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129 lines
4.1 KiB
Python
Executable File
129 lines
4.1 KiB
Python
Executable File
#!/usr/bin/python
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"""
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This program is demonstration for face and object detection using haar-like features.
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The program finds faces in a camera image or video stream and displays a red box around them.
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Original C implementation by: ?
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Python implementation by: Roman Stanchak
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"""
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import sys
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from opencv.cv import *
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from opencv.highgui import *
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# Global Variables
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cascade = None
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storage = cvCreateMemStorage(0)
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cascade_name = "../../data/haarcascades/haarcascade_frontalface_alt.xml"
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input_name = "../c/lena.jpg"
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# Parameters for haar detection
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# From the API:
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# The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned
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# for accurate yet slow object detection. For a faster operation on real video
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# images the settings are:
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# scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING,
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# min_size=<minimum possible face size
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min_size = cvSize(20,20)
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image_scale = 1.3
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haar_scale = 1.2
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min_neighbors = 2
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haar_flags = 0
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def detect_and_draw( img ):
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# allocate temporary images
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gray = cvCreateImage( cvSize(img.width,img.height), 8, 1 )
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small_img = cvCreateImage((cvRound(img.width/image_scale),
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cvRound (img.height/image_scale)), 8, 1 )
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# convert color input image to grayscale
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cvCvtColor( img, gray, CV_BGR2GRAY )
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# scale input image for faster processing
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cvResize( gray, small_img, CV_INTER_LINEAR )
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cvEqualizeHist( small_img, small_img )
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cvClearMemStorage( storage )
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if( cascade ):
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t = cvGetTickCount()
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faces = cvHaarDetectObjects( small_img, cascade, storage,
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haar_scale, min_neighbors, haar_flags, min_size )
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t = cvGetTickCount() - t
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print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.))
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if faces:
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for face_rect in faces:
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# the input to cvHaarDetectObjects was resized, so scale the
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# bounding box of each face and convert it to two CvPoints
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pt1 = cvPoint( int(face_rect.x*image_scale), int(face_rect.y*image_scale))
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pt2 = cvPoint( int((face_rect.x+face_rect.width)*image_scale),
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int((face_rect.y+face_rect.height)*image_scale) )
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cvRectangle( img, pt1, pt2, CV_RGB(255,0,0), 3, 8, 0 )
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cvShowImage( "result", img )
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if __name__ == '__main__':
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if len(sys.argv) > 1:
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if sys.argv[1].startswith("--cascade="):
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cascade_name = sys.argv[1][ len("--cascade="): ]
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if len(sys.argv) > 2:
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input_name = sys.argv[2]
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elif sys.argv[1] == "--help" or sys.argv[1] == "-h":
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print "Usage: facedetect --cascade=\"<cascade_path>\" [filename|camera_index]\n"
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sys.exit(-1)
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else:
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input_name = sys.argv[1]
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# the OpenCV API says this function is obsolete, but we can't
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# cast the output of cvLoad to a HaarClassifierCascade, so use this anyways
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# the size parameter is ignored
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cascade = cvLoadHaarClassifierCascade( cascade_name, cvSize(1,1) )
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if not cascade:
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print "ERROR: Could not load classifier cascade"
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sys.exit(-1)
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if input_name.isdigit():
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capture = cvCreateCameraCapture( int(input_name) )
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else:
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capture = cvCreateFileCapture( input_name )
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cvNamedWindow( "result", 1 )
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if capture:
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frame_copy = None
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while True:
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frame = cvQueryFrame( capture )
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if not frame:
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cvWaitKey(0)
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break
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if not frame_copy:
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frame_copy = cvCreateImage( cvSize(frame.width,frame.height),
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IPL_DEPTH_8U, frame.nChannels )
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if frame.origin == IPL_ORIGIN_TL:
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cvCopy( frame, frame_copy )
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else:
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cvFlip( frame, frame_copy, 0 )
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detect_and_draw( frame_copy )
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if( cvWaitKey( 10 ) >= 0 ):
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break
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else:
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image = cvLoadImage( input_name, 1 )
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if image:
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detect_and_draw( image )
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cvWaitKey(0)
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cvDestroyWindow("result")
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