CODEWITHSUNDEEP
pythonopencvimage-segmentation
Tarcisiofl
Tarcisiofl

Reputation: 143

How to extract the largest connected component using OpenCV and Python?

I am using OpenCV in Python to be able to identify only the Leaf presented on the image. I already be able to segment my image, and now I am currently stuck at "how to crop the largest component after I have detected all of them. Below is the codes, please have a look.

  1. Using scipy.ndimage, I was unable to advance after find the components:

    def undesired_objects ( image ):
    components, n = ndimage.label( image )
    components = skimage.morphology.remove_small_objects( components, min_size = 50 )
    components, n = ndimage.label( components )
    plot.imshow( components )
    plot.show()

  2. Using OpenCV connectedComponentsWithStats:

    def undesired_objects ( image ):
    image = image.astype( 'uint8' )
    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
    sizes = stats[1:, -1]; nb_components = nb_components - 1
    min_size = 150
    img2 = np.zeros(( output.shape ))
    for i in range(0, nb_components):
        if sizes[i] >= min_size:
            img2[output == i + 1] = 255
            plot.imshow( img2 )
            plot.show()

However, in both approaches, I'm still getting more than one component as result. Below, you will find the binary image:

Binary Image

Upvotes: 6

Views: 21060

Answers (2)

yang5
yang5

Reputation: 1235

Using cv2.CC_STAT_AREA for readability:

# Connected components with stats.
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)

# Find the largest non background component.
# Note: range() starts from 1 since 0 is the background label.
max_label, max_size = max([(i, stats[i, cv2.CC_STAT_AREA]) for i in range(1, nb_components)], key=lambda x: x[1])

More here: https://stackoverflow.com/a/35854198/650885

Upvotes: 2

Sunreef
Sunreef

Reputation: 4542

I would replace your code with something like this:

def undesired_objects (image):
    image = image.astype('uint8')
    nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(image, connectivity=4)
    sizes = stats[:, -1]

    max_label = 1
    max_size = sizes[1]
    for i in range(2, nb_components):
        if sizes[i] > max_size:
            max_label = i
            max_size = sizes[i]

    img2 = np.zeros(output.shape)
    img2[output == max_label] = 255
    cv2.imshow("Biggest component", img2)
    cv2.waitKey()

The loop on components now finds the component with the biggest area and displays it at the end of the loop.

Tell me if this works for you as I haven't tested it myself.

Upvotes: 8

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