def saveFaceImage(capture, frequency, display, drawFaces):
img_count = 0
# Create the directory in which we record the training examples.
if not os.path.exists(args.recording_emotion):
os.makedirs(args.recording_emotion)
while True:
flag, frame = capture.read()
if flag:
faceCoordinates = face_detection.getFaceCoordinates(frame)
if faceCoordinates:
image = emotionrecognition.preprocess(frame, faceCoordinates)
# Save the image that will later be used for training.
scipy.misc.imsave(
os.path.join(args.recording_emotion,
args.recording_emotion + str(img_count) + '.png'),
image)
if display:
showFrame(frame, faceCoordinates, None, drawFaces)
img_count = img_count + 1
time.sleep(frequency)
def detectedAndDisplayFaces(capture, net, display=False, drawFaces=False):
recognition = True
# Flag gives us some information about the capture
# Frame is the webcam frame (a numpy image)
flag, frame = capture.read()
# Not sure if there is an error from the cam if we should lock the screen
if flag:
faceCoordinates = face_detection.getFaceCoordinates(frame)
if faceCoordinates and recognition:
emotion = recogintionWork(frame, faceCoordinates, net)
else:
emotion = None
if display:
showFrame(frame, faceCoordinates, emotion, drawFaces)
if faceCoordinates:
return True
else:
return True
def detectedAndDisplayFaces(capture, net, display=False, drawFaces=False):
recognition = True
# Flag gives us some information about the capture
# Frame is the webcam frame (a numpy image)
flag, frame = capture.read()
# Not sure if there is an error from the cam if we should lock the screen
if flag:
faceCoordinates = face_detection.getFaceCoordinates(frame)
if faceCoordinates and recognition:
emotion = recogintionWork(frame, faceCoordinates, net)
else:
emotion = None
if display:
showFrame(frame, faceCoordinates, emotion, drawFaces)
if faceCoordinates:
return True
else:
return True
def saveFaceImage(capture, frequency, display, drawFaces):
img_count = 0
# Create the directory in which we record the training examples.
if not os.path.exists(args.recording_emotion):
os.makedirs(args.recording_emotion)
while True:
flag, frame = capture.read()
if flag:
faceCoordinates = face_detection.getFaceCoordinates(frame)
if faceCoordinates:
image = emotionrecognition.preprocess(frame, faceCoordinates)
# Save the image that will later be used for training.
scipy.misc.imsave(os.path.join(args.recording_emotion,
args.recording_emotion + str(img_count) + '.png'),
image)
if display:
showFrame(frame, faceCoordinates, None, drawFaces)
img_count = img_count + 1
time.sleep(frequency)
