IncrementalCorrelationFilterThinWrapper

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

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)

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)

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