def __init__(self):
self._windowManager = WindowManager('benFinder',
self.onKeypress)
device = depth.CV_CAP_FREENECT
#device = 1
print "device=%d" % device
self._captureManager = CaptureManager(
device, self._windowManager, True)
self._captureManager.channel = depth.CV_CAP_OPENNI_BGR_IMAGE
self._faceTracker = FaceTracker()
self._shouldDrawDebugRects = False
self._backgroundSubtract = False
self._autoBackgroundSubtract = False
self._curveFilter = filters.BGRPortraCurveFilter()
self.background_video_img = None
self.background_depth_img = None
self.autoBackgroundImg = None
self._ts = TimeSeries()
self._frameCount = 0
def __init__(self, save=False, inFile=None):
print "benFinder.__init__()"
print os.path.realpath(__file__)
configPath = "%s/%s" % (os.path.dirname(
os.path.realpath(__file__)), self.configFname)
print configPath
self.cfg = ConfigUtil(configPath, self.configSection)
self.debug = self.cfg.getConfigBool("debug")
if (self.debug): print "Debug Mode"
self._wkdir = self.cfg.getConfigStr("working_directory")
if (self.debug): print "working_directory=%s\n" % self._wkdir
self._tmpdir = self.cfg.getConfigStr("tmpdir")
if (self.debug): print "tmpdir=%s\n" % self._tmpdir
# Check if we are running from live kinect or a file.
if (inFile):
device = depth.CV_CAP_FILE
else:
device = depth.CV_CAP_FREENECT
# Initialise the captureManager
self._captureManager = CaptureManager(device,
None,
True,
inFile=inFile)
self._captureManager.channel = depth.CV_CAP_OPENNI_DEPTH_MAP
# If we are runnign from a file, use the first frame as the
# background image.
if (inFile):
self.saveBgImg()
# If we have asked to save the background image, do that, and exit,
# otherwise initialise the seizure detector.
if (save):
self.saveBgImg()
else:
self.loadBgImg()
self.autoBackgroundImg = None
self._status = self.ALARM_STATUS_OK
self._ts = TimeSeries(
tslen=self.cfg.getConfigInt("timeseries_length"))
self._frameCount = 0
self._outputFrameCount = 0
self._nPeaks = 0
self._ts_time = 0
self._rate = 0
self._ws = webServer.benWebServer(self)
self._ws.setBgImg(
"%s/%s" %
(self._tmpdir, self.cfg.getConfigStr("background_depth")))
self._ws.setChartImg(
"%s/%s" % (self._tmpdir, self.cfg.getConfigStr("chart_fname")))
self._ws.setRawImg(
"%s/%s" %
(self._tmpdir, self.cfg.getConfigStr("raw_image_fname")))
self._ws.setMaskedImg(
"%s/%s" %
(self._tmpdir, self.cfg.getConfigStr("masked_image_fname")))
self._ws.setDataFname(
"%s/%s" % (self._tmpdir, self.cfg.getConfigStr("data_fname")))
self._ws.setAnalysisResults({})
webServer.setRoutes(self._ws)
self.run()
class BenFinder(object):
configFname = "config.ini"
configSection = "benFinder"
ALARM_STATUS_OK = 0 # All ok, no alarms.
ALARM_STATUS_WARN = 1 # Warning status
ALARM_STATUS_FULL = 2 # Full alarm status.
ALARM_STATUS_NOT_FOUND = 3 # Benjamin not found in image
# (area below config area_threshold parameter)
def __init__(self, save=False, inFile=None):
print "benFinder.__init__()"
print os.path.realpath(__file__)
configPath = "%s/%s" % (os.path.dirname(
os.path.realpath(__file__)), self.configFname)
print configPath
self.cfg = ConfigUtil(configPath, self.configSection)
self.debug = self.cfg.getConfigBool("debug")
if (self.debug): print "Debug Mode"
self._wkdir = self.cfg.getConfigStr("working_directory")
if (self.debug): print "working_directory=%s\n" % self._wkdir
self._tmpdir = self.cfg.getConfigStr("tmpdir")
if (self.debug): print "tmpdir=%s\n" % self._tmpdir
# Check if we are running from live kinect or a file.
if (inFile):
device = depth.CV_CAP_FILE
else:
device = depth.CV_CAP_FREENECT
# Initialise the captureManager
self._captureManager = CaptureManager(device,
None,
True,
inFile=inFile)
self._captureManager.channel = depth.CV_CAP_OPENNI_DEPTH_MAP
# If we are runnign from a file, use the first frame as the
# background image.
if (inFile):
self.saveBgImg()
# If we have asked to save the background image, do that, and exit,
# otherwise initialise the seizure detector.
if (save):
self.saveBgImg()
else:
self.loadBgImg()
self.autoBackgroundImg = None
self._status = self.ALARM_STATUS_OK
self._ts = TimeSeries(
tslen=self.cfg.getConfigInt("timeseries_length"))
self._frameCount = 0
self._outputFrameCount = 0
self._nPeaks = 0
self._ts_time = 0
self._rate = 0
self._ws = webServer.benWebServer(self)
self._ws.setBgImg(
"%s/%s" %
(self._tmpdir, self.cfg.getConfigStr("background_depth")))
self._ws.setChartImg(
"%s/%s" % (self._tmpdir, self.cfg.getConfigStr("chart_fname")))
self._ws.setRawImg(
"%s/%s" %
(self._tmpdir, self.cfg.getConfigStr("raw_image_fname")))
self._ws.setMaskedImg(
"%s/%s" %
(self._tmpdir, self.cfg.getConfigStr("masked_image_fname")))
self._ws.setDataFname(
"%s/%s" % (self._tmpdir, self.cfg.getConfigStr("data_fname")))
self._ws.setAnalysisResults({})
webServer.setRoutes(self._ws)
self.run()
def run(self):
"""Run the main loop."""
while (True):
self._captureManager.enterFrame()
frame = self._captureManager.frame
if frame is not None:
if (self.autoBackgroundImg == None):
self.autoBackgroundImg = numpy.float32(frame)
rawFrame = frame.copy()
# First work out the region of interest by
# subtracting the fixed background image
# to create a mask.
#print frame
#print self._background_depth_img
absDiff = cv2.absdiff(frame, self._background_depth_img)
benMask, maskArea = filters.getBenMask(absDiff, 8)
cv2.accumulateWeighted(frame, self.autoBackgroundImg, 0.05)
# Convert the background image into the same format
# as the main frame.
#bg = self.autoBackgroundImg
bg = cv2.convertScaleAbs(self.autoBackgroundImg, alpha=1.0)
# Subtract the background from the frame image
cv2.absdiff(frame, bg, frame)
# Scale the difference image to make it more sensitive
# to changes.
cv2.convertScaleAbs(frame, frame, alpha=100)
# Apply the mask so we only see the test subject.
frame = cv2.multiply(frame, benMask, dst=frame, dtype=-1)
if (maskArea <= self.cfg.getConfigInt('area_threshold')):
bri = (0, 0, 0)
else:
# Calculate the brightness of the test subject.
bri = filters.getMean(frame, benMask)
# Add the brightness to the time series ready for analysis.
self._ts.addSamp(bri[0])
self._ts.addImg(rawFrame)
# Write timeseries to a file every 'output_framecount' frames.
if (self._outputFrameCount >=
self.cfg.getConfigInt('output_framecount')):
# Write timeseries to file
self._ts.writeToFile("%s/%s" % \
( self.cfg.getConfigStr('output_directory'),
self.cfg.getConfigStr('ts_fname')
))
self._outputFrameCount = 0
else:
self._outputFrameCount = self._outputFrameCount + 1
# Only do the analysis every 15 frames (0.5 sec), or whatever
# is specified in configuration file analysis_framecount
# parameter.
if (self._frameCount <
self.cfg.getConfigInt('analysis_framecount')):
self._frameCount = self._frameCount + 1
else:
# Look for peaks in the brightness (=movement).
self._nPeaks, self._ts_time, self._rate = self._ts.findPeaks(
)
#print "%d peaks in %3.2f sec = %3.1f bpm" % \
# (nPeaks,ts_time,rate)
oldStatus = self._status
if (maskArea > self.cfg.getConfigInt('area_threshold')):
# Check for alarm levels
if (self._rate > self.cfg.getConfigInt("rate_warn")):
self._status = self.ALARM_STATUS_OK
elif (self._rate >
self.cfg.getConfigInt("rate_alarm")):
self._status = self.ALARM_STATUS_WARN
else:
self._status = self.ALARM_STATUS_FULL
else:
self._status = self.ALARM_STATUS_NOT_FOUND
if (oldStatus == self.ALARM_STATUS_OK and
self._status == self.ALARM_STATUS_WARN) or \
(oldStatus == self.ALARM_STATUS_WARN and
self._status == self.ALARM_STATUS_FULL):
# Write timeseries to file
self._ts.writeToFile("%s/%s" % \
( self.cfg.getConfigStr('output_directory'),
self.cfg.getConfigStr('alarm_ts_fname')
),bgImg=self._background_depth_img)
# Collect the analysis results together and send them
# to the web server.
resultsDict = {}
resultsDict['fps'] = "%3.0f" % self.fps
resultsDict['bri'] = "%4.0f" % self._ts.mean
resultsDict['area'] = "%6.0f" % maskArea
resultsDict['nPeaks'] = "%d" % self._nPeaks
resultsDict['ts_time'] = self._ts_time
resultsDict['rate'] = "%d" % self._rate
resultsDict['time_t'] = time.ctime()
resultsDict['status'] = self._status
self._ws.setAnalysisResults(resultsDict)
# Write the results to file as a json string
utils.writeJSON(resultsDict,"%s/%s" % \
(self._tmpdir,
self.cfg.getConfigStr("data_fname")))
utils.writeLog(resultsDict,"%s/%s" % \
(self._tmpdir,
"benFinder_alarms.log"))
# Plot the graph of brightness, and save the images
# to disk.
self._ts.plotRawData(
file=True,
fname="%s/%s" % \
(self._tmpdir,self.cfg.getConfigStr("chart_fname")))
cv2.imwrite(
"%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("raw_image_fname")),
rawFrame)
cv2.imwrite(
"%s/%s" %
(self._tmpdir,
self.cfg.getConfigStr("masked_image_fname")), frame)
self._frameCount = 0
else:
print "Null frame received - assuming end of file and exiting"
break
self._captureManager.exitFrame()
@property
def fps(self):
return self._captureManager.fps
@property
def nPeaks(self):
return self._nPeaks
@property
def ts_time(self):
return self._ts_time
@property
def rate(self):
return self._rate
@property
def rawImgFname(self):
return self.cfg.getConfigStr("raw_image_fname")
@property
def maskedImgFname(self):
return self.cfg.getConfigStr("masked_image_fname")
@property
def chartImgFname(self):
return self.cfg.getConfigStr("chart_fname")
def saveBgImg(self):
""" Write a new background image to the appropriate file location."""
if (self._captureManager.hasEnteredFrame):
self._captureManager.exitFrame()
self._captureManager.enterFrame()
print "Writing image to %s." % self.cfg.getConfigStr(
"background_depth")
self._captureManager.writeImage(
"%s/%s" % (self._wkdir, self.cfg.getConfigStr("background_depth")))
print self._captureManager.frame
print self._captureManager.frame.dtype
self._captureManager.exitFrame()
self.loadBgImg()
def loadBgImg(self):
print "Loading background image %s/%s." % \
(self._wkdir,self.cfg.getConfigStr("background_depth"))
self._background_depth_img = cv2.imread("%s/%s" % \
(self._wkdir,self.cfg.getConfigStr("background_depth")),
cv2.CV_LOAD_IMAGE_GRAYSCALE)
# cv2.CV_LOAD_IMAGE_UNCHANGED)
print self._background_depth_img
print self._background_depth_img.dtype
class BenFinder(object):
BACKGROUND_VIDEO_FNAME = "background_video.png"
BACKGROUND_DEPTH_FNAME = "background_depth.png"
def __init__(self):
self._windowManager = WindowManager('benFinder',
self.onKeypress)
device = depth.CV_CAP_FREENECT
#device = 1
print "device=%d" % device
self._captureManager = CaptureManager(
device, self._windowManager, True)
self._captureManager.channel = depth.CV_CAP_OPENNI_BGR_IMAGE
self._faceTracker = FaceTracker()
self._shouldDrawDebugRects = False
self._backgroundSubtract = False
self._autoBackgroundSubtract = False
self._curveFilter = filters.BGRPortraCurveFilter()
self.background_video_img = None
self.background_depth_img = None
self.autoBackgroundImg = None
self._ts = TimeSeries()
self._frameCount = 0
def loadBackgroundImages(self):
""" Load the background images to be used for background subtraction
from disk files.
"""
self.background_video_img = cv2.imread(BenFinder.BACKGROUND_VIDEO_FNAME)
self.background_depth_img = cv2.imread(BenFinder.BACKGROUND_DEPTH_FNAME,
cv2.CV_LOAD_IMAGE_GRAYSCALE)
def showBackgroundImage(self):
""" Display the background image used for subtraction in a separate window
"""
# Load the images from disk if necessary.
if (not self.background_depth_img or not self.background_video_img):
self.loadBackgroundImages()
# Display the correct image
if (self._autoBackgroundSubtract):
cv2.imshow("Auto Background Image", self.autoBackgroundImg)
else:
if (self._captureManager.channel == \
depth.CV_CAP_OPENNI_DEPTH_MAP):
cv2.imshow("background_depth_img",self.background_depth_img)
elif (self._captureManager.channel == \
depth.CV_CAP_OPENNI_BGR_IMAGE):
cv2.imshow("background_video_img",self.background_video_img)
else:
print "Error - Invalid Channel %d." % \
self._captureManager.channel
def run(self):
"""Run the main loop."""
self._windowManager.createWindow()
while self._windowManager.isWindowCreated:
self._captureManager.enterFrame()
frame = self._captureManager.frame
if frame is not None:
if (self._backgroundSubtract):
if (self._autoBackgroundSubtract):
if (self._captureManager.channel == \
depth.CV_CAP_OPENNI_DEPTH_MAP):
if (self.autoBackgroundImg == None):
self.autoBackgroundImg = numpy.float32(frame)
# First work out the region of interest by
# subtracting the fixed background image
# to create a mask.
absDiff = cv2.absdiff(frame,self.background_depth_img)
benMask,maskArea = filters.getBenMask(absDiff,8)
cv2.accumulateWeighted(frame,
self.autoBackgroundImg,
0.05)
# Convert the background image into the same format
# as the main frame.
bg = cv2.convertScaleAbs(self.autoBackgroundImg,
alpha=1.0)
# Subtract the background from the frame image
cv2.absdiff(frame,bg,frame)
# Scale the difference image to make it more sensitive
# to changes.
cv2.convertScaleAbs(frame,frame,alpha=100)
#frame = cv2.bitwise_and(frame,frame,dst=frame,mask=benMask)
frame = cv2.multiply(frame,benMask,dst=frame,dtype=-1)
bri = filters.getMean(frame,benMask)
#print "%4.0f, %3.0f" % (bri[0],self._captureManager.fps)
self._ts.addSamp(bri[0])
if (self._frameCount < 15):
self._frameCount = self._frameCount +1
else:
self._ts.plotRawData()
self._ts.findPeaks()
self._frameCount = 0
else:
print "Auto background subtract only works for depth images!"
else:
if (self._captureManager.channel == \
depth.CV_CAP_OPENNI_DEPTH_MAP):
cv2.absdiff(frame,self.background_depth_img,frame)
benMask = filters.getBenMask(frame,8)
bri = filters.getMean(frame,benMask)
print bri
elif (self._captureManager.channel == \
depth.CV_CAP_OPENNI_BGR_IMAGE):
cv2.absdiff(frame,self.background_video_img,frame)
else:
print "Error - Invalid Channel %d." % \
self._captureManager.channel
#ret,frame = cv2.threshold(frame,200,255,cv2.THRESH_TOZERO)
#self._faceTracker.update(frame)
#faces = self._faceTracker.faces
#if self._shouldDrawDebugRects:
# self._faceTracker.drawDebugRects(frame)
self._captureManager.exitFrame()
self._windowManager.processEvents()
def onKeypress(self, keycode):
"""Handle a keypress.
space -> Take a screenshot.
tab -> Start/stop recording a screencast.
x -> Start/stop drawing debug rectangles around faces.
a -> toggle automatic accumulated background subtraction on or off.
b -> toggle simple background subtraction on or off.
s -> Save current frame as background image.
d -> Toggle between video and depth map view
i -> Display the background image that is being used for subtraction.
escape -> Quit.
"""
print "keycode=%d" % keycode
if keycode == 32: # space
self._captureManager.writeImage('screenshot.png')
elif keycode == 9: # tab
if not self._captureManager.isWritingVideo:
print "Starting Video Recording..."
self._captureManager.startWritingVideo(
'screencast.avi')
else:
print "Stopping video recording"
self._captureManager.stopWritingVideo()
elif keycode == 120: # x
self._shouldDrawDebugRects = \
not self._shouldDrawDebugRects
elif (chr(keycode)=='a'): # Autometic background subtraction
if (self._autoBackgroundSubtract == True):
print "Switching off auto background Subtraction"
self.autoBackgroundImage = None
self._autoBackgroundSubtract = False
else:
print "Switching on auto background subtraction"
self._autoBackgroundSubtract = True
elif (chr(keycode)=='b'): # Simple background subtraction
if (self._backgroundSubtract == True):
print "Switching off background Subtraction"
self._backgroundSubtract = False
else:
print "Switching on background subtraction"
self.loadBackgroundImages()
self._backgroundSubtract = True
elif (chr(keycode)=='d'):
if (self._captureManager.channel == depth.CV_CAP_OPENNI_BGR_IMAGE):
print "switching to depth map..."
self._captureManager.channel = depth.CV_CAP_OPENNI_DEPTH_MAP
else:
print "switching to video"
self._captureManager.channel = depth.CV_CAP_OPENNI_BGR_IMAGE
elif (chr(keycode)=='i'):
self.showBackgroundImage()
elif (chr(keycode)=='s'):
print "Saving Background Image"
if (self._captureManager.channel == depth.CV_CAP_OPENNI_DEPTH_MAP):
self._captureManager.writeImage(BenFinder.BACKGROUND_DEPTH_FNAME)
elif (self._captureManager.channel == depth.CV_CAP_OPENNI_BGR_IMAGE):
self._captureManager.writeImage(BenFinder.BACKGROUND_VIDEO_FNAME)
else:
print "Invalid Channel %d - doing nothing!" \
% self._captureManager.channel
elif keycode == 27: # escape
self._windowManager.destroyWindow()
def __init__(self,save=False, inFile = None):
print "benFinder.__init__()"
print os.path.realpath(__file__)
configPath = "%s/%s" % (os.path.dirname(os.path.realpath(__file__)),
self.configFname)
print configPath
self.cfg = ConfigUtil(configPath,self.configSection)
self.debug = self.cfg.getConfigBool("debug")
if (self.debug): print "Debug Mode"
self._wkdir = self.cfg.getConfigStr("working_directory")
if (self.debug): print "working_directory=%s\n" % self._wkdir
self._tmpdir = self.cfg.getConfigStr("tmpdir")
if (self.debug): print "tmpdir=%s\n" % self._tmpdir
# Check if we are running from live kinect or a file.
if (inFile):
device = depth.CV_CAP_FILE
else:
device = depth.CV_CAP_FREENECT
# Initialise the captureManager
self._captureManager = CaptureManager(
device, None, True, inFile=inFile)
self._captureManager.channel = depth.CV_CAP_OPENNI_DEPTH_MAP
# If we are runnign from a file, use the first frame as the
# background image.
if (inFile):
self.saveBgImg()
# If we have asked to save the background image, do that, and exit,
# otherwise initialise the seizure detector.
if (save):
self.saveBgImg()
else:
self.loadBgImg()
self.autoBackgroundImg = None
self._status = self.ALARM_STATUS_OK
self._ts = TimeSeries(tslen=self.cfg.getConfigInt("timeseries_length"))
self._frameCount = 0
self._outputFrameCount = 0
self._nPeaks = 0
self._ts_time = 0
self._rate = 0
self._ws = webServer.benWebServer(self)
self._ws.setBgImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("background_depth")))
self._ws.setChartImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("chart_fname")))
self._ws.setRawImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("raw_image_fname")))
self._ws.setMaskedImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("masked_image_fname")))
self._ws.setDataFname("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("data_fname")))
self._ws.setAnalysisResults({})
webServer.setRoutes(self._ws)
self.run()
class BenFinder(object):
configFname = "config.ini"
configSection = "benFinder"
ALARM_STATUS_OK = 0 # All ok, no alarms.
ALARM_STATUS_WARN = 1 # Warning status
ALARM_STATUS_FULL = 2 # Full alarm status.
ALARM_STATUS_NOT_FOUND = 3 # Benjamin not found in image
# (area below config area_threshold parameter)
def __init__(self,save=False, inFile = None):
print "benFinder.__init__()"
print os.path.realpath(__file__)
configPath = "%s/%s" % (os.path.dirname(os.path.realpath(__file__)),
self.configFname)
print configPath
self.cfg = ConfigUtil(configPath,self.configSection)
self.debug = self.cfg.getConfigBool("debug")
if (self.debug): print "Debug Mode"
self._wkdir = self.cfg.getConfigStr("working_directory")
if (self.debug): print "working_directory=%s\n" % self._wkdir
self._tmpdir = self.cfg.getConfigStr("tmpdir")
if (self.debug): print "tmpdir=%s\n" % self._tmpdir
# Check if we are running from live kinect or a file.
if (inFile):
device = depth.CV_CAP_FILE
else:
device = depth.CV_CAP_FREENECT
# Initialise the captureManager
self._captureManager = CaptureManager(
device, None, True, inFile=inFile)
self._captureManager.channel = depth.CV_CAP_OPENNI_DEPTH_MAP
# If we are runnign from a file, use the first frame as the
# background image.
if (inFile):
self.saveBgImg()
# If we have asked to save the background image, do that, and exit,
# otherwise initialise the seizure detector.
if (save):
self.saveBgImg()
else:
self.loadBgImg()
self.autoBackgroundImg = None
self._status = self.ALARM_STATUS_OK
self._ts = TimeSeries(tslen=self.cfg.getConfigInt("timeseries_length"))
self._frameCount = 0
self._outputFrameCount = 0
self._nPeaks = 0
self._ts_time = 0
self._rate = 0
self._ws = webServer.benWebServer(self)
self._ws.setBgImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("background_depth")))
self._ws.setChartImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("chart_fname")))
self._ws.setRawImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("raw_image_fname")))
self._ws.setMaskedImg("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("masked_image_fname")))
self._ws.setDataFname("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr("data_fname")))
self._ws.setAnalysisResults({})
webServer.setRoutes(self._ws)
self.run()
def run(self):
"""Run the main loop."""
while(True):
self._captureManager.enterFrame()
frame = self._captureManager.frame
if frame is not None:
if (self.autoBackgroundImg == None):
self.autoBackgroundImg = numpy.float32(frame)
rawFrame = frame.copy()
# First work out the region of interest by
# subtracting the fixed background image
# to create a mask.
#print frame
#print self._background_depth_img
absDiff = cv2.absdiff(frame,self._background_depth_img)
benMask,maskArea = filters.getBenMask(absDiff,8)
cv2.accumulateWeighted(frame,
self.autoBackgroundImg,0.05)
# Convert the background image into the same format
# as the main frame.
#bg = self.autoBackgroundImg
bg = cv2.convertScaleAbs(self.autoBackgroundImg,
alpha=1.0)
# Subtract the background from the frame image
cv2.absdiff(frame,bg,frame)
# Scale the difference image to make it more sensitive
# to changes.
cv2.convertScaleAbs(frame,frame,alpha=100)
# Apply the mask so we only see the test subject.
frame = cv2.multiply(frame,benMask,dst=frame,dtype=-1)
if (maskArea <= self.cfg.getConfigInt('area_threshold')):
bri=(0,0,0)
else:
# Calculate the brightness of the test subject.
bri = filters.getMean(frame,benMask)
# Add the brightness to the time series ready for analysis.
self._ts.addSamp(bri[0])
self._ts.addImg(rawFrame)
# Write timeseries to a file every 'output_framecount' frames.
if (self._outputFrameCount >= self.cfg.getConfigInt('output_framecount')):
# Write timeseries to file
self._ts.writeToFile("%s/%s" % \
( self.cfg.getConfigStr('output_directory'),
self.cfg.getConfigStr('ts_fname')
))
self._outputFrameCount = 0
else:
self._outputFrameCount = self._outputFrameCount + 1
# Only do the analysis every 15 frames (0.5 sec), or whatever
# is specified in configuration file analysis_framecount
# parameter.
if (self._frameCount < self.cfg.getConfigInt('analysis_framecount')):
self._frameCount = self._frameCount +1
else:
# Look for peaks in the brightness (=movement).
self._nPeaks,self._ts_time,self._rate = self._ts.findPeaks()
#print "%d peaks in %3.2f sec = %3.1f bpm" % \
# (nPeaks,ts_time,rate)
oldStatus = self._status
if (maskArea > self.cfg.getConfigInt('area_threshold')):
# Check for alarm levels
if (self._rate > self.cfg.getConfigInt(
"rate_warn")):
self._status= self.ALARM_STATUS_OK
elif (self._rate > self.cfg.getConfigInt(
"rate_alarm")):
self._status= self.ALARM_STATUS_WARN
else:
self._status= self.ALARM_STATUS_FULL
else:
self._status = self.ALARM_STATUS_NOT_FOUND
if (oldStatus == self.ALARM_STATUS_OK and
self._status == self.ALARM_STATUS_WARN) or \
(oldStatus == self.ALARM_STATUS_WARN and
self._status == self.ALARM_STATUS_FULL):
# Write timeseries to file
self._ts.writeToFile("%s/%s" % \
( self.cfg.getConfigStr('output_directory'),
self.cfg.getConfigStr('alarm_ts_fname')
),bgImg=self._background_depth_img)
# Collect the analysis results together and send them
# to the web server.
resultsDict = {}
resultsDict['fps'] = "%3.0f" % self.fps
resultsDict['bri'] = "%4.0f" % self._ts.mean
resultsDict['area'] = "%6.0f" % maskArea
resultsDict['nPeaks'] = "%d" % self._nPeaks
resultsDict['ts_time'] = self._ts_time
resultsDict['rate'] = "%d" % self._rate
resultsDict['time_t'] = time.ctime()
resultsDict['status'] = self._status
self._ws.setAnalysisResults(resultsDict)
# Write the results to file as a json string
utils.writeJSON(resultsDict,"%s/%s" % \
(self._tmpdir,
self.cfg.getConfigStr("data_fname")))
utils.writeLog(resultsDict,"%s/%s" % \
(self._tmpdir,
"benFinder_alarms.log"))
# Plot the graph of brightness, and save the images
# to disk.
self._ts.plotRawData(
file=True,
fname="%s/%s" % \
(self._tmpdir,self.cfg.getConfigStr("chart_fname")))
cv2.imwrite("%s/%s" % (self._tmpdir,
self.cfg.getConfigStr(
"raw_image_fname")),
rawFrame)
cv2.imwrite("%s/%s" % (self._tmpdir,self.cfg.getConfigStr(
"masked_image_fname")),
frame)
self._frameCount = 0
else:
print "Null frame received - assuming end of file and exiting"
break
self._captureManager.exitFrame()
@property
def fps(self):
return self._captureManager.fps
@property
def nPeaks(self):
return self._nPeaks
@property
def ts_time(self):
return self._ts_time
@property
def rate(self):
return self._rate
@property
def rawImgFname(self):
return self.cfg.getConfigStr("raw_image_fname")
@property
def maskedImgFname(self):
return self.cfg.getConfigStr("masked_image_fname")
@property
def chartImgFname(self):
return self.cfg.getConfigStr("chart_fname")
def saveBgImg(self):
""" Write a new background image to the appropriate file location."""
if (self._captureManager.hasEnteredFrame):
self._captureManager.exitFrame()
self._captureManager.enterFrame()
print "Writing image to %s." % self.cfg.getConfigStr("background_depth")
self._captureManager.writeImage("%s/%s" %
(self._wkdir,
self.cfg.getConfigStr("background_depth")
))
print self._captureManager.frame
print self._captureManager.frame.dtype
self._captureManager.exitFrame()
self.loadBgImg()
def loadBgImg(self):
print "Loading background image %s/%s." % \
(self._wkdir,self.cfg.getConfigStr("background_depth"))
self._background_depth_img = cv2.imread("%s/%s" % \
(self._wkdir,self.cfg.getConfigStr("background_depth")),
cv2.CV_LOAD_IMAGE_GRAYSCALE)
# cv2.CV_LOAD_IMAGE_UNCHANGED)
print self._background_depth_img
print self._background_depth_img.dtype