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Hello

This solution is not optimal but ... it work in such case:

import numpy as np import cv2 from matplotlib import pyplot as plt

I = cv2.imread('/home/smile/Downloads/ext_contours.png',cv2.IMREAD_GRAYSCALE)

_,I = cv2.threshold(I,0.,255.,cv2.THRESH_OTSU)
I = cv2.bitwise_not(I)

_,labels,stats,centroid = cv2.connectedComponentsWithStats(I)

result = np.zeros((I.shape[0],I.shape[1],3),np.uint8)

for i in range(0,labels.max()+1):
    mask = cv2.compare(labels,i,cv2.CMP_EQ)

    _,ctrs,_ = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

    result = cv2.drawContours(result,ctrs,-1,(0xFF,0,0))

plt.figure()
plt.imshow(result)    
plt.axis('off')

Hope it helps.

Hello

This solution is not optimal but ... it work in such case:

import numpy as np import cv2 from matplotlib import pyplot as plt

I = cv2.imread('/home/smile/Downloads/ext_contours.png',cv2.IMREAD_GRAYSCALE)
cv2.imread('ext_contours.png',cv2.IMREAD_GRAYSCALE)

_,I = cv2.threshold(I,0.,255.,cv2.THRESH_OTSU)
I = cv2.bitwise_not(I)

_,labels,stats,centroid = cv2.connectedComponentsWithStats(I)

result = np.zeros((I.shape[0],I.shape[1],3),np.uint8)

for i in range(0,labels.max()+1):
    mask = cv2.compare(labels,i,cv2.CMP_EQ)

    _,ctrs,_ = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

    result = cv2.drawContours(result,ctrs,-1,(0xFF,0,0))

plt.figure()
plt.imshow(result)    
plt.axis('off')

Hope it helps.