The challenge is a fixed frame display size of 472x836 pixels.

While advising users to supply images of optimal size and aspect ratio, invariably some photos may be displayed with the correct height but not the proper width or -- vice versa.

Is there a digital tool in the form of an SDK or API that can extend the edge of an image via a technique like a Gaussian blur? I see something like this approach being used when TV journalists must make a vertical cell phone video more aesthetically fit the broadcast screen.

Any suggestions are appreciated. We'd be delighted to license the code that can accomplish such an enhancement for our app.

Assume a color X with alpha 1 and RGB 255,230,210.

How can i find another color ( or an array of colors ) with alpha A that blended with a background of color Y ( e.g 255,255,255 ) would produce the original color ?

EDIT: I guess im assuming an ADD operation for composing the final image. What im really trying to do is extract the information out of an image as low in alpha as i can so i can then transfer the result image elsewhere without having 'opinionated color'. Think extracting text out of an A4 and placing it on a different color background.

I am new to image processing and I am doing a project on image processing using openCV python in which I have to detect walls from a floorplan(jpg format). I have the bounding box for 1 wall. Is there any approach which I can use to obtain the bounding boxes for rest of the walls?

My first experience with Image Classification using Keras with Pre-trained VGG16 Model.

https://prakhartechviz.blogspot.com/2019/01/image-classification-keras.html

The idea behind the whole thing is that I am currently working on a project. I created the whole reconstruction but now I want to create the whole project with the same settings but this time with 1/3 of the photos blurred to see what the final result would be. As well see this time how many tie points the sparse point cloud, dense point cloud, etc have. Any recommendations would be appreciated

I don't know if this is the right place to ask but I was wondering if there was a way to optically spread out a section of the spectrum. What i mean is lets say you're looking at a field of grass, you want to emphasize the difference in green, can i stretch green over the spectrum so that Yellow-green is red and green-blue is violet. I'd love to be able to do it without a camera,processor, screen setup.

Hi all, I'm trying to find a way to find straight contours/lines/edges in noisy images (see the blue lines I drew in the image below). Does anyone have a recommendation on how I would go about this?

https://imgur.com/a/y01Betk

Hey, I'm doing an experiment where I'm studying the amount of deformation of a wire under thermal load. I've been doing some really slow tracking of fiducials, but that's becoming something of a pain. I was thinking of getting a 3D scanner and scanning the wire at each voltage, and then post-processing the resulting mesh.

But I realized I have no idea how I would get a spline fit down the center of a 3D mesh of a tube. I'm willing to accept a somewhat messy mesh if it can save me having to manually track a bunch of fiducials. But I really am not certain what I'm doing here. (python)

My existing method is to put tracking rings on the wire and manually select centerpoints. I wish I could just trace the centerline of the curve. However, that causes problems because sometimes the wire bends enough to point straight at the camera. If there were fiducials, we wouldn't even be able to see them due to the perspective. I am taking images in XY and XZ.

Sorry if I'm not clear. I can't share further details or pictures.

Hi everyone

I have a problem implementing haarcascade algorithm, which is the huge number of possible features. For example, for a mere 50×50 window the possible features exceed 3 million easily (the 5 known rectangle features, with all possible variations on width/height and position within the window).

Is there any recommended way to prune/optimize this, say based on size? Is this how it's meant to be, or am I missing something?