Feature Question/Request : augment image according to mask

🔥 Feature Question/Request

Hello,

I would like to know if 📚 Albumentation 📚 is capable of augment only a certain part of the image, especially according to the mask. I did not find it in the documentation and I don’t know if it is something usual when doing data augmentation but what I would like to do is, for my binary segmentation problem, augment only where the mask is. For example, if I want to segment a clothing someone is wearing, I was thinking that it would be useful to have different color version. I know that there might be some artefact generated compared to the original version but still I think it could be a cool thing to have …

Does someone know if it exists already or if it is possible to integrate it ?

I actually know how to do it my self with python, but I am not sure on how to interface with albumentation … Here, I change randomly the color where the mask is :

image[mask==1,:] = image[mask==1,:] + np.random.randint(low=-50,high=50,size=3)

Thank you !

[Dipet]

Interesting idea. But at this time I do not know how to do so that it would work with any transforms. For now you can use this wrapper:

import numpy as np
import albumentations as A
import matplotlib.pyplot as plt


class MaskWrapper(A.Sequential):
    def __call__(self, **data):
        image = data['image'].copy()

        data = super(MaskWrapper, self).__call__(**data)

        mask = data['mask'].astype(np.bool)
        image[mask] = data['image'][mask]
        data['image'] = image

        return data

transforms = A.Compose([
    MaskWrapper([A.GaussianBlur(p=1), A.RGBShift([50, 50], p=1)]),
    A.LongestMaxSize(512),
    MaskWrapper([A.HueSaturationValue(p=1)])
])

img = A.read_rgb_image("true.png")
mask = np.zeros_like(img)
mask[:500, :500] = 1

res = transforms(image=img, mask=mask)

plt.subplot(211, title="original")
plt.imshow(img, vmin=0, vmax=255)
plt.subplot(212, title="result")
plt.imshow(res['image'], vmin=0, vmax=255)
plt.show()

[Dipet]

I think it is good feature. So let it be open.