Specification#

Analyzers are callbacks passed to match_exhaustive to enable custom analysis workflows of exhaustive searches. Rather than storing all intermediate results in memory, analyzers accumulate only the most relevant data.

Key characteristics of analyzers

  • Called individually for each rotation in the search space

  • Support distributed computation with result merging capabilities

  • Maintain internal state that evolves throughout the matching process

AbstractAnalyzer defines the interface specification that all analyzers must implement.

AbstractAnalyzer

Abstract base class for template matching analyzers.

How Analyzers Work#

Analyzers follow a stateful, three-phase pattern

1. Initialization Phase

The analyzer creates an initial state using AbstractAnalyzer.init_state(). This state contains empty data structures that will accumulate results across all rotations. For example, an analyzer might initialize empty arrays for storing top scores, coordinates, and rotation matrices.

2. Accumulation Phase

For each rotation tested during template matching, the analyzer’s AbstractAnalyzer.__call__() method is invoked with

  • Current state from previous iterations

  • New scores array from the current rotation

  • Rotation matrix that generated these scores

The analyzer updates its state by incorporating the new data according to its specific logic (e.g., keeping only top N scores, filtering by threshold, etc.) and returns the updated state for the next iteration

# For each rotation in the search space
for rotation in rotations:
    # Compute correlation scores for this rotation
    scores = compute_scores(template, target, rotation)

    # Update analyzer state with new results
    analyzer_state = analyzer(analyzer_state, scores, rotation)
3. Finalization Phase

After all rotations are processed, AbstractAnalyzer.result() converts the accumulated state into the final output format. This may include post-processing operations like coordinate transformations or convolution mode corrections.

State Management#

Analyzers use a stateful accumulation pattern that ensures data ownership remains external to analyzer instances. This design maintains compatibility with just-in-time compilation features, particularly for the JAX backend.

Parallel Processing#

Analyzers support parallel processing through

  • Shared Memory: The AbstractAnalyzer.shareable property enables multi-process operation using shared memory buffers, avoiding expensive data copying.

  • Result Merging: The AbstractAnalyzer.merge() class method combines results from different processes or data splits into unified output.

Custom Analyzer Implementation#

To implement a custom analyzer:

  1. Inherit from AbstractAnalyzer:

    class MyAnalyzer(AbstractAnalyzer):
    pass

  2. Define the state structure in init_state()

  3. Implement accumulation logic in __call__()

  4. Handle finalization in result()

  5. Support merging in merge()

  6. Set shareability via the shareable property

This flexible architecture allows for diverse analysis strategies including peak detection, clustering, statistical analysis, and custom filtering without modifying the core template matching algorithms.