def __init__(self,
bpm_limits = [50,160],
data_spike_limit = 13.,
face_detector_smoothness = 10):
super(findFaceGetPulse, self).__init__()
#-----------assembly-level I/O-----------
#input array
self.add("frame_in", Array(iotype="in"))
#output array
self.add("frame_out", Array(iotype="out"))
#array of detected faces (as single frame)
self.add("faces", Array(iotype="out"))
#-----------components-----------
# Each component we want to use must be added to the assembly, then also
# added to the driver's workflow
#splits input color image into R,G,B channels
self.add("RGBsplitter", RGBSplit())
self.driver.workflow.add("RGBsplitter")
#converts input color image to grayscale
self.add("grayscale", Grayscale())
self.driver.workflow.add("grayscale")
#equalizes contast on the grayscale'd input image
self.add("contrast_eq", equalizeContrast())
self.driver.workflow.add("contrast_eq")
#finds faces within the grayscale's and contast-adjusted input image
#Sets smoothness parameter to help prevent 'jitteriness' in the face tracking
self.add("find_faces", faceDetector(smooth = face_detector_smoothness))
self.driver.workflow.add("find_faces")
#collects subimage samples of the detected faces
self.add("grab_faces", frameSlices())
self.driver.workflow.add("grab_faces")
#collects subimage samples of the detected foreheads
self.add("grab_foreheads", frameSlices())
self.driver.workflow.add("grab_foreheads")
#highlights the locations of detected faces using contrast equalization
self.add("highlight_faces", VariableEqualizerBlock(channels=[0,1,2]))
self.driver.workflow.add("highlight_faces")
#highlights the locations of detected foreheads using
#contrast equalization (green channel only)
self.add("highlight_fhd", VariableEqualizerBlock(channels=[1],
zerochannels=[0,2]))
self.driver.workflow.add("highlight_fhd")
#collects data over time to compute a 1d temporal FFT
# 'n' sets the internal buffer length (number of samples)
# 'spike_limit' limits the size of acceptable spikes in the raw measured
# data. When exceeeded due to poor data, the fft component's buffers
# are reset
self.add("fft", BufferFFT(n=425,
spike_limit = data_spike_limit))
self.driver.workflow.add("fft")
#takes in a computed FFT and estimates cardiac data
# 'bpm_limits' sets the lower and upper limits (in bpm) for heartbeat
# detection. 50 to 160 bpm is a pretty fair range here.
self.add("measure_heart", Cardiac(bpm_limits = bpm_limits))
self.driver.workflow.add("measure_heart")
#toggles flashing of the detected foreheads in sync with the detected
#heartbeat. the 'default_a' and 'default_b' set the nominal contrast
#correction that will occur when phase pulsing isn't enabled.
#Pulsing is set by toggling the boolean variable 'state'.
self.add("bpm_flasher", PhaseController(default_a=1.,
default_b=0.,
state = True))
self.driver.workflow.add("bpm_flasher")
self.add("show_bpm_text", showBPMtext())
self.driver.workflow.add("show_bpm_text")
#-----------connections-----------
# here is where we establish the relationships between the components
# that were added above.
#--First, set up the connectivity for components that will do basic
#--input, decomposition, and annotation of the inputted image frame
# pass image frames from the assembly-level input arrays to the RGB
# splitter & grayscale converters (separately)
self.connect("frame_in", "RGBsplitter.frame_in")
self.connect("frame_in", "grayscale.frame_in")
#pass grayscaled image to the contrast equalizer
self.connect("grayscale.frame_out", "contrast_eq.frame_in")
#pass the contrast adjusted grayscale image to the face detector
self.connect("contrast_eq.frame_out", "find_faces.frame_in")
# now pass our original image frame and the detected faces locations
# to the face highlighter
self.connect("frame_in", "highlight_faces.frame_in")
self.connect("find_faces.detected", "highlight_faces.rects_in")
# pass the original image frame and detected face locations
# to the forehead highlighter
self.connect("highlight_faces.frame_out", "highlight_fhd.frame_in")
self.connect("find_faces.foreheads", "highlight_fhd.rects_in")
# pass the original image frame and detected face locations
# to the face subimage collector
self.connect("find_faces.detected", "grab_faces.rects_in")
self.connect("contrast_eq.frame_out", "grab_faces.frame_in")
# --Now we set the connectivity for the components that will do the
# --actual analysis
#pass the green channel of the original image frame and detected
#face locations to the forehead subimage collector
self.connect("find_faces.foreheads", "grab_foreheads.rects_in")
self.connect("RGBsplitter.G", "grab_foreheads.frame_in")
#send the mean of the first detected forehead subimage (green channel)
#to the buffering FFT component
#Should probably be an intermediate component here, but that isn't
#actually necessary - we can do a connection between expressions in
#addition to input/output variables.
#self.connect("grab_foreheads.slices[0]", "fft.data_in")
self.connect("grab_foreheads.zero_mean", "fft.data_in")
#Send the FFT outputs (the fft & associated freqs in hz) to the cardiac
#data estimator
self.connect("fft.fft", "measure_heart.fft_in")
self.connect("fft.freqs", "measure_heart.freqs_in")
#connect the estimated heartbeat phase to the forehead flashing controller
self.connect("measure_heart.phase", "bpm_flasher.phase")
self.connect("fft.ready", "bpm_flasher.state")
#connect the flash controller to the forehead highlighter
self.connect("bpm_flasher.alpha", "highlight_fhd.alpha")
self.connect("bpm_flasher.beta", "highlight_fhd.beta")
#connect collection of all detected faces up to assembly level for output
self.connect("grab_faces.combined", "faces")
# text display of estimated bpm
self.connect("highlight_fhd.frame_out", "show_bpm_text.frame_in")
self.connect("measure_heart.bpm", "show_bpm_text.bpm")
self.connect("find_faces.detected[0][0]", "show_bpm_text.x")
self.connect("find_faces.detected[0][1]", "show_bpm_text.y")
self.connect("fft.fps", "show_bpm_text.fps")
self.connect("fft.size", "show_bpm_text.size")
self.connect("fft.n", "show_bpm_text.n")
self.connect("fft.ready", "show_bpm_text.ready")
self.connect("show_bpm_text.frame_out", "frame_out")
def __init__(self,
bpm_limits=[50, 160],
data_spike_limit=13.,
face_detector_smoothness=10):
super(findFaceGetPulse, self).__init__()
#-----------assembly-level I/O-----------
#input array
self.add("frame_in", Array(iotype="in"))
#output array
self.add("frame_out", Array(iotype="out"))
#array of detected faces (as single frame)
self.add("faces", Array(iotype="out"))
#-----------components-----------
# Each component we want to use must be added to the assembly, then also
# added to the driver's workflow
#splits input color image into R,G,B channels
self.add("RGBsplitter", RGBSplit())
self.driver.workflow.add("RGBsplitter")
#converts input color image to grayscale
self.add("grayscale", Grayscale())
self.driver.workflow.add("grayscale")
#equalizes contast on the grayscale'd input image
self.add("contrast_eq", equalizeContrast())
self.driver.workflow.add("contrast_eq")
#finds faces within the grayscale's and contast-adjusted input image
#Sets smoothness parameter to help prevent 'jitteriness' in the face tracking
self.add("find_faces", faceDetector(smooth=face_detector_smoothness))
self.driver.workflow.add("find_faces")
#collects subimage samples of the detected faces
self.add("grab_faces", frameSlices())
self.driver.workflow.add("grab_faces")
#collects subimage samples of the detected foreheads
self.add("grab_foreheads", frameSlices())
self.driver.workflow.add("grab_foreheads")
#highlights the locations of detected faces using contrast equalization
self.add("highlight_faces", VariableEqualizerBlock(channels=[0, 1, 2]))
self.driver.workflow.add("highlight_faces")
#highlights the locations of detected foreheads using
#contrast equalization (green channel only)
self.add("highlight_fhd",
VariableEqualizerBlock(channels=[1], zerochannels=[0, 2]))
self.driver.workflow.add("highlight_fhd")
#collects data over time to compute a 1d temporal FFT
# 'n' sets the internal buffer length (number of samples)
# 'spike_limit' limits the size of acceptable spikes in the raw measured
# data. When exceeeded due to poor data, the fft component's buffers
# are reset
self.add("fft", BufferFFT(n=322, spike_limit=data_spike_limit))
self.driver.workflow.add("fft")
#takes in a computed FFT and estimates cardiac data
# 'bpm_limits' sets the lower and upper limits (in bpm) for heartbeat
# detection. 50 to 160 bpm is a pretty fair range here.
self.add("measure_heart", Cardiac(bpm_limits=bpm_limits))
self.driver.workflow.add("measure_heart")
#toggles flashing of the detected foreheads in sync with the detected
#heartbeat. the 'default_a' and 'default_b' set the nominal contrast
#correction that will occur when phase pulsing isn't enabled.
#Pulsing is set by toggling the boolean variable 'state'.
self.add("bpm_flasher",
PhaseController(default_a=1., default_b=0., state=True))
self.driver.workflow.add("bpm_flasher")
self.add("show_bpm_text", showBPMtext())
self.driver.workflow.add("show_bpm_text")
#-----------connections-----------
# here is where we establish the relationships between the components
# that were added above.
#--First, set up the connectivity for components that will do basic
#--input, decomposition, and annotation of the inputted image frame
# pass image frames from the assembly-level input arrays to the RGB
# splitter & grayscale converters (separately)
self.connect("frame_in", "RGBsplitter.frame_in")
self.connect("frame_in", "grayscale.frame_in")
#pass grayscaled image to the contrast equalizer
self.connect("grayscale.frame_out", "contrast_eq.frame_in")
#pass the contrast adjusted grayscale image to the face detector
self.connect("contrast_eq.frame_out", "find_faces.frame_in")
# now pass our original image frame and the detected faces locations
# to the face highlighter
self.connect("frame_in", "highlight_faces.frame_in")
self.connect("find_faces.detected", "highlight_faces.rects_in")
# pass the original image frame and detected face locations
# to the forehead highlighter
self.connect("highlight_faces.frame_out", "highlight_fhd.frame_in")
self.connect("find_faces.foreheads", "highlight_fhd.rects_in")
# pass the original image frame and detected face locations
# to the face subimage collector
self.connect("find_faces.detected", "grab_faces.rects_in")
self.connect("contrast_eq.frame_out", "grab_faces.frame_in")
# --Now we set the connectivity for the components that will do the
# --actual analysis
#pass the green channel of the original image frame and detected
#face locations to the forehead subimage collector
self.connect("find_faces.foreheads", "grab_foreheads.rects_in")
self.connect("RGBsplitter.G", "grab_foreheads.frame_in")
#send the mean of the first detected forehead subimage (green channel)
#to the buffering FFT component
#Should probably be an intermediate component here, but that isn't
#actually necessary - we can do a connection between expressions in
#addition to input/output variables.
self.connect("grab_foreheads.slices[0].mean()", "fft.data_in")
#Send the FFT outputs (the fft & associated freqs in hz) to the cardiac
#data estimator
self.connect("fft.fft", "measure_heart.fft_in")
self.connect("fft.freqs", "measure_heart.freqs_in")
#connect the estimated heartbeat phase to the forehead flashing controller
self.connect("measure_heart.phase", "bpm_flasher.phase")
self.connect("fft.ready", "bpm_flasher.state")
#connect the flash controller to the forehead highlighter
self.connect("bpm_flasher.alpha", "highlight_fhd.alpha")
self.connect("bpm_flasher.beta", "highlight_fhd.beta")
#connect collection of all detected faces up to assembly level for output
self.connect("grab_faces.combined", "faces")
# text display of estimated bpm
self.connect("highlight_fhd.frame_out", "show_bpm_text.frame_in")
self.connect("measure_heart.bpm", "show_bpm_text.bpm")
self.connect("find_faces.detected[0][0]", "show_bpm_text.x")
self.connect("find_faces.detected[0][1]", "show_bpm_text.y")
self.connect("fft.fps", "show_bpm_text.fps")
self.connect("fft.size", "show_bpm_text.size")
self.connect("fft.n", "show_bpm_text.n")
self.connect("fft.ready", "show_bpm_text.ready")
self.connect("show_bpm_text.frame_out", "frame_out")