IncrementalCorrelationFilterThinWrapper

class menpofit.clm.IncrementalCorrelationFilterThinWrapper(cf_callable=<function mccf>, icf_callable=<function imccf>)[source]

Bases: object

Wrapper class for defining an Incremental Correlation Filter.

Parameters

  • cf_callable (callable, optional) –

    The correlation filter function. Possible options are:

    Class

    Method

    mccf

    Multi-Channel Correlation Filter

    mosse

    Minimum Output Sum of Squared Errors Filter

  • icf_callable (callable, optional) –

    The incremental correlation filter function. Possible options are:

    Class

    Method

    imccf

    Incremental Multi-Channel Correlation Filter

    imosse

    Incremental Minimum Output Sum of Squared Errors Filter

increment(A, B, n_x, Z, t)[source]

Method that trains the correlation filter.

Parameters

  • A ((N,) ndarray) – The current auto-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels

  • B ((N, N) ndarray) – The current cross-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels

  • n_x (int) – The current number of images.

  • Z (list or (n_images, n_channels, patch_h, patch_w) ndarray) – The training images (patches). If list, then it consists of n_images (n_channels, patch_h, patch_w) ndarray members.

  • t ((1, response_h, response_w) ndarray) – The desired response.

Returns

  • correlation_filter ((n_channels, response_h, response_w) ndarray) – The learned correlation filter.

  • auto_correlation ((N,) ndarray) – The auto-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels

  • cross_correlation ((N, N) ndarray) – The cross-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels

train(X, t)[source]

Method that trains the correlation filter.

Parameters

  • X (list or (n_images, n_channels, patch_h, patch_w) ndarray) – The training images (patches). If list, then it consists of n_images (n_channels, patch_h, patch_w) ndarray members.

  • t ((1, response_h, response_w) ndarray) – The desired response.

Returns

  • correlation_filter ((n_channels, response_h, response_w) ndarray) – The learned correlation filter.

  • auto_correlation ((N,) ndarray) – The auto-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels

  • cross_correlation ((N, N) ndarray) – The cross-correlation array, where N = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels