Using imageDatastore to load Input image and get output as predicted image

Question

Rahul Gulia 2024-4-8

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2104386-using-imagedatastore-to-load-input-image-and-get-output-as-predicted-image

编辑： Matt J 2024-4-9

Hi,

I am trying to see if I can load few images as an input to a supervised ML model and predict an image as output of the model.

I came across imageDatastore() to load images with Label values too. I want to know how I can load my training input and output image dataset to train the supervised ML model.

Here is what I tried,

dataTrain_dir = "D:\RahulGulia\iMHS_rahul\Dataset\Dataset_Warehouse_Config_3_Tensors_cropped_Sample\Perm_tensors"
dataTrainLabel_dir = "D:\RahulGulia\iMHS_rahul\Dataset\Dataset_Warehouse_Config_3_Tensors_cropped_Sample\SINR_tensors"
imds = imageDatastore(data_dir,"LabelSource",dataTrainLabel_dir)

but got an error saying:

Error using imageDatastore

Expected input to match one of these values:

'none', 'foldernames'

Kindly let me know if I can directly train the model in MATLAB using the images as input and also as an output. Or should I send the image as matrix to the ML model. Looking forward to any kind of suggestion. I would really appreciate it.

Thank you,

Rahul

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Matt J 2024-4-8

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2104386-using-imagedatastore-to-load-input-image-and-get-output-as-predicted-image#answer_1438331

编辑：Matt J 2024-4-8

在 MATLAB Online 中打开

and predict an image as output of the model.

Labels are text scalar that are assigned to an image in a classification problem. Conversely your output is an image, so it appears you have an image-to-image regression problem. For that, you would need a CombinedDatastore:

XTrain_dir = "D:\RahulGulia\iMHS_rahul\Dataset\Dataset_Warehouse_Config_3_Tensors_cropped_Sample\Perm_tensors"
YTrain_dir = "D:\RahulGulia\iMHS_rahul\Dataset\Dataset_Warehouse_Config_3_Tensors_cropped_Sample\SINR_tensors"
xstore = imageDatastore(XTrain_dir);
ystore = imageDatastore(yTrain_dir);
xystore=combine(xstore,ystore);

Or, perhaps you have a semantic segmentation problem, in which case you could use a pixelLabelDatastore, assuming you have the Computer Vision Toolbox.