if frame is None:
break
if t>=(max_frames-NUM_FRAMES):
# Convert to floating point
ocv.cvConvert(frame,working)
# Smooth it to remove artifacts
ocv.cvSmooth(working,working,ocv.CV_GAUSSIAN,3,3,0.5)
# Add to accumulator
ocv.cvAcc(working,accumulator)
# Divide accumulator my number of frames for mean background
ocv.cvDiv(accumulator,ones,background,1.0/n)
# Convert background to target (for viewing/saving)
ocv.cvConvert(background,target)
# Increment accumulator divisor
n += 1
# Increment frame
t += 1
if show:
ocv.cvShowImage ('Track', target)
# Break if ESC
if ocv.cvWaitKey (5) & 255 == 27:
break
# Smooth - CRITICAL to remove codec artifacts
ocv.cvSmooth(grayfloat,grayfloat,ocv.CV_GAUSSIAN,3,3,0.5)
#ocv.cvSmooth(gray,gray,ocv.CV_MEDIAN)
# Subtract current frame from background model
ocv.cvAbsDiff(grayfloat, background, grayfloat);
# Normalise image
ocv.cvSet(ones,ocv.cvScalar(1.0,0,0,0),0)
minval = ocv.cvMinMaxLoc(grayfloat,True,False)
maxval = ocv.cvMinMaxLoc(grayfloat,False,True)
#print min,max
ocv.cvSubS(grayfloat,ocv.cvScalar(minval,0,0,0),grayfloat,0)
if ((maxval-minval)!=0): #Stop divide by zero
ocv.cvDiv(grayfloat,ones,grayfloat,1.0/(maxval-minval))
else:
cvZero(grayfloat)
ocv.cvScale(grayfloat,grayfloat,255.0) # Scale back to 0-255
# Convert back to int
ocv.cvConvert(grayfloat,gray)
#ocv.cvMerge(gray,gray,gray,0,output) # Show on output
#Find the "lightest" things in the image
#TODO: find "dip" inbetween
#ocv.cvEqualizeHist(gray,gray)
thresholdat = 70 #30
# Threshold frame
#ocv.cvAdaptiveThreshold(
