Pupil Detection with Python and OpenCV

pupil.py

#Identify pupils. Based on beta 1

import numpy as np
import cv2
import time

cap = cv2.VideoCapture(0) 	#640,480
w = 640
h = 480

while(cap.isOpened()):
    ret, frame = cap.read()
    if ret==True:
	
		#downsample
		#frameD = cv2.pyrDown(cv2.pyrDown(frame))
		#frameDBW = cv2.cvtColor(frameD,cv2.COLOR_RGB2GRAY)
	
		#detect face
		frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
		faces = cv2.CascadeClassifier('haarcascade_eye.xml')
		detected = faces.detectMultiScale(frame, 1.3, 5)
	
		#faces = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
		#detected2 = faces.detectMultiScale(frameDBW, 1.3, 5)
		
		pupilFrame = frame
		pupilO = frame
		windowClose = np.ones((5,5),np.uint8)
		windowOpen = np.ones((2,2),np.uint8)
		windowErode = np.ones((2,2),np.uint8)

		#draw square
		for (x,y,w,h) in detected:
			cv2.rectangle(frame, (x,y), ((x+w),(y+h)), (0,0,255),1)	
			cv2.line(frame, (x,y), ((x+w,y+h)), (0,0,255),1)
			cv2.line(frame, (x+w,y), ((x,y+h)), (0,0,255),1)
			pupilFrame = cv2.equalizeHist(frame[y+(h*.25):(y+h), x:(x+w)])
			pupilO = pupilFrame
			ret, pupilFrame = cv2.threshold(pupilFrame,55,255,cv2.THRESH_BINARY)		#50 ..nothin 70 is better
			pupilFrame = cv2.morphologyEx(pupilFrame, cv2.MORPH_CLOSE, windowClose)
			pupilFrame = cv2.morphologyEx(pupilFrame, cv2.MORPH_ERODE, windowErode)
			pupilFrame = cv2.morphologyEx(pupilFrame, cv2.MORPH_OPEN, windowOpen)

			#so above we do image processing to get the pupil..
			#now we find the biggest blob and get the centriod
			
			threshold = cv2.inRange(pupilFrame,250,255)		#get the blobs
			contours, hierarchy = cv2.findContours(threshold,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
			
			#if there are 3 or more blobs, delete the biggest and delete the left most for the right eye
			#if there are 2 blob, take the second largest
			#if there are 1 or less blobs, do nothing
			
			if len(contours) >= 2:
				#find biggest blob
				maxArea = 0
				MAindex = 0			#to get the unwanted frame 
				distanceX = []		#delete the left most (for right eye)
				currentIndex = 0 
				for cnt in contours:
					area = cv2.contourArea(cnt)
					center = cv2.moments(cnt)
					cx,cy = int(center['m10']/center['m00']), int(center['m01']/center['m00'])
					distanceX.append(cx)	
					if area > maxArea:
						maxArea = area
						MAindex = currentIndex
					currentIndex = currentIndex + 1
		
				del contours[MAindex]		#remove the picture frame contour
				del distanceX[MAindex]
			
			eye = 'right'

			if len(contours) >= 2:		#delete the left most blob for right eye
				if eye == 'right':
					edgeOfEye = distanceX.index(min(distanceX))
				else:
					edgeOfEye = distanceX.index(max(distanceX))	
				del contours[edgeOfEye]
				del distanceX[edgeOfEye]

			if len(contours) >= 1:		#get largest blob
				maxArea = 0
				for cnt in contours:
					area = cv2.contourArea(cnt)
					if area > maxArea:
						maxArea = area
						largeBlob = cnt
					
			if len(largeBlob) > 0:	
				center = cv2.moments(largeBlob)
				cx,cy = int(center['m10']/center['m00']), int(center['m01']/center['m00'])
				cv2.circle(pupilO,(cx,cy),5,255,-1)

	
		#show picture
		cv2.imshow('frame',pupilO)
		cv2.imshow('frame2',pupilFrame)
		if cv2.waitKey(1) & 0xFF == ord('q'):
			break
	
	#else:
		#break

# Release everything if job is finished
cap.release()
cv2.destroyAllWindows()

hi friends, thank for your source code. But I have a trouble like
", line 70, in
del contours[MAindex] # remove the picture frame contour
~~~~~~~~^^^^^^^^^
TypeError: 'tuple' object doesn't support item deletion

if I set cap is a image in 5th line, it's work. But not working with videocapture.
Please, help me if you can.

With the help of github copilot I am working through the issues. My setup is win 11 py 3.12.0. I have added comment with my name. I hopes this helps others.

I'm at troubleshooting this error
File "D:\py\pupil\measure.py", line 100, in
del contours[MAindex]
~~~~~~~~^^^^^^^^^
IndexError: list assignment index out of range

#Identify pupils. Based on beta 1

import numpy as np
import cv2
import time

cap = cv2.VideoCapture(0) #640,480
w = 640
h = 480

while(cap.isOpened()):
ret, frame = cap.read()
if ret==True:

	#downsample
	#frameD = cv2.pyrDown(cv2.pyrDown(frame))
	#frameDBW = cv2.cvtColor(frameD,cv2.COLOR_RGB2GRAY)

	#detect face
	frame = cv2.cvtColor(frame,cv2.COLOR_RGB2GRAY)
	#warprdiv added to resolve file location issue
	#faces = cv2.CascadeClassifier('haarcascade_eye.xml')
	faces = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
	
	detected = faces.detectMultiScale(frame, 1.3, 5)

	#faces = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
	#detected2 = faces.detectMultiScale(frameDBW, 1.3, 5)
	
	pupilFrame = frame
	pupilO = frame
	windowClose = np.ones((5,5),np.uint8)
	windowOpen = np.ones((2,2),np.uint8)
	windowErode = np.ones((2,2),np.uint8)

	#draw square
	for (x,y,w,h) in detected:
		cv2.rectangle(frame, (x,y), ((x+w),(y+h)), (0,0,255),1)	
		cv2.line(frame, (x,y), ((x+w,y+h)), (0,0,255),1)
		cv2.line(frame, (x+w,y), ((x,y+h)), (0,0,255),1)
		#pupilFrame = cv2.equalizeHist(frame[y+(h*.25):(y+h), x:(x+w)])
		pupilFrame = cv2.equalizeHist(frame[int(y+(h*.25)):int(y+h), int(x):int(x+w)])
		pupilO = pupilFrame
		ret, pupilFrame = cv2.threshold(pupilFrame,55,255,cv2.THRESH_BINARY)		#50 ..nothin 70 is better
		pupilFrame = cv2.morphologyEx(pupilFrame, cv2.MORPH_CLOSE, windowClose)
		pupilFrame = cv2.morphologyEx(pupilFrame, cv2.MORPH_ERODE, windowErode)
		pupilFrame = cv2.morphologyEx(pupilFrame, cv2.MORPH_OPEN, windowOpen)

		#so above we do image processing to get the pupil..
		#now we find the biggest blob and get the centriod
		
		threshold = cv2.inRange(pupilFrame,250,255)		#get the blobs
		contours, hierarchy = cv2.findContours(threshold,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
		
		#if there are 3 or more blobs, delete the biggest and delete the left most for the right eye
		#if there are 2 blob, take the second largest
		#if there are 1 or less blobs, do nothing
		
		if len(contours) >= 2:
			#find biggest blob
			maxArea = 0
			MAindex = 0			#to get the unwanted frame 
			distanceX = []		#delete the left most (for right eye)
			currentIndex = 0 
			for cnt in contours:
				area = cv2.contourArea(cnt)
				center = cv2.moments(cnt)
				#warpdriv added take care of divide by zero error
				if center['m00'] != 0:	
					cx,cy = int(center['m10']/center['m00']), int(center['m01']/center['m00'])
				distanceX.append(cx)	
				if area > maxArea:
					maxArea = area
					MAindex = currentIndex
				currentIndex = currentIndex + 1
	
			#del contours[MAindex]		#remove the picture frame contour
			#warpdriv added
			contours = list(contours)
			del contours[MAindex]
			contours = tuple(contours)

			#del distanceX[MAindex]
			#warpdriv added
			distanceX = list(distanceX)
			del distanceX[MAindex]
			distanceX = tuple(distanceX)
		
		eye = 'right'

		if len(contours) >= 2:		#delete the left most blob for right eye
			if eye == 'right':
				edgeOfEye = distanceX.index(min(distanceX))
			else:
				edgeOfEye = distanceX.index(max(distanceX))	

			#warpdriv added
			#del contours[MAindex]		#remove the picture frame contour
			contours = list(contours)
			del contours[MAindex]
			contours = tuple(contours)

			#warpdriv added
			#del distanceX[MAindex]
			distanceX = list(distanceX)
			del distanceX[MAindex]
			distanceX = tuple(distanceX)

		if len(contours) >= 1:		#get largest blob
			maxArea = 0
			for cnt in contours:
				area = cv2.contourArea(cnt)
				if area > maxArea:
					maxArea = area
					largeBlob = cnt
				
		if len(largeBlob) > 0:	
			center = cv2.moments(largeBlob)
			cx,cy = int(center['m10']/center['m00']), int(center['m01']/center['m00'])
			cv2.circle(pupilO,(cx,cy),5,255,-1)


	#show picture
	cv2.imshow('frame',pupilO)
	cv2.imshow('frame2',pupilFrame)
	if cv2.waitKey(1) & 0xFF == ord('q'):
		break

#else:
	#break

Release everything if job is finished

cap.release()
cv2.destroyAllWindows()