Let’s assume there are two images. One is called small image and another one is called big image. I want to randomly generate the small image inside the different parts of the big image one at a time everytime I run. So, currently I have this image. Let’s call it big image I also have smaller image: I have created
Tag: image-processing
how do I (filter and) save many ROIs taken from a larger picture individually to picture files?
I followed this discussion: How extract pictures from an big image in python made mine, this is the output link https://imgur.com/a/GK8747f and .py script below I did frame them up, then how to save each one/qr code Answer I did frame them up, then how to save each one/qr code If you want to save the contents of each rectangle,
numpy slicing using multiple conditions where one of the conditions searches the neighborhood of an element
the problem is to take a black-white image, detect all the places where white borders on black, keep that white, and turn all other white pixels black. I know how to do this using normal for-loops and lists, but I want to do it w/ numpy, which I am not that familiar with. Here is what I have so far:
How to make kornia HomographyWarper behave like OpenCV warpPerspective?
As the title says, I want to use HomographyWarper from kornia so that it gives the same output as OpenCV warpPerspective. With the code above, I get the following output: With normalized_coordinates=False, I get the following output: Apparently the homography transformation is applied differently. I would love to know the difference. Answer You need to make two changes: Use the
how to mark the labels on image using watershed opencv
As this is the output from the watershed and I want to mark the labels like 1,2,3 etc on the regions identified. I have tried to use cv2.puttext as well by using cv2.boudingrect but the labels are not coming in the center of the region identified Through the above code the generated labels are as follows What i want is
Vectorize calculation of density of image regions
I am trying to implement an image stippling algorithm in python, and want to vectorize calculating the density (average luminance) of labelled image regions (Voronoi cells). Currently I’m able to do so using a loop, but this is too computationally intensive for large numbers of regions. How can I vectorize this operation? Answer The problem is not the loop but
Luminance Correction (Prospective Correction)
When I was searching internet for an algorithm to correct luminance I came across this article about prospective correction and retrospective correction. I’m mostly interested in the prospective correction. Basically we take pictures of the scene with image in it(original one), and two other ,one bright and one dark, pictures where we only see the background of the original picture.
How do I crop an image based on custom mask
I start my model and get a mask prediction of the area I would like to crop. This code is the closest I have gotten to the desired image, the problem with this image that is provided is that it doesn’t crop out black background My desired image should be like this Answer You could try this:
Python numba returned data types when calculating MSE
I am using numba to calculate MSE. The input are images which are ready as numpy arrays of uint8. Each element is 0-255. When calculating the squared difference between two images the python function returns (expectedly) a uint8 result, but the same function when using numba returns int64. What’s unclear to me is why the python-only code preserves the data-type
Python: How do I read the data in this multipage TIFF file to produce the desired image?
I am working with TIFF files that represent the readings of detectors in electron microscopy, and I know how this particular image should look, but I’m unsure how to get that result from the raw data in question. The TIFF files in question have several pages corresponding to frames on which data was taken, but when I look at each