Find Signature in Image (3 one above the other)

3-way-signature.py

import cv2 
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt

def check_countour(shape, contour):
    # Get array X:
    arr = list(dict(sorted(Counter(contour[:, 0, 0]).items())).values()) # sort by key and get values (histogram)
    arr = arr[int(len(arr)*0.1):len(arr)-int(len(arr)*0.1)] # crop 10% of the begin and end of array
    # Rect conditions:
    # 1) array variance must be close to zero
    # 2) array lenght must be greater than 25% of the image cols
    # 3) contour area must be greater than 1% of the image area
    if np.var(arr)<0.01 and len(arr)>shape[1]*0.25 and cv2.contourArea(contour)>(shape[0]*shape[1])*0.01:
        return True
    else:
        return False

def pre_process(img):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    img = cv2.GaussianBlur(img,(5,5),0)
    # _, img = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
    # cv2.erode(img, )
    return img

DISPLAY = False
def detect_boxes(input, img):
    boxes = []
    img = cv2.medianBlur(img, 3)
    canny = cv2.Canny(img, 10, 50)
    
    contours, _ = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    for contour in contours:
        if DISPLAY and cv2.contourArea(contour)>900:   
            arr = list(dict(sorted(Counter(contour[:, 0, 0]).items())).values())
            arr = arr[int(len(arr)*0.1):len(arr)-int(len(arr)*0.1)]
            if True: # if np.var(arr)<0.01 and len(arr)>img.shape[1]*0.25 and cv2.contourArea(contour)>(img.shape[0]*img.shape[1])*0.01:
                print('{} - OK'.format(np.var(arr)))
                cv2.drawContours(input,contour,-1,(0,255,0),2)
                cv2.namedWindow('contour', cv2.WINDOW_NORMAL)
                cv2.imshow('contour',input)
                cv2.waitKey()
                arr = list(dict(sorted(Counter(contour[:, 0, 0]).items())).values())
                plt.plot(arr)
                plt.show()

        if check_countour(img.shape, contour):
            x_min, y_min, x_max, y_max = np.min(contour,0)[0][0], np.min(contour,0)[0][1], np.max(contour,0)[0][0], np.max(contour,0)[0][1]
            boxes.append(input[y_min:y_max, x_min:x_max])
    
    print('Found {} boxes'.format(len(boxes)))
    return boxes if len(boxes)==3 else []

input = img = cv2.imread('imgs/1.png')
img   = pre_process(img)
boxes = detect_boxes(input, img)

for box in boxes:
    cv2.imshow('box', box)
    cv2.waitKey()