Image segmentation in python

Question

I have the image shape image

I am looking for python solution to break the shape in this image into smaller parts according to the contour in the image.

I have looked into solution on Canny and findContours in OpenCV but none of them works for me.

Edit:

Code used:

using Canny method

import cv2 import numpy as np

img = cv2.imread('area_of_blob_maxcontrast_white.jpg') edges = cv2.Canny(img, 100, 200)
cv2.imwrite('area_of_blob_maxcontrast_white_edges.jpg',edges)

using findContours method

import numpy as np 
import argparse 
import cv2

image = cv2.imread('area_of_blob_maxcontrast_white.png')

lower = np.array([0, 0, 0]) upper = np.array([15, 15, 15]) shapeMask = cv2.inRange(image, lower, upper)

(_,cnts, _) = cv2.findContours(shapeMask.copy(), cv2.RETR_EXTERNAL,
                            cv2.CHAIN_APPROX_SIMPLE) print "I found %d black shapes" % (len(cnts)) cv2.imshow("Mask", shapeMask)

for c in cnts:
    # draw the contour and show it
    cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
    cv2.imshow("Image", image)
    cv2.waitKey(0)

Please share a sample code of your attempts so people can find and point out where your code needs to be changed. — NOhs
– NOhs, Commented Jul 5, 2018 at 4:36

Andriy Makukha · Accepted Answer · 2018-07-05 11:00:33Z

6

The trick is to make your faint single pixel boundary slightly bolder. I do it by changing any white pixel that has two adjacent black pixels (above, below, to the left or to the right) to black. (I do it extremely slow, though. I'm pretty sure there must be a smarter way to do it with OpenCV or Numpy.)

Here is my code:

#!/usr/bin/env python 

import numpy as np
import cv2

THRESH = 240

orig = cv2.imread("map.png")
img = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)

# Make the faint 1-pixel boundary bolder
rows, cols = img.shape
new_img = np.full_like(img, 255)    # pure white image
for y in range(rows):
    if not (y % 10):
        print ('Row = %d (%.2f%%)' % (y, 100.*y/rows))
    for x in range(cols):
        score  = 1 if y > 0 and img.item(y-1, x) < THRESH else 0
        score += 1 if x > 0 and img.item(y, x-1) < THRESH else 0
        score += 1 if y < rows-1 and img.item(y+1, x) < THRESH else 0
        score += 1 if x < cols-1 and img.item(y, x+1) < THRESH else 0
        if img.item(y, x) < THRESH or score >= 2:
            new_img[y, x] = 0       # black pixels show boundary

cv2.imwrite('thresh.png', new_img)

# Find all contours on the map
_th, contours, hierarchy = cv2.findContours(new_img,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
print "Number of contours detected = %d" % len(contours)

# Fill second level regions on the map
coln = 0
colors = [
    [127, 0, 255],
    [255, 0, 127],
    [255, 127, 0],
    [127, 255, 0],
    [0, 127, 255],
    [0, 255, 127],
]
hierarchy = hierarchy[0]
for i in range(len(contours)):
    area = cv2.contourArea(contours[i])
    if hierarchy[i][3] == 1:
        print (i, area)
        coln = (coln + 1) % len(colors)
        cv2.drawContours(orig, contours, i, colors[coln], -1)

cv2.imwrite("colored_map.png", orig)

Input image:

Output image:

Here I color only the direct descendants of the outmost contour (hierarchy[i][3] == 1). But you can change it to exclude the lakes.

edited Jul 5, 2018 at 11:00

answered Jul 5, 2018 at 10:41

Andriy Makukha

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10 Comments

Esther Over a year ago

Thanks Andriy, that's what I am looking for.

Andriy Makukha Over a year ago

@Esther, I'm glad it helped. Please, consider choosing my answer as "Best answer".

Esther Over a year ago

sorry, i am new to using this. May I know how should i do that to choose your answer as Best answer

Andriy Makukha Over a year ago

@Esther, "To mark an answer as accepted, click on the check mark beside the answer to toggle it from hollow to green". See this screenshot.

Andriy Makukha Over a year ago

NOTE: faster way to make the border bolder would be to apply a custom kernel [[0,1,0],[1,0,1],[0,1,0]] with cv2.filter2D function to compute the score as a matrix.

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