def detect(image):
image_size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(image, grayscale, cv.BGR2GRAY)
# create storage
storage = cv.CreateMemStorage(0)
cv.ClearMemStorage(storage)
# equalize histogram
cv.EqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.LoadHaarClassifierCascade('haarcascade_frontalface_alt.xml',
cv.Size(1, 1))
faces = cv.HaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.HAAR_DO_CANNY_PRUNING, cv.Size(50, 50))
if faces:
print 'face detected!'
for i in faces:
cv.Rectangle(image, cv.Point(int(i.x), int(i.y)),
cv.Point(int(i.x + i.width), int(i.y + i.height)),
cv.RGB(0, 255, 0), 3, 8, 0)
class FaceDetector(WebcamDetector):
def __init__(self, resolution=(640, 480), show_cam=False):
self.setup(resolution, show_cam)
def setup(self, (width, height), show_cam):
"""Setup the cascades."""
WebcamDetector.setup(self, (width, height), show_cam)
# set default face size and body size, according to the camera resolution.
ratio = (640 / self.width)
face_axis = int(50 / ratio)
body_axis = int(125 / ratio)
print(face_axis, body_axis)
self.face_size = cv.Size(face_axis, face_axis)
self.body_size = cv.Size(body_axis, body_axis)
# load cascades
self.face_cascade = cv.LoadHaarClassifierCascade(
'haarcascade_frontalface_alt.xml', cv.Size(1, 1))
self.body_cascade = cv.LoadHaarClassifierCascade(
'haarcascade_upperbody.xml', cv.Size(1, 1))
# no argument on the command line, try to use the camera
capture = cv.CreateCameraCapture (0)
#
### check that capture device is OK
if not capture:
print "Error opening capture device"
sys.exit (1)
#
### capture the 1st frame to get some propertie on it
frame = cv.QueryFrame (capture)
#
### get size of the frame
frame_size = cv.GetSize (frame)
gray = cv.CreateImage( frame_size, 8, 1 )
small_img = cv.CreateImage( cv.Size( int(frame_size.width/image_scale),int(frame_size.height/image_scale)), 8, 1 )
cascade = cv.LoadHaarClassifierCascade( cascade_name, cv.Size(1,1) )
#
while 1: # do forever
# capture the current image
frame = cv.QueryFrame (capture)
if frame is None:
# no image captured... end the processing
break
#
### check OS
if (osName == "nt"):
cv.Flip(frame, frame, 0)
else:
cv.Flip(frame, None, 1)
#
### detecting faces here
def detect(image):
image_size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(image, grayscale, cv.BGR2GRAY)
# create storage
storage = cv.CreateMemStorage(0)
cv.ClearMemStorage(storage)
# equalize histogram
cv.EqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.LoadHaarClassifierCascade('haarcascade_frontalface_alt.xml',
cv.Size(1, 1))
faces = cv.HaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.HAAR_DO_CANNY_PRUNING, cv.Size(50, 50))
if faces:
print 'face detected!'
for i in faces:
cv.Rectangle(image, cv.Point(int(i.x), int(i.y)),
cv.Point(int(i.x + i.width), int(i.y + i.height)),
cv.RGB(0, 255, 0), 3, 8, 0)
# create windows
cv.NamedWindow('Camera', cv.WINDOW_AUTOSIZE)
# create capture device
device = 0 # assume we want first device
capture = cv.CreateCameraCapture(0)
cv.SetCaptureProperty(capture, cv.CAP_PROP_FRAME_WIDTH, 640)
cv.SetCaptureProperty(capture, cv.CAP_PROP_FRAME_HEIGHT, 480)
# check if capture device is OK
if not capture:
print "Error opening capture device"
sys.exit(1)
while 1:
# do forever
# capture the current frame
frame = cv.QueryFrame(capture)
if frame is None:
break
# mirror
cv.Flip(frame, None, 1)
# face detection
detect(frame)
# display webcam image
cv.ShowImage('Camera', frame)
# handle events
k = cv.WaitKey(10)
if k == 0x1b: # ESC
print 'ESC pressed. Exiting ...'
break