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