FA-79011 / Image orientation metadata / Open access

HEIF mirror axis is inverted · case 01

Mirrored HEIC selfies are flipped top-bottom instead of left-right.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

imir 1 (horizontal axis) is given the vertical-axis mapping.

THE FAILURE

imir 1 (horizontal axis) is given the vertical-axis mapping.

Unsuccessful approach: Reusing the unmirrored rotation conversion ignores that a mirror reverses the turn direction.

Case contract

Resolve the displayed orientation tag from metadata sources. For HEIF, the container transforms are authoritative and EXIF/XMP are ignored: irot is anticlockwise quarter turns (default 0) applied first, then imir mirrors about the vertical axis (0, left-right) or the horizontal axis (1, top-bottom); the result is mapped to a tag through (k clockwise quarter turns after m mirrors) = {(0,0):1, (0,1):2, (2,0):3, (2,1):4, (3,1):5, (1,0):6, (1,1):7, (3,0):8}. For other containers a valid integer EXIF value 1..8 wins; otherwise an XMP string that is a single digit 1..8 after trimming whitespace; otherwise 1.

Why this case matters

Camera, phone and scanner images carry an orientation hint separately from the stored pixels; galleries, thumbnailers, editors and upload pipelines must interpret it consistently or photos appear sideways, mirrored or doubly rotated.

1 / The failure

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(meta):
    from_km = {(0, 0): 1, (0, 1): 2, (2, 0): 3, (2, 1): 4, (3, 1): 5, (1, 0): 6, (1, 1): 7, (3, 0): 8}
    c = meta.get('container')
    if c == 'heif':
        rot = meta.get('irot')
        mir = meta.get('imir')
        rot = 0 if rot is None else rot % 4
        if mir is None:
            return from_km[((-rot) % 4, 0)]
        if mir == 1:
            return from_km[(rot, 1)]
        return from_km[((rot + 2) % 4, 1)]
    e = meta.get('exif')
    if isinstance(e, int) and not isinstance(e, bool) and 1 <= e <= 8:
        return e
    x = meta.get('xmp')
    if isinstance(x, str):
        s = x.strip()
        if s in ('1', '2', '3', '4', '5', '6', '7', '8'):
            return int(s)
    return 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[{'container': 'heif', 'exif': 8, 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 1, 'irot': 1, 'imir': 0}, 7], [{'container': 'png'}, 1], [{'container': 'jpeg', 'xmp': '2'}, 2], [{'container': 'heif', 'xmp': '\t5', 'irot': 3}, 6], [{'container': 'tiff'}, 1], [{'container': 'tiff', 'exif': True, 'xmp': '7 '}, 7], [{'container': 'heif', 'exif': True, 'irot': 0, 'imir': 1}, 4]], [[{'container': 'heif', 'exif': 5, 'xmp': ' 8', 'imir': 0}, 2], [{'container': 'heif', 'exif': 3, 'xmp': '0', 'irot': 1, 'imir': 0}, 7], [{'container': 'heif', 'irot': 1, 'imir': 0}, 7], [{'container': 'jpeg', 'exif': 0, 'xmp': '\t5'}, 5], [{'container': 'jpeg', 'exif': 0}, 1], [{'container': 'jpeg', 'xmp': '3\n'}, 3], [{'container': 'heif', 'exif': 3, 'xmp': 'x', 'irot': 0}, 1], [{'container': 'heif', 'xmp': '\t5', 'irot': 0, 'imir': 1}, 4]], [[{'container': 'heif', 'exif': 8, 'xmp': ' 8', 'imir': 0}, 2], [{'container': 'heif', 'exif': 8, 'xmp': '\t5', 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 6, 'xmp': '\t5', 'irot': 3, 'imir': 0}, 5], [{'container': 'jpeg', 'xmp': '7 '}, 7], [{'container': 'tiff'}, 1], [{'container': 'jpeg'}, 1], [{'container': 'jpeg', 'exif': 0}, 1], [{'container': 'heif', 'exif': 3, 'xmp': '2', 'irot': 2, 'imir': 0}, 4]], [[{'container': 'heif', 'exif': 5, 'xmp': '3\n', 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 8, 'xmp': '6', 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'xmp': '0', 'irot': 1, 'imir': 0}, 7], [{'container': 'jpeg', 'exif': 1}, 1], [{'container': 'heif', 'exif': 1, 'xmp': '7 ', 'irot': 2}, 3], [{'container': 'heif', 'xmp': '\t5'}, 1], [{'container': 'jpeg', 'xmp': '7 '}, 7], [{'container': 'heif', 'exif': 0, 'xmp': '7 ', 'irot': 2, 'imir': 0}, 4]], [[{'container': 'heif', 'exif': 6, 'xmp': ' 8', 'irot': 1, 'imir': 1}, 5], [{'container': 'heif', 'exif': 7, 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 5, 'xmp': '2', 'irot': 1, 'imir': 0}, 7], [{'container': 'png', 'xmp': '7 '}, 7], [{'container': 'heif', 'irot': 2}, 3], [{'container': 'heif', 'xmp': ' 8', 'irot': 1}, 8], [{'container': 'jpeg', 'xmp': 'x'}, 1], [{'container': 'heif', 'exif': 7, 'xmp': 'x', 'imir': 1}, 4]]]
labels = ["regression: imir axis meaning", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: imir axis meaning 0	`7`	`5`	Failed
repair trap 1	`5`	`7`	Failed
combined fault 2	`1`	`1`	Passed
control 3	`2`	`2`	Passed
control 4	`6`	`6`	Passed
boundary 5	`1`	`1`	Passed
boundary 6	`7`	`7`	Passed
control 7	`2`	`4`	Failed

SHA-256 / 11b96868096a6ffb8e6e530098511423e843da5086366f5034119725229cfa0c

2 / The unsuccessful fix

Exit 1

"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(meta):
    from_km = {(0, 0): 1, (0, 1): 2, (2, 0): 3, (2, 1): 4, (3, 1): 5, (1, 0): 6, (1, 1): 7, (3, 0): 8}
    c = meta.get('container')
    if c == 'heif':
        rot = meta.get('irot')
        mir = meta.get('imir')
        rot = 0 if rot is None else rot % 4
        if mir is None:
            return from_km[((-rot) % 4, 0)]
        if mir == 0:
            return from_km[((-rot) % 4, 1)]
        return from_km[((rot + 2) % 4, 1)]
    e = meta.get('exif')
    if isinstance(e, int) and not isinstance(e, bool) and 1 <= e <= 8:
        return e
    x = meta.get('xmp')
    if isinstance(x, str):
        s = x.strip()
        if s in ('1', '2', '3', '4', '5', '6', '7', '8'):
            return int(s)
    return 1
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[{'container': 'heif', 'exif': 8, 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 1, 'irot': 1, 'imir': 0}, 7], [{'container': 'png'}, 1], [{'container': 'jpeg', 'xmp': '2'}, 2], [{'container': 'heif', 'xmp': '\t5', 'irot': 3}, 6], [{'container': 'tiff'}, 1], [{'container': 'tiff', 'exif': True, 'xmp': '7 '}, 7], [{'container': 'heif', 'exif': True, 'irot': 0, 'imir': 1}, 4]], [[{'container': 'heif', 'exif': 5, 'xmp': ' 8', 'imir': 0}, 2], [{'container': 'heif', 'exif': 3, 'xmp': '0', 'irot': 1, 'imir': 0}, 7], [{'container': 'heif', 'irot': 1, 'imir': 0}, 7], [{'container': 'jpeg', 'exif': 0, 'xmp': '\t5'}, 5], [{'container': 'jpeg', 'exif': 0}, 1], [{'container': 'jpeg', 'xmp': '3\n'}, 3], [{'container': 'heif', 'exif': 3, 'xmp': 'x', 'irot': 0}, 1], [{'container': 'heif', 'xmp': '\t5', 'irot': 0, 'imir': 1}, 4]], [[{'container': 'heif', 'exif': 8, 'xmp': ' 8', 'imir': 0}, 2], [{'container': 'heif', 'exif': 8, 'xmp': '\t5', 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 6, 'xmp': '\t5', 'irot': 3, 'imir': 0}, 5], [{'container': 'jpeg', 'xmp': '7 '}, 7], [{'container': 'tiff'}, 1], [{'container': 'jpeg'}, 1], [{'container': 'jpeg', 'exif': 0}, 1], [{'container': 'heif', 'exif': 3, 'xmp': '2', 'irot': 2, 'imir': 0}, 4]], [[{'container': 'heif', 'exif': 5, 'xmp': '3\n', 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 8, 'xmp': '6', 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'xmp': '0', 'irot': 1, 'imir': 0}, 7], [{'container': 'jpeg', 'exif': 1}, 1], [{'container': 'heif', 'exif': 1, 'xmp': '7 ', 'irot': 2}, 3], [{'container': 'heif', 'xmp': '\t5'}, 1], [{'container': 'jpeg', 'xmp': '7 '}, 7], [{'container': 'heif', 'exif': 0, 'xmp': '7 ', 'irot': 2, 'imir': 0}, 4]], [[{'container': 'heif', 'exif': 6, 'xmp': ' 8', 'irot': 1, 'imir': 1}, 5], [{'container': 'heif', 'exif': 7, 'irot': 3, 'imir': 0}, 5], [{'container': 'heif', 'exif': 5, 'xmp': '2', 'irot': 1, 'imir': 0}, 7], [{'container': 'png', 'xmp': '7 '}, 7], [{'container': 'heif', 'irot': 2}, 3], [{'container': 'heif', 'xmp': ' 8', 'irot': 1}, 8], [{'container': 'jpeg', 'xmp': 'x'}, 1], [{'container': 'heif', 'exif': 7, 'xmp': 'x', 'imir': 1}, 4]]]
labels = ["regression: imir axis meaning", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(args), expected)
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)

Boundary fixture	Actual	Expected	Outcome
regression: imir axis meaning 0	`7`	`5`	Failed
repair trap 1	`5`	`7`	Failed
combined fault 2	`1`	`1`	Passed
control 3	`2`	`2`	Passed
control 4	`6`	`6`	Passed
boundary 5	`1`	`1`	Passed
boundary 6	`7`	`7`	Passed
control 7	`4`	`4`	Passed

SHA-256 / 0e761d7168c20c405e6fbe7f4f6ce5f581b83d84423d873c65448c4a41ab609b

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

Verification & scope

A deterministic bounded teaching model with a stipulated contract; it makes no claim of conformance to any published specification. This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:49:40.515096+00:00.

Case digest / ca342df648ee6a42e1e631aa703acf3cdcbcb80778e2f163c7d52730b5c4a872