CODEWITHSUNDEEP
pythonc++opencvimage-processingflood-fill
Hadi GhahremanNezhad
Hadi GhahremanNezhad

Reputation: 2445

OpenCV - How to use floodFill with RGB image?

I am trying to use floodFill on an image like below to extract the sky:

enter image description here

But even when I set the loDiff=Scalar(0,0,0) and upDiff=Scalar(255,255,255) the result is just showing the seed point and does not grow larger (the green dot):

enter image description here

code:

Mat flood;
Point seed = Point(180, 80);
flood = imread("D:/Project/data/1.jpeg");
cv::floodFill(flood, seed, Scalar(0, 0, 255), NULL, Scalar(0, 0, 0), Scalar(255, 255, 255));
circle(flood, seed, 2, Scalar(0, 255, 0), CV_FILLED, CV_AA);

This is the result (red dot is the seed):

enter image description here

How can I set the function to get a larger area (like the whole sky)?

Upvotes: 7

Views: 11733

Answers (2)

nathancy
nathancy

Reputation: 46600

Another thing you could do if you want the floodfill to contour as close as possible to contrasting elements in your image is to perform Kmeans color quantization to segment the image into a specified number of clusters. Since the sky and the mountains/trees have a visible color difference, we could segment the image into only three colors which will separate the objects better.

For instance with clusters=3:

Input image -> Kmeans color segmentation

Floodfill result in green

Notice how after segmenting, only three colors define the image. In this way, the floodfill will contour along the mountains/trees better

Code

import cv2
import numpy as np

# Kmeans color segmentation
def kmeans_color_quantization(image, clusters=8, rounds=1):
    h, w = image.shape[:2]
    samples = np.zeros([h*w,3], dtype=np.float32)
    count = 0

    for x in range(h):
        for y in range(w):
            samples[count] = image[x][y]
            count += 1

    compactness, labels, centers = cv2.kmeans(samples,
            clusters, 
            None,
            (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001), 
            rounds, 
            cv2.KMEANS_RANDOM_CENTERS)

    centers = np.uint8(centers)
    res = centers[labels.flatten()]
    return res.reshape((image.shape))

# Load image and perform kmeans
image = cv2.imread('1.jpg')
kmeans = kmeans_color_quantization(image, clusters=3)
result = kmeans.copy()

# Floodfill
seed_point = (150, 50)
cv2.floodFill(result, None, seedPoint=seed_point, newVal=(36, 255, 12), loDiff=(0, 0, 0, 0), upDiff=(0, 0, 0, 0))

cv2.imshow('image', image)
cv2.imshow('kmeans', kmeans)
cv2.imshow('result', result)
cv2.waitKey()

Upvotes: 8

Rotem
Rotem

Reputation: 32084

You need to set loDiff and upDiff arguments correctly.

See floodFill documentation:

loDiff – Maximal lower brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.
upDiff – Maximal upper brightness/color difference between the currently observed pixel and one of its neighbors belonging to the component, or a seed pixel being added to the component.

Here is a Python code sample: 

import cv2
flood = cv2.imread("1.jpeg");

seed = (180, 80)

cv2.floodFill(flood, None, seedPoint=seed, newVal=(0, 0, 255), loDiff=(5, 5, 5, 5), upDiff=(5, 5, 5, 5))
cv2.circle(flood, seed, 2, (0, 255, 0), cv2.FILLED, cv2.LINE_AA);

cv2.imshow('flood', flood)
cv2.waitKey(0)
cv2.destroyAllWindows()

Result:
floor

Upvotes: 4

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