def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w, self.h = 0, 0
self.pressed = 0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot,
"f": self.write_csv
}
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits = [50,160],
data_spike_limit = 13.,
face_detector_smoothness = 10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"s" : self.toggle_search,
"d" : self.toggle_display_plot}
def __init__(self, args):
# assumes connected camera uses cam0 by default
serial = args.serial
baud = args.baud
self.send_serial = False
self.send_udp = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
# self.serial = Serial(port=serial, baudrate=baud)
udp = args.udp
if udp:
self.send_udp = True
if ":" not in udp:
ip = udp
port = 5005
else:
ip, port = udp.split(":")
port = int(port)
self.udp = (ip, port)
self.sock = socket.socket(socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
# Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {#"s": self.toggle_search,
"d": self.toggle_display_plot,
"c": self.toggle_cam,
"f": self.write_csv}
def __init__(self):
self.cap = cv2.VideoCapture(0)
self.fourcc = cv2.cv.CV_FOURCC(*'XVID')
self.out = cv2.VideoWriter('data/' + subject + '/' + video +'/output.avi', -1, 9, (width, height))
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
self.camera = Camera(camera=0) # first camera by default
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
# Maps keystrokes to specified methods
# (A GUI window must have focus for these to work)
self.key_controls = {"s": self.toggle_search,
"d": self.toggle_display_plot,
"f": self.write_csv}
def __init__(self, args):
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
serial = args.serial
baud = args.baud
self.send_serial = False
self.send_udp = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
udp = args.udp
if udp:
self.send_udp = True
if ":" not in udp:
ip = udp
port = 5005
else:
ip, port = udp.split(":")
port = int(port)
self.udp = (ip, port)
self.sock = socket.socket(
socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
# Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot,
"c": self.toggle_cam,
"f": self.write_csv
}
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
self.pressed = 0
self.current_centroid = [0.0,0.0]
self.centroid_1 = [0.0,0.0]
self.centroid_2 = [0.0,0.0]
self.centroid_1_active = False
self.centroid_2_active = False
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"1" : self.set_centroid_1,
"2" : self.set_centroid_2}
def __init__(self, args):
#tried to use droidcam from playstore,this won't work like that
serial = args.serial
baud = args.baud
self.send_serial = False
self.send_udp = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
udp = args.udp
if udp:
self.send_udp = True
if ":" not in udp:
ip = udp
port = 5005
else:
ip, port = udp.split(":")
port = int(port)
self.udp = (ip, port)
self.sock = socket.socket(
socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot,
"c": self.toggle_cam,
"f": self.write_csv
}
def __init__(self, args):
self.COUNTER = 0
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
self.plot_title = "Real-Time Heart Rate"
def __init__(self, args):
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
serial = args.serial
baud = args.baud
self.send_serial = False
self.send_udp = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
# Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot
}
def __init__(self, args):
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
serial = args.serial
baud = args.baud
self.send_serial = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
self.key_controls = {}
class getPulseApp(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
eyesArea = []
perclos = []
eyesCalssfier = CascadeClassifier("cascades/haarcascade_eye.xml")
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
self.pressed = 0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits = [50,160],
data_spike_limit = 2500.,
face_detector_smoothness = 10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"s" : self.toggle_search,
"d" : self.toggle_display_plot,
"f" : self.write_csv}
def write_csv(self):
"""
Writes current data to a csv file
"""
bpm = " " + str(int(self.processor.measure_heart.bpm))
fn = str(datetime.datetime.now()).split(".")[0] + bpm + " BPM.csv"
data = np.array([self.processor.fft.times,
self.processor.fft.samples]).T
np.savetxt(fn, data, delimiter=',')
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces.toggle()
if not state:
self.processor.fft.reset()
print "face detection lock =",not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w,0)
def make_bpm_plot(self):
"""
Creates and/or updates the data display
"""
plotXY([[self.processor.fft.times,
self.processor.fft.samples],
[self.processor.fft.even_times[4:-4],
self.processor.measure_heart.filtered[4:-4]],
[self.processor.measure_heart.freqs,
self.processor.measure_heart.fft]],
labels = [False, False, True],
showmax = [False,False, "bpm"],
label_ndigits = [0,0,0],
showmax_digits = [0,0,1],
skip = [3,3,4],
name = self.plot_title,
bg = self.processor.grab_faces.slices[0])
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
self.pressed = waitKey(10) & 255 #wait for keypress for 10 ms
if self.pressed == 27: #exit program on 'esc'
print "exiting..."
self.camera.cam.release()
exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def get_perclos(self):
# eyesCalssfier = "cascades/haarcascade_eye.xml"
eye = self.getEyesArea(self.processor.grab_faces.slices[0])
if eye!=None:
self.eyesArea.append(eye)
if len(self.eyesArea)>=100:
tempPerclos = self.cal_perclos(self.eyesArea, len(self.eyesArea))
self.perclos.append(tempPerclos)
self.eyesArea[:] = self.eyesArea[0:75]
if len(self.perclos)>=10:
self.perclos[:] = self.perclos[1:10]
def getEyesArea(self,face,classfier=eyesCalssfier):
eyes = []
eyesRects = classfier.detectMultiScale(face)
# 如果检测到人眼，返回，否则返回空
if len(eyesRects)>0:
for (ex,ey,ew,eh) in eyesRects:
eyes.append(ew*eh)
aveEyeArea = float(sum(eyes))/len(eyes)
return aveEyeArea
else:
return None
def handleEyesArea(self,areas):
max_areas=max(areas)
min_areas=min(areas)
#眼球极值
D=max_areas-min_areas
#最大值阈值
threshold_max=min_areas+D*0.8
#大于阈值的都与取做最大值的平均
j=0
vj=0
for area in areas:
if area>=threshold_max:
vj=vj+area
j=j+1
#相对最大值
relative_max=1.0*vj/j
return min_areas,relative_max
#计算PERCLOS值
def cal_perclos(self,areas,count):
min_areas,relative_max=self.handleEyesArea(areas)
pi=[]
eyeclose=0 #眼睛闭上的个数
for mi in areas:
temp=abs(1.0*(mi-min_areas)/(relative_max-min_areas))
# print "temp=",temp
if temp>0.2:
pi.append(0)
else:
pi.append(1)
eyeclose+=1
sigma_pi=0
sum = 1.0
for pii in pi:
if pii==1:
sigma_pi=sigma_pi+1
perclos=1.0*sigma_pi/count
return perclos
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h,self.w,_c = frame.shape
#display unaltered frame
# imshow("Original",frame)
#set current image frame to the processor's input
self.processor.frame_in = frame
#process the image frame to perform all needed analysis
self.processor.run()
#collect the output frame for display
output_frame = self.processor.frame_out
#show the processed/annotated output frame
# imshow("Processed",output_frame)
#create and/or update the raw data display if needed
if self.bpm_plot:
self.make_bpm_plot()
#handle any key presses
self.key_handler()
self.get_perclos()
class AnalyseEmotion(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w, self.h = 0, 0
self.pressed = 0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot,
"f": self.write_csv
}
def write_csv(self):
"""
Writes current data to a csv file
"""
bpm = " " + str(int(self.processor.measure_heart.bpm))
fn = str(datetime.datetime.now()).split(".")[0] + bpm + " BPM.csv"
data = np.array([self.processor.fft.times,
self.processor.fft.samples]).T
np.savetxt(fn, data, delimiter=',')
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces.toggle()
if not state:
self.processor.fft.reset()
print "face detection lock =", not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w, 0)
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
global line2
self.pressed = waitKey(10) & 255 #wait for keypress for 10 ms
if self.pressed == 27 and self.processor.fft.ready == True: #exit program on 'esc'
print "exiting..."
self.camera.cam.release()
#a = sum(self.processor.fft.samples)/float(len(self.processor.fft.samples))
print >> f, str(self.processor.show_bpm_text.bpm)
print >> f, str(line2)
#f.write(str(a) + "\n")
#f.write(str(line2) + "\n")
exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h, self.w, _c = frame.shape
#display unaltered frame
#imshow("Original",frame)
#set current image frame to the processor's input
self.processor.frame_in = frame
#process the image frame to perform all needed analysis
self.processor.run()
#collect the output frame for display
output_frame = self.processor.frame_out
#show the processed/annotated output frame
imshow("Processed", output_frame)
#create and/or update the raw data display if needed
#if self.bpm_plot:
#self.make_bpm_plot()
#print(self.processor.fft.samples)
if (self.processor.fft.ready):
print "Ready to hit escape"
else:
print ".",
img = cv.QueryFrame(capture)
if img:
image = DetectRedEyes(img, faceCascade)
cv.ShowImage("camera", image)
#handle any key presses
self.key_handler()
class getPulseApp(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits = [50,160],
data_spike_limit = 13.,
face_detector_smoothness = 10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"s" : self.toggle_search,
"d" : self.toggle_display_plot}
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces.toggle()
print "face detection lock =",not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w,0)
def make_bpm_plot(self):
"""
Creates and/or updates the data display
"""
plotXY([[self.processor.fft.times,
self.processor.fft.samples],
[self.processor.fft.even_times[4:-4],
self.processor.heart.filtered[4:-4]],
[self.processor.heart.freqs,
self.processor.heart.fft]],
labels = [False, False, True],
showmax = [False,False, "bpm"],
name = self.plot_title,
bg = self.processor.grab_faces.slices[0])
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
pressed = waitKey(10) & 255 #wait for keypress for 10 ms
if pressed == 27: #exit program on 'esc'
quit()
for key in self.key_controls.keys():
if chr(pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h,self.w,_c = frame.shape
#display unaltered frame
#imshow("Original",frame)
#set current image frame to the processor's input
self.processor.frame_in = frame
#process the image frame to perform all needed analysis
self.processor.run()
#collect the output frame for display
output_frame = self.processor.frame_out
#show the processed/annotated output frame
imshow("Processed",output_frame)
#create and/or update the raw data display if needed
if self.bpm_plot:
self.make_bpm_plot()
#handle any key presses
self.key_handler()
class getPulseApp(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w, self.h = 0, 0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=13.,
face_detector_smoothness=10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot
}
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces.toggle()
print "face detection lock =", not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w, 0)
def make_bpm_plot(self):
"""
Creates and/or updates the data display
"""
plotXY([[self.processor.fft.times, self.processor.fft.samples],
[
self.processor.fft.even_times[4:-4],
self.processor.heart.filtered[4:-4]
], [self.processor.heart.freqs, self.processor.heart.fft]],
labels=[False, False, True],
showmax=[False, False, "bpm"],
name=self.plot_title,
bg=self.processor.grab_faces.slices[0])
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
pressed = waitKey(10) & 255 #wait for keypress for 10 ms
if pressed == 27: #exit program on 'esc'
quit()
for key in self.key_controls.keys():
if chr(pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h, self.w, _c = frame.shape
#display unaltered frame
#imshow("Original",frame)
#set current image frame to the processor's input
self.processor.frame_in = frame
#process the image frame to perform all needed analysis
self.processor.run()
#collect the output frame for display
output_frame = self.processor.frame_out
#show the processed/annotated output frame
imshow("Processed", output_frame)
#create and/or update the raw data display if needed
if self.bpm_plot:
self.make_bpm_plot()
#handle any key presses
self.key_handler()
class AnalyseEmotion(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
self.pressed = 0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits = [50,160],
data_spike_limit = 2500.,
face_detector_smoothness = 10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"s" : self.toggle_search,
"d" : self.toggle_display_plot,
"f" : self.write_csv}
def write_csv(self):
"""
Writes current data to a csv file
"""
bpm = " " + str(int(self.processor.measure_heart.bpm))
fn = str(datetime.datetime.now()).split(".")[0] + bpm + " BPM.csv"
data = np.array([self.processor.fft.times,
self.processor.fft.samples]).T
np.savetxt(fn, data, delimiter=',')
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces_toggle()
if not state:
#self.processor.fft.reset()
self.processor.fft = []
print "face detection lock =",not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w,0)
def make_bpm_plot(self):
"""
Creates and/or updates the data display
"""
plotXY([[self.processor.fft.times,
self.processor.fft.samples],
[self.processor.fft.even_times[4:-4],
self.processor.measure_heart.filtered[4:-4]],
[self.processor.measure_heart.freqs,
self.processor.measure_heart.fft]],
labels = [False, False, True],
showmax = [False,False, "bpm"],
label_ndigits = [0,0,0],
showmax_digits = [0,0,1],
skip = [3,3,4],
name = self.plot_title,
bg = self.processor.grab_faces.slices[0])
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
self.pressed = waitKey(10) & 255 #wait for keypress for 10 ms
if self.pressed == 27: #exit program on 'esc'
print "exiting..."
self.camera.cam.release()
exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h,self.w,_c = frame.shape
#display unaltered frame
#imshow("Original",frame)
#set current image frame to the processor's input
self.processor.frame_in = frame
#process the image frame to perform all needed analysis
self.processor.run(frame)
#collect the output frame for display
output_frame = self.processor.frame_out
#show the processed/annotated output frame
imshow("Processed",output_frame)
#create and/or update the raw data display if needed
global smileneighbour, mqLoop, smilecount, eyetot
#if self.bpm_plot:
#self.make_bpm_plot()
if mqLoop >= 1:
x = str(datetime.datetime.now())
sm.write(str(md.datestr2num(x)) + " " + str(smileneighbour) + "\n")
e.write(str(md.datestr2num(x)) + " " + str(eyetot) + "\n")
#hr.write(str(md.datestr2num(x)) + " " + str(self.processor.show_bpm_text.bpm) + "\n")
hr.write(str(md.datestr2num(x)) + " " + str(self.processor.bpm) + "\n")
pulse_estimation_log.write(str(int(round(time.time() * 1000))) + " " + str(self.processor.bpm) + "\n")
smileneighbour+= 2*eyetot
smileneighbour/=100
print "bpm: " + str(self.processor.bpm)
#if (self.processor.show_bpm_text.bpm) > dhr:
if (self.processor.bpm) > dhr:
#print (self.processor.fft.samples[-1]/2, self.processor.fft.samples[-1]-dhr/2)
#overbeat = (self.processor.fft.samples[-1]-dhr)*(self.processor.fft.samples[-1]-dhr)
#smileneighbour += (self.processor.show_bpm_text.bpm-dhr)
smileneighbour += (self.processor.bpm - dhr)
f.write(str(md.datestr2num(x)) + " " + str(smileneighbour) + "\n")
mqLoop = 0
else:
mqLoop+= 0.9
img = cv.QueryFrame(capture)
smileneighbour = 0
eyetot = 0
#if img:
# image = DetectRedEyes(img, faceCascade, smileCascade, eyeCascade)
# cv.ShowImage("camera", image)
#handle any key presses
self.key_handler()
class getPulseApp(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
self.pressed = 0
self.current_centroid = [0.0,0.0]
self.centroid_1 = [0.0,0.0]
self.centroid_2 = [0.0,0.0]
self.centroid_1_active = False
self.centroid_2_active = False
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"1" : self.set_centroid_1,
"2" : self.set_centroid_2}
def set_centroid_1(self):
"""
Sets centroid 1 to the current centroid position.
If centroid 1 is not currently active, makes it active.
"""
if not self.centroid_1_active:
self.centroid_1_active = True
self.centroid_1 = self.current_centroid
def set_centroid_2(self):
"""
Sets centroid 2 to the current centroid position.
If centroid 2 is not currently active, makes it active.
"""
if not self.centroid_2_active:
self.centroid_2_active = True
self.centroid_2 = self.current_centroid
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
self.pressed = waitKey(1) & 255 #wait for keypress for 10 ms
if self.pressed == 27: #exit program on 'esc'
print "exiting..."
self.camera.cam.release()
exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h,self.w,_c= frame.shape
self.current_centroid = centroid(np.sum(frame,2)/3)
#display unaltered frame
#imshow("Original",frame)
#collect the output frame for display
output_frame = process_image(frame,self.current_centroid,self.centroid_1,self.centroid_2,self.centroid_1_active,self.centroid_2_active)
#show the processed/annotated output frame
imshow("Processed",output_frame)
#handle any key presses
self.key_handler()
class AnalyseEmotion(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
self.cap = cv2.VideoCapture(0)
self.fourcc = cv2.cv.CV_FOURCC(*'XVID')
self.out = cv2.VideoWriter('data/' + subject + '/' + video +'/output.avi', -1, 9, (width, height))
self.detector = dlib.get_frontal_face_detector()
self.predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
self.camera = Camera(camera=0) # first camera by default
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
# Maps keystrokes to specified methods
# (A GUI window must have focus for these to work)
self.key_controls = {"s": self.toggle_search,
"d": self.toggle_display_plot,
"f": self.write_csv}
def write_csv(self):
"""
Writes current data to a csv file
"""
bpm = " " + str(int(self.processor.measure_heart.bpm))
fn = str(datetime.datetime.now()).split(".")[0] + bpm + " BPM.csv"
data = np.array([self.processor.fft.times,
self.processor.fft.samples]).T
np.savetxt(fn, data, delimiter=',')
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces.toggle()
if not state:
self.processor.fft.reset()
print "face detection lock =", not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w, 0)
def make_bpm_plot(self):
"""
Creates and/or updates the data display
"""
plotXY([[self.processor.fft.times,
self.processor.fft.samples],
[self.processor.fft.even_times[4:-4],
self.processor.measure_heart.filtered[4:-4]],
[self.processor.measure_heart.freqs,
self.processor.measure_heart.fft]],
labels=[False, False, True],
showmax=[False, False, "bpm"],
label_ndigits=[0, 0, 0],
showmax_digits=[0, 0, 1],
skip=[3, 3, 4],
name=self.plot_title,
bg=self.processor.grab_faces.slices[0])
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
self.pressed = waitKey(1) & 0xFF # wait for keypress for 10 ms
if self.pressed == 27: # exit program on 'esc'
print "exiting..."
self.cap.release()
self.out.release()
self.camera.cam.release()
exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h, self.w, _c = frame.shape
# display unaltered frame
# imshow("Original",frame)
# set current image frame to the processor's input
self.processor.frame_in = frame
# process the image frame to perform all needed analysis
self.processor.run()
# collect the output frame for display
output_frame = self.processor.frame_out
# show the processed/annotated output frame
imshow("Processed", output_frame)
# create and/or update the raw data display if needed
global smileneighbour, mqLoop, smilecount, eyetot
# if self.bpm_plot:
# self.make_bpm_plot()
x = str(datetime.datetime.now())
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = self.detector(gray, 0)
#####Record self-report#####
key = cv2.waitKey(1) & 0xFF
if key == ord("."):
c.write(str(md.datestr2num(x)) + " " + str(500) + "\n")
############################
for rect in rects:
shape = self.predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
shape2 = shape.copy()
newline = ""
for (w,z) in shape2:
cv2.circle(frame, (w, z), 1, (0, 0, 255), -1)
new = str((w,z)) + " "
newline += new
#######################
########Raw Data#######
#######################
raw.write(str(md.datestr2num(x)) + newline + "\n")
#######################
######New Things#######
leftBrow = shape[21]
rightBrow = shape[22]
distance = dist.euclidean(leftBrow, rightBrow)
upper1 = shape[37]
lower1 = shape[41]
right1 = shape[38]
upper2 = shape[43]
lower2 = shape[47]
right2 = shape[44]
average = (dist.euclidean(upper1, lower1) * dist.euclidean(upper1, right1) + dist.euclidean(upper2,
lower2) * dist.euclidean(
upper2, right2))
leftmouth = shape[48]
rightmouth = shape[54]
upperlip = shape[62]
lowerlip = shape[66]
##########End##########
#######################
#Time, Frown, Eye size, Mouth width, Mouth heigth, Brow Raise, Length of face, and Width of face
all.write(str(md.datestr2num(x)) + " " + str(distance) + " " + str(average) + " " + str(
dist.euclidean(leftmouth, rightmouth)) + " " + str(dist.euclidean(upperlip, lowerlip)) + " " + str(
(dist.euclidean(shape[24], shape[44]) + dist.euclidean(shape[19], shape[37]))) + " " + str(dist.euclidean(shape[27], shape[8])) + " " + str(dist.euclidean(shape[2], shape[14])) + "\n")
#######################
######Single File######
cv2.imshow("Frame", frame)
if mqLoop >= 1:
sm.write(str(md.datestr2num(x)) + " " + str(smileneighbour) + "\n")
e.write(str(md.datestr2num(x)) + " " + str(eyetot) + "\n")
hr.write(str(md.datestr2num(x)) + " " + str(self.processor.show_bpm_text.bpm) + "\n")
smileneighbour += 2 * eyetot
smileneighbour /= 100
if (self.processor.show_bpm_text.bpm) > dhr:
# print (self.processor.fft.samples[-1]/2, self.processor.fft.samples[-1]-dhr/2)
# overbeat = (self.processor.fft.samples[-1]-dhr)*(self.processor.fft.samples[-1]-dhr)
smileneighbour += (self.processor.show_bpm_text.bpm - dhr)
f.write(str(md.datestr2num(x)) + " " + str(smileneighbour) + "\n")
mqLoop = 0
else:
mqLoop += 0.9
img = cv.QueryFrame(capture)
smileneighbour = 0
eyetot = 0
if img:
image = DetectRedEyes(img, faceCascade, smileCascade, eyeCascade)
cv.ShowImage("camera", image)
# handle any key presses
self.key_handler()
def __init__(self, args):
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
serial = args.serial
baud = args.baud
self.send_serial = False
self.send_udp = False
self.video_flag = False
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
udp = args.udp
if udp:
self.send_udp = True
if ":" not in udp:
ip = udp
port = 5005
else:
ip, port = udp.split(":")
port = int(port)
self.udp = (ip, port)
self.sock = socket.socket(
socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
self.cameras = []
self.selected_cam = 0
for i in range(3):
camera = Camera(camera=i) # first camera by default
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
break
self.w, self.h = 0, 0
self.pressed = 0
# Containerized analysis of received image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
# Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot,
"c": self.toggle_cam,
"f": self.write_csv,
"v": self.toggle_video
}
self.video = cv2.VideoCapture('video1.avi')
#self.video.set(cv2.CAP_PROP_FPS, 16)
self.csvn = "Webcam-pulse"
fps = self.video.get(cv2.CAP_PROP_FPS)
print "Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(
fps)
#Creates a blank image of 200x200 size to initialise self.irframe
blank_image = np.zeros((400, 400, 3), np.uint8)
self.irframe = blank_image
#self.irframe = None
self.callbackFlag = False
def __init__(self, args):
# Imaging device - must be a connected camera (not an ip camera or mjpeg
# stream)
serial = args.serial
baud = args.baud
video = args.video
self.Tx = False
self.environment = []
self.ser = None
self.kill = False
self.vidname = ""
self.send_serial = False
self.send_udp = False
self.question_number = "-1"
if serial:
self.send_serial = True
if not baud:
baud = 9600
else:
baud = int(baud)
self.serial = Serial(port=serial, baudrate=baud)
udp = args.udp
if udp:
self.send_udp = True
if ":" not in udp:
ip = udp
port = 5005
else:
ip, port = udp.split(":")
port = int(port)
self.udp = (ip, port)
self.sock = socket.socket(
socket.AF_INET, # Internet
socket.SOCK_DGRAM) # UDP
if video:
self.vidname = video
self.cameras = []
self.selected_cam = 0
if args.url is not None:
camera = Camera(camera=args.url)
self.cameras.append(camera)
elif args.video_dir is None:
# Real-time for camera=0, read from one video
# first camera by default
camera = Camera(camera=0, vid=self.vidname)
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
else:
print('Error: No camera was found')
else:
# read all videos from a directory in a sequence
self.video_names = glob.glob(args.video_dir + '/*.mp4')
self.video_names.sort()
for i in range(len(self.video_names)):
# start from the first video
camera = Video(vid=self.video_names[i])
if camera.valid or not len(self.cameras):
self.cameras.append(camera)
self.w, self.h = 0, 0
# self.record = False
self.sz = (int(self.cameras[self.selected_cam].cam.get(
cv2.CAP_PROP_FRAME_WIDTH)),
int(self.cameras[self.selected_cam].cam.get(
cv2.CAP_PROP_FRAME_HEIGHT)))
self.fourcc = cv2.VideoWriter_fourcc(*'MP4V')
self.fps = 25
self.q = 0
# self.out = None
self.pressed = 0
# Containerized analysis of recieved image frames (an openMDAO assembly)
# is defined next.
# This assembly is designed to handle all image & signal analysis,
# such as face detection, forehead isolation, time series collection,
# heart-beat detection, etc.
# Basically, everything that isn't communication
# to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits=[50, 160],
data_spike_limit=2500.,
face_detector_smoothness=10.)
self.processor.init_temp = args.init_temp
# Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Data display - raw signal (top) and PSD (bottom)"
# Maps keystrokes to specified methods
# (A GUI window must have focus for these to work)
self.key_controls = {
"s": self.toggle_search,
"d": self.toggle_display_plot,
"c": self.toggle_cam,
# "g": self.start_record,
# "f": self.stop_record
}
class AnalyseEmotion(object):
"""
Python application that finds a face in a webcam stream, then isolates the
forehead.
Then the average green-light intensity in the forehead region is gathered
over time, and the detected person's pulse is estimated.
"""
def __init__(self):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
self.camera = Camera(camera=0) #first camera by default
self.w,self.h = 0,0
self.pressed = 0
#Containerized analysis of recieved image frames (an openMDAO assembly)
#is defined next.
#This assembly is designed to handle all image & signal analysis,
#such as face detection, forehead isolation, time series collection,
#heart-beat detection, etc.
#Basically, everything that isn't communication
#to the camera device or part of the GUI
self.processor = findFaceGetPulse(bpm_limits = [50,160],
data_spike_limit = 2500.,
face_detector_smoothness = 10.)
#Init parameters for the cardiac data plot
self.bpm_plot = False
self.plot_title = "Cardiac info - raw signal, filtered signal, and PSD"
#Maps keystrokes to specified methods
#(A GUI window must have focus for these to work)
self.key_controls = {"s" : self.toggle_search,
"d" : self.toggle_display_plot,
"f" : self.write_csv}
def write_csv(self):
"""
Writes current data to a csv file
"""
bpm = " " + str(int(self.processor.measure_heart.bpm))
fn = str(datetime.datetime.now()).split(".")[0] + bpm + " BPM.csv"
data = np.array([self.processor.fft.times,
self.processor.fft.samples]).T
np.savetxt(fn, data, delimiter=',')
def toggle_search(self):
"""
Toggles a motion lock on the processor's face detection component.
Locking the forehead location in place significantly improves
data quality, once a forehead has been sucessfully isolated.
"""
state = self.processor.find_faces.toggle()
if not state:
self.processor.fft.reset()
print "face detection lock =",not state
def toggle_display_plot(self):
"""
Toggles the data display.
"""
if self.bpm_plot:
print "bpm plot disabled"
self.bpm_plot = False
destroyWindow(self.plot_title)
else:
print "bpm plot enabled"
self.bpm_plot = True
self.make_bpm_plot()
moveWindow(self.plot_title, self.w,0)
def key_handler(self):
"""
Handle keystrokes, as set at the bottom of __init__()
A plotting or camera frame window must have focus for keypresses to be
detected.
"""
global line2
self.pressed = waitKey(10) & 255 #wait for keypress for 10 ms
if self.pressed == 27 and self.processor.fft.ready == True: #exit program on 'esc'
print "exiting..."
self.camera.cam.release()
#a = sum(self.processor.fft.samples)/float(len(self.processor.fft.samples))
print >>f, str(self.processor.show_bpm_text.bpm)
print >>f, str(line2)
#f.write(str(a) + "\n")
#f.write(str(line2) + "\n")
exit()
for key in self.key_controls.keys():
if chr(self.pressed) == key:
self.key_controls[key]()
def main_loop(self):
"""
Single iteration of the application's main loop.
"""
# Get current image frame from the camera
frame = self.camera.get_frame()
self.h,self.w,_c = frame.shape
#display unaltered frame
#imshow("Original",frame)
#set current image frame to the processor's input
self.processor.frame_in = frame
#process the image frame to perform all needed analysis
self.processor.run()
#collect the output frame for display
output_frame = self.processor.frame_out
#show the processed/annotated output frame
imshow("Processed",output_frame)
#create and/or update the raw data display if needed
#if self.bpm_plot:
#self.make_bpm_plot()
#print(self.processor.fft.samples)
if(self.processor.fft.ready):
print "Ready to hit escape"
else:
print".",
img = cv.QueryFrame(capture)
if img:
image = DetectRedEyes(img, faceCascade)
cv.ShowImage("camera", image)
#handle any key presses
self.key_handler()