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Is there a way to discern an object from the background with OpenCV?

I always wanted to have a device that, from a live camera feed, could detect an object, create a 3D model of it, and then identify it. It would work a lot like the Scanner tool from Subnautica. Imagine my surprise when I found OpenCV, a free-to-use computer vision tool for Python!
My first step is to get the computer to recognize that there is an object at the center of the camera feed. To do this, I found a Canny() function that could detect edges and display them as white lines in a black image, which should make a complete outline of the object in the center. I also used the floodFill() function to fill in the black zone between the white lines with gray, which would show that the computer recognizes that there is an object there. My attempt is in the following image.
The red dot is the center of the live video.
The issue is that the edge lines can have holes in them due to a blur between two colors, which can range from individual pixels to entire missing lines. As a result, the gray gets out and doesn't highlight me as the only object, and instead highlights the entire wall as well. Is there a way to fill those missing pixels in or is there a better way of doing this?

Welcome to SO and the exiting world of machine vision !
What you are describing is a very classical problem in the field, and not a trivial one at all. It depends heavily on the shape and appearance of what you define as the object of interest and the overall structure, homogeneity and color of the background. Remember, the computer has no concept of what an "object" is, the only thing it 'knows' is a matrix of numbers.
In your example, you might start out with selecting the background area by color (or hue, look up HSV). Everything else is your object. This is what classical greenscreening techniques do, and it only works with (a) a homogenous background, which does not share a color with your object and (b) a single or multiple not overlapping objects.
The problem with your edge based approach is that you won't get a closed edge safely, and deciding where the inside and outside of the object is might get tricky.
Advanced ways to do this would get you into Neural Network territory, but maybe try to get the basics down first.
Here are two links to tutorials on converting color spaces and extracting contours:
https://docs.opencv.org/4.x/df/d9d/tutorial_py_colorspaces.html
https://docs.opencv.org/3.4/d4/d73/tutorial_py_contours_begin.html
If you got that figured out, look into stereo vision or 3D imaging in general, and that subnautica scanner might just become reality some day ;)
Good luck !

What features do glitched images have that I could detect?

I'm trying to build a footage filter that only sends only "good" frames to the database.
Here is my current rating function:
def rateImg(img):
try:
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
except:
gray = img
edges = cv2.Canny(gray, 0, 255)
countours, _ = cv2.findContours(
edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
num_of_countours = len(countours)
lap = cv2.Laplacian(gray, cv2.CV_64F).var()
lap = round(lap, 2)
return [lap, num_of_countours]
First off, I use variance of Laplacian to calculate the sharpness of an image from a particular time window.
It should technically provide me a "good" frame, but that's not always the case.
The camera I have to use isn't great and sometimes glitches out like this and frames like this have the highest variance of Laplacian.
So, my current solution is to calculate the number of countours in an image and if an image crosses a particular threshold I classify it as "glitched". But with this approach the algorithm rates images with a lot of objects as "glitched".
Also, I have tried detecting squares and rectangles, but that proved to be much less effective than the countour approach.
Is there any way to detect obvious glitches in an image?
I feel like there should be, because as a human I can easily classify glitched and normal images at a glance. I just can't seem to pin-point what exactly makes them different.

Is there any way to detect obvious glitches in an image?
Yes, but probably not for complex random glitches, have a look in this similar question
In that case, you can detect if there is a large area of the image containing the same color. Photo taken from the camera would never contain the same RGB value although they look similar. However, this would be perfectly normal if the images are arts drawn on a digital devices.
As a human I can easily classify glitched and normal images at a
glance... What exactly makes them (me and a program) different. Is there any way to detect obvious glitches in an image
In fact, you can't identify a glitched image. You try to recognize the objects in it. When you see something "weird" that you don't recognize, you consider it as a glitched image. The machine can neither achieve this. You can train an AI that report images with unrecognizable "parts" as glitched but it will never be 100% accurate

Converting your image to HSV and runnign the Brightness Channel through an edge filter on ImageJ gives me this:
As you can see, the "glitched" region appears pretty uniformly brighter then the rest of the image, and should be detectable in some form. How often do you get a picture from you camera ? Depending on how much change occurs between two pictures, you might get away with subtracting the current one from the one before it to just look at changes.
You have not shown what an image with
a lot of objects
looks like, so you'd have to try if this works for those cases.
OpenCV functions needed for this workflow would be:
cvtColor() with COLOR_BGR2GRAY for color conversion (there might be faster ways to get a good greyvalue then HSV)
one of the edge detectors. Canny() or Sobel() would be the first i'd try
some greyvalue statistics. threshold() and CountNonZero() for a simple approach, which you could refine for sectors on the image or such
PS:
I feel like there should be, because as a human I can easily classify
glitched and normal images at a glance.
That's a common fallacy: Us humans (the sight-centric beings that we are) are fantastic at pattern recognition and interpolation and are rarely aware how much of that (including a lot of error correction) is happening every microsecond. A puny 2D camera can not hope to live up to that. (obligatory XKCD: https://xkcd.com/1425/)

Is there a way to separate and clone an image area with cv2?

I know the title isn't very clear but its kinda a complicated idea and I don't have the slightest clue how else to explain it. If you can think of a better title please, please, please , tell me and ill edit the title.
My problem is this: One of my close coder pals challenged me to re-create this Image to Image translation example. There are a few catches though.
I cant use any pre-trained neural networks
I have to make it run realtime (use webcam)
So far I have made it up to having the face swapped but i need to make it go back on to the non webcam image but there is an issue. to do that I have to re-build and warp the whole image around that image including filling the background behind the face that isn't included in the the original source image. I have tried to use impainting but on some occasions it takes part of the hair and neck and merges it into the background just creating a mess of skin and hair colors. I have also tried expanding my mask on the cv2 impainting function but that results in a big background color square that also looks terrible. I assume the best solution would be to segment off the biggest area around the head with some sort of segmenting algorithm, then clone part of that area to preserve the background texture instead of creating a new bad texture and placing that cloned area inside the mask. This all has to be done in realtime.
In the end I need to copy the person in img1, then remove them (part im stuck on). then take the facial landmarks from img2 and map those landmarks onto img1's clone's face, then add the re-mapped img1 back onto the img1 background. I know i cant communicate these ideas as clearly as someone more qualified as i am in 8th grade so if any clarification is required please ask.

Crop Facebook and Instagram photos from page screenshots

So I have quite an interesting image segmentation problem. Here, I have scraped instagram photos which are stacked vertically.
see image here(too long to post): https://imgur.com/a/gPr2J
What I am trying to do is quite simple. I just want to extract each post image from the screenshot, and save it to some directory. I am trying to find ways to make this work, like cropping by pixel color at a certain height but none of it is working perfectly.
Any method that would quickly segment this image. Python BTW.

I think you should start with segmenting each post out. Use the gaps between each post (which are always uniform) to segment each post out.
Then approach capturing the image inside the post - breaking this down into 2 different problems will make your algorithm simpler in my opinion.

I have a few ideas, not entirely sure how will they work for you, but thought they might give you some leads to try out:
1) All these instagram images seems to have a "heart" shaped icon just below the image you want to extract. Maybe figuring out detecting the heart shape might be good idea? Once you have found the "heart" you can look for the image just above it. Since it is a UI, my hope is that all the images that you want to extract will be a fixed number of pixels above the "heart". Moreover, they should also have the same height and width, I think.
2) Another possible idea is to find the edges in the image. Again, the images you want to extract seem to have a strong edge with respect to their background (but so does text and other UI elements). However, these edges should ideally have the largest area (which is also mostly fixed) enclosed between them. So, after finding the edges, you can use the find contours in function in opencv and then filter out the contours which have an area greater than a threshold. Have you tried something like this?

OpenCV how to replace cut out object with background

I have two images, one image which contains a box and one without. There is a small vertical disparity between the two pictures since the camera was not at the same spot and was translated a bit. I want to cut out the box and replace the hole with the information from the other picture.
I want to achieve something like this (a slide from a computer vision course)
I thought about using the cv2.createBackgroundSubtractorMOG2() method, but it does not seem to work with only 2 pictures.
Simply subtracting the picture from another does not work either because of the disparity.
The course suggests using RANSAC to compute the most likely relationship between two pictures and subtract the area thaht changed a lot. But how do I actually fill in the holes?
Many thanks in advance!!

If you plant ot use only a pair of images (or only a few images), image stitching methods are better than background subtraction.
The steps are:
Calculate homography between the two images.
Warp the second image to overlap the second.
Replace the region with the human with pixels from the warped image.
This link shows a basic example of image stitching. You will need extra work if both images have humans in different places, but otherwise it should not be hard to tweak this code.

You can try this library for background subtraction issues. https://github.com/andrewssobral/bgslibrary
there is python wrappers of this tool.