Documentation says:

We have a Docker image that takes care of setting up image_match and elasticsearch

Looking in the Dockerfile and setup.py, I see no creation of an ElasticSearch container.

Perhaps the documentation should mention it only creates a containerized environment where you can use the Python REPL for execute p

Request

I understand that some support for IHE IID was added here.

Is there documentation or examples/tests?

Here's some of the documentation:

https://wiki.ihe.net/index.php/Invoke_Image_Display
https://en.wikipedia.org/wiki/Invoke_Image_Display
https://www.ihe.net/uploadedFiles/Documents/Radiolog

while ssim_value < 0.95:
optimizer.zero_grad()
ssim_out = -ssim_loss(img1, img2)
ssim_value = - ssim_out.item()
print(ssim_value)
ssim_out.backward()
optimizer.step()
ii = np.transpose(img2[0].detach().cpu().numpy(), (1,2,0))
cv2.imshow("pre", ii255)
print(np.max(ii255))
cv2.waitKey(100)

Description

The ApplyScriptToRemotes script applies a script to all remote modules whose build status reports a successful build.

There are a number of aspects -many of them were already mentioned in PR #781- that could be improved to make the script more robust.

• May be th

I think having guidelines similar to what is implemented in gimp will be useful for manual digitization, visual detection of skewness in images and comparing two different locations on it.

Add entry to check to see if the user is running 32-bit or 64-bit python,.

![whatsapp image 2017-12-01 at 3 03 03 pm](https://user-images.githubusercontent.com/34059153/334768

Feature request: allow passing custom made Matplotlib colormaps

import matplotlib.pyplot as plt
cmap = plt.cm.get_cmap("viridis", 5)

then pass that colormap to the Viewer

(the goal isn't necessarily catecorical colormaps... this is just a simple example. A user might want to make a custom normalized map that are far more complex)

We should probably use a custom function instead of the matrix warping, so that the last pixel in the original image corresponds to the last pixel in the new image.

I was wondering if we should create a mybinder instance for the tutorial. As far as I can tell, we would only need to include the mindboggle_input_example in the Docker image which should be fairly straightforward.

I will happily add this to the documentation, but I need help figuring out how to use it ;).

Basically I want to extract a quadrilateral region from an image and perspective transform it to be a rectangle. Per my other open issue, I've been pointed to the undocumented gm.perspectiveProjection.

Per this operation
https://github.com/PeculiarVentures/GammaCV/blob/8ffa723ef54b297cda8ffb5b21b027

Let's update the documentation to capture that the library can be installed as a module. We should include an explanation of the pip install -e . or python setup.py develop syntax to capture that many developers may want to install in place so they can continue to update and develop the library code.

I think the CaPTk needs to clarify the importance of resampling value in its calculations. Although this is a general concept, but a user should be able to rely on your documentation and use the software.

Please add an explanation about extrapolation/interpolation methods and it's positive and negative aspects for users.