#!/usr/bin/python

from opencv.cv import *
from opencv.highgui import *

class Camera(object):
    def __init__(self, debug=False):
        self.debug = debug
        
        self.cascade = cvLoadHaarClassifierCascade("haarcascade_frontalface_alt.xml", cvSize(1,1))

        self.device = cvCreateCameraCapture(0)
        self.storage = cvCreateMemStorage(0)

        if self.debug:
            cvNamedWindow("result", 1)

        self.frame_copy = None

        # Parameters for haar detection
        # From the API:
        # The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned 
        # for accurate yet slow object detection. For a faster operation on real video 
        # images the settings are: 
        # scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING, 
        # min_size=<minimum possible face size
        self.min_size = cvSize(20,20)
        self.image_scale = 1.3
        self.haar_scale = 1.2
        self.min_neighbors = 2
        self.haar_flags = 0

    def process_one_frame(self):
        if not self.device:
            return False
        
        frame = cvQueryFrame( self.device );
        if not frame:
            return False
        
        if not self.frame_copy:
            self.frame_copy = cvCreateImage(cvSize(frame.width, frame.height),
                                            IPL_DEPTH_8U, frame.nChannels)
            
        if frame.origin == IPL_ORIGIN_TL:
            cvCopy(frame, self.frame_copy)
        else:
            cvFlip(frame, self.frame_copy, 0)

        return self.detect(self.frame_copy)


    def detect(self, img):
        result = False
        
        gray = cvCreateImage(cvSize(img.width, img.height), 8, 1)
        small_img = cvCreateImage(cvSize(cvRound(img.width/self.image_scale),
                                         cvRound(img.height/self.image_scale)),
                                  8, 1)

        cvCvtColor(img, gray, CV_BGR2GRAY)
        cvResize(gray, small_img, CV_INTER_LINEAR)
        
        cvEqualizeHist(small_img, small_img)
        
        cvClearMemStorage(self.storage)

        if self.cascade:
            t = cvGetTickCount();
            faces = cvHaarDetectObjects(small_img, self.cascade, self.storage,
                                        self.haar_scale, self.min_neighbors, self.haar_flags, self.min_size)
            t = cvGetTickCount() - t
            print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.))
            if faces:
                for r in faces:
                    pt1 = cvPoint( int(r.x*self.image_scale), int(r.y*self.image_scale))
                    pt2 = cvPoint( int((r.x+r.width)*self.image_scale), int((r.y+r.height)*self.image_scale) )
                    cvRectangle( img, pt1, pt2, CV_RGB(255,0,0), 3, 8, 0 );

                    result = True
                    

        if self.debug:
            cvShowImage("result", img)

            cvWaitKey( 10 )
        
        return result
    

if __name__ == "__main__":
    c = Camera(debug=True)
    while True:
        print c.process_one_frame()