Face detection with OpenCV

detect.py

import cv2
import numpy as np

cv2.namedWindow("camera", 1)
capture = cv2.VideoCapture(0)

# Set to None to use default size
# To improve performance, can specify a lower resolution. e.g. 320x240
width = 320
height = 240

faceCascade = cv2.CascadeClassifier("/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml")
eyeCascade = cv2.CascadeClassifier("/usr/share/opencv/haarcascades/haarcascade_eye.xml")


if width is None:
    width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
else:
    capture.set(cv2.CAP_PROP_FRAME_WIDTH, width)

if height is None:
    height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
else:
    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

result = np.zeros((height, width, 3), np.uint8)

def DetectRedEyes(image, faceCascade, eyeCascade):
    cas_rejectLevel = 1.3
    cas_levelWeight = 5

    # Convert color input image to grayscale
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Detect faces
    faces = faceCascade.detectMultiScale(gray, cas_rejectLevel, cas_levelWeight)

    # If faces are found
    for (x, y, w, h) in faces:
        cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = image[y:y+h, x:x+w]

        eyes = eyeCascade.detectMultiScale(roi_gray)

        # For each eye found
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)

    return image


while True:
    retval, img = capture.read()

    image = DetectRedEyes(img, faceCascade, eyeCascade)
    cv2.imshow("camera", image)
    if cv2.waitKey(30) & 0xFF == ord('q'):
        break

capture.release()
cv2.destroyAllWindows()