129 lines
4.1 KiB
Python
Executable File
129 lines
4.1 KiB
Python
Executable File
#!/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")
|