How to compare two point of two objects

Since the boxes are touching each other, watershed algorithm works well in this cases to extract the box contours. Then the x coordinates position of the rectangle contours can be used to say whether they are aligned or not.

Steps:

1. Watershed algorithm to get markers.
2. Filter out boxes(rectangles)
3. To see if its aligned or not using standard deviation of all the boxes x coordinates. (the threshold can be set accordingly)

Code:

import cv2
import numpy as np


def watershed_algorithm(image):

    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    # noise removal
    kernel = np.ones((5,5),np.uint8)
    opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)

    # sure background area
    sure_bg = cv2.dilate(opening,kernel,iterations=3)

    # Finding sure foreground area
    dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
    ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)

    # Finding unknown regio3
    sure_fg = np.uint8(sure_fg)
    unknown = cv2.subtract(sure_bg,sure_fg)

    # Marker labelling
    ret, markers = cv2.connectedComponents(sure_fg)

    # Add one to all labels so that sure background is not 0, but 1
    markers = markers+1

    # Now, mark the region of unknown with zero
    markers[unknown==255] = 0

    markers = cv2.watershed(image,markers)
    
    return markers

def is_rect_contour(contour):
    peri = cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, 0.04 * peri, True)
    if len(approx) == 4:
        return True
    else:
        return False
    
def get_rectangle_contours(markers, shape):
    rectangle_contours = []
    
    for marker in np.unique(markers):
        if marker == 0 or marker==-1:
            continue
        mask = np.zeros(shape, dtype="uint8")
        mask[markers == marker] = 255
        cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
            cv2.CHAIN_APPROX_SIMPLE)
        c = max(cnts[0], key=cv2.contourArea)
        if is_rect_contour(c):
            rectangle_contours.append(c)
    
    return rectangle_contours

def draw_and_display_contours(contours, image):
    for i,c in enumerate(contours):
        (x, y, w, h) = cv2.boundingRect(c)
        cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
        cv2.putText(image, "#{}".format(i+1), (int(x) - 10, int(y)),
            cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
    
    cv2.imshow('Contours', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
    
def is_boxes_aligned(rectangle_contours):
    x_coord_list = []
    for c in rectangle_contours:
        (x, y, w, h) = cv2.boundingRect(c)
        x_coord_list.append(x)
    
    threshold_std_deviation_in_X_coordinates = 10
    if np.std(x_coord_list) < threshold_std_deviation_in_X_coordinates:
        print("Box are aligned in same column with a standard deviation of {}".format(np.std(x_coord_list)))
    else:
        print("Box are not aligned in same column")

    

image = cv2.imread("./cubes.jpg")
markers = watershed_algorithm(image)
rectangle_contours = get_rectangle_contours(markers, image.shape[:2])
draw_and_display_contours(rectangle_contours,image)
is_boxes_aligned(rectangle_contours)

Output:

Box are aligned in same column with a standard deviation of 4.027681991198191