def setup(self, config=None): # Components self.video = None if config.TrialMovie != None: self.video = CVVideo.CVFileVideo(config.TrialMovie) self.detector = SkinHaarDetector(config.HaarCascade, config.HaarSize, config.HaarSkin) self.tracker = TrivialTracker(config) self.window = CVWindow(config.UIName, config.UISaveTo) # Determine minimization algorithm to be used if config.MinProblem == 'IP': self.ai = IP(constraints=config.MinProblemConstraints) elif config.MinProblem == 'Gap2': self.ai = GapApproximation() elif config.MinProblem == 'Trivial': self.ai = TrivialAI() else: raise 'Minimization method not specified!' # Recognizer self.recognizers = [] for i in range(0, config.PhotoBag): self.recognizers.append(LBPRecognizer(cversion=True)) self.recognizers = BagRecognizerSVM(config.PhotoPath, self.recognizers, config.BoundingBox) self.track = [] self.prev = [] self.config = config self.dist = None
class VRoster:
def __init__(self, alpha=0, beta=0, priorSize=0, frames=300, skipFrames=5, ui=False, output=None):
self.alpha = alpha
self.beta = beta
self.priorSize = priorSize
self.frames = frames
self.skipFrames = skipFrames
self.ui = ui
self.profile = Profile()
if output == None:
self.output = open('output.txt', 'w+')
else:
self.output = output
def setup(self, config=None):
# Components
self.video = None
if config.TrialMovie != None:
self.video = CVVideo.CVFileVideo(config.TrialMovie)
self.detector = SkinHaarDetector(config.HaarCascade, config.HaarSize, config.HaarSkin)
self.tracker = TrivialTracker(config)
self.window = CVWindow(config.UIName, config.UISaveTo)
# Determine minimization algorithm to be used
if config.MinProblem == 'IP':
self.ai = IP(constraints=config.MinProblemConstraints)
elif config.MinProblem == 'Gap2':
self.ai = GapApproximation()
elif config.MinProblem == 'Trivial':
self.ai = TrivialAI()
else:
raise 'Minimization method not specified!'
# Recognizer
self.recognizers = []
for i in range(0, config.PhotoBag):
self.recognizers.append(LBPRecognizer(cversion=True))
self.recognizers = BagRecognizerSVM(config.PhotoPath, self.recognizers, config.BoundingBox)
self.track = []
self.prev = []
self.config = config
self.dist = None
def update(self, frame):
frameGray = cv.CreateImage(cv.GetSize(frame), cv.IPL_DEPTH_8U, 1)
cv.CvtColor(frame, frameGray, cv.CV_RGB2GRAY)
# Get objects
observations = self.detector.detect(frame)
objects = self.tracker.update(observations)
objectImages = Image.extractSubImages(frameGray, objects, self.config.BoundingBox)
detectedCount = len(objectImages)
# Generate recognition matrix
recognized = []
for image in objectImages:
recognized.append(self.recognizers.query(image))
# Attempt to find best matching
s = numpy.abs(numpy.matrix(recognized))
# Temporal variables
b = numpy.eye(detectedCount)
da = numpy.zeros((detectedCount, detectedCount))
ds = numpy.zeros(s.shape)
# Calculate temporal variables
if len(self.prev)>0:
for t in range(0, len(self.prev)):
for i in range(0, detectedCount):
for j in range(0, len(self.prev[0][1])):
da[i,j] += numpy.sum(numpy.power(objectImages[i]-self.prev[t][1][j],2))**.5
da /= (len(self.prev)*1.0)
for i in range(0, ds.shape[0]):
for j in range(0, ds.shape[1]):
tmp = []
for k in range(0, len(self.prev[0][0])):
tmp2 = 0
for t in range(0, len(self.prev)):
tmp2 += (1 - 2*int(self.prev[t][0][k]==j))
tmp.append(tmp2/(len(self.prev)*1.0)*b[i,k]*da[i,k])
ds[i,j] = numpy.sum(tmp)
# Create cost matrix
s = s + self.beta*ds
# Unknown column is added
a = numpy.zeros((detectedCount, 1)) + numpy.average(s,1)*self.alpha - numpy.std(s,1)*.5
s = numpy.hstack((s,a))
# Solve the pairing problem (assuming B is correct)
c = self.ai.predict(s, unknown=True)
# Calculate new B
if len(self.prev)>0:
w = numpy.zeros((detectedCount, detectedCount))
for i in range(0, detectedCount):
for j in range(0, len(self.prev[0][0])):
w[i,j] = da[i,j] * int(c[i]!=self.prev[0][0][j])
# Solve the pairing problem for B (assuming C is correct)
newB = self.ai.predict(w, unknown=False)
# Update B matrix with results
b = numpy.eye(detectedCount)
for i in range(0, len(newB)):
b[i, newB[i]] = 1
# Update prior data
self.prev.append([c, objectImages])
if len(self.prev)>self.priorSize:
self.prev.pop()
# Save tracking information for display and output
currentTrack = []
if len(objects)>0:
for (i,l) in enumerate(c):
tmp = [objects[i][0]+objects[i][2]/2.0, objects[i][1]+objects[i][3]/2.0, l]
currentTrack.append(tmp)
self.output.write('%d %d %d; '%(tmp[0], tmp[1], tmp[2]))
self.output.write('\n')
return (objects, c)
# Main code that cycles through frames performing the recognition and tracking
def run(self, config=None):
self.setup(config)
for fid in range(0, self.frames):
print fid, '%.02f%%'%(fid/(self.frames*1.0))
for i in range(0, max(self.skipFrames, 1)):
frame = self.video.next()
if frame==None:
print 'Movie ended!'
break
# Track/Recognize
objects, predicted = self.update(frame)
# If UI is enabled, display results
if self.ui == True:
canvas = CVCanvas(frame)
if len(objects)>0:
for i in range(0, len(objects)):
label = predicted[i]
canvas.drawText('[%02d]=%d'%(i, label), (objects[i][0], objects[i][1]-3), (255, 255, 0))
canvas.drawRect(objects[i], (255,0,0))
self.window.update(canvas)
return track
