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 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()
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()