Mirror Reversibility Obverse-Reverse Checker
Compare exact registered palette-ID grids with horizontal obverse reversal; assumes both views have same top and hoist-side registration, not image resampling or print reversibility certification.
Description
Mirror reversibility compares registered obverse and reverse designs under horizontal reflection.
Mirror reversibility compares registered obverse and reverse designs under horizontal reflection.1
Exact palette grids make that comparison reproducible when both views share the same top and hoist registration.
When to use Mirror Reversibility Obverse-Reverse Checker
Use this tool when you need to reproduce the stated calculation from measured records or independently selected model inputs. Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
How to use Mirror Reversibility Obverse-Reverse Checker and understand its results
- Establish the flag dimensions, orientation, specification, palette, or visual annotations needed for this particular calculation.
- Enter obversePaletteGrid, reversePaletteGrid, 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 |
|---|---|---|---|
| obversePaletteGrid | Obverse palette grid | numeric matrix | Required |
| reversePaletteGrid | Reverse palette grid | numeric matrix | Required |
Use equally sized nonempty rectangular numeric grids with integer palette IDs from zero to one million. Compare obverse cell [row,column] with reverse [row,width−1−column]. Return equality and every mismatched obverse [row,column] pair.
match(y,x)=(obverse[y][x]=reverse[y][width−1−x]) Symbols follow the input and output meanings and the calculation rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Calculated observation or reference | object |
| result.reverseEqualsHorizontalMirror | Reverse Equals Horizontal Mirror | boolean |
| result.mismatchedMirrorCells | Mismatched Mirror Cells | list |
{
"input": {
"obversePaletteGrid": [
[
1,
2
],
[
3,
4
]
],
"reversePaletteGrid": [
[
2,
1
],
[
4,
3
]
]
},
"output": {
"result": {
"reverseEqualsHorizontalMirror": true,
"mismatchedMirrorCells": []
}
}
}Limitations and assumptions
The calculation uses supplied geometry, observations, or annotations. It does not establish an official construction specification, national identity, or universal design quality.
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 matrices are limited to 128 rows and 128 entries per row, with rectangularity required where stated. Extremely large composite outputs are rejected.
Alternative or Complementary methods
For a related review, use Two-Sided Design Difference Recorder: Two flag faces can be compared only after their attributes have been aligned under the same registration convention. Exact attribute differences then provide a reproducible record of which positions changed. For a complementary step, use Flag Difference Hamming Pixel Comparator: Hamming distance counts exact mismatches between categorical cells. For registered flag grids it measures label differences without introducing resampling or perceptual color judgments.
References
Similar or alternative tools
- Two-Sided Design Difference Recorder
Two flag faces can be compared only after their attributes have been aligned under the same registration convention. Exact attribute differences then provide a reproducible record of which positions changed.
- Flag Difference Hamming Pixel Comparator
Hamming distance counts exact mismatches between categorical cells. For registered flag grids it measures label differences without introducing resampling or perceptual color judgments.