def main(pixThreshold, frameRate, videoStream):
expDuration = 600000 # duration of experiment, in seconds; only relevant for live feed
saveFreq = 4500 # how often to save data, in frames
i, m = imageTools.loadImageAndMask()
m, w = imageTools.convertMaskToWeights(m)
videoType, displayDiffs = imageTools.getVideoType(videoStream)
cap = cv2.VideoCapture(videoStream)
print 'Camera resolution is %s x %s' % (str(m.shape[1]), str(m.shape[0]))
cap.set(3, m.shape[1])
cap.set(4, m.shape[0])
ret, frame = cap.read()
storedFrame = imageTools.grayBlur(frame)
pixThreshold = int(np.floor(pixThreshold * storedFrame.shape[0]))
print('PixelThreshold is %i') % pixThreshold
pixData = np.zeros([saveFreq, len(np.unique(w)) + 1])
i = 0 # a counter for saving chunks of data
totalFrames = 0
startTime = datetime.now()
oldTime = startTime
elapsed = 0
print('Analyzing motion data...')
moviedeq = []
while (cap.isOpened()):
ret, frame = cap.read()
if ret == False:
print 'End of Video'
break
currentFrame = imageTools.grayBlur(frame)
moviedeq.append(currentFrame)
diff = imageTools.diffImage(storedFrame, currentFrame, pixThreshold,
displayDiffs)
timeDiff = 1. / frameRate
elapsed = elapsed + timeDiff
counts = np.bincount(w, weights=diff.ravel())
pixData[i, :] = np.hstack((elapsed, counts))
totalFrames += 1
storedFrame = currentFrame # comment out if nothing is in first frame
i += 1
pixData = pixData[:i, :]
pixData = pixData[:, 2:] # get rid of timing column and background column
file = open(videoStream + ".motion2", 'w')
file.write("12/8/2015" + '\015')
for x in range(0, 285):
#print "x", x
for y in range(0, 96):
file.write(str(int(pixData[x, :][y])) + '\n')
cap.release()
cv2.destroyAllWindows()
vidInfo = {}
return vidInfo
def main(pixThreshold, frameRate, videoStream):
expDuration = 600000 # duration of experiment, in seconds; only relevant for live feed
saveFreq = 4500 # how often to save data, in frames
i, m = imageTools.loadImageAndMask()
# convert mask to integer values for bincount weights
m, w = imageTools.convertMaskToWeights(m)
#print m,w
# start camera or open video
videoType, displayDiffs = imageTools.getVideoType(videoStream)
cap = cv2.VideoCapture(videoStream)
# adjust video resolution if necessary (sized to mask)
print 'Camera resolution is %s x %s' % (str(m.shape[1]), str(m.shape[0]))
cap.set(3, m.shape[1])
cap.set(4, m.shape[0])
# Set Pixel Threshold
ret, frame = cap.read()
storedFrame = imageTools.grayBlur(frame)
pixThreshold = int(np.floor(pixThreshold * storedFrame.shape[0]))
print('PixelThreshold is %i') % pixThreshold
# Acquire data
if saveFreq / frameRate > expDuration: # do shorter of expDuration vs. saveFreq
saveFreq = expDuration * frameRate
pixData = np.zeros([saveFreq, len(np.unique(w)) + 1])
i = 0 # a counter for saving chunks of data
totalFrames = 0
startTime = datetime.now()
oldTime = startTime
elapsed = 0
print('Analyzing motion data...')
moviedeq = []
while (cap.isOpened()):
ret, frame = cap.read()
if ret == False:
print 'End of Video'
break
currentFrame = imageTools.grayBlur(frame)
moviedeq.append(currentFrame)
# check if i bigger than saveFreq. If yes, save and reset values
## if i >= saveFreq:
# save data
## timeStamp = datetime.now()
# make a directory to save stuff in? %m%d%y%H%M
## fname = 'data' + videoStream + timeStamp.strftime('%m%d%y%H%M%S') + '.npy'
#fname = 'data' + timeStamp.strftime('%m%d%y%H%M%S') + '.npy'
## np.save(fname,pixData)
# reset pixData and i
## pixData = np.zeros([ saveFreq, len(np.unique(w)) + 1])
## i = 0
# stop experiment if user presses 'q' or if experiment duration is up
if (cv2.waitKey(1) & 0xFF == ord('q') or len(sys.argv) == 1 and
datetime.now() > startTime + timedelta(seconds=expDuration)):
break
# record pixel differences in all of the ROIs
diff = imageTools.diffImage(storedFrame, currentFrame, pixThreshold,
displayDiffs)
timeDiff = 1. / frameRate
elapsed = elapsed + timeDiff
#print elapsed
# calculate and record pixel differences
counts = np.bincount(w, weights=diff.ravel())
#print counts # output
pixData[i, :] = np.hstack((elapsed, counts))
totalFrames += 1
storedFrame = currentFrame # comment out if nothing is in first frame
#oldTime = newTime
i += 1
# done recording. Remove empty rows (those bigger than i) from PixData
pixData = pixData[:i, :]
#print np.shape(pixData)
#print pixData
#testing = calc_mode(moviedeq, np.zeros([660,1088]))
#cv2.imwrite('test_img.jpg', testing)
# Write data to file with timestamp:
# np.save(movieName[:-4] + '.npy', pixData)
##timeStamp = datetime.now()
##fname = 'data' + videoStream + timeStamp.strftime('%m%d%y%H%M%S') + '.npy'
#fname = 'data' + timeStamp.strftime('%m%d%y%H%M%S') + '.npy'
##np.save(fname,pixData)
pixData = pixData[:, 2:] # get rid of timing column and background column
#print np.shape(pixData)
#print pixData
#pixData = deltaPix[:,1:] # get rid of background column
file = open(videoStream + ".motion2", 'w')
file.write("12/8/2015" + '\015')
for x in range(0, 285):
#print "x", x
for y in range(0, 96):
#print "y", y
#print str(int(pixData[x,:][y]))
file.write(str(int(pixData[x, :][y])) + '\n')
# Save info (elapsed time and framerate) for later use
vidInfo = {}
#analysisTime = timeStamp - startTime
#vidInfo['analysisTime'] = float(str(analysisTime.seconds) + '.' + str(analysisTime.microseconds))
vidInfo['TotalFrames'] = totalFrames
#vidInfo['fps'] = int(totalFrames/vidInfo['analysisTime'])
vidInfo['pixThreshold'] = pixThreshold
vidInfo['CameraResolution'] = '%s x %s' % (str(m.shape[1]), str(
m.shape[0]))
vidInfo['NamePrefix'] = videoStream
#vidInfo['NamePrefix'] = videoStream.split('.')[0]
#print ('Analyzed %i frames in %f seconds') % (vidInfo['TotalFrames'],vidInfo['analysisTime'])
#print('FrameRate is about %i fps') % vidInfo['fps']
print 'Motion threshold is %i pixels' % int(pixThreshold)
print 'Camera resolution is %s' % vidInfo['CameraResolution']
# release camera
cap.release()
cv2.destroyAllWindows()
return vidInfo
def main(pixThreshold,frameRate,videoStream):
#elate_loc={(1,1):1, (1,2):2, (1,3):3, (1,4):4, (1:5):5, (1,6):6, (1,7):7, (1,8):8, (1,9):9,(1,10):10,(1,11):11,(1,12):12,(2,1):13,(2,2):14,(2,3):15,(2,4):16,(2:5):17,(2,6):18,(2,7):19,(2,8):20,(2,9):21,(2,10):22,(2,11):23,(2,12):24,(3,1):25,(3,2):26,(3,3):27,(3,4):28,(3,5):29,(3,6):30,(3,7)}
rowold={1:0,2:12,3:24,4:36,5:48,6:60,7:72,8:84}
row={0:0,1:12,2:24,3:36,4:48,5:60,6:72,7:84}
expDuration = 600000 # duration of experiment, in seconds; only relevant for live feed
saveFreq = 4500 # how often to save data, in frames
i,m = imageTools.loadImageAndMask()
e = imageTools.loadModeImage()
roimask = np.zeros((660,1088))
(maxxysnp, minxysnp) = max_min()
print "maxxysnp: ", maxxysnp
print "minxysnp: ", minxysnp
maxxs = []
minxs = []
maxys = []
minys = []
for j in range (0, numberofwells*2,2):
maxx = maxxysnp[j]
maxxs.append(maxxysnp[j])
maxy = maxxysnp[j+1]
maxys.append(maxxysnp[j+1])
minx = minxysnp[j]
minxs.append(minxysnp[j])
miny = minxysnp[j+1]
minys.append(minxysnp[j+1])
roimask[miny:maxy,minx:maxx] = j+1
maxxs.sort()
maxys.sort()
minxs.sort()
minys.sort()
#smaxxs = []
#sminxs = []
#smaxys = []
#sminys = []
#rx = 8
#cy = 12
#realmaxx = 0
#realmaxxs = []
#for z in range(0, len(maxxs)):
# if z == cy - 1:
# realmaxxs.append(realmaxx)
# realmaxx = 0
# if maxxs[z] > realmaxx:
# realmaxx = maxxs[z]
np.set_printoptions(threshold=np.nan) # printing entire array
print roimask
# print maxx,maxy,minx,miny
#print e
#cv2.imwrite('testinge.jpg', e)
#cv2.imwrite('testingm.jpg', m)
#cv2.imwrite('testingi.jpg', i)
# convert mask to integer values for bincount weights
#print "mask1: ", np.shape(m), m
m,w = imageTools.convertMaskToWeights(m)
rm,roimaskweights = imageTools.convertMaskToWeights(roimask)
#print "mask2: ", np.shape(m), m
#print "weights: ", np.shape(w), w
unique = np.unique(m)
print "unique: ", unique
unique2 = np.unique(rm)
print "unique2: ", unique2
#print np.shape(m)
rminr = set()
rminc = set()
rmaxr = set()
rmaxc = set()
for x in unique2:
#print "x: ", x
#if x == 0:
# continue
#print np.shape(np.where(m==x))
#print np.where(m==x)
rmaxdimc = np.amax(np.where(rm==x)[0])
rmaxc.add(rmaxdimc)
#print "max dimc: ", np.amax(np.where(m==x)[0])
rmaxdimr = np.amax(np.where(rm==x)[1])
rmaxr.add(rmaxdimr)
#print "max dimr: ", np.amax(np.where(m==x)[1])
rmindimc = np.amin(np.where(rm==x)[0])
rminc.add(rmindimc)
#print "min dimc: ", np.amin(np.where(m==x)[0])
rmindimr = np.amin(np.where(rm==x)[1])
rminr.add(rmindimr)
#print "min dimr: ", np.amin(np.where(m==x)[1])
rlminx = list(rminr)
rlminx.sort()
rlmaxx = list(rmaxr)
rlmaxx.sort()
rlminy = list(rminc)
rlminy.sort()
rlmaxy = list(rmaxc)
rlmaxy.sort()
print rlminx, rlmaxx, rlminy, rlmaxy
minr = set()
minc = set()
maxr = set()
maxc = set()
for x in unique:
#print "x: ", x
if x == 0:
continue
#print np.shape(np.where(m==x))
#print np.where(m==x)
maxdimc = np.amax(np.where(m==x)[0])
maxc.add(maxdimc)
#print "max dimc: ", np.amax(np.where(m==x)[0])
maxdimr = np.amax(np.where(m==x)[1])
maxr.add(maxdimr)
#print "max dimr: ", np.amax(np.where(m==x)[1])
mindimc = np.amin(np.where(m==x)[0])
minc.add(mindimc)
#print "min dimc: ", np.amin(np.where(m==x)[0])
mindimr = np.amin(np.where(m==x)[1])
minr.add(mindimr)
#print "min dimr: ", np.amin(np.where(m==x)[1])
#print minr, maxr, minc, maxc
lminx = list(minr)
lminx.sort()
lmaxx = list(maxr)
lmaxx.sort()
lminy = list(minc)
lminy.sort()
lmaxy = list(maxc)
lmaxy.sort()
print "real mask: ", lminx, lmaxx, lminy, lmaxy
#print lminr, lmaxr, lminc, lmaxc
#print np.where(m==75)
#for x in len(m):
#print m[x]
#print "mask2: ", np.shape(m), m
#print "mask: ", np.nonzero(m)
#print m,w
# start camera or open video
videoType, displayDiffs = imageTools.getVideoType(videoStream)
cap = cv2.VideoCapture(videoStream)
# adjust video resolution if necessary (sized to mask)
print 'Camera resolution is %s x %s' % (str(m.shape[1]),str(m.shape[0]))
cap.set(3,m.shape[1])
cap.set(4,m.shape[0])
# Set Pixel Threshold
ret,frame = cap.read()
storedImage = np.array(e * 255, dtype = np.uint8)
# have to convert the float32 to uint8
storedMode = imageTools.Blur(storedImage)
storedFrame = imageTools.grayBlur(frame)
pixThreshold = int(np.floor( pixThreshold * storedFrame.shape[0] ))
print('PixelThreshold is %i') % pixThreshold
cenData = np.zeros([ saveFreq, len(np.unique(w))*2 -2])
pixData = np.zeros([ saveFreq, len(np.unique(w)) + 1])
i = 0 # a counter for saving chunks of data
totalFrames = 0
startTime = datetime.now()
oldTime = startTime
elapsed = 0
print('Analyzing motion data...')
frame_roi = []
while(cap.isOpened()):
#print "frames", totalFrames
ret,frame = cap.read()
if ret == False:
print 'End of Video'
break
currentFrame = imageTools.grayBlur(frame)
currentFrame2 = imageTools.grayBlur(frame)
diffpix = imageTools.diffImage(storedFrame,currentFrame2,pixThreshold,displayDiffs)
#print np.shape(diffpix)
#print diffpix # This is 660x1088
diff = imageTools.trackdiffImage(storedMode,currentFrame,pixThreshold,displayDiffs)
diff.dtype = np.uint8
_,contours,hierarchy = cv2.findContours(diff, cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
MIN_THRESH = 20.0
MIN_THRESH_P = 20.0
#if cv2.contourArea(contours[0]) > MIN_THRESH:
# M = cv2.moments(contours[0])
# cX = int(M["m10"] / M["m00"])
# cY = int(M["m01"] / M["m00"])
# print cX,cY
roi_dict = {}
for r in range(0,numberofwells):
roi_dict[r+1] = []
for cs in range(0,len(contours)):
if cv2.contourArea(contours[cs]) > MIN_THRESH or cv2.arcLength(contours[cs],True) > MIN_THRESH_P:
#if cv2.contourArea(contours[cs]) > MIN_THRESH and cv2.arcLength(contours[cs],True) > MIN_THRESH_P:
M = cv2.moments(contours[cs])
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
print i, " cX and cY:", cX, cY
#print lmaxx
#print lmaxy
r=1
c=1
for x in range(0,len(lmaxx)):
if cX > lmaxx[x]:
r=x+2
print "Lx,cX,lmaxx[x],lmin[x] ",x, cX, lmaxx[x], lminx[x]
for y in range(0, len(lmaxy)):
if cY > lmaxy[y]:
c=y+2
print "Ly,cY,maxy[y],lmin[x],r,c ",y, cY, lmaxy[y], lminy[y],r,c
area = cv2.contourArea(contours[cs])
perim = cv2.arcLength(contours[cs],True)
perim = cv2.arcLength(contours[cs],True)
print "L r + c + row[c]: ", r, c, rowold[c]," final well: ", r + rowold[c]
if not roi_dict[r+rowold[c]]:
# roi_dict[r+row[c]].append((area*perim))
roi_dict[r+rowold[c]].append((area*perim,cX,cY))
#roi_dict[r+rowold[c]].append((area*perim,contours[cs]))
else:
if roi_dict[r+rowold[c]] < area*perim:
roi_dict[r+rowold[c]][0] = (area*perim,cX,cY)
print len(maxxs), maxxs, maxys, minxs, minys
for x in range(0,len(maxxs)):
if cX > maxxs[x]:
r=x+1 # maybe DONT ADD TWO?
#r=x+2 # maybe DONT ADD TWO?
print "x,cX,maxx[x],minx[x],r,c: ",x, cX, maxxs[x], minxs[x],r,c
for y in range(0, len(maxys)):
if cY > maxys[y]:
c=y+1
#c=y+2
print "y,cY,maxy[y].miny[y],r,c", y, cY, maxys[y], minys[y],r,c
area = cv2.contourArea(contours[cs])
perim = cv2.arcLength(contours[cs],True)
print "r + c + r/8+1 + c/12: ", r, c, r/8+1, c/12, " row[c/12]: ", row[c/12], " final well: ", r/8 + 1 + row[c/12]
if not roi_dict[r/8+1+row[c/12]]:
# roi_dict[r+row[c]].append((area*perim))
roi_dict[r/8+1+row[c/12]].append((area*perim,cX,cY))
#roi_dict[r+row[c]].append((area*perim,contours[cs]))
else:
if roi_dict[r/8+1+row[c/12]] < area*perim:
roi_dict[r/8+1+row[c/12]][0] = (area*perim,cX,cY)
frame_roi.append(roi_dict)
timeDiff = 1. / frameRate
elapsed = elapsed + timeDiff
pixcounts = []
pixcounts = np.bincount(w, weights=diffpix.ravel())
pixData[i,:] = np.hstack((elapsed,pixcounts))
counts = []
keys = roi_dict.keys()
keys.sort()
for k in keys:
x = -10000
y = -10000
if roi_dict[k]:
x = roi_dict[k][0][1]
y = roi_dict[k][0][2]
counts.append(x)
counts.append(y)
cv2.line(storedImage,(x,y),(x,y),(255,255,255),2)
if i == 284:
cv2.imwrite('trackedimagewithlines_' + str(i) + ".png", storedImage)
cenData[i,:] = np.asarray(counts)
totalFrames += 1
storedFrame = currentFrame
i += 1
file = open(videoStream + ".centroid2",'w')
for x in range(0,285):
for y in range(0,192):
file.write(str(int(cenData[x,:][y])) + '\n')
pixData = pixData[:i,:]
pixData = pixData[:,2:] # get rid of timing column and background column
file = open(videoStream + ".motion2",'w')
file.write("12/8/2015" + '\015')
for x in range(0,285):
#print "x", x
for y in range(0,numberofwells):
file.write(str(int(pixData[x,:][y])) + '\n')
vidInfo = {}
# release camera
cap.release()
cv2.destroyAllWindows()
return vidInfo
def main(pixThreshold, frameRate, videoStream):
#elate_loc={(1,1):1, (1,2):2, (1,3):3, (1,4):4, (1:5):5, (1,6):6, (1,7):7, (1,8):8, (1,9):9,(1,10):10,(1,11):11,(1,12):12,(2,1):13,(2,2):14,(2,3):15,(2,4):16,(2:5):17,(2,6):18,(2,7):19,(2,8):20,(2,9):21,(2,10):22,(2,11):23,(2,12):24,(3,1):25,(3,2):26,(3,3):27,(3,4):28,(3,5):29,(3,6):30,(3,7)}
col = {1: 0, 2: 12, 3: 24, 4: 36, 5: 48, 6: 60, 7: 72, 8: 84}
expDuration = 600000 # duration of experiment, in seconds; only relevant for live feed
saveFreq = 4500 # how often to save data, in frames
i, m = imageTools.loadImageAndMask()
e = imageTools.loadModeImage()
#print e
#cv2.imwrite('testinge.jpg', e)
#cv2.imwrite('testingm.jpg', m)
#cv2.imwrite('testingi.jpg', i)
# convert mask to integer values for bincount weights
np.set_printoptions(threshold=np.nan) # printing entire array
#print "mask1: ", np.shape(m), m
m, w = imageTools.convertMaskToWeights(m)
#print "mask2: ", np.shape(m), m
unique = np.unique(m)
#print np.shape(m)
minr = set()
minc = set()
maxr = set()
maxc = set()
for x in unique:
#print "x: ", x
if x == 0:
continue
#print np.shape(np.where(m==x))
#print np.where(m==x)
maxdimc = np.amax(np.where(m == x)[0])
maxc.add(maxdimc)
#print "max dimc: ", np.amax(np.where(m==x)[0])
maxdimr = np.amax(np.where(m == x)[1])
maxr.add(maxdimr)
#print "max dimr: ", np.amax(np.where(m==x)[1])
mindimc = np.amin(np.where(m == x)[0])
minc.add(mindimc)
#print "min dimc: ", np.amin(np.where(m==x)[0])
mindimr = np.amin(np.where(m == x)[1])
minr.add(mindimr)
#print "min dimr: ", np.amin(np.where(m==x)[1])
#print minr, maxr, minc, maxc
lminx = list(minr)
lminx.sort()
lmaxx = list(maxr)
lmaxx.sort()
lminy = list(minc)
lminy.sort()
lmaxy = list(maxc)
lmaxy.sort()
#print lminr, lmaxr, lminc, lmaxc
#print np.where(m==75)
#for x in len(m):
#print m[x]
#print "mask2: ", np.shape(m), m
#print "mask: ", np.nonzero(m)
#print m,w
# start camera or open video
videoType, displayDiffs = imageTools.getVideoType(videoStream)
cap = cv2.VideoCapture(videoStream)
# adjust video resolution if necessary (sized to mask)
print 'Camera resolution is %s x %s' % (str(m.shape[1]), str(m.shape[0]))
cap.set(3, m.shape[1])
cap.set(4, m.shape[0])
# Set Pixel Threshold
ret, frame = cap.read()
storedImage = np.array(e * 255, dtype=np.uint8)
# have to convert the float32 to uint8, hopefully this is doing it correctly (found online)
storedFrame = imageTools.Blur(storedImage)
#storedFrame = imageTools.grayBlur(frame)
pixThreshold = int(np.floor(pixThreshold * storedFrame.shape[0]))
print('PixelThreshold is %i') % pixThreshold
# Acquire data
if saveFreq / frameRate > expDuration: # do shorter of expDuration vs. saveFreq
saveFreq = expDuration * frameRate
pixData = np.zeros([saveFreq, len(np.unique(w)) * 2 - 2])
i = 0 # a counter for saving chunks of data
totalFrames = 0
startTime = datetime.now()
oldTime = startTime
elapsed = 0
print('Analyzing motion data...')
moviedeq = []
frame_roi = []
while (cap.isOpened()):
ret, frame = cap.read()
if ret == False:
print 'End of Video'
break
currentFrame = imageTools.grayBlur(frame)
moviedeq.append(currentFrame)
# stop experiment if user presses 'q' or if experiment duration is up
if (cv2.waitKey(1) & 0xFF == ord('q') or len(sys.argv) == 1 and
datetime.now() > startTime + timedelta(seconds=expDuration)):
break
diff = imageTools.trackdiffImage(storedFrame, currentFrame,
pixThreshold, displayDiffs)
diff.dtype = np.uint8
_, contours, hierarchy = cv2.findContours(diff, cv2.RETR_TREE,
cv2.CHAIN_APPROX_NONE)
MIN_THRESH = 20.0
MIN_THRESH_P = 20.0
#if cv2.contourArea(contours[0]) > MIN_THRESH:
# M = cv2.moments(contours[0])
# cX = int(M["m10"] / M["m00"])
# cY = int(M["m01"] / M["m00"])
# print cX,cY
roi_dict = {}
for r in range(0, 96):
roi_dict[r + 1] = []
for cs in range(0, len(contours)):
if cv2.contourArea(contours[cs]) > MIN_THRESH or cv2.arcLength(
contours[cs], True) > MIN_THRESH_P:
#if cv2.contourArea(contours[cs]) > MIN_THRESH and cv2.arcLength(contours[cs],True) > MIN_THRESH_P:
M = cv2.moments(contours[cs])
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
#print i, " cX and cY:", cX, cY
#print lmaxx
#print lmaxy
r = 1
c = 1
for x in range(0, len(lmaxx)):
if cX > lmaxx[x]:
r = x + 2
# print x, cX, lmaxx[x], lminx[x]
for y in range(0, len(lmaxy)):
if cY > lmaxy[y]:
c = y + 2
# print y, cY, lmaxy[y], lminy[y]
area = cv2.contourArea(contours[cs])
perim = cv2.arcLength(contours[cs], True)
if not roi_dict[r + col[c]]:
# roi_dict[r+col[c]].append((area*perim))
roi_dict[r + col[c]].append((area * perim, cX, cY))
#roi_dict[r+col[c]].append((area*perim,contours[cs]))
else:
if roi_dict[r + col[c]] < area * perim:
roi_dict[r + col[c]][0] = (area * perim, cX, cY)
frame_roi.append(roi_dict)
timeDiff = 1. / frameRate
elapsed = elapsed + timeDiff
counts = []
keys = roi_dict.keys()
keys.sort()
for k in keys:
x = -10000
y = -10000
if roi_dict[k]:
x = roi_dict[k][0][1]
y = roi_dict[k][0][2]
counts.append(x)
counts.append(y)
cv2.line(storedImage, (x, y), (x, y), (255, 255, 255), 2)
cv2.imwrite('withlines' + str(i) + ".png", storedImage)
pixData[i, :] = np.asarray(counts)
totalFrames += 1
file = open(videoStream + ".centroid2", 'w')
for x in range(0, 285):
for y in range(0, 192):
file.write(str(int(pixData[x, :][y])) + '\n')
# Save info (elapsed time and framerate) for later use
vidInfo = {}
analysisTime = timeStamp - startTime
vidInfo['analysisTime'] = float(
str(analysisTime.seconds) + '.' + str(analysisTime.microseconds))
vidInfo['TotalFrames'] = totalFrames
vidInfo['fps'] = int(totalFrames / vidInfo['analysisTime'])
vidInfo['pixThreshold'] = pixThreshold
vidInfo['CameraResolution'] = '%s x %s' % (str(m.shape[1]), str(
m.shape[0]))
vidInfo['NamePrefix'] = videoStream
#vidInfo['NamePrefix'] = videoStream.split('.')[0]
print('Analyzed %i frames in %f seconds') % (vidInfo['TotalFrames'],
vidInfo['analysisTime'])
print('FrameRate is about %i fps') % vidInfo['fps']
print 'Motion threshold is %i pixels' % int(pixThreshold)
print 'Camera resolution is %s' % vidInfo['CameraResolution']
# release camera
cap.release()
cv2.destroyAllWindows()
return vidInfo
def main(pixThreshold, frameRate, videoStream):
expDuration = 600000 # duration of experiment, in seconds; only relevant for live feed
saveFreq = 4500 # how often to save data, in frames
i, m = imageTools.loadImageAndMask()
# convert mask to integer values for bincount weights
m, w = imageTools.convertMaskToWeights(m)
#print m,w
moviedeq = []
i2 = 0
for file in glob.glob("*_4*avi"):
if i2 == 15:
break
#for x in range(0,5):
# start camera or open video
videoType, displayDiffs = imageTools.getVideoType(videoStream)
# print "testing: ", file
#print "testing: ", videoStream.split('-')[x]
cap = cv2.VideoCapture(file)
#cap = cv2.VideoCapture(videoStream.split('-')[x])
# adjust video resolution if necessary (sized to mask)
# print 'Camera resolution is %s x %s' % (str(m.shape[1]),str(m.shape[0]))
# cap.set(3,m.shape[1])
# cap.set(4,m.shape[0])
# Set Pixel Threshold
ret, frame = cap.read()
storedFrame = imageTools.grayBlur(frame)
#pixThreshold = int(np.floor( pixThreshold * storedFrame.shape[0] ))
#print('PixelThreshold is %i') % pixThreshold
# Acquire data
if saveFreq / frameRate > expDuration: # do shorter of expDuration vs. saveFreq
saveFreq = expDuration * frameRate
pixData = np.zeros([saveFreq, len(np.unique(w)) + 1])
#i = 0 # a counter for saving chunks of data
totalFrames = 0
startTime = datetime.now()
oldTime = startTime
elapsed = 0
print('Analyzing motion data...')
#moviedeq = []
while (cap.isOpened()):
ret, frame = cap.read()
if ret == False:
print 'End of Video'
break
currentFrame = imageTools.grayBlur(frame)
moviedeq.append(currentFrame)
# stop experiment if user presses 'q' or if experiment duration is up
if (cv2.waitKey(1) & 0xFF == ord('q')
or len(sys.argv) == 1 and datetime.now() >
startTime + timedelta(seconds=expDuration)):
break
# record pixel differences in all of the ROIs
#diff = imageTools.diffImage(storedFrame,currentFrame,pixThreshold,0)
timeDiff = 1. / frameRate
elapsed = elapsed + timeDiff
# print elapsed
# calculate and record pixel differences
#counts = np.bincount(w, weights=diff.ravel())
# print counts # output
#pixData[i,:] = np.hstack((elapsed,counts))
totalFrames += 1
storedFrame = currentFrame # comment out if nothing is in first frame
#oldTime = newTime
i2 += 1
# done recording. Remove empty rows (those bigger than i) from PixData
#pixData = pixData[:i,:]
#print pixData
testing = calc_mode(moviedeq, np.zeros([660, 1088]))
print "saving mode.png"
cv2.imwrite('mode.png', testing)
# Save info (elapsed time and framerate) for later use
vidInfo = {}
# analysisTime = timeStamp - startTime
# vidInfo['analysisTime'] = float(str(analysisTime.seconds) + '.' + str(analysisTime.microseconds))
# vidInfo['TotalFrames'] = totalFrames
# vidInfo['fps'] = int(totalFrames/vidInfo['analysisTime'])
# #vidInfo['pixThreshold']=pixThreshold
# vidInfo['CameraResolution']='%s x %s' % (str(m.shape[1]),str(m.shape[0]))
#vidInfo['NamePrefix'] = videoStream.split('-')[x]
#vidInfo['NamePrefix'] = videoStream.split('.')[0]
# print ('Analyzed %i frames in %f seconds') % (vidInfo['TotalFrames'],vidInfo['analysisTime'])
# print('FrameRate is about %i fps') % vidInfo['fps']
# print 'Motion threshold is %i pixels' % int(pixThreshold)
# print 'Camera resolution is %s' % vidInfo['CameraResolution']
# release camera
cap.release()
cv2.destroyAllWindows()
return vidInfo