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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, os
from opencv.cv import *
from opencv.highgui import *
from PIL import Image
# Global Variables
cascade = None
storage = cvCreateMemStorage(0)
cascade_name = "/usr/share/opencv/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 = 1.3
min_neighbors = 2
haar_flags = CV_HAAR_DO_CANNY_PRUNING
def crop_count(facecropped, gcount):
#get image dimensions
iheight = int(facecropped.height / 2);
iwidth = int(facecropped.width);
iy = int(facecropped.width / 2);
#create a crop rect
crop1 = cvRect(60,iy,iwidth/2,iheight);
#select crop rect area from original image
halff = cvGetSubRect(facecropped,crop1);
#get new image dimensions
mheight = int(halff.height /2);
mwidth = int(halff.width);
my = int(halff.height / 2);
#create a crop box for mouth
crop2 = cvRect(0,my,mwidth,mheight);
#select crop rect area from image
mouth = cvGetSubRect(halff,crop2);
#create temp images
source = mouth;
grey = cvCreateImage (cvSize (mouth.width, mouth.height), 8, 1)
output = cvCreateImage (cvSize (mouth.width, mouth.height), 8, 1)
# Convert to greyscale
cvCvtColor (source, grey, CV_BGR2GRAY)
# Convert to black and white based on threshold of 50
cvThreshold(grey, output, 50, 255, CV_THRESH_BINARY);
count = 0
# Count number of dark pixels
for i in output:
for j in i:
if j == 255:
count += 1
print "Number of dark pixels: " + str(count)
#left for testing
if gcount < 10:
gcount = "0%s" % gcount
cvSaveImage("face-%s.jpg" % gcount, output);
def detect_and_draw( img, gcount ):
# allocate temporary images
gray = cvCreateImage( cvSize(img.width,img.height), 8, 1 );
small_img = cvCreateImage( cvSize( 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 CvPo
# 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) )
x = int(face_rect.x*image_scale);
y = int(face_rect.y*image_scale);
h = int(face_rect.height*image_scale);
w = int(face_rect.width*image_scale);
print x,y,w,h
face = cvRect(x,y,w,h);
crop = cvGetSubRect(img,face);
#cvSaveImage("face.jpg", crop);
crop_count(crop, gcount);
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 );
if( capture ):
frame_copy = None
i = 0;
while True:
frame = cvQueryFrame( capture );
print i;
if( not frame ):
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, i );
i = i+1;
if( cvWaitKey( 10 ) >= 0 ):
break;
else:
image = cvLoadImage( input_name, 1 );
if( image ):
detect_and_draw( image );
cvWaitKey(0);
cvDestroyWindow("result");