X-Git-Url: http://git.maemo.org/git/?p=opencv;a=blobdiff_plain;f=samples%2Fswig_python%2Ffacedetect.py;fp=samples%2Fswig_python%2Ffacedetect.py;h=73eef6e18b78b638730b98e4b6cc974b0bf546ed;hp=0000000000000000000000000000000000000000;hb=e4c14cdbdf2fe805e79cd96ded236f57e7b89060;hpb=454138ff8a20f6edb9b65a910101403d8b520643

diff --git a/samples/swig_python/facedetect.py b/samples/swig_python/facedetect.py
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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")