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
Multi-Channel Correlation Filter
Minimum Output Sum of Squared Errors Filter
icf_callable (callable, optional) –
The incremental correlation filter function. Possible options are:
Class
Method
Incremental Multi-Channel Correlation Filter
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, whereN = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels
B (
(N, N)
ndarray) – The current cross-correlation array, whereN = (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, whereN = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels
cross_correlation (
(N, N)
ndarray) – The cross-correlation array, whereN = (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, whereN = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels
cross_correlation (
(N, N)
ndarray) – The cross-correlation array, whereN = (patch_h+response_h-1) * (patch_w+response_w-1) * n_channels