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#!/usr/bin/python
"""
This program is demonstration for face and object detection using haar-like features.
The program finds faces in a camera image or video stream and displays a red box around them.
Original C implementation by: ?
Python implementation by: Roman Stanchak
"""
import sys
from opencv.cv import *
from opencv.highgui import *
# Global Variables
cascade = None
storage = cvCreateMemStorage(0)
cascade_name = "../../data/haarcascades/haarcascade_frontalface_alt.xml"
input_name = "../c/lena.jpg"
# 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
min_size = cvSize(20,20)
image_scale = 1.3
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
def detect_and_draw( img ):
# allocate temporary images
gray = cvCreateImage( cvSize(img.width,img.height), 8, 1 )
small_img = cvCreateImage((cvRound(img.width/image_scale),
cvRound (img.height/image_scale)), 8, 1 )
# convert color input image to grayscale
cvCvtColor( img, gray, CV_BGR2GRAY )
# scale input image for faster processing
cvResize( gray, small_img, CV_INTER_LINEAR )
cvEqualizeHist( small_img, small_img )
cvClearMemStorage( storage )
if( cascade ):
t = cvGetTickCount()
faces = cvHaarDetectObjects( small_img, cascade, storage,
haar_scale, min_neighbors, haar_flags, min_size )
t = cvGetTickCount() - t
print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.))
if faces:
for face_rect in faces:
# the input to cvHaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = cvPoint( int(face_rect.x*image_scale), int(face_rect.y*image_scale))
pt2 = cvPoint( int((face_rect.x+face_rect.width)*image_scale),
int((face_rect.y+face_rect.height)*image_scale) )
cvRectangle( img, pt1, pt2, CV_RGB(255,0,0), 3, 8, 0 )
cvShowImage( "result", img )
if __name__ == '__main__':
if len(sys.argv) > 1:
if sys.argv[1].startswith("--cascade="):
cascade_name = sys.argv[1][ len("--cascade="): ]
if len(sys.argv) > 2:
input_name = sys.argv[2]
elif sys.argv[1] == "--help" or sys.argv[1] == "-h":
print "Usage: facedetect --cascade=\"<cascade_path>\" [filename|camera_index]\n"
sys.exit(-1)
else:
input_name = sys.argv[1]
# the OpenCV API says this function is obsolete, but we can't
# cast the output of cvLoad to a HaarClassifierCascade, so use this anyways
# the size parameter is ignored
cascade = cvLoadHaarClassifierCascade( cascade_name, cvSize(1,1) )
if not cascade:
print "ERROR: Could not load classifier cascade"
sys.exit(-1)
if input_name.isdigit():
capture = cvCreateCameraCapture( int(input_name) )
else:
capture = cvCreateFileCapture( input_name )
cvNamedWindow( "result", 1 )
if capture:
frame_copy = None
while True:
frame = cvQueryFrame( capture )
if not frame:
cvWaitKey(0)
break
if not frame_copy:
frame_copy = cvCreateImage( cvSize(frame.width,frame.height),
IPL_DEPTH_8U, frame.nChannels )
if frame.origin == IPL_ORIGIN_TL:
cvCopy( frame, frame_copy )
else:
cvFlip( frame, frame_copy, 0 )
detect_and_draw( frame_copy )
if( cvWaitKey( 10 ) >= 0 ):
break
else:
image = cvLoadImage( input_name, 1 )
if image:
detect_and_draw( image )
cvWaitKey(0)
cvDestroyWindow("result")