Eye Detection in openCV-Python Using a Webcam

As mentioned in the first post, it’s quite easy to move from detecting eyes in images to detecting them in video via a webcam – which is exactly what we will detail in this post.

Pre-requisites:

1. OpenCV installed (see the previous blog post for details)
2. A working webcam

The Code

Let’s dive straight into the code

import cv2
import sys

cascPath = sys.argv[1]
eyeCascade = cv2.CascadeClassifier(cascPath)

video_capture = cv2.VideoCapture(0)

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    eyes = eyeCascade.detectMultiScale(gray, 1.3, 5)
    # Draw a rectangle around the faces
    for (x, y, w, h) in eyes:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('Video', frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

Now let’s break it down…

import cv2
import sys

cascPath = sys.argv[1]
eyeCascade = cv2.CascadeClassifier(cascPath)

This should be familiar to you. We are creating a eye cascade, as we did in the image example.

video_capture = cv2.VideoCapture(0)

This line sets the video source to the default webcam, which OpenCV can easily capture.

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()

Here, we capture the video. The read() function reads one frame from the video source, which in this example is the webcam. This returns:

1. The actual video frame read (one frame on each loop)
2. A return code

The return code tells us if we have run out of frames, which will happen if we are reading from a file. This doesn’t matter when reading from the webcam, since we can record forever, so we will ignore it.

# Capture frame-by-frame
ret, frame = video_capture.read()

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

eyes = eyeCascade.detectMultiScale(gray, 1.3, 5)

# Draw a rectangle around the faces
for (x, y, w, h) in eyes:
    cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

# Display the resulting frame
cv2.imshow('Video', frame)

Again, this code should be familiar. We are merely searching for the eyes in our captured frame.

if cv2.waitKey(1) & 0xFF == ord('q'):
    break

We wait for the ‘q’ key to be pressed. If it is, we exit the script.

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

Let’s test against:

$ python eye_detect.py haarcascade_mcs_eyes.xml

Here check the output on video:

So, that’s me with my driver’s license in my hand. And you can see that the algorithm tracks both the real me and the photo me. Note that when I move slowly, the algorithm can keep up. When I move my hand up to my face a bit faster, though, it gets confused and mistakes my wrist for a face.