Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Home Page: https://albumentations.ai
License: MIT License
After augmentation, the boxes are no longer tight around the cat & dog. Is this normal for an affine transform?
There's an off-by-one in this check (line 103) and how the slicing is done img[y_min:y_max, x_min:x_max].
Forcing x_max < width and y_max < height means we always crop the right-most column and bottom-most line.
Reason: building composition of transformations from json file, but json only has list as sequences.
Example: it would be great to be able to do RandomScale(scale_limit=[0.5, 3], p=1)
How:
def to_tuple(param, low=None):
if isinstance(param, (tuple, list)):
return tuple(param)
else:
return (-param if low is None else low, param)
Add Shift/Scale/Rotate operations support for bounding boxes
Hello, thank you very much for your work before anything else. I'm having problems applying random crops and center crops when going from a given shape to a smaller one obviously. Also happens with Resize(). The output is as follows:
Outputs:
Where original sample shape is (64,64,3)
Currently passing mask = None as argument causes an error.
It results in sofistication code that must be common for train and test data. Somthing like this:
if mask is not None: #validation pipeline
augm = MyAugmentation(image = img, mask = mask)
augm_img, augm_mask = augm['image'], augm['mask']
else: #production pipeline
augm = MyAugmentation(image = img)
augm_img = augm['image']It can be simplified if augmentations will accept None parameters (and return None result for them):
augm = MyAugmentation(image = img, mask = mask) # both validation and production pipelines
augm_img, augm_mask = augm['image'], augm['mask']I'd like to use IAAAffine and IAAPerspective to augment images for segmentation. I have the label masks one-hot encoded as 8-bit values, and the missing mask-only transform from DualIAATransform interpolates the values in the the label masks, making them invalid (i.e. more than one bit set for a given pixel)
Unexpected behavior of the OpticalDistortion transformation. Rotates the input image on 90 degrees.
Hello all,
thanks for working on such a nice project, I have tried imgaug, augmentor and keras, but wasn't satisfied since I am doing a lot of patch segmentation.
I would have wanted to know if it were possible to add albumentations in the repository of Anaconda, so that we could install it and keep track of the versions easily?
Thanks in advance,
Nicolas
When I install the albumentations on windows7 for keras, the bug is jump on face.
pip, source):(keras) $ pip install imgaug
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting imgaug
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/af/fc/c56a7da8c23122b7c5325b941850013880a7a93c21dc95e2b1ecd4750108/imgaug-0.2.7-py3-
none-any.whl (644kB)
100% |โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 645kB 2.9MB/s
Requirement already satisfied: Pillow in d:\software\anaconda3\envs\keras\lib\site-packages (from imgaug) (5.4.1)
Requirement already satisfied: imageio in d:\software\anaconda3\envs\keras\lib\site-packages (from imgaug) (2.4.1)
Requirement already satisfied: numpy>=1.7.0 in d:\software\anaconda3\envs\keras\lib\site-packages (from imgaug) (1.15.4)
Requirement already satisfied: scikit-image>=0.11.0 in d:\software\anaconda3\envs\keras\lib\site-packages (from imgaug) (0.14.1)
Collecting Shapely (from imgaug)
Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a2/fb/7a7af9ef7a35d16fa23b127abee272cfc483ca89029b73e92e93cdf36e6b/Shapely-1.6.4.pos
t2.tar.gz (225kB)
100% |โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ| 235kB 6.4MB/s
Complete output from command python setup.py egg_info:
Traceback (most recent call last):
File "", line 1, in
File "C:\Users\WangZhao\AppData\Local\Temp\pip-install-h64vi3v5\Shapely\setup.py", line 80, in
from shapely.buildcfg import geos_version_string, geos_version,
File "C:\Users\WangZhao\AppData\Local\Temp\pip-install-h64vi3v5\Shapely\shapely_buildcfg.py", line 200, in
lgeos = CDLL("geos_c.dll")
File "D:\Software\Anaconda3\envs\keras\lib\ctypes_init.py", line 348, in init
self._handle = _dlopen(self._name, mode)
OSError: [WinError 126] ๆพไธๅฐๆๅฎ็ๆจกๅใ
----------------------------------------
Command "python setup.py egg_info" failed with error code 1 in C:\Users\WangZhao\AppData\Local\Temp\pip-install-h64vi3v5\Shapely\
Can you, please, add an example for pytorch dataloader?
Can't figure out how to use albumentations for gray images, due to error in functions like RandomContrast which try to cast image to gray => raise an error
And also ToGray is impossible to use in Compose:
im = np.random.randint(0,255,(224,224,3))
aug = Compose([ToGray(p=1.)],p=1.)
augmented = ToGray()(image = im) # works
augmented2 = aug(image=im) # fails with opencv error:
Unsupported depth of input image:
> 'VDepth::contains(depth)'
> where
> 'depth' is 4 (CV_32S)
it looks like the problem is due to cast from int8 to float32 before calling this transform
python env:
Python 3.6.6 |Anaconda, Inc.| (default, Jun 28 2018, 17:14:51)
[GCC 7.2.0] on linux
Name Version Build Channel
albumentations 0.1.2
I flow notebooks/showcase.ipynb
my code:
import albumentations as A
import random
import cv2
random.seed(42)
image = cv2.imread('images/parrot.jpg')
tfms = [ A.Resize(256, 256),
A.RandomContrast(p=1),
A.RandomGamma(p=1),
A.RGBShift(),
A.CLAHE(p=1)]
aug = A.Compose(tfms, p=1)
r = augment_and_show(aug, image)
run output error:
~/anaconda3/envs/pytorch/lib/python3.6/site-packages/albumentations/core/transforms_interface.py in apply_to_mask(self, img, **params)
68
69 def apply_to_mask(self, img, **params):
---> 70 return self.apply(img, **{k: cv2.INTER_NEAREST if k == 'interpolation' else v for k, v in params.items()})
71
72
~/anaconda3/envs/pytorch/lib/python3.6/site-packages/albumentations/augmentations/transforms.py in apply(self, img, interpolation, **params)
238
239 def apply(self, img, interpolation=cv2.INTER_LINEAR, **params):
--> 240 return F.resize(img, height=self.height, width=self.width, interpolation=interpolation)
241
242 def apply_to_bbox(self, bbox, **params):
~/anaconda3/envs/pytorch/lib/python3.6/site-packages/albumentations/augmentations/functional.py in resize(img, height, width, interpolation)
81
82 def resize(img, height, width, interpolation=cv2.INTER_LINEAR):
---> 83 img = cv2.resize(img, (width, height), interpolation=interpolation)
84 return img
85
error: OpenCV(3.4.2) /io/opencv/modules/imgproc/src/resize.cpp:4044: error: (-215:Assertion failed) !ssize.empty() in function 'resize'
Bug appears when bboxes are used.
def strong_aug(p=.5):
return Compose([
HorizontalFlip(p=0.001),
# IAAPiecewiseAffine(p=1.0),
OneOf([
# OpticalDistortion(p=0.1),
# GridDistortion(p=0.1),
# IAAPerspective(p=1.0),
# IAAAffine(p=1.0),
IAAPiecewiseAffine(p=1.0),
], p=0.0),
],
bbox_params={'format': 'pascal_voc',
'min_area': 1,
'min_visibility': 0.1,
'label_fields': ['labels']},
p=p)
x0 = 0
x1 = 200
y0 = 10
y1 = 200
bb = np.array([[int(x0), int(y0), int(x1), int(y1)]])
augm = strong_aug(p=1.0)(image=img, bboxes=bb, labels=['labels'])
img = augm['image']
bboxes = np.array(augm['bboxes'])
Error message:
\site-packages\albumentations\augmentations\bbox_utils.py:42: RuntimeWarning: overflow encountered in long_scalars
area = (x_max - x_min) * (y_max - y_min)
Returned boxes are empty.
It works fine in this case:
return Compose([
HorizontalFlip(p=0.001),
IAAPiecewiseAffine(p=1.0),
],
bbox_params={'format': 'pascal_voc',
'min_area': 1,
'min_visibility': 0.1,
'label_fields': ['labels']},
p=p)
It returns empty boxes in this case (without warning):
return Compose([
HorizontalFlip(p=0.001),
IAAPiecewiseAffine(p=1.0),
OneOf([
# OpticalDistortion(p=0.1),
# GridDistortion(p=0.1),
# IAAPerspective(p=1.0),
IAAAffine(p=1.0),
# IAAPiecewiseAffine(p=1.0),
], p=0.0),
],
bbox_params={'format': 'pascal_voc',
'min_area': 1,
'min_visibility': 0.1,
'label_fields': ['labels']},
p=p)
It returns incorrect boxes which looks like ([['0.0' '0.173015873015873' '0.9742857142857143' '0.9285714285714286' 'labels']]):
return Compose([
# HorizontalFlip(p=0.001),
# IAAPiecewiseAffine(p=1.0),
OneOf([
OpticalDistortion(p=0.1),
GridDistortion(p=0.1),
# IAAPerspective(p=1.0),
# IAAAffine(p=1.0),
# IAAPiecewiseAffine(p=1.0),
], p=0.0),
],
bbox_params={'format': 'pascal_voc',
'min_area': 1,
'min_visibility': 0.1,
'label_fields': ['labels']},
p=p)
conda, pip, source): pipIn IAAAffine shear works incorrect. It does shear on the same angle.
I have a piece of code which I am trying to use to augment some images. If I try to augment my images sequentially, everything works perfectly but as soon as I try to use multiprocessing, everything breaks.
Here is the code I am using to do multiprocessing:
def strong_aug(p=.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([IAAAdditiveGaussianNoise(), GaussNoise(),], p=0.2),
OneOf([MotionBlur(p=.2), MedianBlur(blur_limit=3, p=.1), Blur(blur_limit=3, p=.1),], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=.2),
OneOf([OpticalDistortion(p=0.3), GridDistortion(p=.1), IAAPiecewiseAffine(p=0.3),], p=0.2),
OneOf([CLAHE(clip_limit=2), IAASharpen(), IAAEmboss(), RandomBrightness(),], p=0.3),
HueSaturationValue(p=0.3),],
p=p)
aug = strong_aug(p=1)
class ImageProcessor:
def __init__(self,aug):
self._aug = aug
def __call__(self,filename):
name = "aug_" + filename
img = cv2.imread(str(train_dir / filename))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
image = aug(image=img)['image']
imsave("augmented_train_images/" + name, image)
final_images = .... # list containing all the filenames corresponding to the images
proc=ImageProcessor(aug)
pool=multiprocessing.Pool()
pool.map(proc,final_images) Here is the error log:
<ipython-input-22-eab8d2bb2f8e> in <module>()
7 proc=ImageProcessor(aug)
8 pool=multiprocessing.Pool()
----> 9 pool.map(proc,final_images)
10 print("Augmentation done for label: ", k)
~/miniconda2/envs/py36/lib/python3.6/multiprocessing/pool.py in map(self, func, iterable, chunksize)
264 in a list that is returned.
265 '''
--> 266 return self._map_async(func, iterable, mapstar, chunksize).get()
267
268 def starmap(self, func, iterable, chunksize=None):
~/miniconda2/envs/py36/lib/python3.6/multiprocessing/pool.py in get(self, timeout)
642 return self._value
643 else:
--> 644 raise self._value
645
646 def _set(self, i, obj):
~/miniconda2/envs/py36/lib/python3.6/multiprocessing/pool.py in _handle_tasks(taskqueue, put, outqueue, pool, cache)
422 break
423 try:
--> 424 put(task)
425 except Exception as e:
426 job, idx = task[:2]
~/miniconda2/envs/py36/lib/python3.6/multiprocessing/connection.py in send(self, obj)
204 self._check_closed()
205 self._check_writable()
--> 206 self._send_bytes(_ForkingPickler.dumps(obj))
207
208 def recv_bytes(self, maxlength=None):
~/miniconda2/envs/py36/lib/python3.6/multiprocessing/reduction.py in dumps(cls, obj, protocol)
49 def dumps(cls, obj, protocol=None):
50 buf = io.BytesIO()
---> 51 cls(buf, protocol).dump(obj)
52 return buf.getbuffer()
53
AttributeError: Can't pickle local object 'Sharpen.<locals>.create_matrices'
I spent an hour until I realized you are dividing the mask by 255 if it's uint8.
I don't see why...
I suggest to leave the mask unmodified
{"img": type_img, "mask": type_mask}Hi, if I would like to randomly zoom out my image like with iaa.Scale((0.5, 1.0)), how would I do this with albumentations? I could pad the image first, but the amount of padding would not be random.
I stack 4 single-channel image to a 4-D image and transform it. It fails.
How to transform a image more than 3 channels?
Or is there some method to transform the 4 single-channel image with the same random transformation?
Hi,
I've noticed I receive different augmentation results between two identical runs, although my seeds are fixed. I set tensorflow (which shouldn't be related) and numpy random seeds.
Is there an additional seed needs to be set for albumentations?
Thanks,
Hello! I just found that example from https://albumentations.readthedocs.io/en/latest/examples.html sometime gives me TypeError and OpenCV related errors
Here is minimal example
import albumentations
from albumentations import (
HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine,
IAASharpen, IAAEmboss, RandomContrast, RandomBrightness, Flip, OneOf, Compose
)
import numpy as np
def strong_aug(p=.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=.2),
MedianBlur(blur_limit=3, p=.1),
Blur(blur_limit=3, p=.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=.1),
IAAPiecewiseAffine(p=0.3),
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomContrast(),
RandomBrightness(),
], p=0.3),
HueSaturationValue(p=0.3),
], p=p)
print(albumentations.__version__)
for i in range(1000):
print(i)
image = np.ones((300, 300))
mask = np.ones((300, 300))
whatever_data = "my name"
augmentation = strong_aug(p=0.9)
data = {"image": image, "mask": mask, "whatever_data": whatever_data, "additional": "hello"}
augmented = augmentation(**data)
image, mask, whatever_data, additional = augmented["image"], augmented["mask"], augmented["whatever_data"], augmented["additional"]
First full error message
Traceback (most recent call last):
File "a.py", line 49, in <module>
augmented = augmentation(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/composition.py", line 17, in __call__
data = t(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/transforms_interface.py", line 22, in __call__
return {key: self.targets.get(key, lambda x, **p: x)(arg, **params) for key, arg in kwargs.items()}
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/transforms_interface.py", line 22, in <dictcomp>
return {key: self.targets.get(key, lambda x, **p: x)(arg, **params) for key, arg in kwargs.items()}
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/augmentations/transforms.py", line 414, in apply
assert image.dtype == np.uint8 or self.hue_shift_limit < 1.
TypeError: '<' not supported between instances of 'tuple' and 'float'
Second full error message
OpenCV(3.4.1) Error: Unsupported format or combination of formats () in medianBlur, file /io/opencv/modules/imgproc/src/smooth.cpp, line 4597
Traceback (most recent call last):
File "a.py", line 49, in <module>
augmented = augmentation(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/composition.py", line 17, in __call__
data = t(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/composition.py", line 33, in __call__
data = t(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/transforms_interface.py", line 22, in __call__
return {key: self.targets.get(key, lambda x, **p: x)(arg, **params) for key, arg in kwargs.items()}
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/transforms_interface.py", line 22, in <dictcomp>
return {key: self.targets.get(key, lambda x, **p: x)(arg, **params) for key, arg in kwargs.items()}
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/augmentations/transforms.py", line 551, in apply
return F.median_blur(image, ksize)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/augmentations/functional.py", line 193, in median_blur
return cv2.medianBlur(img, ksize)
cv2.error: OpenCV(3.4.1) /io/opencv/modules/imgproc/src/smooth.cpp:4597: error: (-210) in function medianBlur
Third full error message
OpenCV(3.4.1) Error: Assertion failed (depth == 0 || depth == 2 || depth == 5) in cvtColor, file /io/opencv/modules/imgproc/src/color.cpp, line 11109
Traceback (most recent call last):
File "a.py", line 49, in <module>
augmented = augmentation(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/composition.py", line 17, in __call__
data = t(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/composition.py", line 33, in __call__
data = t(**data)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/transforms_interface.py", line 22, in __call__
return {key: self.targets.get(key, lambda x, **p: x)(arg, **params) for key, arg in kwargs.items()}
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/core/transforms_interface.py", line 22, in <dictcomp>
return {key: self.targets.get(key, lambda x, **p: x)(arg, **params) for key, arg in kwargs.items()}
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/augmentations/transforms.py", line 597, in apply
return F.clahe(img, clip_limit, self.tile_grid_size)
File "/home/daiver/.local/lib/python3.6/site-packages/albumentations/augmentations/functional.py", line 156, in clahe
img = cv2.cvtColor(img, cv2.COLOR_RGB2LAB)
cv2.error: OpenCV(3.4.1) /io/opencv/modules/imgproc/src/color.cpp:11109: error: (-215) depth == 0 || depth == 2 || depth == 5 in function cvtColor
My Python version: 3.6.3
albumentations version: 0.0.4
PS Sorry for my poor English
Hi, any plans to add https://github.com/NVIDIA/DALI support?
When using RandomSizedCrop(), is it better to use padding if a suggested crop region is bigger than the original image size, rather than raise ValueError?
For example, the augmentation method used for achieving cityscapes good result uses padding.
https://github.com/PkuRainBow/OCNet/blob/8184d9d55ec474c7e22278256f1db8b3d7ebce88/dataset/cityscapes.py#L121
So I suggest changing this part;
https://github.com/albu/albumentations/blob/8626026910d6b0236bccd09af276ca9432af5d22/albumentations/augmentations/functional.py#L207-L221
to
def random_crop(img, crop_height, crop_width, h_start, w_start, pad_value=(0.0,)):
height, width = img.shape[:2]
if height < crop_height or width < crop_width:
pad_h = max(crop_height - height, 0)
pad_w = max(crop_width - width, 0)
img = cv2.copyMakeBorder(img, 0, pad_h, 0,
pad_w, cv2.BORDER_CONSTANT,
value=pad_value) # if gt label, pad_value should be ignore index.
x1, y1, x2, y2 = get_random_crop_coords(height, width, crop_height, crop_width, h_start, w_start)
img = img[y1:y2, x1:x2]
return img
pip install --force-reinstall albumentations, but still can't import resize. As discussed at sbsj, open a ticket. Could you please check?
Current implementation of Jpeg compression works only with uint8 data, so it can break some pipelines where data is casted to [0, 1] floats from the very beginning.
In [29]: from albumentations.augmentations import functional as f_aug
In [30]: img = np.random.rand(100, 100).astype('float32')
In [31]: f_aug.jpeg_compression(img, 95).dtype
Out[31]: dtype('uint8')
Please let me know if you'd like to get PR on this issue.
P.S. BTW, it may be a good idea to check for input and output dtype equality to make the debugging of similar issues easier.
Problem lies in these lines: https://github.com/albu/albumentations/blob/3a294a3c993109aaabeca5b4901e90f03f1079ce/albumentations/core/composition.py#L47-L50
If data['bboxes'] is an empty list (or does not exist at all), this code raises error.
Hi,
Very interesting and thorough package for playing with images.
I am curious to know what is the purpose of the function mask_to_tensor in .torch.functional? More specifically, what does it mean to create a mask, like:
for c in range(mask.shape[2]):
long_mask[mask[..., c] > 0] = c
and where/why would I use such a weird looking mask?
Please bear in mind that I am not asking how this code work but that why would someone ever need to create such a mask? Application of the mask. Few examples would be enlightening, thanks!
https://github.com/albu/albumentations/blob/master/albumentations/torch/functional.py#L15
version: albumentations-0.1.7
call:
distortion = albumentations.GridDistortion(num_steps=32, distort_limit=0.7, border_mode=cv2.BORDER_CONSTANT, p=0.3)
sample = distortion(image=sample)['image']
image is 256x256x3
Thrown exception:
File "C:\Users\owner\AppData\Local\conda\conda\envs\allegro_env\lib\site-packages\albumentations\augmentations\functional.py", line 412, in grid_distortion
cur = prev + x_step * xsteps[idx]
IndexError: list index out of range
After applying the Resize transform to the binary mask, we get a non-binary mask:
In: np.unique(mask), mask.shape
Out: (array([ 0, 255], dtype=uint8), (101, 101))
In: t_mask = Resize(256, 256)(image=img, mask=mask)['mask']
In: np.unique(t_mask), t_mask.shape
Out: (array([ 0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12, 13,
14, 15, 17, 18, 20, 22, 23, 24, 25, 26, 27, 29, 32,
33, 34, 35, 36, 37, 38, 40, 42, 43, 44, 48, 50, 52,
53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 66, 68, 69,
72, 74, 75, 79, 81, 82, 83, 84, 86, 87, 88, 89, 90,
91, 93, 94, 95, 96, 100, 103, 111, 112, 118, 119, 120, 121,
126, 132, 133, 134, 135, 136, 137, 138, 141, 142, 143, 144, 147,
151, 154, 155, 156, 158, 159, 162, 164, 165, 166, 167, 168, 169,
173, 179, 180, 181, 182, 183, 186, 187, 190, 193, 194, 195, 196,
197, 199, 201, 205, 207, 208, 212, 213, 215, 216, 217, 220, 221,
222, 223, 224, 226, 227, 228, 229, 232, 233, 234, 235, 236, 237,
238, 239, 240, 242, 243, 244, 245, 246, 250, 251, 252, 253, 254,
255], dtype=uint8), (256, 256))
I think the code https://github.com/albu/albumentations/blob/master/albumentations/augmentations/transforms.py#L213-L214 needs to be changed:
def apply(self, img, interpolation, **params):
return F.resize(img, height=self.height, width=self.width, interpolation=interpolation)
Hi,
If I have a pair of images (with a single mask image), how can I apply the same transform on the images?
Thanks,
Adi
When installed with pip, incompatible versions of numpy and skimage may be installed.
Steps to reproduce the behavior:
pip install -U albumentationspythonimport albumentationsroot@11e08e49dff0:/app# pip install -U albumentations
Requirement already up-to-date: albumentations in /usr/local/lib/python3.6/dist-packages (0.1.10)
Requirement already satisfied, skipping upgrade: numpy>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from albumentations) (1.16.0)
Requirement already satisfied, skipping upgrade: scipy in /usr/local/lib/python3.6/dist-packages (from albumentations) (1.2.0)
Requirement already satisfied, skipping upgrade: opencv-python in /usr/local/lib/python3.6/dist-packages (from albumentations) (4.0.0.21)
Requirement already satisfied, skipping upgrade: imgaug>=0.2.5 in /usr/local/lib/python3.6/dist-packages (from albumentations) (0.2.7)
Requirement already satisfied, skipping upgrade: scikit-image>=0.11.0 in /usr/local/lib/python3.6/dist-packages (from imgaug>=0.2.5->albumentations) (0.14.1)
Requirement already satisfied, skipping upgrade: six in /usr/local/lib/python3.6/dist-packages (from imgaug>=0.2.5->albumentations) (1.12.0)
Requirement already satisfied, skipping upgrade: matplotlib in /usr/local/lib/python3.6/dist-packages (from imgaug>=0.2.5->albumentations) (3.0.2)
Requirement already satisfied, skipping upgrade: Pillow in /usr/local/lib/python3.6/dist-packages (from imgaug>=0.2.5->albumentations) (5.4.1)
Requirement already satisfied, skipping upgrade: imageio in /usr/local/lib/python3.6/dist-packages (from imgaug>=0.2.5->albumentations) (2.4.1)
Requirement already satisfied, skipping upgrade: Shapely in /usr/local/lib/python3.6/dist-packages (from imgaug>=0.2.5->albumentations) (1.6.4.post2)
Requirement already satisfied, skipping upgrade: networkx>=1.8 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug>=0.2.5->albumentations) (2.2)
Requirement already satisfied, skipping upgrade: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug>=0.2.5->albumentations) (1.0.1)
Requirement already satisfied, skipping upgrade: dask[array]>=0.9.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug>=0.2.5->albumentations) (1.0.0)
Requirement already satisfied, skipping upgrade: cloudpickle>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.11.0->imgaug>=0.2.5->albumentations) (0.6.1)
Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imgaug>=0.2.5->albumentations) (2.3.1)
Requirement already satisfied, skipping upgrade: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imgaug>=0.2.5->albumentations) (1.0.1)
Requirement already satisfied, skipping upgrade: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imgaug>=0.2.5->albumentations) (2.7.5)
Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->imgaug>=0.2.5->albumentations) (0.10.0)
Requirement already satisfied, skipping upgrade: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=1.8->scikit-image>=0.11.0->imgaug>=0.2.5->albumentations) (4.3.0)
Requirement already satisfied, skipping upgrade: toolz>=0.7.3; extra == "array" in /usr/local/lib/python3.6/dist-packages (from dask[array]>=0.9.0->scikit-image>=0.11.0->imgaug>=0.2.5->albumentations) (0.9.0)
Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->imgaug>=0.2.5->albumentations) (40.6.3)
root@11e08e49dff0:/app# python
Python 3.6.8 (default, Dec 24 2018, 19:24:27)
[GCC 5.4.0 20160609] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import albumentations
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.6/dist-packages/albumentations/__init__.py", line 5, in <module>
from .core.composition import *
File "/usr/local/lib/python3.6/dist-packages/albumentations/core/composition.py", line 11, in <module>
from albumentations.imgaug.transforms import DualIAATransform
File "/usr/local/lib/python3.6/dist-packages/albumentations/imgaug/transforms.py", line 1, in <module>
import imgaug as ia
File "/usr/local/lib/python3.6/dist-packages/imgaug/__init__.py", line 2, in <module>
from imgaug.imgaug import *
File "/usr/local/lib/python3.6/dist-packages/imgaug/imgaug.py", line 22, in <module>
import skimage.draw
File "/usr/local/lib/python3.6/dist-packages/skimage/__init__.py", line 167, in <module>
from .util.dtype import (img_as_float32,
File "/usr/local/lib/python3.6/dist-packages/skimage/util/__init__.py", line 8, in <module>
from .arraycrop import crop
File "/usr/local/lib/python3.6/dist-packages/skimage/util/arraycrop.py", line 8, in <module>
from numpy.lib.arraypad import _validate_lengths
ImportError: cannot import name '_validate_lengths'
Working installation of the library
pip install -U numpy==1.15.4 fixes the problem
Hi.
I've found some unobvious behavior when using bbox-aware Compose along with ToTensor augmentation for PyTorch. The problem is that boxes_postprocessing() method assumes image has numpy format (HxWxC), but it is not if when ToTensor was applied.
As a workaround, bbox modifying augs should be separrated from ToTensor(). For example
full_aug = Compose([VerticalFlip(p=1), ToTensor()], bbox_params=bbox_conf)
should be rewritten as
bbox_modifying_aug = alb.Compose([alb.VerticalFlip(p=1)], bbox_params=box_conf)
full_aug = alb.Compose([bbox_modifying_aug, ToTensor()])
Complete snipped reproducing the problem and demonstrating workaround in action is available here.
As far as I know, this thing is not mentioned anywhere in docs, but it should be if it is intentional behavior.
I think it should not be left like that and should be changed.
There is RuntimeWarning when I use example for a loop.
code:
from albumentations import (
HorizontalFlip, IAAPerspective, ShiftScaleRotate, CLAHE, RandomRotate90,
Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
IAAAdditiveGaussianNoise, GaussNoise, MotionBlur, MedianBlur, IAAPiecewiseAffine,
IAASharpen, IAAEmboss, RandomContrast, RandomBrightness, Flip, OneOf, Compose
)
import numpy as np
def strong_aug(p=0.5):
return Compose([
RandomRotate90(),
Flip(),
Transpose(),
OneOf([
IAAAdditiveGaussianNoise(),
GaussNoise(),
], p=0.2),
OneOf([
MotionBlur(p=0.2),
MedianBlur(blur_limit=3, p=0.1),
Blur(blur_limit=3, p=0.1),
], p=0.2),
ShiftScaleRotate(shift_limit=0.0625, scale_limit=0.2, rotate_limit=45, p=0.2),
OneOf([
OpticalDistortion(p=0.3),
GridDistortion(p=0.1),
IAAPiecewiseAffine(p=0.3),
], p=0.2),
OneOf([
CLAHE(clip_limit=2),
IAASharpen(),
IAAEmboss(),
RandomContrast(),
RandomBrightness(),
], p=0.3),
HueSaturationValue(p=0.3),
], p=p)
import cv2
image = cv2.imread("test.jpg")
augmentation = strong_aug(p=0.9)
for i in range(10000):
data = {"image": image}
augmented = augmentation(**data)
aug_img= augmented["image"]
cv2.namedWindow("1", 2)
cv2.imshow("1", aug_img.astype(np.uint8))
cv2.waitKey(1)
cv2.destroyAllWindows()
The warning information below:
albumentations\augmentations\functional.py:257: RuntimeWarning: invalid value encountered in true_divide
return cv2.filter2D(img, -1, kernel / np.sum(kernel))
I found it in the function of motion_blur. That means something wrong in the function of MotionBlur?
Am I get bug in my code?
Could you please help me to solve the problem?
Any chance to get speed benchmarks also for solt (https://mipt-oulu.github.io/solt/index.html#) and Augmentor (https://github.com/mdbloice/Augmentor) libraries?
Currently, you take size of the images with such code:
params.update({'cols': kwargs['image'].shape[1], 'rows': kwargs['image'].shape[0]})
But that is not always correct. What if user wants to have a list of images to augment as this one: {"image1": image1, "image2": image2}
Augmentation squeeze target shape:
import numpy as np
import albumentations
from albumentations import Flip, Compose
sample = {
'image': np.ones((224, 224, 1)),
'mask': np.ones((224, 224, 1)),
}
aug = Compose([Flip(p=0.5)], p=1.)
sample_1 = aug(**sample)
sample_2 = aug(**sample)
print(sample_1['mask'].shape) # (224, 224, 1) - augmentation not applied
print(sample_2['mask'].shape) # (224, 224)Is it possible to fix this behavior?
Obvious solution - squeeze before aug and expand after. But maybe you have another ideas (smth like ShapeGuard)?
Collecting Shapely (from imgaug)
Using cached https://files.pythonhosted.org/packages/a2/fb/7a7af9ef7a35d16fa23b127abee272cfc483ca89029b73e92e93cdf36e6b/Shapely-1.6.4.post2.tar.gz
Complete output from command python setup.py egg_info:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\ADMINI~1\AppData\Local\Temp\pip-install-kdsk2c_6\Shapely\setup.py", line 80, in <module>
from shapely._buildcfg import geos_version_string, geos_version, \
File "C:\Users\ADMINI~1\AppData\Local\Temp\pip-install-kdsk2c_6\Shapely\shapely\_buildcfg.py", line 200, in <module>
lgeos = CDLL("geos_c.dll")
File "C:\ProgramData\Anaconda3\envs\pytorch04_py36\lib\ctypes\__init__.py", line 348, in __init__
self._handle = _dlopen(self._name, mode)
OSError: [WinError 126] The specified module could not be found
Windows Server 2016
python 3.6.6
albumentations version 0.1.8
Steps to reproduce the behavior:
pip install imgaug
(or pip install albumentations )
The error is probably due to missing C compiler required for building the dll.
Workaround:
ref: https://gis.stackexchange.com/questions/62925/why-is-shapely-not-installing-correctly
I'm using PyTorch to augment a dataset. I have downloaded the dataset and loaded it using the torchvision.datasets.ImageFolder 'n torch.utils.data.DataLoader functions. If I use a transformation from PyTorch the data is loaded, transformed and plotted without any errors, but if I apply some transformation from Albumentation I receive the following error:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-15-6a0e45004b2c> in <module>
4 for item in train_vectors:
5 # Get epoch
----> 6 images, labels = iter(item['data']).next()
7
8 # Plot images
TypeError: Traceback (most recent call last):
File "/home/pc/.pyenv/versions/anaconda3-2018.12/envs/vida/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 106, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/pc/.pyenv/versions/anaconda3-2018.12/envs/vida/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 106, in <listcomp>
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/pc/.pyenv/versions/anaconda3-2018.12/envs/vida/lib/python3.6/site-packages/torchvision/datasets/folder.py", line 103, in __getitem__
sample = self.transform(sample)
File "/home/pc/.pyenv/versions/anaconda3-2018.12/envs/vida/lib/python3.6/site-packages/torchvision/transforms/transforms.py", line 49, in __call__
img = t(img)
TypeError: __call__() takes 1 positional argument but 2 were givenor
tensor is not a torch imageThe transformation list:
transform_train = [
albumentations.augmentations.transforms.JpegCompression(),
torchvision.transforms.Resize((height, width)),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0,485, 0,456, 0,406), std=(0,229, 0,224, 0,225))
]The loading:
torch.utils.data.DataLoader(
dataset=torchvision.datasets.ImageFolder(
root=path,
transform=transform
),
batch_size=batch,
shuffle=shuffle,
num_workers=workers
)The iteration:
for item in train_vectors:
# Get epoch
images, labels = iter(item['data']).next()I'm using PyTorch 0.4.1, Albumentations 0.1.11 and Img Aug 0.2.7 (I'm running the notebooks inside a virtual environment created based on a virtual image installed using Pyenv).
I researched, but I couldn't figure out why I'm receiving this error, I have tried to iterate using enumerate, range and so on and nothing works, the complete code and references can be found here.
Do I need to iterate over the dataset of a different way or convert the data to another format... ?
The current recommended way to install is
pip install -U git+https://github.com/albu/albumentations
This requires git, something not everyone might have installed (particularly on windows).
If you change it to
pip install -U https://github.com/albu/albumentations/archive/master.zip
The installation can be done without git.
Thanks for you work, I have other question, how to use your Albumentations to make one image generate more different photos from each other in one batch?
This issue is a bit mysterious from my point of view, as it's kind of complicated to reproduce it a single try.
In [1]: from albumentations import MedianBlur
...: import numpy as np
...:
...: success, fail = 0, 0
...: for _ in range(100):
...: img = np.random.rand(100, 100)
...: try:
...: img = MedianBlur()(image=img).get('image')
...: success += 1
...: except Exception:
...: fail += 1
...: print(success, fail)
...:
OpenCV Error: Assertion failed (src.depth() == CV_8U && (cn == 1 || cn == 3 || cn == 4)) in medianBlur, file /Users/travis/build/skvark/opencv-python/opencv/modules/imgproc/src/smooth.cpp, line 3538
OpenCV Error: Unsupported format or combination of formats () in medianBlur, file /Users/travis/build/skvark/opencv-python/opencv/modules/imgproc/src/smooth.cpp, line 3529
OpenCV Error: Unsupported format or combination of formats () in medianBlur, file
// some errors removed from the log
48 52
I've reproduced this stochastic issue on two different computers, both using opencv-python==3.3.0.10.
Thanks,
Hi.
Is there a recommended approach to do test time augmentation? I would like to pick several random augmentations and apply them to test and validation sets for later stacking. I'm not sure how to proceed. How do I make sure that one set of augmentations is applied to the whole test set and then to validation set and not random augmentations to every image?
Thanks.
Can you please add support of Cutout augmentation, that will be very useful.
why is albumentations faster than imgaug, while both use the opencv as backend?
Hi, thank you for this amazing library. I was wondering can we use this library for on 3D small volumes such as 64x64x64. Thank you for your time,
Vural
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