Structure for the Background Subtractor operation's initialization parameters.
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#include <opencv2/gapi/video.hpp>
Structure for the Background Subtractor operation's initialization parameters.
◆ BackgroundSubtractorParams() [1/2]
cv::gapi::video::BackgroundSubtractorParams::BackgroundSubtractorParams |
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◆ BackgroundSubtractorParams() [2/2]
cv::gapi::video::BackgroundSubtractorParams::BackgroundSubtractorParams |
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BackgroundSubtractorType |
op, |
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int |
histLength, |
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double |
thrshld, |
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bool |
detect, |
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double |
lRate |
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Full constructor
- Parameters
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op | MOG2/KNN Background Subtractor type. |
histLength | Length of the history. |
thrshld | For MOG2: Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. For KNN: Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample. |
detect | If true, the algorithm will detect shadows and mark them. It decreases the speed a bit, so if you do not need this feature, set the parameter to false. |
lRate | 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. |
◆ detectShadows
bool cv::gapi::video::BackgroundSubtractorParams::detectShadows = true |
If true, the algorithm will detect shadows and mark them.
◆ history
int cv::gapi::video::BackgroundSubtractorParams::history = 500 |
◆ learningRate
double cv::gapi::video::BackgroundSubtractorParams::learningRate = -1 |
The value between 0 and 1 that indicates how fast the background model is learnt. Negative parameter value makes the algorithm use some automatically chosen learning rate.
◆ operation
Type of the Background Subtractor operation.
◆ threshold
double cv::gapi::video::BackgroundSubtractorParams::threshold = 16 |
For MOG2: Threshold on the squared Mahalanobis distance between the pixel and the model to decide whether a pixel is well described by the background model. For KNN: Threshold on the squared distance between the pixel and the sample to decide whether a pixel is close to that sample.
The documentation for this struct was generated from the following file: