doxe.py

import numpy as np
import cv2
import math

# Define lower and upper bounds for HSV color range
hsv_low = np.array([1, 107, 149], np.uint8)
hsv_high = np.array([18, 255, 255], np.uint8)

# Define hierarchy indices
NEXT = 0
PREVIOUS = 1
FIRST_CHILD = 2
PARENT = 3

def find_largest_contour_and_child(contours, hierarchy):
    
    largest_contour_index = max(range(len(contours)), key=lambda i: cv2.contourArea(contours[i]))
    child_index = hierarchy[largest_contour_index][FIRST_CHILD]
    biggest_child_contour_index = -1
    biggest_child_contour_area = 0

    while child_index != -1:
        child_contour = contours[child_index]
        child_contour_area = cv2.contourArea(child_contour)

        if child_contour_area > biggest_child_contour_area:
            biggest_child_contour_area = child_contour_area
            biggest_child_contour_index = child_index

        child_index = hierarchy[child_index][NEXT]
    
    return (largest_contour_index ,biggest_child_contour_index)

img = cv2.imread('1.jpg')

# Convert image to HSV color space
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# Create a mask based on the specified HSV color range
mask = cv2.inRange(hsv, hsv_low, hsv_high)

contours, hierarchy = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
# Draw all contours on the original image
cv2.drawContours(img, contours, -1, (255, 0, 255), 3)

# Process only if there are contours detected
if len(contours) != 0:
    hierarchy = hierarchy[0]
    # Find the largest contour and child contour within the largest contour
    largest_contour_index, biggest_child_contour_index = find_largest_contour_and_child(contours, hierarchy) 
    cv2.drawContours(img, contours, largest_contour_index, (255, 0, 0), 5)
    # Draw the largest child contour
    if biggest_child_contour_index != -1:
        biggest_child_contour = contours[biggest_child_contour_index]
        cv2.drawContours(img, [biggest_child_contour], 0, (0, 255, 0), 2)

        outer_contour = contours[largest_contour_index]
        inner_contour = contours[biggest_child_contour_index]
        # Check if both outer and inner contours have areas greater than 3600
        if (cv2.contourArea(outer_contour) > 3600) and (cv2.contourArea(inner_contour) > 3600):
            x, y, w, h = cv2.boundingRect(outer_contour)
            outer_aspect_ratio = float(w) / h
            outer_center = (int(x+(w/2)), int(y+(h/2)))
            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

            x, y, w, h = cv2.boundingRect(inner_contour)
            inner_aspect_ratio = float(w) / h
            inner_center = (int(x+(w/2)), int(y+(h/2)))
            cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)

            # Check if the aspect ratios and distance between centers meet the criteria
            if (abs(outer_aspect_ratio - inner_aspect_ratio) < 0.2) and (math.dist(outer_center, inner_center) < 8):
                image = cv2.putText(img, 'probably note', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
    else:
        print("There is no child contour :(")

cv2.imshow("img",img)
cv2.imshow("mask",mask)
cv2.waitKey(0)