CODEWITHSUNDEEP
pythonopencv
Sharonica
Sharonica

Reputation: 193

Find edges (graph edges) in a binary image using OpenCV

I have a binary image, and I am trying to represent a graph, such that the white parts of the image are the vertices and edges, where the big area white spots are the vertices, and the edges are the white parts that connect between the big white parts that I detected as vertices. I managed to find the center of the big white parts by using OpenCV functions such as erosion, findContours and moments, using moments centroids. So I have the vertices of the graph. My next goal is to get the edges, meaning finding lines that are IN WHITE AREAS only, represented by 2 points, (x1,y1) and (x2,y2). I tried using all kinds of function such as: cv2.Canny() cv2.findLine cv2.findContour with different parameters on the binary image

For understanding my goal one can think about it as a maze, where the beginning of it is the biggest white spot in the image, and the end of the maze is the second biggest white spot, and the places you can walk through are all white areas of the image.

Some code segments I used in my project: First finds the edges, given a binary image (finalImage) and return the centroids

def findCentroids(finalImage):
_, contours0, hierarchy = cv2.findContours(finalImage.copy(), cv2.RETR_LIST,                    cv2.CHAIN_APPROX_NONE)
moments = [cv2.moments(cnt) for cnt in contours0]
centroids = []
for M in moments:
    if M["m00"] != 0:
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])
        centroids.append((cX, cY))
return centroids

So like I found centroids, I want to find more centroids (to make the image less erosed) and then perhaps find all edges that connect between these centroids. This doesn't seem like a good method so I hope to get better approaches in answers.

EDIT So I thought about another idea, which is to use the connected components method. I try to use the connected components supplied by cv2, likewise:

output = cv2.connectedComponentsWithStats((imageForEdges), 8, cv2.CV_32S)

But the outcome is that only black spots are recognized as components, which is the opposite as what I need. I tried to use the inverted image and it gave the same results, as I assume the algorithm prefers the spots that are completely bounded, and not the background (which is the white color in my case, and the whole purpose of me using it, that it finds areas that are not bounded )

Upvotes: 3

Views: 1531

Answers (1)

Soltius
Soltius

Reputation: 2261

Did you check out Iwanowski's algorithm ? https://pdfs.semanticscholar.org/cd14/22f1e33022b0bede3f4a03844bc7dcc979ed.pdf

The paper describes a method for the analysis of the content of a binary image in order to find its structure. The class of images it deals with consists of images showing at its foreground groups of objects connected one to another forming a graph-like structure. Described method extract automatically this structure from image bitmap and produces a matrix containing connections between all the objects shown on the input image

Upvotes: 1

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