python - Getting a centre of an irregular shape

Question

I have a irregular shape like below:

I need to get the centre of that white area, I just tried with contour in openCV like below

img=cv.imread('mypic',0)
ret,thresh = cv.threshold(img,127,255,cv.THRESH_BINARY_INV)
cnts = cv.findContours(thresh.copy(), cv.RETR_EXTERNAL,
    cv.CHAIN_APPROX_SIMPLE)
cnt = contours[0]
x,y,w,h = cv.boundingRect(cnt)
res_img = cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
cv.imwrite('output.png',res_img)

But those cnts doeesn't give me very good results, as you can see the original image and its two small black point below the picture. Can someone point me to a good solution to get a centre of an irregular shape like above?

question from:https://stackoverflow.com/questions/65930839/getting-a-centre-of-an-irregular-shape

Answer 1

As I suggested, perform an erosion followed by dilation (an opening operation) on the binary image, then compute central moments and use this information to calculate the centroid. These are the steps:

1. Get a binary image from the input via Otsu's Thresholding
2. Compute central moments using cv2.moments
3. Compute the blob's centroid using the previous information

Let's see the code:

import cv2
import numpy as np

# Set image path
path = "C:/opencvImages/"
fileName = "pn43H.png"

# Read Input image
inputImage = cv2.imread(path+fileName)

# Convert BGR to grayscale:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)

# Threshold via Otsu + bias adjustment:
threshValue, binaryImage = cv2.threshold(grayscaleImage, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

This is the binary image you get. Notice the small noise:

The opening operation will get rid of the smalls blobs. A rectangular structuring element will suffice, let's use 3 iterations:

# Apply an erosion + dilation to get rid of small noise:

# Set kernel (structuring element) size:
kernelSize = 3

# Set operation iterations:
opIterations = 3

# Get the structuring element:
maxKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernelSize, kernelSize))

# Perform closing:
openingImage = cv2.morphologyEx(binaryImage, cv2.MORPH_OPEN, maxKernel, None, None, opIterations, cv2.BORDER_REFLECT101)

This is the filtered image:

Now, compute the central moments and then the blob's centroid:

# Calculate the moments
imageMoments = cv2.moments(openingImage)

# Compute centroid
cx = int(imageMoments['m10']/imageMoments['m00'])
cy = int(imageMoments['m01']/imageMoments['m00'])

# Print the point:
print("Cx: "+str(cx))
print("Cy: "+str(cy))

Additionally, let's draw this point onto the binary image to check out the results:

# Draw centroid onto BGR image:
bgrImage = cv2.cvtColor(binaryImage, cv2.COLOR_GRAY2BGR)
bgrImage = cv2.line(bgrImage, (cx,cy), (cx,cy), (0,255,0), 10)

This is the result: