View Source Evision.BackgroundSubtractor (Evision v0.1.7)
Link to this section Summary
Functions
Raising version of apply/2
.
Raising version of apply/3
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Raising version of getBackgroundImage/1
.
Link to this section cv
Computes a foreground mask.
Positional Arguments
image:
Evision.Mat
.Next video frame.
Keyword Arguments
learningRate:
double
.The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.
Return
fgmask:
Evision.Mat
.The output foreground mask as an 8-bit binary image.
Python prototype (for reference):
apply(image[, fgmask[, learningRate]]) -> fgmask
Computes a foreground mask.
Positional Arguments
image:
Evision.Mat
.Next video frame.
Keyword Arguments
learningRate:
double
.The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm to use some automatically chosen learning rate. 0 means that the background model is not updated at all, 1 means that the background model is completely reinitialized from the last frame.
Return
fgmask:
Evision.Mat
.The output foreground mask as an 8-bit binary image.
Python prototype (for reference):
apply(image[, fgmask[, learningRate]]) -> fgmask
Computes a background image.
Return
backgroundImage:
Evision.Mat
.The output background image.
Note: Sometimes the background image can be very blurry, as it contain the average background statistics.
Python prototype (for reference):
getBackgroundImage([, backgroundImage]) -> backgroundImage