def outfacing(lock, receiver, ttsque=None, finder=finder):
cv2.namedWindow('visuals')
processor = faceprocessor()
extractor = modelInferance()
processor.setSource(cv2.VideoCapture(0))
processor.setFinder(finder().nextFaces)
faceDict = FaceDictionary()
datawindow = window()
dnn = model()
dnn.load_previous()
generator = datagenerator()
clf, revmap = load_classifier(lock)
cacher = feature_cacher()
kill = False
cacheFlag = False
label = None
cv2.namedWindow('visual')
ix = 0
while cv2.waitKey(10) < 0 and not kill:
temp = processor.getFaces()
if receiver and receiver.poll(0.001):
signal = receiver.recv()
print(signal)
if signal.find('label:') >= 0:
label = signal[signal.find(':') + 1:]
cacheFlag = True
if signal == 'STOPCACHE':
cacheFlag = False
if signal == 'KILL':
kill = True
if signal == 'Done Thinking':
clf, revmap = load_classifier(lock)
print('HOT SWAPPED CLASSIFIER')
if temp is None:
continue
frame, fbbs, wfaces = temp
feats = extractor.getFeatures(wfaces)
#faceDict.update(fbbs,feats)
#index,fbbs,feats = faceDict.getobjs()
index = np.ones(len(fbbs))
clss, score, tmap = handledata(dnn, cacher, generator, cacheFlag,
label, feats, wfaces, datawindow)
if tmap is not None:
revmap = tmap
if score is not None:
isUnknown = score < 8.0
else:
isUnknown = False
sendmesg(isUnknown, 'What are you doing here? Relinquish thine name. ',
ttsque)
ix += 1
visualize(frame, fbbs, wfaces, clss, revmap, isUnknown, index)
# print(clss)
extractor.kill()
return 1
def outfacing(lock, receiver, finder=finder):
cv2.namedWindow('visuals')
processor = faceprocessor()
extractor = modelInferance()
processor.setSource(cv2.VideoCapture(0))
processor.setFinder(finder().nextFaces)
#processor.setTracker(tracker())
clf, revmap = load_classifier(lock)
cacher = feature_cacher()
kill = False
cacheFlag = False
label = None
cv2.namedWindow('visual')
try:
while cv2.waitKey(10) < 0 and not kill:
temp = processor.getFaces()
if receiver and receiver.poll(0.001):
signal = receiver.recv()
print(signal)
if signal.find('label:') >= 0:
label = signal[signal.find(':') + 1:]
cacheFlag = True
if signal == 'STOPCACHE':
cacheFlag = False
if signal == 'KILL':
kill = True
if signal == 'Done Thinking':
clf, revmap = load_classifier(lock)
print('HOT SWAPPED CLASSIFIER')
if temp is None:
continue
frame, fbbs, wfaces = temp
feats = extractor.getFeatures(wfaces)
pair = wfaces[0].shape
vizframe = np.zeros((pair[0], pair[1] * len(wfaces), 3),
dtype=np.uint8)
cv2.imshow('visual', frame)
clss = clf.predict_proba(feats)
if cacheFlag:
pairs = zip(feats, wfaces)
cacher.cachePairs(pairs, label)
i = 0
for face, probs in zip(wfaces, clss):
#retrieve the top 2 classes based on probabilty
top2 = probs.argsort()[-2:]
for ix, cls in enumerate(top2):
cv2.putText(face,
revmap[round(cls)] + ':' + str(probs[cls]),
(0, 45 + ix * 50), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0))
vizframe[:, i * 96:(i + 1) * 96] = face
i += 1
cv2.imshow('visuals', vizframe)
finally:
extractor.kill()
return 1
def __init__(self,finder=None,
processor=faceprocessor,
extractor=modelInferance,
cachePath="/home/avail/data/facerecognition/cache"):
self.finder = finder
if self.finder is not None:
self.processor = processor()
self.processor.setFinder(finder().nextFaces)
self.extractor = extractor()
self.saveDir = cachePath