I am trying to make a function that will solarize an image, but before I can do that I need to know how to solarize an image.
According to Wiki:
"the image recorded on a negative or on a photographic print is wholly or partially reversed in tone. Dark areas appear light or light areas appear dark"
I am running a python framework which allows me to access all the pixels in an image and their RGB values. From there does anyone have any suggestions on how to achieve this?
Here's some good information on how that effect is digitally recreated
Related
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 !
For an analysis, I'd like to take the a bunch of TIF images I have, and fill in the black background to create a perfect square.
I would like to keep the same general pattern of the image when I fill in the black spots, instead of just filling in the black with random bits of white and blue. My first thought for doing this is to impose some sort of symmetrical "reflection" of the image onto the black portions - the concept is detailed below.
The thing is, I'm not sure how to go about this - my first thought was to convert the image to a NumPy array and copy the individual rows of pixels over for a pseudo-reflection, but that could take a lot of time since I would be accounting for the length of the black portion in each row, and it wouldn't even be the desired result. I was wondering if there was a package or method that did something like this already, perhaps in PIL.
Any ideas are appreciated, as I am not to familiar with image processing in Python (or in general) - thank you!
EDIT: Here is a google drive link for the example image in question.
EDIT 2: Here is a google drive link for another example, this time with two "overlapping" black background areas (in other words, the actual data has a corner)
I have a FOV camera that has approximately 195*130 degree. So this 'lens' will put in a circle holder and the lens should not see the holder. Here's the image of not I want.
I draw 4 rectangle in Paint. There are 4 black spots which is the holder. The full red one is for censorship not there actually
If the camera image streams like that, that's a no. I need to detect that black spots and if it is like this it should gives me a error message or simply 'false'. I searched google and couldn't found this. I'm a noob of this subject but if you explain me how to do this I can connect the dots.
Thank you for your helps.
And I get the stream via USB-Capture Card. It acts like webcam.
#UPDATE1: I cropped the four corners of image then get the threshold. Made a basic if else logic and get what I want. Thank you anyway.
Try detection by generating custom HAAR filters.
Or make it simply by applying a threshold (nearly black) and look if some tiny squares in the corneres are completely black.
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.
There are many photos are dark, These photos does not make sense to the viewer.
So I want to use opencv identified it, how to do that by opencv?
Any python source example will good:)
Perhaps you can transform the image to the HSV color space and use the V values for measuring the amount of light in the scene. Extract a histogram of the V values and compare it between light and dark images to see the differences.
your question is a little unclear as of what you wish to do. Can you give an example of the scene? I wish to see how dark it is before coming to a clear conclusion.
But if you just want to determine if an image is dark as via your title, its simple, draw an Histogram. Dark images tends to have peaks on the left hand side of the histogram, where the intensity of the pixels are pretty much in the value range of 0 to 20 or maybe can go up to 40/50, the right hand side of the histogram should pretty much have nothing should the image is really dark as what you mentioned. Following which, do a threshold on the image to determine whether the image is dark or not.
If you want to make the image clearer to the human eye, you can do histogram normalization. But that depends on how bad the image really is.
Was that what you are looking for?