def handleWBCapture(self, img, thumbImg):
t = FXTimeUtil.getT()
dtString = FXTimeUtil.T2DateString(t)
filename = dtString + ".jpg"
cv2.imwrite(filename, img)
filename = dtString + "_thumb.jpg"
cv2.imwrite(filename, thumbImg)
def movementCheckProcess(self):
interval = self.param['movementCheck']['interval']
while True:
sTime = FXTimeUtil.getT()
img = self.getMovementCheckImg()
if img==None:
continue
imgGray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
if self.movementCheckImg == None:
self.movementCheckImg = imgGray
diffImg = cv2.absdiff(imgGray, self.movementCheckImg)
val = cv2.mean(diffImg)
val = float(val[0])
duration = FXTimeUtil.getT() - sTime
if duration < interval:
time.sleep(interval - duration)
duration = interval
self.movementVal = val
w = duration/1.0
w = min(w, 1.0)
self.movementValMean = w * val + (1.0-w) * self.movementValMean
self.movementCheckImg = imgGray
if self.debugLevel >= 3:
print "movement: %f (mean %f)" % (self.movementVal, self.movementValMean)
if useRoyWeb:
ps.send('movement', self.movementVal, 'v', 'movementVal')
ps.send('movementMean', self.movementValMean, 'v', 'movementValMean')
def audioProcess(self):
t = FXTimeUtil.getT()
audioAngleImg = self.getAudioMapImg()
self.currentAudioAngleImg = audioAngleImg
if self.currentAudioAngleSumImg == None:
self.currentAudioAngleSumImg = self.currentAudioAngleImg.copy()
else:
processInterval = t - self.currentAudioProcessTime
timeRange = 1.0
processInterval = min (processInterval, timeRange)
w = processInterval / timeRange
self.currentAudioAngleSumImg = cv2.addWeighted(self.currentAudioAngleSumImg, (1.0 - w),\
self.currentAudioAngleImg, w, 0)
self.currentAudioProcessTime = t
def _threadProcessObserve(self):
interval = 1.0 #secs
currentT = 0.
while True:
if self.threadFlag == False:
break
t = FXTimeUtil.getT()
diffT = t - currentT
if diffT < interval:
time.sleep(interval - diffT)
self.observeProcess()
cv2.waitKey(1)
currentT = t
print "thread process killed [observe]\n"
def trackProcess(self):
t = FXTimeUtil.getT()
floorImg = self.getFloorImage()
if floorImg == None:
return
viewPort = self.trackInfo['viewPort']
floorImg8UC3, floorImgBin = FXImageUtil.createColorMap(floorImg, 0., viewPort['z']['max'])
noiseReduction = self.trackInfo['noiseReduction']
if noiseReduction > 0:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (noiseReduction, noiseReduction))
floorImgBin = cv2.morphologyEx(floorImgBin, cv2.MORPH_OPEN, kernel)
floorImgBin = cv2.morphologyEx(floorImgBin, cv2.MORPH_CLOSE, kernel)
binDilate = self.trackInfo['binDilate']
if binDilate > 0:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (binDilate, binDilate))
floorImgBin = cv2.dilate(floorImgBin, kernel)
binErode = self.trackInfo['binErode']
if binErode > 0:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (binErode, binErode))
floorImgBin = cv2.erode(floorImgBin, kernel)
floorImgBinOrig = floorImgBin.copy()
contours, hierarchy = cv2.findContours(floorImgBin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
gaussianSize = self.trackInfo['gaussianSize']
if gaussianSize > 0:
floorImgGaussian = cv2.GaussianBlur(floorImg,(gaussianSize,gaussianSize),0)
else:
floorImgGaussian = floorImg.copy()
#floorImg = floorImgGaussian
#floorImgGaussian8UC3 = FXImageUtil.createColorMap(floorImgGaussian, 0., viewPort['z']['max'])[0]
#cv2.imshow("floorImgGaussian", floorImgGaussian8UC3)
######## audioのマップを作成する
self.audioProcess()
########トラッキング処理
trackedData = {'dataId': 'kinectTracker', 't': t}
points = []
for contour in contours:
if len(contour) < 5:
continue #too small
box = cv2.fitEllipse(contour)
#検出したサイズをmmに (minSize, maxSize)
objectSizeMM = (min(box[1]) * self.trackInfo['pixel2mm'], max(box[1]) * self.trackInfo['pixel2mm'])
#print objectSize
#サイズのチェック
if objectSizeMM[0] < self.trackInfo['objectSize']['min']: #小さい方の大きさ
continue
if objectSizeMM[1] > self.trackInfo['objectSize']['max']: #大きい方の大きさ
continue
ellipseImgBin = np.zeros(floorImgBin.shape).astype(np.uint8)
cv2.ellipse(ellipseImgBin, box, (255), -1)
minMaxLoc = cv2.minMaxLoc(floorImgGaussian, mask=ellipseImgBin)
#人物のシルエット
personBinImg = floorImgBin * ellipseImgBin
#人物のシルエットの面積/楕円の面積
areaSize_ellipse = np.count_nonzero(ellipseImgBin)
areaSize_personBin = np.count_nonzero(personBinImg)
areaSizeRate = 0.
if areaSize_ellipse > 0:
areaSizeRate = float(areaSize_personBin) / float(areaSize_ellipse)
#print box #((centerx, centery), (w,h), angle?)
boxCenter = box[0]
object_height = int(minMaxLoc[1])
maxLoc = minMaxLoc[3]
object_mPos = [maxLoc[0], maxLoc[1]]
#object_mPos = [int(boxCenter[0]), int(boxCenter[1])]
object_wPos = self.kinectFloorMap.mapPos2wPos(object_mPos, object_height)
audioScore = 0.
if self.currentAudioAngleSumImg != None:
audioScore = float(self.currentAudioAngleSumImg[object_mPos[1]][object_mPos[0]])
tData = {}
tData['x'] = object_wPos[0]
tData['y'] = object_wPos[1]
tData['z'] = object_wPos[2]
tData['width'] = objectSizeMM[1] #大きい方
tData['height'] = objectSizeMM[0] #小さい方
mData = {}
mData['x'] = object_mPos[0]
mData['y'] = object_mPos[1]
mData['box'] = box
mData['areaSizeRate'] = areaSizeRate
audioData = {}
audioData['score'] = audioScore
#print areaSizeRate
point = {'trackedData': tData, 'trackedMapData': mData, 'audio': audioData}
points.append(point)
trackedData['data'] = points
self.putTrackedIds(self.currentTrackedData, trackedData)
###### visualize
audioAngleSumImg8UC3 = cv2.cvtColor(self.currentAudioAngleSumImg, cv2.COLOR_GRAY2BGR)
floorImgBin8UC3 = cv2.cvtColor(floorImgBinOrig, cv2.COLOR_GRAY2BGR)
#floorImg8UC3に描画
if trackedData.has_key('data'):
points = trackedData['data']
for point in points:
mPos = [point['trackedMapData']['x'], point['trackedMapData']['y']]
box = point['trackedMapData']['box']
cv2.ellipse(floorImg8UC3, box, (0, 0, 255), 1)
cv2.circle(floorImg8UC3, (mPos[0], mPos[1]), 3, (0,0,255), -1)
trackStr = "(" + point['trackedId'] + ")" + str(object_wPos[2])
cv2.putText(floorImg8UC3, trackStr, (mPos[0]+5, mPos[1]), cv2.FONT_HERSHEY_PLAIN, 0.8, (0,0,255))
#cv2.ellipse(audioAngleSumImg8UC3, box, (0, 0, 255), 1)
cv2.circle(audioAngleSumImg8UC3, (mPos[0], mPos[1]), 3, (0,0,255), -1)
cv2.ellipse(floorImgBin8UC3, box, (0, 0, 255), 1)
cv2.circle(floorImgBin8UC3, (mPos[0], mPos[1]), 3, (0,0,255), -1)
cv2.imshow("floorImg", floorImg8UC3)
cv2.imshow("audioAngleImg", audioAngleSumImg8UC3)
cv2.imshow("floorImgBin", floorImgBin8UC3)
self.currentTrackedData = trackedData
def diffCheckProcess(self):
interval = self.param['interval']
while True:
sTime = FXTimeUtil.getT()
if self.movementValMean < self.param['movementCheck']['thr']:
if self.debugLevel >= 2:
print "no movements. diff check"
img = self.getImg()
if img==None:
continue
imgGray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
imgGray = cv2.GaussianBlur(imgGray, (5,5),0)
lapImg = cv2.Laplacian(imgGray, cv2.CV_32F, ksize=3)
#lapImg = lapImg.astype(np.uint8)
#conjunction
#kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5))
#lapImg = cv2.dilate(lapImg, kernel, iterations=2)
#lapImg = cv2.erode(lapImg, kernel, iterations=2)
if self.diffCheckImg == None:
self.diffCheckImg = lapImg
diffImg = cv2.absdiff(lapImg, self.diffCheckImg)
ret, diffImg = cv2.threshold(diffImg, 10, 255, cv2.THRESH_BINARY)
diffImg = diffImg.astype(np.uint8)
#noise reduction
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
diffImg = cv2.erode(diffImg, kernel, iterations=2)
diffImg = cv2.dilate(diffImg, kernel, iterations=2)
diffImg = cv2.bitwise_and(diffImg, self.diffCheckMask)
if self.debugLevel >= 1:
dispImg = diffImg.copy()
dispImg = FXImageUtil.scaleImage(diffImg, 0.25)
cv2.imshow("diffImg", dispImg)
val = cv2.mean(diffImg)
val = float(val[0])
self.diffVal = val
self.diffCheckImg = lapImg
if self.debugLevel >= 2:
print "HD difference val: %f" % self.diffVal
if useRoyWeb:
ps.send('diff', self.diffVal, 'v', 'diffVal')
if self.diffVal > self.param['thr']:
print "difference is detected"
thumbSize = self.param['dstThumb']['size']
thumbImg = cv2.resize(img, (thumbSize[0], thumbSize[1]))
if self.thumbRectFlag:
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (100, 100))
diffImgArea = cv2.dilate(diffImg, kernel)
diffImgArea = cv2.erode(diffImgArea, kernel)
contours, hierarchy = cv2.findContours(diffImgArea, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
#get max rectangle
areas = [cv2.contourArea(contour) for contour in contours]
cnt_max = [contours[areas.index(max(areas))]][0]
rect = cv2.boundingRect(cnt_max)
#draw rectangle
offset = 20
pt1 = (int((rect[0]-offset) * self.thumbScale), int((rect[1]-offset) * self.thumbScale))
pt2 = (int((rect[0]+rect[2]+offset) * self.thumbScale), int((rect[1]+rect[3]+offset) * self.thumbScale))
cv2.rectangle(thumbImg, pt1, pt2, self.thumbRectColor, thickness=self.thumbRectThickness)
self.handleWBCapture(img, thumbImg)
if self.debugLevel >= 1:
dispImg = FXImageUtil.scaleImage(img, 0.25)
cv2.imshow("captured wb", dispImg)
dispImg = FXImageUtil.scaleImage(diffImg, 0.25)
cv2.imshow("captured wb diff", dispImg)
else:
if self.debugLevel >= 2:
print "movement is detected. skip diff check"
duration = FXTimeUtil.getT() - sTime
if duration < interval:
time.sleep(interval - duration)
duration = interval
cv2.waitKey(1)
