Detecting pupils

We are going to take a different approach here. Pupils are too generic to take the Haar cascade approach. We will also get a sense of how to detect things based on their shape. Following is what the output will look like:

Let's see how to build the pupil detector:

import math

import cv2
import numpy as np

img = cv2.imread('input.jpg')
scaling_factor = 0.7

img = cv2.resize(img, None, fx=scaling_factor, fy=scaling_factor, interpolation=cv2.INTER_AREA)
cv2.imshow('Input', img)
gray = cv2.cvtColor(~img, cv2.COLOR_BGR2GRAY)

ret, thresh_gray = cv2.threshold(gray, 220, 255, cv2.THRESH_BINARY)
contours, hierarchy = cv2.findContours(thresh_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for contour in contours:
    area = cv2.contourArea(contour)
    rect = cv2.boundingRect(contour)
    x, y, width, height = rect
    radius = 0.25 * (width + height)

    area_condition = (100 <= area <= 200)
    symmetry_condition = (abs(1 - float(width)/float(height)) <= 0.2)
    fill_condition...