Using OpenCV detectMultiScale to find my face

Question

I'm pretty sure I have the general theme correct, but I'm not finding any faces. My code reads from c=cv2.VideoCapture(0), i.e. the computer's videocamera. I then have the following set up to yield where the faces are. As you can see, I'm looping through different scaleFactors and minNeighbors but rects always comes back empty. I've also tried each of the four different haarcascade xml files included in the opencv/data/haarcascades package.

Any tips?

while(1):
    ret, frame = c.read()
    rects = find_face_from_img(frame)

def detect(img, cascade):
    for scale in [float(i)/10 for i in range(11, 15)]:
        for neighbors in range(2,5):
            rects = cascade.detectMultiScale(img, scaleFactor=scale, minNeighbors=neighbors,
                                             minSize=(20, 20), flags=cv2.cv.CV_HAAR_SCALE_IMAGE)
            print 'scale: %s, neighbors: %s, len rects: %d' % (scale, neighbors, len(rects))

def find_face_from_img(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.equalizeHist(gray)
    rects = detect(gray, cascade)

Answer 1

I changed your code a little in order to make it run on my pc. When I run is at such I get results

import cv2
import cv2.cv as cv
import getopt, sys

def detect(img, cascade):
    for scale in [float(i)/10 for i in range(11, 15)]:
        for neighbors in range(2,5):
            rects = cascade.detectMultiScale(img, scaleFactor=scale, minNeighbors=neighbors,
                                             minSize=(20, 20), flags=cv2.cv.CV_HAAR_SCALE_IMAGE)

            print 'scale: %s, neighbors: %s, len rects: %d' % (scale, neighbors, len(rects))


def find_face_from_img(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.equalizeHist(gray)
    rects = detect(gray, cascade)


if __name__ == '__main__':

    args, video_src = getopt.getopt(sys.argv[1:], '', ['cascade=', 'nested-cascade='])
    try: video_src = video_src[0]
    except: video_src = 0
    args = dict(args)


    cascade_fn = args.get('--cascade', "cascades/haarcascade_frontalface_alt.xml")
    cascade = cv2.CascadeClassifier(cascade_fn)

    c=cv2.VideoCapture(0)
    while(1):
        ret, frame = c.read()
        rects = find_face_from_img(frame)
        if 0xFF & cv2.waitKey(5) == 27:
                break

Output:

scale: 1.2, neighbors: 2, len rects: 1
scale: 1.2, neighbors: 3, len rects: 1
scale: 1.2, neighbors: 4, len rects: 1
scale: 1.3, neighbors: 2, len rects: 1
scale: 1.3, neighbors: 3, len rects: 1
scale: 1.3, neighbors: 4, len rects: 0
scale: 1.4, neighbors: 2, len rects: 1
scale: 1.4, neighbors: 3, len rects: 0
scale: 1.4, neighbors: 4, len rects: 0
scale: 1.1, neighbors: 2, len rects: 1
scale: 1.1, neighbors: 3, len rects: 1
scale: 1.1, neighbors: 4, len rects: 1
scale: 1.2, neighbors: 2, len rects: 1
scale: 1.2, neighbors: 3, len rects: 1
scale: 1.2, neighbors: 4, len rects: 1
scale: 1.3, neighbors: 2, len rects: 1

Some advice: Don't pick your minSize too low ... else every small item which resembles a face will be detected.

I assume you are running through all these parameters to find the ones that are the best. I found out the minNeighors shouldn't be too high, else it won't find any.

Make sure your cascade xml file is linked to correctly. If it doesn't find it, it won't give an error, it will just find no faces.