Filling contours but leaving contained regions unfilled

I have this python code that supposedly fills the contours of an image, but leaves the holes contained in it unfilled. This is what I want:

what I want (holes unfilled)

But this is what I get:

what I get (the whole thing filled)

I've tried specifying the contour hierarchies for filling with cv2, but I can't get the result I want.

This is what I've tried:


import numpy as np
import cv2

# Load the PNG image
img = cv2.imread('slice.png')

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)

# Find the contours in the binary image
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)

# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
    # Check if the contour has a parent
    if hierarchy[0][i][3] == -1:
        # If the contour doesn't have a parent, fill it with pixel value 255
        cv2.drawContours(filled_img, [contour], -1, 255, cv2.FILLED)

# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

I've tried modifying the -1, 0, 1 values for the 'if hierarchy[0][i][3] == -1:' part, but it either fills the smaller holes, or fills the entire bigger contour like the first pic I posted.

Update

I also would like to fill with white the inside of lesser hierarchy contours, like this:

Solution

The issue is that cv2.drawContours fills the entire inner part of a closed contour, regardless if there is an inner contour.

Instead of filling the contours without a parent with white, we may start with white contour, and fill the contours without a child with black.

Assuming we know that the inner part should be black, we may apply the following stages:

Find contours using cv2.RETR_EXTERNAL, and fill the outer contour with white.
Find contours using cv2.RETR_TREE.
Iterate the contours hierarchy, and fill with black color only contours that doesn't have a child contour (fill with black the most inner contours).

Code sample:

import numpy as np
import cv2

# Load the PNG image
img = cv2.imread('slice.png')

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)

# Find the outer contours in the binary image (using cv2.RETR_EXTERNAL)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)

# Fill the outer contour with white color
cv2.drawContours(filled_img, contours, -1, 255, cv2.FILLED)

# Find contours with hierarchy, this time use cv2.RETR_TREE
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
    # Check if the contour has no child
    if hierarchy[0][i][2] < 0:
        # If contour has no child, fill the contour with black color
        cv2.drawContours(filled_img, [contour], -1, 0, cv2.FILLED)

# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result filled_img:

Note:
In case we don't know the color of the most inner contour, we may draw a white contour on a black background, and use the result as a mask - use the mask for copying the original content of the input image.

Update:

Support contours that don't have a child:

For supporting both contours that have a child and contours without a child, we may fill with black color, only contours that match both conditions:

Contours has no child contour.
Contour has a grandparent contour (look for grandparent instead of a parent because an empty contour has an inner contour and its parent is the outer contour).

Code sample:

import numpy as np
import cv2

# Load the PNG image
img = cv2.imread('slice.png')

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)

# Find the outer contours in the binary image (using cv2.RETR_EXTERNAL)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)

# Fill the outer contour with white color
cv2.drawContours(filled_img, contours, -1, 255, cv2.FILLED)

# Find contours with hierarchy, this time use cv2.RETR_TREE
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
    has_grandparent = False
    has_parent = hierarchy[0][i][3] >= 0
    if has_parent:
        # Check if contour has a grandparent
        parent_idx = hierarchy[0][i][3]
        has_grandparent = hierarchy[0][parent_idx][3] >= 0

    # Check if the contour has no child
    if hierarchy[0][i][2] < 0 and has_grandparent:
        # If contour has no child, fill the contour with black color
        cv2.drawContours(filled_img, [contour], -1, 0, cv2.FILLED)

# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Update:

Fill with white the inside of lesser hierarchy contours:

Before filling the contour with black color, we may check is the nominated contour has black pixels inside.
Fill with black only if it has no child, has a grandparent and has black inside.

For testing if has black pixels inside we may draw the contour (with white color) over temporary image.
Then check if the minimum value is 0 (value where drawn contour is white).

tmp = np.zeros_like(thresh)
cv2.drawContours(tmp, [contour], -1, 255, cv2.FILLED)
has_innder_black_pixels = (thresh[tmp==255].min() == 0)

Code sample:

import numpy as np
import cv2

# Load the PNG image
img = cv2.imread('slice.png')

# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Threshold the image to create a binary image
ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)

# Find the outer contours in the binary image (using cv2.RETR_EXTERNAL)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Create a blank image with the same dimensions as the original image
filled_img = np.zeros(img.shape[:2], dtype=np.uint8)

# Fill the outer contour with white color
cv2.drawContours(filled_img, contours, -1, 255, cv2.FILLED)

# Find contours with hierarchy, this time use cv2.RETR_TREE
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Iterate over the contours and their hierarchies
for i, contour in enumerate(contours):
    has_grandparent = False
    has_parent = hierarchy[0][i][3] >= 0
    if has_parent:
        # Check if contour has a grandparent
        parent_idx = hierarchy[0][i][3]
        has_grandparent = hierarchy[0][parent_idx][3] >= 0

    # Draw the contour over temporary image first (for testing if it has black pixels inside).
    tmp = np.zeros_like(thresh)
    cv2.drawContours(tmp, [contour], -1, 255, cv2.FILLED)
    has_innder_black_pixels = (thresh[tmp==255].min() == 0)  # If the minimum value is 0 (value where draw contour is white) then the contour has black pixels inside

    if hierarchy[0][i][2] < 0 and has_grandparent and has_innder_black_pixels:
        # If contour has no child and has a grandparent and it has black inside, fill the contour with black color
        cv2.drawContours(filled_img, [contour], -1, 0, cv2.FILLED)

# Display the result
cv2.imshow('Original Image', img)
cv2.imshow('Filled Regions', filled_img)
cv2.waitKey(0)
cv2.destroyAllWindows()