def recognize(self, contours):
# using maximal area and convexity defect depths to
# recognize between palm and fist.
x, y, r, b = im.find_max_rectangle(contours)
max_area, contours = im.max_area(contours)
print 'area: ', float(max_area)/((r-x)*(b-y))
hull = im.find_convex_hull(contours)
mean_depth = 0
if hull:
cds = im.find_convex_defects(contours, hull)
if len(cds) != 0:
mean_depth = sum([cd[3] for cd in cds])/len(cds)
if not self.isFist(max_area,
mean_depth) and not self.isPalm(max_area, mean_depth):
if self.gestures_buffer:
self.gestures_buffer.pop(0)
return Gesture('Not Sure'), max_area, mean_depth
# The majority of votes in self.gestures_buffer will
# determine which gesture should be in this frame
FIST = 0
PALM = 1
if self.isFist(max_area, mean_depth):
self.gestures_buffer.append(FIST)
elif self.isPalm(max_area, mean_depth):
self.gestures_buffer.append(PALM)
self.gestures_buffer = self.gestures_buffer[-self.buffer_size:]
avg = float(sum(self.gestures_buffer))/self.buffer_size
if avg > 0.5:
ges = 'Palm'
else:
ges = 'Fist'
return Gesture(ges, 'Short'), max_area, mean_depth
def recognize(self, contours):
# using maximal area and convexity defect depths to
# recognize between palm and fist.
x, y, r, b = im.find_max_rectangle(contours)
max_area, contours = im.max_area(contours)
print 'area: ', float(max_area) / ((r - x) * (b - y))
hull = im.find_convex_hull(contours)
mean_depth = 0
if hull:
cds = im.find_convex_defects(contours, hull)
if len(cds) != 0:
mean_depth = sum([cd[3] for cd in cds]) / len(cds)
if not self.isFist(max_area, mean_depth) and not self.isPalm(
max_area, mean_depth):
if self.gestures_buffer:
self.gestures_buffer.pop(0)
return Gesture('Not Sure'), max_area, mean_depth
# The majority of votes in self.gestures_buffer will
# determine which gesture should be in this frame
FIST = 0
PALM = 1
if self.isFist(max_area, mean_depth):
self.gestures_buffer.append(FIST)
elif self.isPalm(max_area, mean_depth):
self.gestures_buffer.append(PALM)
self.gestures_buffer = self.gestures_buffer[-self.buffer_size:]
avg = float(sum(self.gestures_buffer)) / self.buffer_size
if avg > 0.5:
ges = 'Palm'
else:
ges = 'Fist'
return Gesture(ges, 'Short'), max_area, mean_depth
def mainLoop():
input_video_fn = get_input_video_filename()
print 'input video filename:', input_video_fn
# Setting up the window objects and environment
proc_win_name = "Processing window"
cam_win_name = "Capture from camera"
proc_win = cv.NamedWindow(proc_win_name, 1)
cam_win = cv.NamedWindow(cam_win_name, 1)
if input_video_fn:
cam = cv.CaptureFromFile(input_video_fn)
else:
cam = cv.CaptureFromCAM(0)
cv.SetMouseCallback(proc_win_name, handle_mouse)
cv.SetMouseCallback(cam_win_name, handle_mouse)
msdelay = 3
initHueThreshold = 42
initIntensityThreshold = 191
skin_detector = skin.SkinDetector()
skin_detector.setHueThreshold(initHueThreshold)
skin_detector.setIntensityThreshold(initIntensityThreshold)
cv.CreateTrackbar('hueThreshold',
proc_win_name,
initHueThreshold,
255,
skin_detector.setHueThreshold)
cv.CreateTrackbar('intensityThreshold',
proc_win_name,
initIntensityThreshold,
255,
skin_detector.setIntensityThreshold)
session = ImageProcessSession(skin_detector)
ga = gesture.GestureAnalyzer()
grammar = Grammar()
gfn = get_grammar_filename()
if not gfn:
print 'usage: python GestureLock.py -g grammar_file.gmr'
exit(0)
answer_grammer = read_grammar(gfn)
im_orig_writer = ImageWriter(output_folder=get_output_folder())
im_contour_writer = ImageWriter(output_folder='out2')
prev = []
while True:
k = cv.WaitKey(msdelay)
k = chr(k) if k > 0 else 0
if handle_keyboard(k) < 0:
break
bgrimg = cv.QueryFrame(cam)
if not bgrimg:
break
im_orig_writer.write(bgrimg)
cv.Flip(bgrimg, None, 1)
contours = session.process(bgrimg)
img = cv.CreateImage((bgrimg.width, bgrimg.height), 8, 3)
if contours:
ges, area, depth = ga.recognize(contours)
x, y, r, b = im.find_max_rectangle(contours)
cv.Rectangle(img, (x,y), (r, b), im.color.RED)
cv.DrawContours(img, contours, im.color.RED, im.color.GREEN, 1,
thickness=3)
print ges
currentInput = grammar.instantGes(ges)
print currentInput
if len(prev)>=2:
for i,g in enumerate(currentInput):
im.puttext(prev[0], str(g), 30, 70+40*i)
im_contour_writer.write(prev[0])
prev.append( img )
prev.pop(0)
else:
prev.append( img )
if grammar == answer_grammer:
for i,g in enumerate(currentInput):
im.puttext(prev[0], str(g), 30, 70+40*i)
im_contour_writer.write(prev[0])
im.puttext(prev[0], 'AUTHENTICATED!', 30, 70+40*len(currentInput))
im_contour_writer.write(prev[0])
print 'AUTHENTICATED!!!!'
break
cv.ShowImage(proc_win_name, img)
