im = highgui.cvQueryFrame(camera)

detect(im)

#convert Ipl image to PIL image

# Find out how large the file is, as the underlying C-based code

# needs to allocate memory in the following steps

image_size = opencv.cvGetSize(image)

# create grayscale version - this is also the point where the allegation about

# facial recognition being racist might be most true. A caucasian face would have more

# definition on a webcam image than an African face when greyscaled.

# I would suggest that adding in a routine to overlay edge-detection enhancements may

# help, but you would also need to do this to the training images as well.

grayscale = opencv.cvCreateImage(image_size, 8, 1)

opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)

# create storage (It is C-based so you need to do this sort of thing)

storage = opencv.cvCreateMemStorage(0)

opencv.cvClearMemStorage(storage)

# equalize histogram

opencv.cvEqualizeHist(grayscale, grayscale)

# detect objects - Haar cascade step

# In this case, the code uses a frontal_face cascade - trained to spot faces that look directly

# at the camera. In reality, I found that no bearded or hairy person must have been in the training

# set of images, as the detection routine turned out to be beardist as well as a little racist!

cascade = opencv.cvLoadHaarClassifierCascade('haarcascade_frontalface_alt.xml', opencv.cvSize(1,1))

faces = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))

if faces:

for face in faces:

# Hmm should I do a min-size check?

# Draw a Chartreuse rectangle around the face - Chartruese rocks

opencv.cvRectangle(image, opencv.cvPoint( int(face.x), int(face.y)),

opencv.cvPoint(int(face.x + face.width), int(face.y + face.height)),