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):
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):
#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, ip, user, password):
#Imaging device - must be a connected camera (not an ip camera or mjpeg
#stream)
# self.camera = ipCamera(ip,
# user=user,
# password=password)
self.camera = VideoFile(
'C:\Users\Ashok Fernandez\Desktop\PulseDetector\Bill_Clinton.avi')
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=25.,
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, filename=(sys.argv[1] if len(sys.argv) > 1 else None)):
if filename is None:
raise Exception("Filename unset")
self.camera = VideoStream(filename=filename) # first camera by default
self.bpms = []
# 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=2500.0, face_detector_smoothness=10.0)
while self.main_loop():
sys.stderr.write(". ")
sys.stderr.write("\n\n")
def __init__(self,filename=(sys.argv[1] if len(sys.argv) > 1 else None)):
if (filename is None):
raise Exception("Filename unset")
self.camera = VideoStream(filename=filename) #first camera by default
self.bpms = []
#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.)
while (self.main_loop()):
sys.stderr.write(". ")
sys.stderr.write("\n\n")
from lib.processors import findFaceGetPulse
import networkx as nx
"""
Simple tool to visualize the design of the real-time image analysis
Everything needed to produce the graph already exists in an instance of the
assembly.
"""
#get the component/data dependancy graph (depgraph) of the assembly
assembly = findFaceGetPulse()
graph = assembly._depgraph._graph
#prune a few unconnected nodes not related to the actual analysis
graph.remove_node("@xin")
graph.remove_node("@xout")
graph.remove_node("driver")
#plot the graph to disc as a png image
ag = nx.to_agraph(graph)
ag.layout('dot')
ag.draw('design.png')
from lib.processors import findFaceGetPulse
import cv2
cam = cv2.VideoCapture(0)
image,frame = cam.read()
cam.release()
proc = findFaceGetPulse(bpm_limits = [50,160],
data_spike_limit = 2500.,
face_detector_smoothness = 10.)
proc.frame_in = frame
proc.run()
output_frame = proc.frame_out
print output_frame
exit
