Image-Segmentation | Image segmentation application | Machine Learning library

I am trying to code the following image classification code: https://www.thepythoncode.com/article/kmeans-for-image-segmentation-opencv-python

but my question is; is there a way to write a loop such that for each cluster that you use, you get a new image that blackens out this part of the image?

I was trying for example this:

I want to solve a multiclass segmentation task using deep learning (in python). Here, is a summary of vgg_unet model that is mainly collected from GitHub. So, in my dataset 8 labels are available. So, at the last convolution layer, there are 8 channels for the categorical classification of every class. The summary of my model is below,

I am using unet for image segmentation, using the code outlined herein.

My input images are 256x256x3. while the corresponding segmentation masks are 256x256.

I have changed the size for the input to Unet:

I have a UNet++(view in private, code for model at the bottom of the article) which I'm trying to reconfigure. I'm getting some artifacts in some images so I'm following this article which suggests doing upsampling then a convolution operation.

I'm replacing the up-sample layers with sequential operation shown below but my model isn't learning. I suspect its to do with how I've configured the channels so I'd like another opinion.

Old up-sample operation:

Problem Statement: I have an image and a pixel of the image can belong to only(either) one of Band5','Band6', 'Band7' (see below for details). Hence, I have a pytorch multi-class problem but I am unable to understand how to set the targets which needs to be in form [batch, w, h]

My dataloader return two values:

I'm using image-segmentation on some images, and sometimes it would be nice to be able to plot the borders of the segments.

I have a 2D NumPy array that I plot with Matplotlib, and the closest I've gotten, is using contour-plotting. This makes corners in the array, but is otherwise perfect.

Can Matplotlib's contour-function be made to only plot vertical/horizontal lines, or is there some other way to do this?

An example can be seen here:

I've been working on a segmentation problem for many days and after finally finding out how to properly read the dataset, I ran into this problem: "ValueError: Error when checking target: expected activation_1(Softmax) to have 3 dimensions, but got array with shape (32, 416, 608, 3)" I used the functional API, since I took the FCNN architecture from : https://github.com/divamgupta/image-segmentation-keras/blob/master/Models/FCN32.py.

It is slightly modified and adapted in accordance with my task(IMAGE_ORDERING = "channels_last"(TensorFlow backend)). Can anyone please help me? Massive thanks in advance. The architecture below is for FCNN, which I try to implement for the purpose of the segmentation. Here is the architecture(after calling model.summary()).
[1]: https://i.stack.imgur.com/2Ou5z.png
[2]: https://i.stack.imgur.com/zOFAz.png
[3]: The specific error is : https://i.stack.imgur.com/DVo2k.png
[4]: "Importing the dataset" function :https://i.stack.imgur.com/UY2FE.png
[5]: "Fit_Generator method calling" :https://i.stack.imgur.com/VskLj.png

I am using tensorflow's object detection api with faster_rcnn_resnet101 and get the following error when trying to train:

tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: Input to reshape is a tensor with 36 values, but the requested shape requires a multiple of 16

[[{{node Reshape_13}}]]

[[IteratorGetNext]]

[[IteratorGetNext/_7243]]

(1) Invalid argument: Input to reshape is a tensor with 36 values, but the requested shape requires a multiple of 16

[[{{node Reshape_13}}]]

[[IteratorGetNext]]

0 successful operations. 0 derived errors ignored.

I am using a slightly modified version of the pets-train.sh file to run the training (only paths have been altered). I am trying to train on tf.record files containing jpg images of size (1280, 720) and have made no changes to the network architecture (I have confirmed that all images in the record are of this size).

Curiously, I can successfully run inference on these images when I do something equivalent to what's in the tutorial file detect_pets.py. This makes me think something is wrong with the way that I've created the tf.record files (code below) rather than anything to do with the shape of the images, despite the error having to do with reshape. However,I've successfully trained on tf.records created in the same way before (from images of size (600, 600), (1024, 1024), and (720, 480), all with the same network). Moreover, I've previously encountered a similar error (only the numbers were different but the error was still with node Reshape_13) on a different data set of images with size (600, 600).

I am using python 3.7, tf version 1.14.0, cuda 10.2, Ubuntu 18.04

I've looked extensively at various other posts (here, here, here, here, and here) but I wasn't able to make any progress.

I've tried adjusting the keep_aspect_ratio_resizer parameters (originally min_dimension=600, max_dimension=1024 but I've also tried min, max = (720, 1280) and have tried pad_to_max_dimension: true with both of these min/max choices as well).

This is the code I'm using to create the tf.record file (apologies or indentations being off here):

I am running a CNN of a regressive type which inputs and outputs images of different dimensions (so not a an Image-segmentation problem) based on a dataset of samples and corresponding labels. As a result the last dense layer of my network has the height and width of the labels multiplied together. Now, I have been training the network for a while now and I wanted to see what the images looked like so to see how good or bad my model is. Is there a function that provides me with this option or do I have to hard-code it? How do I do it? Down below is attached the code of my network and the network summary as well.

conv2d_1 (Conv2D) (None, 54, 1755, 4) 20

activation_1 (Activation) (None, 54, 1755, 4) 0

max_pooling2d_1 (MaxPooling2 (None, 18, 585, 4) 0

batch_normalization_1 (Batch (None, 18, 585, 4) 16

conv2d_2 (Conv2D) (None, 17, 584, 8) 136

activation_2 (Activation) (None, 17, 584, 8) 0

max_pooling2d_2 (MaxPooling2 (None, 8, 292, 8) 0

batch_normalization_2 (Batch (None, 8, 292, 8) 32

conv2d_3 (Conv2D) (None, 7, 291, 16) 528

activation_3 (Activation) (None, 7, 291, 16) 0

max_pooling2d_3 (MaxPooling2 (None, 3, 145, 16) 0

batch_normalization_3 (Batch (None, 3, 145, 16) 64

conv2d_4 (Conv2D) (None, 2, 144, 32) 2080

activation_4 (Activation) (None, 2, 144, 32) 0

max_pooling2d_4 (MaxPooling2 (None, 1, 72, 32) 0

batch_normalization_4 (Batch (None, 1, 72, 32) 128

flatten_1 (Flatten) (None, 2304) 0

dropout_1 (Dropout) (None, 2304) 0

dense_1 (Dense) (None, 19316) 44523380

activation_5 (Activation) (None, 19316) 0

=================================================================

Total params: 44,526,384 Trainable params: 44,526,264 Non-trainable params: 120

Thanks in advance!