if max_frames:
if t > max_frames:
break
# capture the next frame
frame = ocv.cvQueryFrame(cap)
# if we've reached the end of the video
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 frame is None:
break
# update outputs to the new frame
ocv.cvCopy(frame,output)
# Mask - if you do this here you get edge artifacts
#ocv.cvCopy(frame,m_frame,mask)
# Convert frame to grayscale
ocv.cvConvertImage(frame,gray)
# Convert grayscale to float
ocv.cvConvert(gray,grayfloat)
# 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)