Exhaustive#

Exhaustive template matching evaluates similarity along a provided set of rotations and all possible translations are sampled using Fast Fourier Transform (FFT) operations. Therefore, the algorithm is guaranteed to evaluate a provided set of configurations and to find a global optimum with a sufficiently high angular sampling rate.

Within pytme, exhaustive template matching is modularized into three primary stages: setup, scoring, and callback.

  1. Setup: Creates a context where template matching is poised to be carried out efficiently. Setup functions involve data preparation, configuring FFT operations and pre-computing shared parameters.

  2. Scoring: Sample scoring function across translational and rotational degrees of freedom.

  3. Callback: Custom on the fly processing of template matching results using analyzers.

If you wish to integrate custom template matching methods into pytme, please refer to the Adding Custom Methods section.

Methods#

match_exhaustive orchestrates the matching process, supporting parallel processing and post-scoring operations. Depending on user specification, parallelization can be performed by splitting the search region into subsets, and/or by distribution the angular search.

match_exhaustive(matching_data, ...[, ...])

Run exhaustive template matching over all translations and a subset of rotations specified in matching_data.

Setup functions#

cc_setup(matching_data, fast_shape, ...)

Setup function for computing the unnormalized cross-correlation between target (f) and template (g)

lcc_setup(matching_data, **kwargs)

Setup function for computing the laplace cross-correlation between target (f) and template (g)

corr_setup(matching_data, template_filter, ...)

Setup for computing a normalized cross-correlation between a target (f), a template (g) given template_mask (m)

cam_setup(matching_data, **kwargs)

Like corr_setup() but with standardized target and template

flc_setup(matching_data, fast_shape, ...)

Setup function for flc_scoring().

flcSphericalMask_setup(matching_data, ...)

Like flc_setup() for rotation invariant masks

mcc_setup(matching_data, fast_shape, ...)

Setup function for mcc_scoring().

Scoring functions#

corr_scoring(template, template_filter, ...)

Calculates a normalized cross-correlation between a target f and a template g.

flc_scoring(template, template_mask, ...[, ...])

Computes a normalized cross-correlation between target (f), template (g), and template_mask (m)

mcc_scoring(template, template_mask, ...[, ...])

Computes a normalized cross-correlation score between target (f), template (g), template_mask (m) and target_mask (t)

Adding Custom Methods#

New scoring methods need to be registered via register_matching_exhaustive(). This enables developers to specify a unique name, setup function, scoring function, and a custom memory estimation class for their method. This ensures the modular and extensible design of pytme, allowing developers to continuously expand tme’s capabilities.

Adding a new template matching methods requires defining the following parameters:

  • matching: Name of the matching method.

  • matching_setup: The setup function associated with the name.

  • matching_scoring: The scoring function associated with the name.

  • memory_class: A custom memory estimation class, which inherits from MatchingMemoryUsage

register_matching_exhaustive(matching, ...)

Registers a new matching scheme.