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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, James Bowman
"""
import sys
import cv2.cv as cv
from optparse import OptionParser
# Parameters for haar detection
# From the API:
# The default parameters (scale_factor=2, 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 = (20, 20)
image_scale = 2
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
def detect_and_draw(img, cascade):
# allocate temporary images
gray = cv.CreateImage((img.width,img.height), 8, 1)
small_img = cv.CreateImage((cv.Round(img.width / image_scale),
cv.Round (img.height / image_scale)), 8, 1)
# convert color input image to grayscale
cv.CvtColor(img, gray, cv.CV_BGR2GRAY)
# scale input image for faster processing
cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)
cv.EqualizeHist(small_img, small_img)
if(cascade):
t = cv.GetTickCount()
faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
haar_scale, min_neighbors, haar_flags, min_size)
t = cv.GetTickCount() - t
print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
if faces:
for ((x, y, w, h), n) in faces:
# the input to cv.HaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
pt1 = (int(x * image_scale), int(y * image_scale))
pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)
cv.ShowImage("result", img)
if __name__ == '__main__':
parser = OptionParser(usage = "usage: %prog [options] [filename|camera_index]")
parser.add_option("-c", "--cascade", action="store", dest="cascade", type="str", help="Haar cascade file, default %default", default = "../data/haarcascades/haarcascade_frontalface_alt.xml")
(options, args) = parser.parse_args()
cascade = cv.Load(options.cascade)
if len(args) != 1:
parser.print_help()
sys.exit(1)
input_name = args[0]
if input_name.isdigit():
capture = cv.CreateCameraCapture(int(input_name))
else:
capture = None
cv.NamedWindow("result", 1)
if capture:
frame_copy = None
while True:
frame = cv.QueryFrame(capture)
if not frame:
cv.WaitKey(0)
break
if not frame_copy:
frame_copy = cv.CreateImage((frame.width,frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if frame.origin == cv.IPL_ORIGIN_TL:
cv.Copy(frame, frame_copy)
else:
cv.Flip(frame, frame_copy, 0)
detect_and_draw(frame_copy, cascade)
if cv.WaitKey(10) >= 0:
break
else:
image = cv.LoadImage(input_name, 1)
detect_and_draw(image, cascade)
cv.WaitKey(0)
cv.DestroyWindow("result")
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