Detecting overlapping blobs in image?

Question

I have read the following two posts related to my question; however, my problem is slightly different and I'm having a hard time solving it.

1. Finding if two polygons Intersect in python?
2. Getting intersection of circles using shapely?

I have an image with irregular shapes such as these:

Now, with this image, I'd like to be able to identify the separate blobs, even if they're adjoined. In particular, I want to count individual blobs that are tightly overlapping. Like for instance: For instance, in that image, I want to be able to count 7 separate blobs. However, edge detection only counts 1. If I use some solidity cutoff (area of blob/area of convex hull), I'm able to count 2.

I'm not so concerned about the separate non-overlapping ones. But I'm having a really hard time detecting them using edge detection. I tried using Opencv's Canny function and HoughesCircles function. Neither of them proved effective.

Any idea how to proceed?

Answer 1

I tried out an algorithm based in azimuths and second derivatives, by using contours circulars, and it works well. PyQGIS code is as follows:

layer = iface.activeLayer()

feat = layer.getFeatures().next()

points = feat.geometry().asPolyline()

azimuths = [ points[i].azimuth(points[i+1]) for i in range(len(points)-1) ]

az_diff = [ azimuths[i+1] - azimuths[i] for i in range(len(azimuths)-1) ]

sum = 0

for item in az_diff:
    if item < 0:
        sum += 1

blobs_number = sum/2 + 1

print 'blobs_number: ', blobs_number 

For one circular "blob":

For two overlapped "blobs":

For seven overlapped "blobs":

Results were as expected.

Answer 2

I am aware you already found the solution, so this is just for future reader (if any) who might be interested in. (As this can also be seen as an image processing).

In bio and medical area, ImageJ is well-know for such a task. Not frequently, but there are some discussions in this forum with subjects related to remote sensing.

Using ImageJ workflow would be;

1. Download and install ImageJ.
2. Load your image file (drag and drop the image to ImageJ menu bar).
3. Make it binary (0,1) image Process | Binary | Make Binary
4. Break-apart (isolate) fused particles. Process | Binary | Watershed.
5. Count particles by Analyze | Analyze Particles.(minimum size~ 500 pixels)
6. AS the process (3) flips black/white, you may want to reverse the B/W back to original by Edit | Invert.