Symmetry Type Classifier (vertical/horizontal/rotational)
Retain every independently witnessed flag symmetry, not image symmetry extraction. Feature columns, independently annotated in order: vertical mirror independently witnessed, horizontal mirror independently witnessed, half-turn independently witnessed. Unknown evidence and compatible classes retained; not automated semantic, historical, identity, legal or safety determination.
Description
A flag may exhibit vertical reflection, horizontal reflection, and half-turn symmetry at the same time.
A flag may exhibit vertical reflection, horizontal reflection, and half-turn symmetry at the same time.1
This checker keeps every symmetry supported by independent observations.
When to use Symmetry Type Classifier (vertical/horizontal/rotational)
Use this tool when you need to review independently annotated design features against a fixed pattern. Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
How to use Symmetry Type Classifier (vertical/horizontal/rotational) and understand its results
- Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
- Enter observedFeatureFlags, featureKnownFlags, following the field table and rules below, then run the tool.
- Read result. Use the output meanings below to interpret units, signs, flags, and any nested result fields.
- Correct reported input issues and run again. For a successful result, check the stated evidence coverage and assumptions before using it in your review.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedFeatureFlags | Observed feature flags | list of number | Required |
| featureKnownFlags | Feature known flags | list of number | Required |
Enter binary observed-feature and feature-known lists in the fixed column order listed below. Their length must match the fixed rules. Known mismatches exclude a candidate; unknown columns leave it compatible. A candidate is witnessed only when every required column is known and matches. Ignored columns do not require evidence. Return compatible classes, witnessed classes, and zero-based unknown-feature indices. This consumes annotations, not pixels.
compatible_c=∀j(rule_cj=−1 or known_j=0 or feature_j=rule_cj); witnessed_c=∀j(rule_cj=−1 or (known_j=1 and feature_j=rule_cj)) Symbols follow the input and output meanings and the calculation rules above.
Fixed feature-column order: vertical mirror independently witnessed, horizontal mirror independently witnessed, half-turn independently witnessed. The fixed classes are vertical-mirror, horizontal-mirror, and half-turn. Each requires only its corresponding observed feature; the other feature columns are ignored.
| Output field | Meaning | Value type |
|---|---|---|
| result | Calculated observation or reference | object |
| result.compatibleClasses | Compatible Classes | list |
| result.witnessedClasses | Witnessed Classes | list |
| result.unknownFeatureIndices | Unknown Feature Indices | list |
{
"input": {
"observedFeatureFlags": [
1,
1,
1
],
"featureKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [
"vertical-mirror",
"horizontal-mirror",
"half-turn"
],
"witnessedClasses": [
"vertical-mirror",
"horizontal-mirror",
"half-turn"
],
"unknownFeatureIndices": []
}
}
}Limitations and assumptions
Annotations must be independently established. Candidate labels describe only the fixed tested features; they are not country identification, official membership, or an image-recognition result.
Missing required fields, unknown fields, wrong types, and violated calculation rules are input issues. Numeric values must be finite; nonfinite results are rejected. Evidence arrays must be dense, with no missing cells. Numeric observation lists allow at most 10,000 entries, subject to any smaller bounds stated above. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related review, use Rotational Symmetry Order Detector: A tested rotational order describes how many repeated orientations were independently witnessed. The largest witnessed tested order is an evidence summary, not discovery of a complete symmetry group. For a complementary step, use Mirror Reversibility Obverse-Reverse Checker: Mirror reversibility compares registered obverse and reverse designs under horizontal reflection. Exact palette grids make that comparison reproducible when both views share the same top and hoist registration.
References
Similar or alternative tools
- Rotational Symmetry Order Detector
A tested rotational order describes how many repeated orientations were independently witnessed. The largest witnessed tested order is an evidence summary, not discovery of a complete symmetry group.
- Mirror Reversibility Obverse-Reverse Checker
Mirror reversibility compares registered obverse and reverse designs under horizontal reflection. Exact palette grids make that comparison reproducible when both views share the same top and hoist registration.