How to do color rgb segmentation with your trainGenerator ?

[arthurPrvst]

Hi, I'm trying to do a color segmentation (I have grayscale image, and their labeled image with colors).

I changed mask_color_mode to "rgb", and num_class to 3 (because I have 3 differents colors) in data.py, and I changed input_size from 256,256,1 to 256,256,3 in model.py, but I still have errors..

"expected input_1 to have shape (None,256,256,3) but got array with shape (2,256,256,1).
I rune the debugger and, mask.shape gave me : (2,256,256,3) and omg.shape gave me : (2,256,256,1)

Any idea ? Thanks in advance :)

[ajithvcoder]

@arthurPrvst did u solve the issue ?

[lillux]

How did you solve the issue? Il mar 19 giu 2018, 19:27 Ajith <notifications@github.com> ha scritto:

…

@arthurPrvst <https://github.com/arthurPrvst> did u solve the issue ? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#47 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AQ-p9GF0T_dp3RYCIYlAHzgbyI4zsjqfks5t-TSVgaJpZM4UpibM> .

[arthurPrvst]

I finally decided to go back to grasycale to reduce calculation in my network (I converted RGB to grayscale with nearest interpolation : the depth of the image was not useful).
But, you can bypass this problem by adding this modifications :

• Set input layer to 512,512,1
• Modify last conv9 : conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9).
• Modify conv10 : conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9) I have 3 classes here.

This modification are used of the code of this depot before his last commit (the add of generator for huge dataset). I would advise you to do the same and to do your own generator. Here is a good tutorial : https://stanford.edu/~shervine/blog/keras-how-to-generate-data-on-the-fly.html

Enjoy