CODEWITHSUNDEEP
pythonpython-3.xalgorithmimageoptimization
Jake
Jake

Reputation: 2912

Efficient way to detect white background in image

I wrote a script to detect if an image has a white background, by totalling all the pixels that are white and if it exceeds a threshold percentage of the total pixels.

This process takes time and especially long if I have a lot of images. Is there a more efficient way in numpy or opencv that can do it, rather than just using parallel processing?

def find_white_background(imgpath, threshold="0.3"):
    """remove images with transparent or white background"""
    imgArr = cv2.imread(imgpath)
    w, h, alpha = imgArr.shape

    total = w * h
    background = np.array([255, 255, 255])

    cnt = 0
    for row in imgArr:
        for pixel in row:
            if np.array_equal(pixel, background):
                cnt += 1

    percent = cnt / total
    if percent >= threshold:
        return True
    else:
        return False

Upvotes: 3

Views: 3401

Answers (1)

Chris
Chris

Reputation: 16147

This should provide greater efficiency by comparing the entire array to your background color array at once instead of looping.

def find_white_background(imgpath, threshold=0.3):
    """remove images with transparent or white background"""
    imgArr = cv2.imread(imgpath)
    background = np.array([255, 255, 255])
    percent = (imgArr == background).sum() / imgArr.size
    if percent >= threshold:
        print(percent)
        return True
    else:
        return False

Upvotes: 3

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