Image segmentation with Canny Edge Opencv

Question

I’m trying to target the edge of both the iris and the pupil using Canny Edge from Opencv.

However all the parameters I used do not meet the criterion of leaving well delimited the two edges. The only way I could find to target at least the edge of the iris was to binarize it and then apply the Canny Edge, and actually make a good return.

• Is there any way to improve the result ? Or is it trial and error ?

• How to segment the pupil?

Attempt to segment only the pupil binarizing in the dark interval:

#'guarda' o pixel se ele estiver no intervalo que é preto
for x in range(0,suavizada.shape[0]):
    for y in range(0,suavizada.shape[1]):
        if img[x][y] >=0 and suavizada[x][y] <65:
            escala[x][y] = 255

Code to segment the iris:

img = cv2.imread('path',0)
suavizada = cv2.medianBlur(img,7)
_,limites = cv2.threshold(suavizada,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

bordas = cv2.Canny(limites,0,0)


cv2.imshow('bordas',bordas)
cv2.waitKey(0)
cv2.destroyAllWindows()

Original image:

Image after binarizing and passing by Canny:

OBS.: I am using Python 3.x with the library installed via Pypi.

Answer 1

The Canny Edge the way it is being implemented is leaving a pupil very small and similar to the noises created by eyelashes.

I think the black color Slice is the best option to extract the pupil, then use the Hough Circles to find the circle of the pupil and the Grab Cut to extract the pupil.

Slice in Black

Extract from the image whatever is between (0, 0, 0) and (60, 60, 60) of the BGR color space

Detection of circles

Detects the pupil circle with Hough Circles

Region of Interest and Grab Cut

Create the region of interest (ROI) with the coordinates of the circle found and use Grab Cut to extract the pupil

Code

import cv2
import numpy as np
import urllib.request


resp = urllib.request.urlopen("https://i.stack.imgur.com/NL10Y.jpg")
img = np.asarray(bytearray(resp.read()), dtype="uint8")
img = cv2.imdecode(img, cv2.IMREAD_COLOR)

copia = img.copy()
# Extrai as cores entre o intervalo BGR definido
mask = cv2.inRange(img, (0, 0, 0), (60, 60, 60))
## slice no preto
imask = mask > 0
preto = np.zeros_like(img, np.uint8)
preto[imask] = img[imask]

preto = cv2.cvtColor(preto, cv2.COLOR_BGR2GRAY)
cv2.imshow('Preto', preto)

# detecção de círculos
circles = cv2.HoughCircles(preto, cv2.HOUGH_GRADIENT, 1, 100,
                           param1=30, param2=30, minRadius=20, maxRadius=100)

#param do Grab Cut
bgdModel = np.zeros((1, 65), np.float64)
fgdModel = np.zeros((1, 65), np.float64)

# pelo menos um círculo encontrado
if circles is not None:
    # converte para int
    circles = np.round(circles[0, :]).astype("int")

    # loop nas coordenadas (x, y) e raio dos círculos encontrados
    for (x, y, r) in circles:
        roi = img.copy()
        r=r+50
        # Desenha o círculo encontrado
        cv2.circle(copia, (x, y), r - 25, (0, 255, 0), 4)
        # Desenha o retângulo do centro do círculo
        cv2.rectangle(copia, (x - 5, y - 5), (x + 5, y + 5), (0, 128, 255), -1)
        # Região de interesse com a pupila
        roi = roi[y-r:y+r, x-r:x+r]
        roi_x, roi_y, _ = roi.shape
        #Grab Cut da Pupila
        mask = np.zeros(roi.shape[:2], np.uint8)
        rect = (10, 10,roi_x-10, roi_y-10)
        cv2.grabCut(roi, mask, rect, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
        mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
        roi_grab = roi * mask2[:, :, np.newaxis]
        # Mostra o Grab Cut
        cv2.imshow("Grab Cut", np.hstack([roi, roi_grab]))

    # Mostra a imagem com o círcuto e centro encontrado pelo Hough Circle
    cv2.imshow("output", np.hstack([img, copia]))
    cv2.waitKey(0)