Detect and fit 2 intersecting ellipses -- prototype 1

import cv2
import numpy as np


def process_blob(n, contour, input_img, output_img):
    MAJOR_DEFECT_THRESHOLD = 2.0

    cv2.drawContours(output_img, [contour], 0, (191,0,0), 2)

    hull = cv2.convexHull(contour)
    cv2.drawContours(output_img, [hull], 0, (0,191,0), 2)

    hull_idx = cv2.convexHull(contour, returnPoints=False)
    defects = cv2.convexityDefects(contour, hull_idx)

    intersections = []
    for i,defect in enumerate(np.squeeze(defects, 1)):
        first_idx, last_idx, far_idx, far_dist = defect
        real_far_dist = far_dist / 256.0
        if real_far_dist >= MAJOR_DEFECT_THRESHOLD:
            print(("DEFECT #%d " % i), first_idx, last_idx, far_idx, real_far_dist)
            intersections.append(far_idx)

    if len(intersections) == 0:
        print("One ellipse")
        ellipse = cv2.fitEllipse(contour)
        cv2.ellipse(output_img, ellipse, (0,0,255), 2)
        return [ellipse]

    if len(intersections) == 4:
        print("Two ellipses")
        contour_segments = [
            contour[intersections[0]:intersections[1]+1]
            , contour[intersections[1]:intersections[2]+1]
            , contour[intersections[2]:intersections[3]+1]
            , np.vstack([contour[intersections[3]:],contour[:intersections[0]+1]])
        ]
        cv2.polylines(output_img, [contour_segments[0]], False, (0,255,0), 4)
        cv2.polylines(output_img, [contour_segments[1]], False, (0,255,255), 4)
        cv2.polylines(output_img, [contour_segments[2]], False, (255,255,0), 4)
        cv2.polylines(output_img, [contour_segments[3]], False, (255,0,255), 4)

        split_contours = [
            np.vstack([contour_segments[0], contour_segments[2]])
            , np.vstack([contour_segments[1], contour_segments[3]])
        ]
        ellipses = [cv2.fitEllipse(c) for c in split_contours]
        for ellipse in ellipses:
            cv2.ellipse(output_img, ellipse, (0,0,255), 2)
        return ellipses

    print("Invalid scenario")
    return []


image = cv2.imread('input.png', cv2.IMREAD_GRAYSCALE)

_,img_binary = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)

contours, _ = cv2.findContours(img_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

print("Found %d blob(s)." % len(contours))

PADDING = 20

for i,contour in enumerate(contours):
    orig_x, orig_y, width, height = cv2.boundingRect(contour)
    offset_x, offset_y = orig_x - PADDING, orig_y - PADDING
    offset_contour = contour - (offset_x, offset_y)

    input_img = cv2.copyMakeBorder(image[orig_y:orig_y+height,orig_x:orig_x+width]
        , PADDING, PADDING, PADDING, PADDING, cv2.BORDER_CONSTANT, None, 255)
    #output_img = np.zeros((PADDING * 2 + height, PADDING * 2 + width, 3), np.uint8)
    output_img = cv2.cvtColor(cv2.add(input_img, 127), cv2.COLOR_GRAY2BGR)

    process_blob(i, offset_contour, input_img, output_img)
    cv2.imshow('', output_img)
    cv2.waitKey()