scale=(0, 0.05), per_channel=0.5),
iaa.AdditiveLaplaceNoise(scale=(0, 0.05), per_channel=0.5),
iaa.AdditivePoissonNoise(lam=(0, 8), per_channel=0.5),
iaa.Dropout(p=(0, 0.05), per_channel=0.5),
iaa.ImpulseNoise(p=(0, 0.05)),
iaa.SaltAndPepper(p=(0, 0.05)),
iaa.Salt(p=(0, 0.05)),
iaa.Pepper(p=(0, 0.05))
]),
#iaa.Sometimes(p=0.5, iaa.JpegCompression((0, 30)), None),
],
random_order=True)
return seq.augment_image(img)
def main():
# datapath为存放训练图片的地方
datapath = '/home/zhex/data/OID_origin/train/Umbrella/'
# original_file为需要被增强的
original_file = '/home/zhex/data/OID_origin/tools/new_txt/Umbrella.txt' # 需要被增强的训练真值txt
# aug_file只记录了新增的增强后图片的box，要得到原始+增强的所有label：cat original_file augfile>finalfile(txt拼接)
# aug_file输出是pdpd需要的格式，pytorch需要另行转换(可以拼接得到finalfile后直接将finalfile转换)
aug_file = 'augfile_Umbrella.txt'
dict_before = readlist(original_file)
new_fp = open(aug_file, 'w')
# augscene = {'Umbrellad':10,'hat':2} # 需要哪些场景，新增几倍数量的新数据
augscene = {'Umbrella': 5}
for scene in augscene: # scene = Umbrella img_id = scene
for i in range(augscene[scene]):
for img_id in dict_before.keys():
img = Image.open(datapath + img_id)
img = np.array(img)
bbs = ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=x, y1=y, x2=x + w, y2=y + h)
for [x, y, w, h] in dict_before[img_id]
],
shape=img.shape)
# 设置数据增强方式
seq = iaa.SomeOf(
(1, 3),
[
iaa.Crop(px=(0, 16)), #裁剪
iaa.Multiply((0.7, 1.3)), #改变色调
iaa.Affine(scale=(0.5, 0.7)), #放射变换
iaa.GaussianBlur(sigma=(0, 1.5)), #高斯模糊
# iaa.AddToHueAndSaturation(value=(25,-25)),
iaa.ChannelShuffle(1), # RGB三通道随机交换
iaa.ElasticTransformation(alpha=0.1),
# iaa.Grayscale(alpha=(0.2, 0.5)),
iaa.Pepper(p=0.03),
iaa.AdditiveGaussianNoise(scale=(0.03 * 255,
0.05 * 255)),
iaa.Dropout(p=(0.03, 0.05)),
iaa.Salt(p=(0.03, 0.05)),
iaa.AverageBlur(k=(1, 3)),
iaa.Add((-10, 10)),
iaa.CoarseSalt(size_percent=0.01)
])
seq_det = seq.to_deterministic(
) # 保持坐标和图像同步改变，每个batch都要调用一次，不然每次的增强都是一样的
image_aug = seq_det.augment_images([img])[0]
bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
pic_name = img_id.split('.')[0]
# datapath = '/home/zhex/OID/train/Umbrella'
if not os.path.exists(datapath + 'myaug/'):
os.makedirs(datapath + 'myaug/')
new_img_id = 'myaug/' + pic_name + '_{}'.format(i) + '.jpg'
print('new_img_id = ', new_img_id)
Image.fromarray(image_aug).save(datapath + new_img_id)
new_fp = writelist(new_fp, new_img_id, bbs_aug.bounding_boxes)
def chapter_augmenters_salt():
fn_start = "arithmetic/salt"
aug = iaa.Salt(0.1)
run_and_save_augseq(fn_start + ".jpg",
aug, [ia.quokka(size=(128, 128)) for _ in range(8)],
cols=4,
rows=2,
quality=95)
def transform_op(transform):
if transform == 'flip_h':
seq = iaa.Sequential([
iaa.Fliplr(1),
])
elif transform == 'invert':
seq = iaa.Sequential([
iaa.Invert(1, True),
])
elif transform == 'brigth':
seq = iaa.Sequential([
iaa.Multiply(1.5, True),
])
elif transform == 'dark':
seq = iaa.Sequential([
iaa.Multiply(0.5, True),
])
elif transform == 'blur':
seq = iaa.Sequential([
iaa.GaussianBlur(0.5),
])
elif transform == 'sharp':
seq = iaa.Sequential([
iaa.Sharpen(1, 1.25),
])
elif transform == 'dark_sharp':
seq = iaa.Sequential([
iaa.Sharpen(1, 0.25),
])
elif transform == 'gauss_noise':
seq = iaa.Sequential([
iaa.AdditiveGaussianNoise(0.03, 10, True),
])
elif transform == 'dropout':
seq = iaa.Sequential([
iaa.Dropout(0.08, True),
])
elif transform == 'salt':
seq = iaa.Sequential([
iaa.Salt(0.08, True),
])
elif transform == 'salt_pepper':
seq = iaa.Sequential([
iaa.SaltAndPepper(0.08, True),
])
elif transform == 'contrast':
seq = iaa.Sequential([
iaa.ContrastNormalization(1.5, True),
])
else:
# So far it's only implemented horizontal flips
print("Sorry, only those operations listed in help are implemented. \
Check data_augmentation.py -help for further instructions")
return None
return seq
def __getitem__(self, idx):
img_pil = Image.open(
os.path.join(self.img_dir,
self.img_name[idx] + '.jpg')).convert('RGB')
# read targets, coordinates: x1, y1, x2, y2
targets = pd.read_csv(
os.path.join(self.label_dir, self.img_name[idx] + '.txt'),
sep=" ",
header=None,
names=['class', 'left', 'top', 'right', 'bottom'])
targets_boxes = targets[['left', 'top', 'right', 'bottom']]
# number of object on image
num_objs = len(targets_boxes)
# get bounding box for each object on image
list_boxes = []
for i in range(num_objs):
list_boxes.append(targets_boxes.iloc[i].tolist())
# convert boxes to tensor
boxes = torch.as_tensor(list_boxes, dtype=torch.float32)
# there is only one class
labels = torch.ones((num_objs, ), dtype=torch.int64)
# img transform
img = self.img_transform(img_pil)
target = {}
target['boxes'] = boxes
target['labels'] = labels
if self.train:
# convert image to numpy array
img = np.array(img_pil)
# define augmentation
# applies the given augmenter in 50% of all cases
sometimes = lambda aug: iaa.Sometimes(0.3, aug)
sequence = iaa.Sequential([
iaa.Resize(size=224),
sometimes(iaa.Salt(p=0.10)),
sometimes(iaa.Multiply((0.25, 0.50)))
])
# boxes transform
bbs = self._get_list_bbs(list_boxes)
boxes = ia.BoundingBoxesOnImage(bounding_boxes=bbs,
shape=img.shape)
boxes = sequence.augment_bounding_boxes(boxes)
boxes = boxes.to_xyxy_array(dtype=np.float32)
boxes = torch.from_numpy(boxes)
target['boxes'] = boxes
# image transform
transform = self.img_transform
img = sequence.augment_image(img)
img = transform(img.copy())
return img, target
def generate_background_noise(image_shape):
random_state = int(np.random.rand() * 100000)
background_noise = np.zeros(image_shape, dtype=np.uint8)
background_noise[:] = [0, 0, 0, 255]
noise_augmentation = iaa.Sequential([
iaa.Salt(p=0.041, random_state=random_state),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.MotionBlur(angle=90, k=8),
iaa.GaussianBlur(sigma=0.6)
])
return noise_augmentation(image=background_noise)
def aug_before_prepare(self, img, bboxes, masks, polys):
aug = iaa.SomeOf((1, 3), [
iaa.Multiply(((0.7, 1.3))),
iaa.GaussianBlur(sigma=(0, 1.5)),
iaa.ChannelShuffle(1),
iaa.Pepper(p=0.03),
iaa.AdditiveGaussianNoise(scale=(0.03 * 255, 0.05 * 255)),
iaa.Dropout(p=(0.03, 0.05)),
iaa.Salt(p=(0.03, 0.05)),
iaa.Add((-10, 10)),
iaa.AverageBlur(k=(1, 3))
])
if (random.random() > 0.5):
img, bboxes, masks = self.random_rotate(img, np.array(polys), masks, 90)
img = aug.augment_image(img)
return img, bboxes, masks
def salt(image, prob, keys):
""" Adding salt noise """
r = random.uniform(1, 5) * 0.05
aug = iaa.Sequential([
iaa.Dropout(p=(0, r)),
iaa.CoarseDropout(p=0.001, size_percent=0.01),
iaa.Salt(0.001),
iaa.AdditiveGaussianNoise(scale=0.1 * 255)
])
aug.add(iaa.Multiply(random.uniform(0.25, 1.5)))
x = random.randrange(-10, 10) * .01
y = random.randrange(-10, 10) * .01
aug.add(
iaa.Affine(scale=random.uniform(.7, 1.1),
translate_percent={
"x": x,
"y": y
},
cval=(0, 255)))
seq_det = aug.to_deterministic()
image_aug = seq_det.augment_images([image])[0]
keys = ia.KeypointsOnImage([
ia.Keypoint(x=keys[0], y=keys[1]),
ia.Keypoint(x=keys[2], y=keys[3]),
ia.Keypoint(x=keys[4], y=keys[5]),
ia.Keypoint(x=keys[6], y=keys[7]),
ia.Keypoint(x=keys[8], y=keys[9])
],
shape=image.shape)
keys_aug = seq_det.augment_keypoints([keys])[0]
k = keys_aug.keypoints
output = [
k[0].x, k[0].y, k[1].x, k[1].y, k[2].x, k[2].y, k[3].x, k[3].y, k[4].x,
k[4].y
]
index = 0
for i in range(0, len(prob)):
output[index] = output[index] * prob[i]
output[index + 1] = output[index + 1] * prob[i]
index = index + 2
output = np.array(output)
return image_aug, output
def do_all_aug(image):
do_aug(image, iaa.Noop(name="origin"))
do_aug(image, iaa.Crop((0, 10))) # 切边
do_aug(image, iaa.GaussianBlur((0, 3)))
do_aug(image, iaa.AverageBlur(1, 7))
do_aug(image, iaa.MedianBlur(1, 7))
do_aug(image, iaa.Sharpen())
do_aug(image, iaa.BilateralBlur()) # 既噪音又模糊，叫双边
do_aug(image, iaa.MotionBlur())
do_aug(image, iaa.MeanShiftBlur())
do_aug(image, iaa.GammaContrast())
do_aug(image, iaa.SigmoidContrast())
do_aug(image,
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge")) # shear：x轴往左右偏离的像素书，(a,b)是a,b间随机值，[a,b]是二选一
do_aug(image,
iaa.Affine(shear={
'x': (-10, 10),
'y': (-10, 10)
}, mode="edge")) # shear：x轴往左右偏离的像素书，(a,b)是a,b间随机值，[a,b]是二选一
do_aug(image, iaa.Rotate(rotate=(-10, 10), mode="edge"))
do_aug(image, iaa.PiecewiseAffine()) # 局部点变形
do_aug(image, iaa.Fog())
do_aug(image, iaa.Clouds())
do_aug(image, iaa.Snowflakes(flake_size=(0.1, 0.2),
density=(0.005, 0.025)))
do_aug(
image,
iaa.Rain(
nb_iterations=1,
drop_size=(0.05, 0.1),
speed=(0.04, 0.08),
))
do_aug(
image,
iaa.ElasticTransformation(alpha=(0.0, 20.0),
sigma=(3.0, 5.0),
mode="nearest"))
do_aug(image, iaa.AdditiveGaussianNoise(scale=(0, 10)))
do_aug(image, iaa.AdditiveLaplaceNoise(scale=(0, 10)))
do_aug(image, iaa.AdditivePoissonNoise(lam=(0, 10)))
do_aug(image, iaa.Salt((0, 0.02)))
do_aug(image, iaa.Pepper((0, 0.02)))
def __init__(self):
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.seq = iaa.Sequential([
sometimes(iaa.Crop(px=(0, 0, 8, 0), keep_size=True)),
sometimes(iaa.Pad(px=(0, 0, 0, 5), keep_size=False)),
iaa.Multiply((0.8, 1.2), per_channel=0.5),
sometimes(iaa.PerspectiveTransform(scale=(0.01, 0.05))),
sometimes(
iaa.OneOf([
iaa.CoarseDropout((0.01, 0.03), size_percent=(0.1, 0.3)),
iaa.CoarseDropout((0.01, 0.03), size_percent=(0.1, 0.3), per_channel=1.0),
iaa.Dropout((0.03,0.05)),
iaa.Salt((0.03,0.05))
])
),
iaa.Multiply((0.8, 1.2), per_channel=0.5),
sometimes(iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.8, 1.2), per_channel=0.5),
second=iaa.ContrastNormalization((0.8, 1.5))
)
),
sometimes(
iaa.OneOf([
iaa.MotionBlur(k=(3,4),angle=(0, 360)),
iaa.GaussianBlur((0, 1.2)),
iaa.AverageBlur(k=(2, 3)),
iaa.MedianBlur(k=(3, 5))
])
),
sometimes(
iaa.CropAndPad(
percent=(-0.05, 0.1),
pad_mode='constant',
pad_cval=(0, 255)
),
),
sometimes(iaa.ElasticTransformation(alpha=(1.0, 2.0), sigma=(2.0, 3.0))), # move pixels locally around (with random strengths)
sometimes(iaa.PiecewiseAffine(scale=(0.01, 0.02), mode='constant')), # sometimes move parts of the image around
sometimes(iaa.AdditiveGaussianNoise((0.02, 0.1))),
sometimes(iaa.AdditivePoissonNoise((0.02,0.05))),
iaa.Invert(p=0.5)
])
def __init__(self):
sometimes = lambda aug: iaa.Sometimes(0.5, aug)
self.seq = iaa.Sequential([
iaa.Multiply((0.8, 1.2), per_channel=0.5),
sometimes(
iaa.OneOf([
iaa.CoarseDropout((0.01, 0.03), size_percent=(0.1, 0.3)),
iaa.CoarseDropout((0.01, 0.03), size_percent=(0.1, 0.3), per_channel=1.0),
iaa.Dropout((0.03,0.05)),
iaa.Salt((0.03,0.05))
])
),
sometimes(iaa.FrequencyNoiseAlpha(
exponent=(-4, 0),
first=iaa.Multiply((0.8, 1.2), per_channel=0.5),
second=iaa.ContrastNormalization((0.8, 1.5))
)
),
sometimes(
iaa.OneOf([
iaa.MotionBlur(k=(3,4),angle=(0, 360)),
iaa.GaussianBlur((0, 1.2)),
iaa.AverageBlur(k=(2, 3)),
iaa.MedianBlur(k=(3, 5))
])
),
sometimes(
iaa.CropAndPad(
percent=(-0.02, 0.02),
pad_mode='constant',
pad_cval=(0, 255)
),
),
sometimes(iaa.AdditiveGaussianNoise((0.02, 0.1))),
sometimes(iaa.AdditivePoissonNoise((0.02,0.05))),
iaa.Invert(p=0.5)
])
def salt(image, keys):
""" Adding noise """
r = random.randint(1, 5) * 0.1
aug = iaa.Sequential([
iaa.Dropout(p=(0, r)),
iaa.CoarseDropout(p=0.02, size_percent=0.5),
iaa.Salt(0.05)
])
aug.add(iaa.Multiply(random.uniform(0.35, 1.5)))
aug.add(iaa.Affine(rotate=random.randint(-180, 180)))
aug.add(iaa.Affine(scale=random.uniform(.2, 1.2), cval=(0, 255)))
seq_det = aug.to_deterministic()
keys = ia.KeypointsOnImage(
[ia.Keypoint(x=keys[0], y=keys[1]),
ia.Keypoint(x=keys[2], y=keys[3])],
shape=image.shape)
image_aug = seq_det.augment_images([image])[0]
keys_aug = seq_det.augment_keypoints([keys])[0]
k = keys_aug.keypoints
keys_aug = [k[0].x, k[0].y, k[1].x, k[1].y]
keys_aug = np.asarray(keys_aug)
return image_aug, keys_aug
# for the rest from the same poisson distribution:
"Additive_Poisson_Noise": lambda lo, hi, percent:
iaa.AdditivePoissonNoise(lam=(lo, hi), per_channel=percent),
# Adds salt and pepper noise to an image, i.e. some white-ish and black-ish pixels.
# Replaces percent of all pixels with salt and pepper noise
"Salt_And_Pepper": lambda percent: iaa.SaltAndPepper(percent),
# Adds coarse salt and pepper noise to image, i.e. rectangles that contain noisy white-ish and black-ish pixels
# Replaces percent of all pixels with salt/pepper in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Salt_And_Pepper": lambda percent, lo, hi: iaa.CoarseSaltAndPepper(percent, size_percent=(lo, hi)),
# Adds salt noise to an image, i.e white-ish pixels
# Replaces percent of all pixels with salt noise
"Salt": lambda percent: iaa.Salt(percent),
# Adds coarse salt noise to image, i.e. rectangles that contain noisy white-ish pixels
# Replaces percent of all pixels with salt in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Salt": lambda percent, lo, hi: iaa.CoarseSalt(percent, size_percent=(lo, hi)),
# Adds Pepper noise to an image, i.e Black-ish pixels
# Replaces percent of all pixels with Pepper noise
"Pepper": lambda percent: iaa.Pepper(percent),
# Adds coarse pepper noise to image, i.e. rectangles that contain noisy black-ish pixels
# Replaces percent of all pixels with salt in an image that has lo to hi percent of the input image size,
# then upscales the results to the input image size, leading to large rectangular areas being replaced.
"Coarse_Pepper": lambda percent, lo, hi: iaa.CoarsePepper(percent, size_percent=(lo, hi)),
def salt(img):
seq = iaa.Sequential(
[iaa.Salt(per_channel=True, p=random.uniform(0, 0.5))])
return seq.augment_image(img)
#!/usr/bin/env python
#-*- coding:utf-8 -*-
#author: wu.zheng midday.me
from imgaug import augmenters as iaa
import cv2
import imgaug as ia
from imgaug.augmentables.segmaps import SegmentationMapOnImage
from imgaug.augmentables.bbs import BoundingBox, BoundingBoxesOnImage
import random
seq = iaa.SomeOf((1, 4), [
iaa.Salt(p=(0.2, 0.4)),
iaa.GaussianBlur(sigma=(0, 2.0)),
iaa.CoarseDropout(p=(0.02, 0.1), size_percent=(0.2, 0.6)),
iaa.JpegCompression(compression=(30, 50)),
])
def augment_with_segmap(image, segmap, num_classes):
if random.random() < 0.3:
return image, segmap
segmap = SegmentationMapOnImage(segmap,
shape=image.shape,
nb_classes=num_classes)
image_aug, segmap_aug = seq(image=image, segmentation_maps=segmap)
return image_aug, None
def main():
parser = argparse.ArgumentParser(description="Check augmenters visually.")
parser.add_argument(
"--only",
default=None,
help=
"If this is set, then only the results of an augmenter with this name will be shown. "
"Optionally, comma-separated list.",
required=False)
args = parser.parse_args()
images = [
ia.quokka_square(size=(128, 128)),
ia.imresize_single_image(data.astronaut(), (128, 128))
]
keypoints = [
ia.KeypointsOnImage([
ia.Keypoint(x=50, y=40),
ia.Keypoint(x=70, y=38),
ia.Keypoint(x=62, y=52)
],
shape=images[0].shape),
ia.KeypointsOnImage([
ia.Keypoint(x=55, y=32),
ia.Keypoint(x=42, y=95),
ia.Keypoint(x=75, y=89)
],
shape=images[1].shape)
]
bounding_boxes = [
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[0].shape),
ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=10, y1=10, x2=20, y2=20),
ia.BoundingBox(x1=40, y1=50, x2=70, y2=60)
],
shape=images[1].shape)
]
augmenters = [
iaa.Sequential([
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="Sequential"),
iaa.SomeOf(2,
children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="SomeOf"),
iaa.OneOf(children=[
iaa.CoarseDropout(p=0.5, size_percent=0.05),
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
iaa.Crop(percent=0.1)
],
name="OneOf"),
iaa.Sometimes(0.5,
iaa.AdditiveGaussianNoise(scale=0.1 * 255),
name="Sometimes"),
iaa.WithColorspace("HSV",
children=[iaa.Add(20)],
name="WithColorspace"),
iaa.WithChannels([0], children=[iaa.Add(20)], name="WithChannels"),
iaa.AddToHueAndSaturation((-20, 20),
per_channel=True,
name="AddToHueAndSaturation"),
iaa.Noop(name="Noop"),
iaa.Resize({
"width": 64,
"height": 64
}, name="Resize"),
iaa.CropAndPad(px=(-8, 8), name="CropAndPad-px"),
iaa.Pad(px=(0, 8), name="Pad-px"),
iaa.Crop(px=(0, 8), name="Crop-px"),
iaa.Crop(percent=(0, 0.1), name="Crop-percent"),
iaa.Fliplr(0.5, name="Fliplr"),
iaa.Flipud(0.5, name="Flipud"),
iaa.Superpixels(p_replace=0.75, n_segments=50, name="Superpixels"),
iaa.Grayscale(0.5, name="Grayscale0.5"),
iaa.Grayscale(1.0, name="Grayscale1.0"),
iaa.GaussianBlur((0, 3.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=10, name="BilateralBlur"),
iaa.Sharpen(alpha=(0.1, 1.0), lightness=(0, 2.0), name="Sharpen"),
iaa.Emboss(alpha=(0.1, 1.0), strength=(0, 2.0), name="Emboss"),
iaa.EdgeDetect(alpha=(0.1, 1.0), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.1, 1.0),
direction=(0, 1.0),
name="DirectedEdgeDetect"),
iaa.Add((-50, 50), name="Add"),
iaa.Add((-50, 50), per_channel=True, name="AddPerChannel"),
iaa.AddElementwise((-50, 50), name="AddElementwise"),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.0, 0.1 * 255),
name="AdditiveGaussianNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.Multiply((0.5, 1.5), per_channel=True, name="MultiplyPerChannel"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.0, 0.1), name="Dropout"),
iaa.CoarseDropout(p=0.05,
size_percent=(0.05, 0.5),
name="CoarseDropout"),
iaa.Invert(p=0.5, name="Invert"),
iaa.Invert(p=0.5, per_channel=True, name="InvertPerChannel"),
iaa.ContrastNormalization(alpha=(0.5, 2.0),
name="ContrastNormalization"),
iaa.SaltAndPepper(p=0.05, name="SaltAndPepper"),
iaa.Salt(p=0.05, name="Salt"),
iaa.Pepper(p=0.05, name="Pepper"),
iaa.CoarseSaltAndPepper(p=0.05,
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.CoarseSalt(p=0.05, size_percent=(0.01, 0.1), name="CoarseSalt"),
iaa.CoarsePepper(p=0.05, size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Affine(scale={
"x": (0.8, 1.2),
"y": (0.8, 1.2)
},
translate_px={
"x": (-16, 16),
"y": (-16, 16)
},
rotate=(-45, 45),
shear=(-16, 16),
order=ia.ALL,
cval=(0, 255),
mode=ia.ALL,
name="Affine"),
iaa.PiecewiseAffine(scale=0.03,
nb_rows=(2, 6),
nb_cols=(2, 6),
name="PiecewiseAffine"),
iaa.PerspectiveTransform(scale=0.1, name="PerspectiveTransform"),
iaa.ElasticTransformation(alpha=(0.5, 8.0),
sigma=1.0,
name="ElasticTransformation"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=False,
name="Alpha"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Add(100),
second=iaa.Dropout(0.5),
per_channel=True,
name="AlphaPerChannel"),
iaa.Alpha(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaAffine"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=False,
name="AlphaElementwise"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Add(50),
second=iaa.ContrastNormalization(2.0),
per_channel=True,
name="AlphaElementwisePerChannel"),
iaa.AlphaElementwise(factor=(0.0, 1.0),
first=iaa.Affine(rotate=(-45, 45)),
per_channel=True,
name="AlphaElementwiseAffine"),
iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0),
per_channel=False,
name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha(first=iaa.EdgeDetect(1.0),
per_channel=False,
name="FrequencyNoiseAlpha")
]
augmenters.append(
iaa.Sequential([iaa.Sometimes(0.2, aug.copy()) for aug in augmenters],
name="Sequential"))
augmenters.append(
iaa.Sometimes(0.5, [aug.copy() for aug in augmenters],
name="Sometimes"))
for augmenter in augmenters:
if args.only is None or augmenter.name in [
v.strip() for v in args.only.split(",")
]:
print("Augmenter: %s" % (augmenter.name, ))
grid = []
for image, kps, bbs in zip(images, keypoints, bounding_boxes):
aug_det = augmenter.to_deterministic()
imgs_aug = aug_det.augment_images(
np.tile(image[np.newaxis, ...], (16, 1, 1, 1)))
kps_aug = aug_det.augment_keypoints([kps] * 16)
bbs_aug = aug_det.augment_bounding_boxes([bbs] * 16)
imgs_aug_drawn = [
kps_aug_one.draw_on_image(img_aug)
for img_aug, kps_aug_one in zip(imgs_aug, kps_aug)
]
imgs_aug_drawn = [
bbs_aug_one.draw_on_image(img_aug)
for img_aug, bbs_aug_one in zip(imgs_aug_drawn, bbs_aug)
]
grid.append(np.hstack(imgs_aug_drawn))
ia.imshow(np.vstack(grid))
tip = amp._get_tip_queue()
augmenters = [
# blur images with a sigma between 0 and 3.0
iaa.Noop(),
iaa.GaussianBlur(sigma=(0.5, 2.0)),
iaa.Add((-50.0, 50.0), per_channel=False),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.07 * 255, 0.07 * 255),
per_channel=False),
iaa.Dropout(p=0.07, per_channel=False),
iaa.CoarseDropout(p=(0.05, 0.15),
size_percent=(0.1, 0.9),
per_channel=False),
iaa.SaltAndPepper(p=(0.05, 0.15), per_channel=False),
iaa.Salt(p=(0.05, 0.15), per_channel=False),
iaa.Pepper(p=(0.05, 0.15), per_channel=False),
iaa.ContrastNormalization(alpha=(iap.Uniform(0.02, 0.03),
iap.Uniform(1.7, 2.1))),
iaa.ElasticTransformation(alpha=(0.5, 2.0)),
]
seq = iaa.Sequential(iaa.OneOf(augmenters), )
def get_data_from_tip(tip, batch_size):
features = []
labels = []
descriptions = []
for i in range(batch_size):
data = tip.get()
d, f, l = data
def main():
# datapath为存放训练图片的地方
datapath = '/home/zhex/data/yuncong/'
# original_file为需要被增强的
original_file = '/home/zhex/data/yuncong/Mall_train.txt' # 需要被增强的训练真值txt
# aug_file只记录了新增的增强后图片的box，要得到原始+增强的所有label：cat original_file augfile>finalfile(txt拼接)
# aug_file输出是pdpd需要的格式，pytorch需要另行转换(可以拼接得到finalfile后直接将finalfile转换)
aug_file = 'augfile_Mall.txt'
dict_before = readlist(original_file)
new_fp = open(aug_file, 'w')
# augscene = {'Mall': 3, 'Part_B': 10, 'Part_A': 13} # 需要哪些场景，新增几倍数量的新数据
augscene = {'Mall': 3}
for scene in augscene:
for i in range(augscene[scene]):
for img_id in dict_before.keys():
if scene in img_id:
print(img_id)
img = Image.open(
datapath +
img_id) # img.convert('RGB')->img.save('filename.jpg')
img = np.array(img)
bbs = ia.BoundingBoxesOnImage([
ia.BoundingBox(x1=x, y1=y, x2=x + w, y2=y + h)
for [x, y, w, h] in dict_before[img_id]
],
shape=img.shape)
# 设置数据增强方式
# import imgaug.augmenters as iaa
# List augmenter that applies only some of its children to images
'''
iaa.SomeOf(n=None,
children=None,
random_order=False,
name=None,
deterministic=False,
random_state=None)
n: 从总的Augmenters中选择多少个来处理图片,类型可以是int,tuple,list,或者随机值
random_order: 是否每次顺序一样,默认值False(即每次顺序一样)
'''
seq = iaa.SomeOf(
(1, 3),
[ #每次使用1~3个Augmenter来处理图片,每个batch的顺序一样
iaa.Crop(px=(0, 16)), #裁剪
iaa.Multiply((0.7, 1.3)), #改变色调
iaa.Affine(scale=(0.5, 0.7)), #仿射变换
iaa.GaussianBlur(sigma=(0, 1.5)), #高斯模糊
iaa.AddToHueAndSaturation(value=(25, -25)),
iaa.ChannelShuffle(1), # RGB三通道随机交换
iaa.ElasticTransformation(alpha=0.1),
iaa.Grayscale(alpha=(0.2, 0.5)),
iaa.Pepper(p=0.03),
iaa.AdditiveGaussianNoise(scale=(0.03 * 255,
0.05 * 255)),
iaa.Dropout(p=(0.03, 0.05)),
iaa.Salt(p=(0.03, 0.05)),
iaa.AverageBlur(k=(1, 3)),
iaa.Add((-10, 10)),
iaa.CoarseSalt(size_percent=0.01)
],
random_order=False)
seq_det = seq.to_deterministic(
) # 保持坐标和图像同步改变，每个batch都要调用一次，不然每次的增强都是一样的
image_aug = seq_det.augment_images([img])[0]
bbs_aug = seq_det.augment_bounding_boxes([bbs])[0]
pic_name = img_id.split('/')[-1].split('.')[0]
pic_dir = img_id.split(pic_name)[0]
if not os.path.exists(datapath + 'myaug/' + pic_dir):
os.makedirs(datapath + 'myaug/' + pic_dir)
new_img_id = 'myaug/' + pic_dir + pic_name + '_{}'.format(
i) + '.jpg'
Image.fromarray(image_aug).save(datapath + new_img_id)
new_fp = writelist(new_fp, new_img_id,
bbs_aug.bounding_boxes)
def augment(train_x, train_y_prob, train_y_keys):
aug = iaa.Sequential()
x = random.uniform(-10, 10) * .01
y = random.uniform(-10, 10) * .01
aug.add(iaa.Affine(translate_percent={"x": x, "y": y}, # (-10, 10) -> (-1, 3)
scale=random.uniform(.7, 1.1), # (.7, 1.1) -> (.99, 1.01)
rotate=random.uniform(-10, 10), # (-10, 10) -> (-5, 5)
shear=random.uniform(-15, 15), # (-15, 15) -> (.5, .5)
cval=(0, 255))) # (0 ~ 255)
""" random brightness """
brightness = random.uniform(.5, 1.5) # (.5, 1.5) -> (.995, 1.005)
aug.add(iaa.Multiply(brightness))
""" random pixel dropout """
aug.add(iaa.CoarseDropout(p=.001, size_percent=0.005))
aug.add(iaa.Dropout(p=(0, random.uniform(1, 5) * 0.005)))
""" salt noise """
aug.add(iaa.Salt(.001))
""" additive gaussian noise """
aug.add(iaa.AdditiveGaussianNoise(scale=random.uniform(.01, .1) * 255))
""" crop """
pixel = 5
aug.add(iaa.Crop(px=((0, random.randint(0, pixel)), (0, random.randint(0, pixel)),
(0, random.randint(0, pixel)), (0, random.randint(0, pixel)))))
seq_det = aug.to_deterministic()
image_aug = []
keys_aug = []
for i in range(0, train_x.shape[0]):
image = train_x[i, :, :, :]
prob = train_y_prob[i, :]
keys = train_y_keys[i, :]
image_aug.append(seq_det.augment_images([image])[0])
koi = ia.KeypointsOnImage([ia.Keypoint(x=keys[0], y=keys[1]),
ia.Keypoint(x=keys[2], y=keys[3]),
ia.Keypoint(x=keys[4], y=keys[5]),
ia.Keypoint(x=keys[6], y=keys[7]),
ia.Keypoint(x=keys[8], y=keys[9])], shape=image.shape)
k = seq_det.augment_keypoints([koi])[0]
k = k.keypoints
keys = [k[0].x, k[0].y, k[1].x, k[1].y, k[2].x, k[2].y, k[3].x, k[3].y, k[4].x, k[4].y]
index = 0
prob = prob.T[0][:-1] # last index is a direction parameter
for j in range(0, len(prob)):
keys[index] = keys[index] * prob[j]
keys[index + 1] = keys[index + 1] * prob[j]
index = index + 2
keys_aug.append(keys)
image_aug = np.asarray(image_aug)
keys_aug = np.asarray(keys_aug)
keys_aug = np.expand_dims(keys_aug, axis=-1)
return image_aug, keys_aug
def create_augmenters(height, width, height_augmentable, width_augmentable,
only_augmenters):
def lambda_func_images(images, random_state, parents, hooks):
return images
def lambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return heatmaps
def lambda_func_keypoints(keypoints, random_state, parents, hooks):
return keypoints
def assertlambda_func_images(images, random_state, parents, hooks):
return True
def assertlambda_func_heatmaps(heatmaps, random_state, parents, hooks):
return True
def assertlambda_func_keypoints(keypoints, random_state, parents, hooks):
return True
augmenters_meta = [
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=False,
name="Sequential_2xNoop"),
iaa.Sequential([iaa.Noop(), iaa.Noop()],
random_order=True,
name="Sequential_2xNoop_random_order"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=False,
name="SomeOf_3xNoop"),
iaa.SomeOf((1, 3),
[iaa.Noop(), iaa.Noop(), iaa.Noop()],
random_order=True,
name="SomeOf_3xNoop_random_order"),
iaa.OneOf([iaa.Noop(), iaa.Noop(), iaa.Noop()], name="OneOf_3xNoop"),
iaa.Sometimes(0.5, iaa.Noop(), name="Sometimes_Noop"),
iaa.WithChannels([1, 2], iaa.Noop(), name="WithChannels_1_and_2_Noop"),
iaa.Noop(name="Noop"),
iaa.Lambda(func_images=lambda_func_images,
func_heatmaps=lambda_func_heatmaps,
func_keypoints=lambda_func_keypoints,
name="Lambda"),
iaa.AssertLambda(func_images=assertlambda_func_images,
func_heatmaps=assertlambda_func_heatmaps,
func_keypoints=assertlambda_func_keypoints,
name="AssertLambda"),
iaa.AssertShape((None, height_augmentable, width_augmentable, None),
name="AssertShape"),
iaa.ChannelShuffle(0.5, name="ChannelShuffle")
]
augmenters_arithmetic = [
iaa.Add((-10, 10), name="Add"),
iaa.AddElementwise((-10, 10), name="AddElementwise"),
#iaa.AddElementwise((-500, 500), name="AddElementwise"),
iaa.AdditiveGaussianNoise(scale=(5, 10), name="AdditiveGaussianNoise"),
iaa.AdditiveLaplaceNoise(scale=(5, 10), name="AdditiveLaplaceNoise"),
iaa.AdditivePoissonNoise(lam=(1, 5), name="AdditivePoissonNoise"),
iaa.Multiply((0.5, 1.5), name="Multiply"),
iaa.MultiplyElementwise((0.5, 1.5), name="MultiplyElementwise"),
iaa.Dropout((0.01, 0.05), name="Dropout"),
iaa.CoarseDropout((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseDropout"),
iaa.ReplaceElementwise((0.01, 0.05), (0, 255),
name="ReplaceElementwise"),
#iaa.ReplaceElementwise((0.95, 0.99), (0, 255), name="ReplaceElementwise"),
iaa.SaltAndPepper((0.01, 0.05), name="SaltAndPepper"),
iaa.ImpulseNoise((0.01, 0.05), name="ImpulseNoise"),
iaa.CoarseSaltAndPepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSaltAndPepper"),
iaa.Salt((0.01, 0.05), name="Salt"),
iaa.CoarseSalt((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarseSalt"),
iaa.Pepper((0.01, 0.05), name="Pepper"),
iaa.CoarsePepper((0.01, 0.05),
size_percent=(0.01, 0.1),
name="CoarsePepper"),
iaa.Invert(0.1, name="Invert"),
# ContrastNormalization
iaa.JpegCompression((50, 99), name="JpegCompression")
]
augmenters_blend = [
iaa.Alpha((0.01, 0.99), iaa.Noop(), name="Alpha"),
iaa.AlphaElementwise((0.01, 0.99), iaa.Noop(),
name="AlphaElementwise"),
iaa.SimplexNoiseAlpha(iaa.Noop(), name="SimplexNoiseAlpha"),
iaa.FrequencyNoiseAlpha((-2.0, 2.0),
iaa.Noop(),
name="FrequencyNoiseAlpha")
]
augmenters_blur = [
iaa.GaussianBlur(sigma=(1.0, 5.0), name="GaussianBlur"),
iaa.AverageBlur(k=(3, 11), name="AverageBlur"),
iaa.MedianBlur(k=(3, 11), name="MedianBlur"),
iaa.BilateralBlur(d=(3, 11), name="BilateralBlur"),
iaa.MotionBlur(k=(3, 11), name="MotionBlur")
]
augmenters_color = [
# InColorspace (deprecated)
iaa.WithColorspace(to_colorspace="HSV",
children=iaa.Noop(),
name="WithColorspace"),
iaa.WithHueAndSaturation(children=iaa.Noop(),
name="WithHueAndSaturation"),
iaa.MultiplyHueAndSaturation((0.8, 1.2),
name="MultiplyHueAndSaturation"),
iaa.MultiplyHue((-1.0, 1.0), name="MultiplyHue"),
iaa.MultiplySaturation((0.8, 1.2), name="MultiplySaturation"),
iaa.AddToHueAndSaturation((-10, 10), name="AddToHueAndSaturation"),
iaa.AddToHue((-10, 10), name="AddToHue"),
iaa.AddToSaturation((-10, 10), name="AddToSaturation"),
iaa.ChangeColorspace(to_colorspace="HSV", name="ChangeColorspace"),
iaa.Grayscale((0.01, 0.99), name="Grayscale"),
iaa.KMeansColorQuantization((2, 16), name="KMeansColorQuantization"),
iaa.UniformColorQuantization((2, 16), name="UniformColorQuantization")
]
augmenters_contrast = [
iaa.GammaContrast(gamma=(0.5, 2.0), name="GammaContrast"),
iaa.SigmoidContrast(gain=(5, 20),
cutoff=(0.25, 0.75),
name="SigmoidContrast"),
iaa.LogContrast(gain=(0.7, 1.0), name="LogContrast"),
iaa.LinearContrast((0.5, 1.5), name="LinearContrast"),
iaa.AllChannelsCLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
name="AllChannelsCLAHE"),
iaa.CLAHE(clip_limit=(2, 10),
tile_grid_size_px=(3, 11),
to_colorspace="HSV",
name="CLAHE"),
iaa.AllChannelsHistogramEqualization(
name="AllChannelsHistogramEqualization"),
iaa.HistogramEqualization(to_colorspace="HSV",
name="HistogramEqualization"),
]
augmenters_convolutional = [
iaa.Convolve(np.float32([[0, 0, 0], [0, 1, 0], [0, 0, 0]]),
name="Convolve_3x3"),
iaa.Sharpen(alpha=(0.01, 0.99), lightness=(0.5, 2), name="Sharpen"),
iaa.Emboss(alpha=(0.01, 0.99), strength=(0, 2), name="Emboss"),
iaa.EdgeDetect(alpha=(0.01, 0.99), name="EdgeDetect"),
iaa.DirectedEdgeDetect(alpha=(0.01, 0.99), name="DirectedEdgeDetect")
]
augmenters_edges = [iaa.Canny(alpha=(0.01, 0.99), name="Canny")]
augmenters_flip = [
iaa.Fliplr(1.0, name="Fliplr"),
iaa.Flipud(1.0, name="Flipud")
]
augmenters_geometric = [
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=0,
mode="constant",
cval=(0, 255),
name="Affine_order_0_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
name="Affine_order_1_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=3,
mode="constant",
cval=(0, 255),
name="Affine_order_3_constant"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="edge",
cval=(0, 255),
name="Affine_order_1_edge"),
iaa.Affine(scale=(0.9, 1.1),
translate_percent={
"x": (-0.05, 0.05),
"y": (-0.05, 0.05)
},
rotate=(-10, 10),
shear=(-10, 10),
order=1,
mode="constant",
cval=(0, 255),
backend="skimage",
name="Affine_order_1_constant_skimage"),
# TODO AffineCv2
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="constant",
name="PiecewiseAffine_4x4_order_1_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=0,
mode="constant",
name="PiecewiseAffine_4x4_order_0_constant"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=4,
nb_cols=4,
order=1,
mode="edge",
name="PiecewiseAffine_4x4_order_1_edge"),
iaa.PiecewiseAffine(scale=(0.01, 0.05),
nb_rows=8,
nb_cols=8,
order=1,
mode="constant",
name="PiecewiseAffine_8x8_order_1_constant"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=False,
name="PerspectiveTransform"),
iaa.PerspectiveTransform(scale=(0.01, 0.05),
keep_size=True,
name="PerspectiveTransform_keep_size"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=0,
mode="constant",
cval=0,
name="ElasticTransformation_order_0_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="constant",
cval=0,
name="ElasticTransformation_order_1_constant"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="nearest",
cval=0,
name="ElasticTransformation_order_1_nearest"),
iaa.ElasticTransformation(
alpha=(1, 10),
sigma=(0.5, 1.5),
order=1,
mode="reflect",
cval=0,
name="ElasticTransformation_order_1_reflect"),
iaa.Rot90((1, 3), keep_size=False, name="Rot90"),
iaa.Rot90((1, 3), keep_size=True, name="Rot90_keep_size")
]
augmenters_pooling = [
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=False,
name="AveragePooling"),
iaa.AveragePooling(kernel_size=(1, 16),
keep_size=True,
name="AveragePooling_keep_size"),
iaa.MaxPooling(kernel_size=(1, 16), keep_size=False,
name="MaxPooling"),
iaa.MaxPooling(kernel_size=(1, 16),
keep_size=True,
name="MaxPooling_keep_size"),
iaa.MinPooling(kernel_size=(1, 16), keep_size=False,
name="MinPooling"),
iaa.MinPooling(kernel_size=(1, 16),
keep_size=True,
name="MinPooling_keep_size"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=False,
name="MedianPooling"),
iaa.MedianPooling(kernel_size=(1, 16),
keep_size=True,
name="MedianPooling_keep_size")
]
augmenters_segmentation = [
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="cubic",
name="Superpixels_max_size_64_cubic"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=64,
interpolation="linear",
name="Superpixels_max_size_64_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=128,
interpolation="linear",
name="Superpixels_max_size_128_linear"),
iaa.Superpixels(p_replace=(0.05, 1.0),
n_segments=(10, 100),
max_size=224,
interpolation="linear",
name="Superpixels_max_size_224_linear"),
iaa.UniformVoronoi(n_points=(250, 1000), name="UniformVoronoi"),
iaa.RegularGridVoronoi(n_rows=(16, 31),
n_cols=(16, 31),
name="RegularGridVoronoi"),
iaa.RelativeRegularGridVoronoi(n_rows_frac=(0.07, 0.14),
n_cols_frac=(0.07, 0.14),
name="RelativeRegularGridVoronoi"),
]
augmenters_size = [
iaa.Resize((0.8, 1.2), interpolation="nearest", name="Resize_nearest"),
iaa.Resize((0.8, 1.2), interpolation="linear", name="Resize_linear"),
iaa.Resize((0.8, 1.2), interpolation="cubic", name="Resize_cubic"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="CropAndPad_edge"),
iaa.CropAndPad(percent=(-0.2, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="CropAndPad_keep_size"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
keep_size=False,
name="Pad"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="edge",
pad_cval=(0, 255),
keep_size=False,
name="Pad_edge"),
iaa.Pad(percent=(0.05, 0.2),
pad_mode="constant",
pad_cval=(0, 255),
name="Pad_keep_size"),
iaa.Crop(percent=(0.05, 0.2), keep_size=False, name="Crop"),
iaa.Crop(percent=(0.05, 0.2), name="Crop_keep_size"),
iaa.PadToFixedSize(width=width + 10,
height=height + 10,
pad_mode="constant",
pad_cval=(0, 255),
name="PadToFixedSize"),
iaa.CropToFixedSize(width=width - 10,
height=height - 10,
name="CropToFixedSize"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="nearest",
name="KeepSizeByResize_CropToFixedSize_nearest"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="linear",
name="KeepSizeByResize_CropToFixedSize_linear"),
iaa.KeepSizeByResize(iaa.CropToFixedSize(height=height - 10,
width=width - 10),
interpolation="cubic",
name="KeepSizeByResize_CropToFixedSize_cubic"),
]
augmenters_weather = [
iaa.FastSnowyLandscape(lightness_threshold=(100, 255),
lightness_multiplier=(1.0, 4.0),
name="FastSnowyLandscape"),
iaa.Clouds(name="Clouds"),
iaa.Fog(name="Fog"),
iaa.CloudLayer(intensity_mean=(196, 255),
intensity_freq_exponent=(-2.5, -2.0),
intensity_coarse_scale=10,
alpha_min=0,
alpha_multiplier=(0.25, 0.75),
alpha_size_px_max=(2, 8),
alpha_freq_exponent=(-2.5, -2.0),
sparsity=(0.8, 1.0),
density_multiplier=(0.5, 1.0),
name="CloudLayer"),
iaa.Snowflakes(name="Snowflakes"),
iaa.SnowflakesLayer(density=(0.005, 0.075),
density_uniformity=(0.3, 0.9),
flake_size=(0.2, 0.7),
flake_size_uniformity=(0.4, 0.8),
angle=(-30, 30),
speed=(0.007, 0.03),
blur_sigma_fraction=(0.0001, 0.001),
name="SnowflakesLayer")
]
augmenters = (augmenters_meta + augmenters_arithmetic + augmenters_blend +
augmenters_blur + augmenters_color + augmenters_contrast +
augmenters_convolutional + augmenters_edges +
augmenters_flip + augmenters_geometric + augmenters_pooling +
augmenters_segmentation + augmenters_size +
augmenters_weather)
if only_augmenters is not None:
augmenters_reduced = []
for augmenter in augmenters:
if any([
re.search(pattern, augmenter.name)
for pattern in only_augmenters
]):
augmenters_reduced.append(augmenter)
augmenters = augmenters_reduced
return augmenters
augmenters = [
iaa.GaussianBlur(sigma=(1.0, 3.0)), # blur images with a sigma of 0 to 2.0
iaa.MotionBlur((3, 5)), # blur image
iaa.AdditiveGaussianNoise(
scale=(0, 0.05 * 255)
), # Add gaussian noise to an image, sampled once per pixel from a normal distribution N(0, 0.05*255)
iaa.AdditiveLaplaceNoise(scale=(0, 0.05 * 255)),
iaa.Multiply((0.5, 1.5), per_channel=0.5),
iaa.Multiply(
(0.5,
1.5)), # Multiply each image with a random value between 0.5 and 1.5:
iaa.Dropout(
p=(0.05, 0.15)
), # Sample per image a value p from the range 0.05<=p<=0.15 and then drop p percent of all pixels in the image (i.e. convert them to black pixels):
iaa.ImpulseNoise(p=(0.03, 0.06)),
iaa.Salt(p=(0.03, 0.05)),
iaa.Add((-30, 30)), # adding random value to pixels
iaa.SigmoidContrast(6.1, 0.5),
iaa.PerspectiveTransform(scale=0.02),
iaa.PerspectiveTransform(scale=0.01),
iaa.PerspectiveTransform(scale=0.015),
iaa.PerspectiveTransform(scale=0.012),
iaa.PiecewiseAffine(scale=0.015),
iaa.PiecewiseAffine(scale=0.005),
iaa.PiecewiseAffine(scale=0.01),
iaa.Crop(px=(10, 15), keep_size=True),
iaa.Crop(px=(10, 15))
]
def check_dir_or_create(dir):
def train(tip, iters=None, learning_rate=0.001, batch_norm=False):
import tensorflow as tf
random.seed(datetime.datetime.now())
tf.set_random_seed(seed())
default_device = '/gpu:0'
# default_device = '/cpu:0'
# hyperparams
batch_size = 94
training = True
batch_norms_training = False
# steps
light_summary_steps = 25
heavy_summary_steps = 250
checkpoint_steps = 500
# stats / logging
model_version = 1
time_str = datetime.datetime.now().strftime('%m-%d--%H%M%S')
best_loss = 0.6
batch_num = 0
best_acc = 0
models_to_keep = 3
# glob vars
heavy_sum = []
light_sum = []
models_history = []
train_only_dense = False
dense_size = 64
dropout = True
learning_rate = 1e-6
augmentation = True
with tf.device(default_device):
# config = tf.ConfigProto()
config = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
# please do not use the totality of the GPU memory
config.gpu_options.per_process_gpu_memory_fraction = 0.98
# config = tf.ConfigProto(device_count = {'GPU': 0})
config.gpu_options.allow_growth = True
with tf.Session(graph=tf.Graph(), config=config) as sess:
with tf.name_scope("inputs"):
# _images = tf.placeholder(tf.float32, [None, 224, 224, 1])
_images = tf.placeholder(tf.float32, [batch_size, 224, 224, 1])
_is_training = tf.placeholder(tf.bool, name='is_training')
model = vggish.Vggish(_images,
classes=2,
trainable=training,
batch_norm=batch_norm,
dropout=dropout,
only_dense=train_only_dense,
dense_size=dense_size,
bn_trainable=batch_norms_training)
with tf.name_scope("targets"):
_labels = tf.placeholder(tf.float32, shape=(None, 2), name='labels')
with tf.name_scope("outputs"):
logits = model.fc3l
# predictions = tf.nn.softmax(logits, name='predictions')
predictions = tf.nn.sigmoid(logits, name='predictions')
tvars = tf.trainable_variables()
for v in tvars:
print(v)
if 'weights' in v.name:
heavy_sum.append(tf.summary.histogram(v.name, v))
# if 'conv1_1' in v.name:
# light_sum.append(tf.summary.histogram(v.name, v))
for v in tvars:
if 'bias' in v.name:
heavy_sum.append(tf.summary.histogram(v.name, v))
# if 'conv1_1' in v.name:
# light_sum.append(tf.summary.histogram(v.name, v))
light_sum.append(tf.summary.histogram("predictions", predictions))
light_sum.extend(model.summaries)
heavy_sum.extend(model.heavy_summaries)
with tf.name_scope("0_cost"):
cross_entropy = tf.nn.sigmoid_cross_entropy_with_logits(
logits=logits,
labels=_labels,
name='cross_entropy'
)
tvars = tf.trainable_variables()
L2 = [tf.nn.l2_loss(v) for v in tvars if 'bias' not in v.name]
lossL2 = tf.add_n(L2) * 0.01
cost = tf.reduce_mean(cross_entropy, name='cost') + lossL2
light_sum.append(tf.summary.scalar("cost", cost))
def my_capper(t):
print(t)
# return t
if t is None:
return None
return tf.clip_by_value(t, -5., 5.)
log_string = 'logs/{}-vggish/{}-lr-{:.8f}'.format(model_version,
time_str,
learning_rate)
with tf.name_scope("0_train"):
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
vars_to_train = model.get_vars_to_train()
global_step = tf.Variable(0, name='global_step')
learning_rate = tf.train.exponential_decay(learning_rate, global_step, 100000, 0.96, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)
grads_and_vars = optimizer.compute_gradients(cost, var_list=vars_to_train)
grads_and_vars = [(my_capper(gv[0]), gv[1]) for gv in grads_and_vars]
optimizer = optimizer.apply_gradients(grads_and_vars, global_step=global_step)
correct_predictions = tf.equal(tf.argmax(predictions, 1),
tf.argmax(_labels, 1),
name='correct_predictions')
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32),
name='accuracy')
grad_norm = tf.norm(grads_and_vars[0][0])
light_sum.append(tf.summary.scalar("accuracy", accuracy))
light_sum.append(tf.summary.scalar("gradient", grad_norm))
light_summary = tf.summary.merge(light_sum)
heavy_summary = tf.summary.merge(light_sum + heavy_sum)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(tf.global_variables(), max_to_keep=None)
iteration = 0
try:
print('[+] loading startup.json')
startup = json.load(open('startup.vggish.json', 'r'))
print('[+] loading path:', startup['path'])
state = json.load(open(startup['path'], 'r'))
print('[+] loading checkpoint:', state['checkpoint_path'])
last_checkpoint = os.path.dirname(state['checkpoint_path'])
weights_to_load = vggish.conv_vars + vggish.fc_vars
if train_only_dense:
weights_to_load = vggish.conv_vars
model.load_weights(last_checkpoint, vars_names=weights_to_load)
iteration = state['iteration']
best_loss = state['best_loss']
if 'train_loss' in state:
best_loss = state['best_loss']
checkpoint_path = state['checkpoint_path']
except:
print('[!] no models to checkpoint from..')
raise
writer = tf.summary.FileWriter(log_string)
augmenters = [
# blur images with a sigma between 0 and 3.0
iaa.Noop(),
iaa.GaussianBlur(sigma=(0.5, 2.0)),
iaa.Add((-50.0, 50.0), per_channel=False),
iaa.AdditiveGaussianNoise(loc=0,
scale=(0.07*255, 0.07*255),
per_channel=False),
iaa.Dropout(p=0.07, per_channel=False),
iaa.CoarseDropout(p=(0.05, 0.15),
size_percent=(0.1, 0.9),
per_channel=False),
iaa.SaltAndPepper(p=(0.05, 0.15), per_channel=False),
iaa.Salt(p=(0.05, 0.15), per_channel=False),
iaa.Pepper(p=(0.05, 0.15), per_channel=False),
iaa.ContrastNormalization(alpha=(iap.Uniform(0.02, 0.03),
iap.Uniform(1.7, 2.1))),
iaa.ElasticTransformation(alpha=(0.5, 2.0)),
]
if not augmentation:
augmenters = [
iaa.Noop(),
]
seq = iaa.Sequential(
iaa.OneOf(augmenters),
)
while True:
if iters and iteration >= iters:
return
seq.reseed(seed())
np.random.seed(seed())
descriptions, features, labels = get_data_from_tip(tip, batch_size)
iteration += 1
# import pdb; pdb.set_trace()
mean = 0.172840994091
std = 0.206961060284
merged_summaries = light_summary
if iteration % heavy_summary_steps == 0:
merged_summaries = heavy_summary
try:
with np.errstate(all='raise'):
for i in range(5):
newfeatures = seq.augment_images(features * 255) / 255
if not np.isnan(newfeatures).any():
break
print('[!] has nan in newfeatures, retrying', i)
if np.isnan(newfeatures).any():
print('[!] could not get rid of nan.. skipping this batch')
iteration -= 1
continue
features = newfeatures
except Exception:
print("[!] Warning detected augmenting, skipping..")
tb = traceback.format_exc()
open("numpy_warns.log", 'ab').write(str(descriptions).encode('utf-8'))
open("numpy_warns.log", 'ab').write(str(tb).encode('utf-8'))
open("numpy_warns.log", 'a').write('------------------------------------')
# import pdb; pdb.set_trace()
continue
feed_dict = {
_images: features,
_labels: labels,
_is_training: training
}
train_loss, val_acc, _, p, summary, grad, _corr_pred = sess.run(
[cost,
accuracy,
optimizer,
logits,
merged_summaries,
grad_norm,
correct_predictions],
feed_dict=feed_dict
)
print('[+] iteration {}'.format(iteration))
if iteration % light_summary_steps == 0:
if os.path.isfile('nomix_pdb'):
import pdb
pdb.set_trace()
print('[+] writing summary')
writer.add_summary(summary, iteration)
print('\tIteration {} Accuracy: {} Loss: {}/{}'.format(iteration,
val_acc,
train_loss,
best_loss))
# if train_loss < best_loss:
if iteration % checkpoint_steps == 0:
# print('\t\tNew Best Loss!')
best_loss = train_loss
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
checkpoint_dir = os.path.join('D:\\checkpoint', timestamp)
checkpoint_path = os.path.join('D:\\checkpoint', timestamp, 'model.ckpt')
print('\t\tSaving model to:' + checkpoint_path)
saver.save(sess, checkpoint_path, global_step=batch_num)
state = {
'iteration': iteration,
'best_acc': float(best_acc),
'best_loss': float(best_loss),
'val_acc': float(val_acc),
'train_loss': float(train_loss),
'checkpoint_path': checkpoint_path,
'log_string': log_string,
}
# state_path = os.path.join('save', timestamp, 'state.json')
state_path = os.path.join('D:\\checkpoint', timestamp, 'state.json')
open(state_path, 'w').write(json.dumps(state))
startup = {
'path': state_path,
}
open('startup.vggish.json', 'w').write(json.dumps(startup))
models_history.append(checkpoint_dir)
while len(models_history) > models_to_keep:
try:
path_to_del = models_history.pop(0)
print('[+] deleting model', path_to_del)
shutil.rmtree(path_to_del)
except:
print('[+] failed to delete')
traceback.print_exc()
def draw_per_augmenter_images():
print("[draw_per_augmenter_images] Loading image...")
#image = misc.imresize(ndimage.imread("quokka.jpg")[0:643, 0:643], (128, 128))
image = ia.quokka_square(size=(128, 128))
keypoints = [ia.Keypoint(x=34, y=15), ia.Keypoint(x=85, y=13), ia.Keypoint(x=63, y=73)] # left ear, right ear, mouth
keypoints = [ia.KeypointsOnImage(keypoints, shape=image.shape)]
print("[draw_per_augmenter_images] Initializing...")
rows_augmenters = [
(0, "Noop", [("", iaa.Noop()) for _ in sm.xrange(5)]),
(0, "Crop\n(top, right,\nbottom, left)", [(str(vals), iaa.Crop(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Pad\n(top, right,\nbottom, left)", [(str(vals), iaa.Pad(px=vals)) for vals in [(2, 0, 0, 0), (0, 8, 8, 0), (4, 0, 16, 4), (8, 0, 0, 32), (32, 64, 0, 0)]]),
(0, "Fliplr", [(str(p), iaa.Fliplr(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Flipud", [(str(p), iaa.Flipud(p)) for p in [0, 0, 1, 1, 1]]),
(0, "Superpixels\np_replace=1", [("n_segments=%d" % (n_segments,), iaa.Superpixels(p_replace=1.0, n_segments=n_segments)) for n_segments in [25, 50, 75, 100, 125]]),
(0, "Superpixels\nn_segments=100", [("p_replace=%.2f" % (p_replace,), iaa.Superpixels(p_replace=p_replace, n_segments=100)) for p_replace in [0, 0.25, 0.5, 0.75, 1.0]]),
(0, "Invert", [("p=%d" % (p,), iaa.Invert(p=p)) for p in [0, 0, 1, 1, 1]]),
(0, "Invert\n(per_channel)", [("p=%.2f" % (p,), iaa.Invert(p=p, per_channel=True)) for p in [0.5, 0.5, 0.5, 0.5, 0.5]]),
(0, "Add", [("value=%d" % (val,), iaa.Add(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Add\n(per channel)", [("value=(%d, %d)" % (vals[0], vals[1],), iaa.Add(vals, per_channel=True)) for vals in [(-55, -35), (-35, -15), (-10, 10), (15, 35), (35, 55)]]),
(0, "AddToHueAndSaturation", [("value=%d" % (val,), iaa.AddToHueAndSaturation(val)) for val in [-45, -25, 0, 25, 45]]),
(0, "Multiply", [("value=%.2f" % (val,), iaa.Multiply(val)) for val in [0.25, 0.5, 1.0, 1.25, 1.5]]),
(1, "Multiply\n(per channel)", [("value=(%.2f, %.2f)" % (vals[0], vals[1],), iaa.Multiply(vals, per_channel=True)) for vals in [(0.15, 0.35), (0.4, 0.6), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "GaussianBlur", [("sigma=%.2f" % (sigma,), iaa.GaussianBlur(sigma=sigma)) for sigma in [0.25, 0.50, 1.0, 2.0, 4.0]]),
(0, "AverageBlur", [("k=%d" % (k,), iaa.AverageBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "MedianBlur", [("k=%d" % (k,), iaa.MedianBlur(k=k)) for k in [1, 3, 5, 7, 9]]),
(0, "BilateralBlur\nsigma_color=250,\nsigma_space=250", [("d=%d" % (d,), iaa.BilateralBlur(d=d, sigma_color=250, sigma_space=250)) for d in [1, 3, 5, 7, 9]]),
(0, "Sharpen\n(alpha=1)", [("lightness=%.2f" % (lightness,), iaa.Sharpen(alpha=1, lightness=lightness)) for lightness in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "Emboss\n(alpha=1)", [("strength=%.2f" % (strength,), iaa.Emboss(alpha=1, strength=strength)) for strength in [0, 0.5, 1.0, 1.5, 2.0]]),
(0, "EdgeDetect", [("alpha=%.2f" % (alpha,), iaa.EdgeDetect(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(0, "DirectedEdgeDetect\n(alpha=1)", [("direction=%.2f" % (direction,), iaa.DirectedEdgeDetect(alpha=1, direction=direction)) for direction in [0.0, 1*(360/5)/360, 2*(360/5)/360, 3*(360/5)/360, 4*(360/5)/360]]),
(0, "AdditiveGaussianNoise", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "AdditiveGaussianNoise\n(per channel)", [("scale=%.2f*255" % (scale,), iaa.AdditiveGaussianNoise(scale=scale * 255, per_channel=True)) for scale in [0.025, 0.05, 0.1, 0.2, 0.3]]),
(0, "Dropout", [("p=%.2f" % (p,), iaa.Dropout(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Dropout\n(per channel)", [("p=%.2f" % (p,), iaa.Dropout(p=p, per_channel=True)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(3, "CoarseDropout\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseDropout\n(p=0.2, per channel)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseDropout(p=0.2, size_percent=size_percent, per_channel=True, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "SaltAndPepper", [("p=%.2f" % (p,), iaa.SaltAndPepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Salt", [("p=%.2f" % (p,), iaa.Salt(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "Pepper", [("p=%.2f" % (p,), iaa.Pepper(p=p)) for p in [0.025, 0.05, 0.1, 0.2, 0.4]]),
(0, "CoarseSaltAndPepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSaltAndPepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarseSalt\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarseSalt(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "CoarsePepper\n(p=0.2)", [("size_percent=%.2f" % (size_percent,), iaa.CoarsePepper(p=0.2, size_percent=size_percent, min_size=2)) for size_percent in [0.3, 0.2, 0.1, 0.05, 0.02]]),
(0, "ContrastNormalization", [("alpha=%.1f" % (alpha,), iaa.ContrastNormalization(alpha=alpha)) for alpha in [0.5, 0.75, 1.0, 1.25, 1.50]]),
(0, "ContrastNormalization\n(per channel)", [("alpha=(%.2f, %.2f)" % (alphas[0], alphas[1],), iaa.ContrastNormalization(alpha=alphas, per_channel=True)) for alphas in [(0.4, 0.6), (0.65, 0.85), (0.9, 1.1), (1.15, 1.35), (1.4, 1.6)]]),
(0, "Grayscale", [("alpha=%.1f" % (alpha,), iaa.Grayscale(alpha=alpha)) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(6, "PerspectiveTransform", [("scale=%.3f" % (scale,), iaa.PerspectiveTransform(scale=scale)) for scale in [0.025, 0.05, 0.075, 0.10, 0.125]]),
(0, "PiecewiseAffine", [("scale=%.3f" % (scale,), iaa.PiecewiseAffine(scale=scale)) for scale in [0.015, 0.03, 0.045, 0.06, 0.075]]),
(0, "Affine: Scale", [("%.1fx" % (scale,), iaa.Affine(scale=scale)) for scale in [0.1, 0.5, 1.0, 1.5, 1.9]]),
(0, "Affine: Translate", [("x=%d y=%d" % (x, y), iaa.Affine(translate_px={"x": x, "y": y})) for x, y in [(-32, -16), (-16, -32), (-16, -8), (16, 8), (16, 32)]]),
(0, "Affine: Rotate", [("%d deg" % (rotate,), iaa.Affine(rotate=rotate)) for rotate in [-90, -45, 0, 45, 90]]),
(0, "Affine: Shear", [("%d deg" % (shear,), iaa.Affine(shear=shear)) for shear in [-45, -25, 0, 25, 45]]),
(0, "Affine: Modes", [(mode, iaa.Affine(translate_px=-32, mode=mode)) for mode in ["constant", "edge", "symmetric", "reflect", "wrap"]]),
(0, "Affine: cval", [("%d" % (int(cval*255),), iaa.Affine(translate_px=-32, cval=int(cval*255), mode="constant")) for cval in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(
2, "Affine: all", [
(
"",
iaa.Affine(
scale={"x": (0.5, 1.5), "y": (0.5, 1.5)},
translate_px={"x": (-32, 32), "y": (-32, 32)},
rotate=(-45, 45),
shear=(-32, 32),
mode=ia.ALL,
cval=(0.0, 1.0)
)
)
for _ in sm.xrange(5)
]
),
(1, "ElasticTransformation\n(sigma=0.2)", [("alpha=%.1f" % (alpha,), iaa.ElasticTransformation(alpha=alpha, sigma=0.2)) for alpha in [0.1, 0.5, 1.0, 3.0, 9.0]]),
(0, "Alpha\nwith EdgeDetect(1.0)", [("factor=%.1f" % (factor,), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0))) for factor in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(4, "Alpha\nwith EdgeDetect(1.0)\n(per channel)", [("factor=(%.2f, %.2f)" % (factor[0], factor[1]), iaa.Alpha(factor=factor, first=iaa.EdgeDetect(1.0), per_channel=0.5)) for factor in [(0.0, 0.2), (0.15, 0.35), (0.4, 0.6), (0.65, 0.85), (0.8, 1.0)]]),
(15, "SimplexNoiseAlpha\nwith EdgeDetect(1.0)", [("", iaa.SimplexNoiseAlpha(first=iaa.EdgeDetect(1.0))) for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]]),
(9, "FrequencyNoiseAlpha\nwith EdgeDetect(1.0)", [("exponent=%.1f" % (exponent,), iaa.FrequencyNoiseAlpha(exponent=exponent, first=iaa.EdgeDetect(1.0), size_px_max=16, upscale_method="linear", sigmoid=False)) for exponent in [-4, -2, 0, 2, 4]])
]
print("[draw_per_augmenter_images] Augmenting...")
rows = []
for (row_seed, row_name, augmenters) in rows_augmenters:
ia.seed(row_seed)
#for img_title, augmenter in augmenters:
# #aug.reseed(1000)
# pass
row_images = []
row_keypoints = []
row_titles = []
for img_title, augmenter in augmenters:
aug_det = augmenter.to_deterministic()
row_images.append(aug_det.augment_image(image))
row_keypoints.append(aug_det.augment_keypoints(keypoints)[0])
row_titles.append(img_title)
rows.append((row_name, row_images, row_keypoints, row_titles))
# matplotlib drawin routine
"""
print("[draw_per_augmenter_images] Plotting...")
width = 8
height = int(1.5 * len(rows_augmenters))
fig = plt.figure(figsize=(width, height))
grid_rows = len(rows)
grid_cols = 1 + 5
gs = gridspec.GridSpec(grid_rows, grid_cols, width_ratios=[2, 1, 1, 1, 1, 1])
axes = []
for i in sm.xrange(grid_rows):
axes.append([plt.subplot(gs[i, col_idx]) for col_idx in sm.xrange(grid_cols)])
fig.tight_layout()
#fig.subplots_adjust(bottom=0.2 / grid_rows, hspace=0.22)
#fig.subplots_adjust(wspace=0.005, hspace=0.425, bottom=0.02)
fig.subplots_adjust(wspace=0.005, hspace=0.005, bottom=0.02)
for row_idx, (row_name, row_images, row_keypoints, row_titles) in enumerate(rows):
axes_row = axes[row_idx]
for col_idx in sm.xrange(grid_cols):
ax = axes_row[col_idx]
ax.cla()
ax.axis("off")
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
if col_idx == 0:
ax.text(0, 0.5, row_name, color="black")
else:
cell_image = row_images[col_idx-1]
cell_keypoints = row_keypoints[col_idx-1]
cell_image_kp = cell_keypoints.draw_on_image(cell_image, size=5)
ax.imshow(cell_image_kp)
x = 0
y = 145
#ax.text(x, y, row_titles[col_idx-1], color="black", backgroundcolor="white", fontsize=6)
ax.text(x, y, row_titles[col_idx-1], color="black", fontsize=7)
fig.savefig("examples.jpg", bbox_inches="tight")
#plt.show()
"""
# simpler and faster drawing routine
"""
output_image = ExamplesImage(128, 128, 128+64, 32)
for (row_name, row_images, row_keypoints, row_titles) in rows:
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
misc.imsave("examples.jpg", output_image.draw())
"""
# routine to draw many single files
seen = defaultdict(lambda: 0)
markups = []
for (row_name, row_images, row_keypoints, row_titles) in rows:
output_image = ExamplesImage(128, 128, 128+64, 32)
row_images_kps = []
for image, keypoints in zip(row_images, row_keypoints):
row_images_kps.append(keypoints.draw_on_image(image, size=5))
output_image.add_row(row_name, row_images_kps, row_titles)
if "\n" in row_name:
row_name_clean = row_name[0:row_name.find("\n")+1]
else:
row_name_clean = row_name
row_name_clean = re.sub(r"[^a-z0-9]+", "_", row_name_clean.lower())
row_name_clean = row_name_clean.strip("_")
if seen[row_name_clean] > 0:
row_name_clean = "%s_%d" % (row_name_clean, seen[row_name_clean] + 1)
fp = os.path.join(IMAGES_DIR, "examples_%s.jpg" % (row_name_clean,))
#misc.imsave(fp, output_image.draw())
save(fp, output_image.draw())
seen[row_name_clean] += 1
markup_descr = row_name.replace('"', '') \
.replace("\n", " ") \
.replace("(", "") \
.replace(")", "")
markup = '' % (markup_descr, fp, markup_descr)
markups.append(markup)
for markup in markups:
print(markup)
def arithmetic(image_array: ndarray):
seq = iaa.Sequential([
iaa.Salt(p=0.03)
])
return seq.augment_image(image_array)
