Iris detection from an eye photo

iris_detection.py

# Numpy is needed because OpenCV images in python are actually numpy arrays.
import numpy
import cv2

class iris_detection():
    def __init__(self, image_path):
        '''
        initialize the class and set the class attributes
        '''
        self._img = None
        self._img_path = image_path
        self._pupil = None

    def load_image(self):
        '''
        load the image based on the path passed to the class
        it should use the method cv2.imread to load the image
        it should also detect if the file exists
        '''
        self._img = cv2.imread(self._img_path)
        # If the image doesn't exists or is not valid then imread returns None
        if type(self._img) is type(None):
            return False
        else:
            return True
        
    def convert_to_gray_scale(self):
        self._img = cv2.cvtColor(self._img, cv2.COLOR_BGR2GRAY)

    def detect_pupil(self):
        '''
        This method should use cv2.findContours and cv2.HoughCircles() function from cv2 library to find the pupil
        and then set the coordinates for pupil circle coordinates
        '''
        # First binarize the image so that findContours can work correctly.
        _, thresh = cv2.threshold(self._img, 100, 255, cv2.THRESH_BINARY)
        # Now find the contours and then find the pupil in the contours.
        contours, _, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        c = cv2.HoughCircles(contours, cv2.HOUGH_GRADIENT, 2, self._img.shape[0]/2)
        # Then mask the pupil from the image and store it's coordinates.
        for l in c:
            # OpenCV returns the circles as a list of lists of circles
            for circle in l:
                center = (circle[0], circle[1])
                radius = circle[2]
                cv2.circle(self._img, center, radius, (0, 0, 0), thickness = -1)
                self._pupil = (center[0], center[1], radius)
        
    def detect_iris(self):
        '''
        This method should use the background subtraction technique to isolate the iris from the original image
        It should use the coordinates from the detect_pupil to get a larger circle using cv2.HoughCircles()
        '''
        _, t = cv2.threshold(self._img, 195, 255, cv2.THRESH_BINARY)
        contours, _, _ = cv2.findContours(t, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        # Find the iris using the radius of the pupil as input.
        c = cv2.HoughCircles(contours, cv2.HOUGH_GRADIENT, 2, self._pupil[2] * 2, param2 = 150)

        for l in c:
            for circle in l:
                center = (self._pupil[0], self._pupil[1])
                radius = circle[2]
                # This creates a black image and draws an iris-sized white circle in it.
                mask = numpy.zeros((self._img.shape[0], self._img.shape[1], 1), numpy.uint8)
                cv2.circle(mask, center, radius, (255, 255, 255), thickness = -1)
                # Mask the iris and crop everything outside of its radius.
                self._img = cv2.bitwise_and(self._img, mask)

    def start_detection(self):
        '''
        This is the main method that will be called to detect the iris
        it will call all the previous methods in the following order:
        load_image
        convert_to_gray_scale
        detect_pupil
        detect_iris
        then it should display the resulting image with the iris only
        using the method cv2.imshow
        '''
        if(self.load_image()):
            self.convert_to_gray_scale()
            self.detect_pupil()
            self.detect_iris()
            cv2.imshow("Result", self._img)
            cv2.waitKey(0)
        else:
            print('Image file "' + self._img_path + '" could not be loaded.')
        
#id = iris_detection('c:\\temp\\eye_image.jpg')
id = iris_detection('eye_image.jpg')
id.start_detection()

I had to modify part of this code to get it to run

    NoneType = type(None)
    if type(self._img) == NoneType:
        return False
    else:
        return True

and now I have another problem.

I'm trying to run this code, but I get the error

File "C:/Users/wmj/Documents/Python Scripts/Biometrics/IRIS/Raw/Esmitt method/iris_detection.py", line 60, in detect_iris
for l in c:

TypeError: 'NoneType' object is not iterable

I am trying to solve this now.

did u solve the problem?

I had to modify part of this code to get it to run

    NoneType = type(None)
    if type(self._img) == NoneType:
        return False
    else:
        return True

and now I have another problem.

I'm trying to run this code, but I get the error

File "C:/Users/wmj/Documents/Python Scripts/Biometrics/IRIS/Raw/Esmitt method/iris_detection.py", line 60, in detect_iris
for l in c:

TypeError: 'NoneType' object is not iterable

I am trying to solve this now.

Replace "==" by "is" . This would be worked !

Can you provide the photo you used to detect the iris?

am always getting this error with contours...
contours, _, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
ValueError: not enough values to unpack (expected 3, got 2)
Plz help...

Well, since opencv 3.2 source image is not modified by the function findContours . Maybe it depends of the version of your OpenCV

@esmitt Too bad I have no experience in OpenCV.

am always getting this error with contours... contours, _, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) ValueError: not enough values to unpack (expected 3, got 2) Plz help...

Hi,
Incase you didnt solve the issue, please use contours,_ instead of the 3 variables.

Need help to solve that

Traceback (most recent call last):
File "c:\Users\MAC\Music\Iris-recognition-with-python-and-opencv-master\code\images\new.py", line 92, in
id.start_detection()
File "c:\Users\MAC\Music\Iris-recognition-with-python-and-opencv-master\code\images\new.py", line 83, in start_detection
self.detect_pupil()
File "c:\Users\MAC\Music\Iris-recognition-with-python-and-opencv-master\code\images\new.py", line 39, in detect_pupil
c = cv2.HoughCircles(contours, cv2.HOUGH_GRADIENT, 2, self._img.shape[0]/2)
cv2.error: OpenCV(4.5.5) 👎 error: (-5:Bad argument) in function 'HoughCircles'

Overload resolution failed:

• image is not a numerical tuple
• Expected Ptrcv::UMat for argument 'image'

Need help to solve that

Traceback (most recent call last): File "c:\Users\MAC\Music\Iris-recognition-with-python-and-opencv-master\code\images\new.py", line 92, in id.start_detection() File "c:\Users\MAC\Music\Iris-recognition-with-python-and-opencv-master\code\images\new.py", line 83, in start_detection self.detect_pupil() File "c:\Users\MAC\Music\Iris-recognition-with-python-and-opencv-master\code\images\new.py", line 39, in detect_pupil c = cv2.HoughCircles(contours, cv2.HOUGH_GRADIENT, 2, self._img.shape[0]/2) cv2.error: OpenCV(4.5.5) 👎 error: (-5:Bad argument) in function 'HoughCircles'

Overload resolution failed:

• image is not a numerical tuple
• Expected Ptrcv::UMat for argument 'image'

Was there ever a solution discovered for this bug? I'm running into it too, and I assume it'll happen again with detecting the iris later in the code.

def detect_pupil(self):
    '''
    This method should use cv2.findContours and cv2.HoughCircles() function from cv2 library to find the pupil
    and then set the coordinates for pupil circle coordinates
    '''
    # First binarize the image so that findContours can work correctly.
    _, thresh = cv2.threshold(self._img, 100, 255, cv2.THRESH_BINARY)
    # Now find the contours and then find the pupil in the contours.
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    # Make a copy image of the original and then use the drawContours function to actually apply the contours found in the previous step
    img_with_contours = np.copy(self._img)
    cv2.drawContours(img_with_contours, contours, -1, (0, 255, 0))
    c = cv2.HoughCircles(img_with_contours, cv2.HOUGH_GRADIENT, 2, self._img.shape[0] / 2, maxRadius=150)
    # Then mask the pupil from the image and store it's coordinates.
    for l in c:
        # OpenCV returns the circles as a list of lists of circles
        for circle in l:
            center = (int(circle[0]), int(circle[1]))
            radius = int(circle[2])
            cv2.circle(self._img, center, radius, (0, 0, 0), thickness=-1)
            self._pupil = (center[0], center[1], radius)

Here's the fix for the issue btw, solved it a few weeks back and forgot to come back with it. OpenCV changed since the original code so you need to take the output array from contours and draw them one with the drawContours function, I used a temporary image to make a copy in this instance. Note you'll also have to do the sames step for find Iris later down in the code.

need help got the same problem with @swathigowroju

need help got the same problem with @swathigowroju

@antoni27 You need to delete the last variable throwaway.

contours, _, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
Should be
contours, _ = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
Because findContours only returns two variables now in the most updated version of OpenCV

Also you'll need to use drawContours I'm pretty sure after that call, I can't fully remember since its been like two weeks since I tried working with this program. But just look up the documentation is pretty well written.

i got the same problem now is there any solution yet?
thanks btw

i got the same problem now is there any solution yet? thanks btw

Which problem is that? Is it the same error? Show the error message and I'll try my best to help.

Hi this solution dosen't detect the iris properly! please submit the best allgorithm to do it