I have an Image (or several hundreds of them) that need to be analyzed. The goal is to find all black spots close to each other.
For example all black spots with a Horizontal distance of 160 pixel and vertical 40 pixel.
For now I just look at each Pixel and if there is a black pixel I call a recursive Method to find its neighbours (i can post the code too if you want to)
It works, but its very slow. At the moment the script runs about 3-4 minutes depending on image size.
Is there some easy/fast way to accomplish this (best would be a scikit-image method to help out here) I'm using Python.
edit: I tried to use
Disclaimer: I'm not proficient with the scikit image library at all, but I've tackled similar problems using MATLAB so I've searched for the equivalent methods in scikit, and I hope my findings below help you.
First you can use skimage.measure.label which returns
label_image, i.e. an image where all connected regions are labelled with the same number. I believe you should call this function with
background=255 because from your description it seems that the background in your images is the while region (hence the value 255).
This is essentially an image where the
background pixels are assigned the value
0 and the pixels that make up each (connected) spot are assigned the value of an integer label, so all the pixels of one spot will be labelled with the value
1, the pixels of another spot will be labelled with the value
2, and so on. Below I'll refer to "spots" and "labelled regions" interchangeably.
You can then call skimage.measure.regionprops, that takes as input the
label_image obtained in the previous step. This function returns a list of
RegionProperties (one for each labelled region), which is a summary of properties of a labelled region.
Depending on your definition of
The goal is to find all black spots close to each other.
there are different fields of the
RegionProperties that you can use to help solve your problem:
bboxgives you the set of coordinates of the bounding box that contains that labelled region,
centroidgives you the coordinates of the centroid pixel of that labelled region,
local_centroidgives you the centroid relative to the bounding box
(Note there are also
bbox_area properties which you can use to decide whether to throw away very small spots that you might not be interested in, thus reducing computation time when it comes to comparing proximity of each pair of spots)
If you're looking for a coarse comparison, then comparing the
local_centroid between each pair of labelled regions might be enough.
Otherwise you can use the
bbox coordinates to measure the exact distance between the outer bounds of any two regions.
If you want to base the decision on the precise distance between the pixel(s) of each pair of regions that are closest to each other, then you'll likely have to use the