Finding thick objects on binary image
Question:
In the context of analysing images to find zones with movement, here’s what I’ve got as an intermediate result, using opencv with python (assume these are 100% binary):
So my question is: is there a way to locate blobs of white with a specific “thickness” threshold ?
Here’s what it could look like, roughly:
I’ve been looking for transforms and manipulations such as connected components and morphological transformations but those won’t work and I can’t quite figure out where to start other than that.
Answers:
The morphological opening is ideal for this problem. It removes all the white parts thinner than a given diameter.
In OpenCV it is implemented in cv2.morphologyEx using op=cv2.MORPH_OPEN:
kernel = cv2.getStructuringElement(cv2.cv.MORPH_ELLIPSE, diameter)
output = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
But note that this removes parts of objects that are thin, it doesn’t leave the full object if a part of it is wide enough. That can be done using an opening by reconstruction, an erosion followed by a morphological reconstruction (also known as geodesic dilation).
OpenCV doesn’t have that algorithm. This Q&A gives a rough outline for how to implement it in OpenCV, but that is a very expensive algorithm, there are much more efficient ones.
There might be an implementation in Scikit-image, I haven’t looked for it.
DIPlib (with Python bindings called PyDIP) (also, I’m an author) has a dip.OpeningByReconstruction. Install the Python module with pip install diplib.
In the context of analysing images to find zones with movement, here’s what I’ve got as an intermediate result, using opencv with python (assume these are 100% binary):
So my question is: is there a way to locate blobs of white with a specific “thickness” threshold ?
Here’s what it could look like, roughly:
I’ve been looking for transforms and manipulations such as connected components and morphological transformations but those won’t work and I can’t quite figure out where to start other than that.
The morphological opening is ideal for this problem. It removes all the white parts thinner than a given diameter.
In OpenCV it is implemented in cv2.morphologyEx using op=cv2.MORPH_OPEN:
kernel = cv2.getStructuringElement(cv2.cv.MORPH_ELLIPSE, diameter)
output = cv2.morphologyEx(image, cv2.MORPH_OPEN, kernel)
But note that this removes parts of objects that are thin, it doesn’t leave the full object if a part of it is wide enough. That can be done using an opening by reconstruction, an erosion followed by a morphological reconstruction (also known as geodesic dilation).
OpenCV doesn’t have that algorithm. This Q&A gives a rough outline for how to implement it in OpenCV, but that is a very expensive algorithm, there are much more efficient ones.
There might be an implementation in Scikit-image, I haven’t looked for it.
DIPlib (with Python bindings called PyDIP) (also, I’m an author) has a dip.OpeningByReconstruction. Install the Python module with pip install diplib.