def __init__(self):
#self.currentrect = (320, 80, 127, 170)
self.pt1 = (320, 80)
self.pt2 = (447, 250)
self.key = -1
self.detector = LKDetector(5, 5)
f = open('___learned3', 'r')
self.net = pickle.load(f)
self.ninsize = (80, 50)
f.close()
def __init__(self):
#self.currentrect = (320, 80, 127, 170)
self.ninsize = (25, 25)
self.pt1 = (320, 80)
self.pt2 = (447, 250)
self.additionslds = ClassificationDataSet(self.ninsize[0] * self.ninsize[1], nb_classes=2)
self.key = -1
self.detector = LKDetector(5, 5)
f = open('WorkingNNs/l17bak_25_25_1by1', 'r')
#f = open('___learned3', 'r')
self.net = pickle.load(f)
#self.net = buildNetwork(self.ninsize[0] * self.ninsize[1], 96, self.additionslds.outdim, outclass=SoftmaxLayer)
f.close()
self.stage = 0
self.numoflearning = 0
'''
class videowork:
def __init__(self):
#self.currentrect = (320, 80, 127, 170)
self.pt1 = (320, 80)
self.pt2 = (447, 250)
self.key = -1
self.detector = LKDetector(5, 5)
f = open('___learned3', 'r')
self.net = pickle.load(f)
self.ninsize = (80, 50)
f.close()
def work(self, image):
width = self.pt2[0] - self.pt1[0]
height = self.pt2[1] - self.pt1[1]
currentrect = (self.pt1[0], self.pt1[1], width, height)#self.currentrect
print currentrect
image_size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.CreateMemStorage(0)
cv.EqualizeHist(grayscale, grayscale)
frame = np.asarray(image[:, :])
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mask = np.zeros_like(frame_gray)
mask[self.pt1[1]:self.pt2[1], self.pt1[0]:self.pt2[0]] = 255
st = time.time()
tracks = self.detector.detect(frame_gray, mask)
print 'detect time', time.time() - st
cv.SetImageROI(grayscale, currentrect)
cv.Rectangle (image, (currentrect[0], currentrect[1]), (currentrect[2] + currentrect[0], currentrect[3] + currentrect[1]), (0, 255, 0), 1)
cv.SetImageROI(image, currentrect)
tmp = cv.CreateImage((currentrect[2], currentrect[3]), 8, 1)
cv.Copy(grayscale, tmp)
#print len(tracks)
st = time.time()
out = self.net.activate(FeaturesFromImg(tmp, self.ninsize)).argmax()
print out, '|time', time.time() - st
cv.ResetImageROI(image)
cv.ResetImageROI(grayscale)
if out == 1:
cv.Rectangle (image, (currentrect[0], currentrect[1]), (currentrect[2] + currentrect[0], currentrect[3] + currentrect[1]), (0, 255, 255), 5)
self.pt1, self.pt2 = PatchBoundary(tracks, self.pt1, self.pt2)
if self.key == 113:
cv.SaveImage('img.bmp', tmp)
self.key = 255
cv.Rectangle (image, (currentrect[0], currentrect[1]), (currentrect[2] + currentrect[0], currentrect[3] + currentrect[1]), (0, 255, 255), 1)
for item in tracks:
cv.Circle(image, (item[-1][0], item[-1][1]), 2, (0, 255, 0), -1)
#pt1 = (400, 50)
#pt2 = (500, 250)
return image
def run(self):
DEVICE = 0 #/dev/video0
# create windows
cv.NamedWindow('Camera')
# create capture device
device = 0 # assume we want first device
capture = cv.CreateCameraCapture(DEVICE)
k = ''
while k !='q' :
frame = cv.QueryFrame(capture)#cv.RetrieveFrame(capture)
if frame is None:
break
cv.Flip(frame, None, 1)
frame = self.work(frame)
# display webcam image
cv.ShowImage('Camera', frame)
k = cv.WaitKey(10) % 0x100
if k !=255 :
self.key = k
print 'pressed', k
class videowork:
def __init__(self):
#self.currentrect = (320, 80, 127, 170)
self.ninsize = (25, 25)
self.pt1 = (320, 80)
self.pt2 = (447, 250)
self.additionslds = ClassificationDataSet(self.ninsize[0] * self.ninsize[1], nb_classes=2)
self.key = -1
self.detector = LKDetector(5, 5)
f = open('WorkingNNs/l17bak_25_25_1by1', 'r')
#f = open('___learned3', 'r')
self.net = pickle.load(f)
#self.net = buildNetwork(self.ninsize[0] * self.ninsize[1], 96, self.additionslds.outdim, outclass=SoftmaxLayer)
f.close()
self.stage = 0
self.numoflearning = 0
'''
0 - nothing
1 - learning with lk
2 - learning without lk
'''
#self.addlflag = False
#print self.additionslds.outdim
#exit()
def ImageFromRect(self, image, rect):
cv.SetImageROI(image, rect)
tmp = cv.CreateImage((rect[2], rect[3]), 8, 1)
cv.Copy(image, tmp)
cv.ResetImageROI(image)
return tmp
def ClassifyImage(self, image):
features = FeaturesFromImg(image, self.ninsize)
'''if self.addlflag:
self.additionslds.addSample(features, [1])'''
return self.net.activate(features).argmax()
def ClassifyWindow(self, image, rect):
tmp = self.ImageFromRect(image, rect)
'''if self.addlflag:
for i in xrange(2):
badwin = self.ImageFromRect(image, RandomRect(rect))
features = FeaturesFromImg(badwin, self.ninsize)
self.additionslds.addSample(features, [0])
print 'len of new dataset', len(self.additionslds)
'''
return self.ClassifyImage(tmp)
def SearchObject(self, image, startrect):
#print cv.GetSize(image), 'SIZE'
windowstep = 10
def NewRects(rect, alreadycreated):
res = []
res.append((rect[0] + windowstep, rect[1], rect[2], rect[3]))
res.append((rect[0], rect[1] + windowstep, rect[2], rect[3]))
res.append((rect[0] - windowstep, rect[1], rect[2], rect[3]))
res.append((rect[0], rect[1] - windowstep, rect[2], rect[3]))
res = [x for x in res if (x not in alreadycreated) and rangecheck(x)]
for x in res:
alreadycreated[x] = 1
#print res
return res
searchlimit = 256
alreadycreated = {}
qu = Queue()
qu.put(startrect)
isdetected = False
res = None
while (not isdetected) and (not qu.empty()) and (searchlimit > 0):
rect = qu.get()
searchlimit -= 1
#print rect
isdetected = self.ClassifyWindow(image, rect)
#cv.Rectangle(image, (rect[0], rect[1]) ,(rect[0] + rect[2], rect[1] + rect[3]),(255, 255, 0))
if isdetected:
res = rect
else:
li = NewRects(rect, alreadycreated)
for x in li: qu.put(x)
return res
def work(self, image):
width = self.pt2[0] - self.pt1[0]
height = self.pt2[1] - self.pt1[1]
currentrect = (self.pt1[0], self.pt1[1], width, height)#self.currentrect
#print currentrect
image_size = cv.GetSize(image)
# create grayscale version
grayscale = cv.CreateImage(image_size, 8, 1)
cv.CvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.CreateMemStorage(0)
cv.EqualizeHist(grayscale, grayscale)
frame = np.asarray(image[:, :])
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
mask = np.zeros_like(frame_gray)
mask[self.pt1[1]:self.pt2[1], self.pt1[0]:self.pt2[0]] = 255
st = time.time()
tracks = self.detector.detect(frame_gray, mask)
print 'detect time', time.time() - st
cv.Rectangle (image, self.pt1, self.pt2, (0, 255, 0), 1)
cv.SetImageROI(image, currentrect)
st = time.time()
out = self.ClassifyWindow(grayscale, currentrect)
print out, '|time', time.time() - st
cv.ResetImageROI(image)
if out == 1:
cv.Rectangle (image, (currentrect[0], currentrect[1]), (currentrect[2] + currentrect[0], currentrect[3] + currentrect[1]), (0, 255, 255), 5)
self.pt1, self.pt2 = PatchBoundary(tracks, self.pt1, self.pt2)
tmp = self.ImageFromRect(grayscale, currentrect)
features = FeaturesFromImg(grayscale, self.ninsize)
self.additionslds.addSample(features, [1])
for i in xrange(2):
badwin = self.ImageFromRect(grayscale, RandomRect(currentrect))
features = FeaturesFromImg(badwin, self.ninsize)
self.additionslds.addSample(features, [0])
print 'len of new dataset', len(self.additionslds)
'''
else:
rect = self.SearchObject(grayscale, currentrect)
if rect:
self.pt1, self.pt2 = RectToPoints(rect)
'''
if self.stage > 0:
tmp = self.ImageFromRect(grayscale, currentrect)
features = FeaturesFromImg(grayscale, self.ninsize)
self.additionslds.addSample(features, [1])
for i in xrange(2):
badwin = self.ImageFromRect(grayscale, RandomRect(currentrect))
features = FeaturesFromImg(badwin, self.ninsize)
self.additionslds.addSample(features, [0])
print 'len of new dataset', len(self.additionslds)
if len(self.additionslds) > 20:
self.additionslds._convertToOneOfMany( )
self.additionslds.outdim = self.net.outdim
#net = buildNetwork(self.ninsize[0] * self.ninsize[1], 96, self.additionslds.outdim, outclass=SoftmaxLayer)
trainer = RPropMinusTrainer(
self.net, dataset=self.additionslds,
momentum=0.1, verbose=True, weightdecay=0.01)
'''trainer = BackpropTrainer(
self.net, dataset=self.additionslds,
momentum=0.1, verbose=True, weightdecay=0.01)'''
trainer.trainEpochs( 3 )
self.additionslds.clear()
self.numoflearning += 1
if self.key == 113:
cv.SaveImage('img.bmp', image)
self.key = 255
if self.key == 119:
self.stage = 1
self.key = 255
if self.stage == 1 and self.numoflearning > 2:
self.stage = 2
'''
for item in tracks:
cv.Circle(image, (item[-1][0], item[-1][1]), 2, (0, 255, 0), -1)
'''
return image
def run(self):
DEVICE = 0 #/dev/video0
# create windows
cv.NamedWindow('Camera')
# create capture device
device = 0 # assume we want first device
capture = cv.CreateCameraCapture(DEVICE)
k = ''
while k !='q' :
frame = cv.QueryFrame(capture)#cv.RetrieveFrame(capture)
if frame is None:
break
cv.Flip(frame, None, 1)
frame = self.work(frame)
# display webcam image
cv.ShowImage('Camera', frame)
k = cv.WaitKey(10) % 0x100
if k !=255 :
self.key = k
print 'pressed', k
