Identify an embedded image within an image in Python [closed]

I have, for example, the following image:

Is there any way of identifying the embedded image between the text?

I know you can use OpenCV’s match_by_template but that is if you already know what the embedded image is. I have spent a lot of time searching how to identify an image within an image in Python when not already knowing what the embedded image is, but I have not been able to find anything. Please let me know what I can do.

Answer

Here’s my attempt. This is how it works:

1. To detect text, check how different pixels are from their neighbors. This is done using an absolute difference.
2. The previous step only detects the edges of text. Expand this with a gaussian blur.
3. Threshold this, and remove text.
4. Crop remaining whitespace.

It uses numpy, opencv, and scipy to do it.

Full code:

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
import scipy.ndimage

img_orig = cv.imread('image_extract.png')

def find_text(gray, gaussian_size_px=10, text_threshold=10):
    flat = gray.flatten().astype('int16')
    # difference each pixel against the pixel 1 position forward
    differenced_image = np.abs(flat - np.roll(flat, 1)).reshape(gray.shape)
    differenced_image = scipy.ndimage.gaussian_filter(differenced_image, sigma=gaussian_size_px)
    is_text = differenced_image > text_threshold
    return is_text


def remove_text(img, minpool_size=3):
    is_text = find_text(img)
    image_only = np.where(is_text, 0, img)
    # filter out small bright pixels with convolved minimum
    image_only = scipy.ndimage.minimum_filter(image_only, size=minpool_size)
    return image_only


def find_subimage(img_orig):
    gray = cv.cvtColor(img_orig, cv.COLOR_BGR2GRAY)
    # invert colors so white = 0 and black = 255
    gray = np.max(gray) - gray
    
    image_only = remove_text(gray)

    coords = cv.findNonZero(image_only)
    x, y, w, h = cv.boundingRect(coords)
    cropped = img_orig[y:y+h, x:x+w]
    return cropped

plt.imshow(find_subimage(img_orig))

Output: