def threshold_image(image, n=[]):
"""Record the first 5 images to get a background, then diff current frame with the last saved frame.
"""
if len(n) < 5:
# n[4] will be our background
# First capture a few images
n.append(cv.cvCloneMat(image))
if len(n) == 5:
# last time here
# could do averaging here.
pass
return image
original = n[4]
differenceImage = cv.cvCloneMat(image)
cv.cvAbsDiff(image, original, differenceImage)
"""The threshold value determines the amount of "Change" required
before something will show up"""
thresholdValue = 50 # 32
cv.cvThreshold(differenceImage, differenceImage, thresholdValue, 255,
cv.CV_THRESH_BINARY)
# Convert to one channel
gray = cv.cvCreateImage(cv.cvGetSize(differenceImage), 8, 1)
cv.cvCvtColor(differenceImage, gray, cv.CV_BGR2GRAY)
# Use median filter to remove salt and pepper noise.
cv.cvSmooth(gray, gray, cv.CV_MEDIAN, 15)
# Dilate and the threshold image
# It adds a border to the object.
#cv.cvDilate(gray,gray, None, 9)
# Add a bit of Blur to the threshold mask
cv.cvSmooth(gray, gray, cv.CV_GAUSSIAN, 5)
result = cv.cvCloneMat(image)
cv.cvSetZero(result)
cv.cvAnd(image, image, result, gray)
return result
def threshold_image(image, n=[]):
"""Record the first 5 images to get a background, then diff current frame with the last saved frame.
"""
if len(n) < 5:
# n[4] will be our background
# First capture a few images
n.append(cv.cvCloneMat(image))
if len(n) == 5:
# last time here
# could do averaging here.
pass
return image
original = n[4]
differenceImage = cv.cvCloneMat( image )
cv.cvAbsDiff( image, original, differenceImage )
"""The threshold value determines the amount of "Change" required
before something will show up"""
thresholdValue = 50 # 32
cv.cvThreshold( differenceImage, differenceImage, thresholdValue, 255, cv.CV_THRESH_BINARY )
# Convert to one channel
gray = cv.cvCreateImage( cv.cvGetSize(differenceImage), 8, 1 )
cv.cvCvtColor( differenceImage, gray, cv.CV_BGR2GRAY )
# Use median filter to remove salt and pepper noise.
cv.cvSmooth(gray, gray, cv.CV_MEDIAN, 15)
# Dilate and the threshold image
# It adds a border to the object.
#cv.cvDilate(gray,gray, None, 9)
# Add a bit of Blur to the threshold mask
cv.cvSmooth(gray, gray, cv.CV_GAUSSIAN, 5)
result = cv.cvCloneMat( image)
cv.cvSetZero(result)
cv.cvAnd(image,image, result, gray)
return result
