To get the center mask from an image of masks using Python3 and opencv

Question

Suppose I have these sets of images:

I want to take the center coordinate mask, ie, the circular one which has the cell:

To finally isolate the cell

How to achieve these using Python3 and opencv?

Answer 1

The Concept

1. Detect the contours of the objects.

2. Loop through the contours and find the one that encloses the center of the image.

3. Using that contours, create a mask for the image and mask the image.

The Code

import cv2
import numpy as np

def process(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_canny = cv2.Canny(img_gray, 0, 50)
    img_dilate = cv2.dilate(img_canny, None, iterations=1)
    img_erode = cv2.erode(img_dilate, None, iterations=1)
    return img_erode

def get_masked(img):
    h, w, _ = img.shape
    center = h // 2, w // 2
    contours, _ = cv2.findContours(process(img), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    for cnt in contours:
        if cv2.contourArea(cnt) > 100:
            if cv2.pointPolygonTest(cnt, center, False) > 0:
                mask = np.zeros((h, w), 'uint8')
                cv2.drawContours(mask, [cnt], -1, 255, -1) 
                return cv2.bitwise_and(img, img, mask=mask)

img = cv2.imread("blobs.png")
cv2.imshow("img_processed", get_masked(img))
cv2.waitKey(0)

The Output

The Explanation

1. Import the necessary libraries:

import cv2
import numpy as np

2. Define a function to process the image into a binary image that will allow optimal results when detecting the contours of the image:

def process(img):
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img_canny = cv2.Canny(img_gray, 0, 50)
    img_dilate = cv2.dilate(img_canny, None, iterations=1)
    img_erode = cv2.erode(img_dilate, None, iterations=1)
    return img_erode

3. Define a function that will loop though the contours of the image (using the process function defined before to process the image) , and for every contour with contour area greater than 100 (to filter out noise) , check if the center of the image is inside the contour (done by checking if the result from calling the cv2.pointPolygonTest returns a positive number) , create the mask, mask the image and return the masked image:

def get_masked(img):
    h, w, _ = img.shape
    center = h // 2, w // 2
    contours, _ = cv2.findContours(process(img), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    for cnt in contours:
        if cv2.contourArea(cnt) > 100:
            if cv2.pointPolygonTest(cnt, center, False) > 0:
                mask = np.zeros((h, w), 'uint8')
                cv2.drawContours(mask, [cnt], -1, 255, -1) 
                return cv2.bitwise_and(img, img, mask=mask)

4. Finally, read in your image, apply the get_masked function define before and show the image:

img = cv2.imread("blobs.png")
cv2.imshow("img_processed", get_masked(img))
cv2.waitKey(0)