Using all three color spaces

[chokevin8]

So I calculated my dataset statistics by using dataset_statistics.py for all three color spaces (RGB, HSV, HED), and then created a yaml file that looked exactly like this. When trying to use this, do I just fix this line (line 50) so that self.color_space is selected randomly out of the three color spaces? Then, when I put it inside my transforms function like below, the transforms will select one color space, augment & normalize and then re-normalize with Reinhard method? I'd appreciate some help with this, thanks!

#randstain_all.yaml is my yaml folder
val_transforms = transforms.Compose([RandStainNA(
yaml_file="randstainna_all.yaml",
std_hyper=-0.3,
probability=0.8,
distribution="normal",
is_train=True
)])

[chokevin8]

@yiqings @lyl010221 @lyl750697268 Hello, I would really appreciate any guidance from you guys, thank you in advance!

[yiqings]

@chokevin8 Thanks for your input. Could you specify your questions, I am afraid I didn't really catch your question.

[chokevin8]

@yiqings Sorry for a belated response, I've looked back at this and the problem was in the .yaml file. I generated my own .yaml file after calculating the statistics for my own training dataset. I have the same exact .yaml file like yours here. The problem is that for color_space = Random, it will create an error in the RandStainNA class in the randstainna.py file rightly so, since Random is not a color_space. To recreate your work, do I just need to create a random number generator with equal probability p = 1/3 so that everytime the class is called one of the three color spaces are selected?

[TumVink]

Heyy @chokevin8
I want to do the same thing as you. After looking into the very wrapped package folders, I found that the authors have implemented a method to augment and norm the images into random color space (HSV HED LAB).

In lines 121-145 of file "/classification/timm/data/transforms_factory.py" you may find this function. The probabilities of norm_jitter are different depending on the settings, for example in "r/classification/[scripts_CRC_final]", you may find the p=0.5 in the last exp-settings.

But please remember: this is not an official reply from the package author, though I like this work a lot ;)

Jingsong