FAILURE MAP
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FA-79286 / Image orientation metadata / Open access

Tap mapping ignores the axis swap for transpose tags · case 01

On tag 5 and 7 photos the fit and bounds use the stored aspect ratio.

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

ROOT CAUSE

Only the pure rotations swap displayed width and height.

VERIFIED REPAIR

Swap for every tag from 5 to 8.

Unsuccessful approach: A strict > 5 test still misses tag 5.

Case contract

A viewer letterboxes the displayed image (contain fit, centred) inside a view [vw, vh]. Map a tap [tx, ty] in view coordinates to the stored pixel [x, y] of an image of stored size [w, h] with orientation tag 1..8 (invalid -> 1); tags 5..8 swap displayed axes. Taps in the letterbox bars or on the far edge return None; the displayed pixel is floor of the exact position, then mapped back through the inverse of the orientation.

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
import math
from fractions import Fraction
N = 1
observations = []
def solve(view, size, tag, tap):
    vw, vh = view
    w, h = size
    tx, ty = tap
    if tag not in range(1, 9):
        tag = 1
    dw, dh = (h, w) if tag in (6, 8) else (w, h)
    s = min(Fraction(vw, dw), Fraction(vh, dh))
    ox = (vw - dw * s) / 2
    oy = (vh - dh * s) / 2
    dx = (tx - ox) / s
    dy = (ty - oy) / s
    if dx < 0 or dy < 0 or dx >= dw or dy >= dh:
        return None
    px, py = math.floor(dx), math.floor(dy)
    if tag == 1:
        return [px, py]
    if tag == 2:
        return [w - 1 - px, py]
    if tag == 3:
        return [w - 1 - px, h - 1 - py]
    if tag == 4:
        return [px, h - 1 - py]
    if tag == 5:
        return [py, px]
    if tag == 6:
        return [py, h - 1 - px]
    if tag == 7:
        return [w - 1 - py, h - 1 - px]
    return [w - 1 - py, px]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[([35, 16], [9, 2], 5, [3, 11]), None], [([29, 25], [7, 6], 5, [23, 24]), [6, 5]], [([40, 25], [5, 4], 8, [32, 13]), None], [([13, 34], [5, 3], 1, [10, 31]), None], [([17, 19], [3, 9], 1, [7, 11]), [0, 5]], [([13, 26], [7, 5], 8, [9, 3]), None], [([22, 31], [4, 9], 8, [16, 29]), None], [([8, 10], [4, 8], 5, [4, 8]), None]], [[([26, 22], [9, 7], 7, [0, 13]), None], [([24, 14], [5, 8], 5, [7, 8]), [2, 2]], [([16, 30], [4, 2], 5, [9, 29]), [3, 1]], [([40, 21], [2, 9], 3, [13, 12]), None], [([26, 29], [2, 4], 6, [11, 12]), [0, 2]], [([25, 26], [2, 9], 6, [18, 0]), None], [([21, 19], [8, 6], 3, [19, 2]), [0, 5]], [([40, 35], [8, 6], 5, [27, 26]), [5, 4]]], [[([17, 21], [6, 3], 5, [7, 3]), [0, 1]], [([39, 28], [2, 4], 5, [14, 12]), [0, 1]], [([31, 21], [7, 5], 5, [2, 18]), None], [([11, 19], [8, 2], 8, [8, 11]), None], [([17, 38], [4, 3], 4, [2, 16]), [0, 2]], [([35, 17], [7, 6], 1, [31, 5]), None], [([37, 32], [8, 3], 3, [9, 25]), None], [([29, 33], [4, 5], 5, [5, 29]), None]], [[([27, 39], [2, 3], 7, [8, 5]), None], [([31, 38], [2, 6], 5, [4, 16]), [0, 0]], [([27, 31], [6, 9], 5, [15, 23]), [5, 5]], [([22, 29], [9, 2], 3, [8, 26]), None], [([35, 16], [3, 9], 2, [10, 1]), None], [([39, 33], [9, 7], 1, [6, 23]), [1, 5]], [([20, 23], [3, 9], 0, [20, 12]), None], [([17, 29], [5, 8], 5, [14, 8]), None]], [[([26, 16], [5, 6], 7, [22, 12]), [1, 0]], [([31, 9], [5, 9], 5, [10, 8]), [4, 1]], [([22, 21], [2, 4], 5, [18, 6]), [0, 3]], [([29, 20], [4, 2], 6, [24, 13]), None], [([29, 29], [7, 9], 8, [12, 2]), None], [([36, 16], [8, 2], 3, [32, 16]), None], [([31, 20], [7, 7], 6, [7, 16]), [5, 6]], [([15, 21], [6, 7], 7, [6, 5]), [5, 4]]]]
labels = ["regression: displayed axis swap", "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 fixtureActualExpectedOutcome
regression: displayed axis swap 0[1, 0]NoneFailed
repair trap 1[5, 5][6, 5]Failed
combined fault 2NoneNonePassed
control 3NoneNonePassed
control 4[0, 5][0, 5]Passed
boundary 5NoneNonePassed
boundary 6NoneNonePassed
control 7[6, 2]NoneFailed

SHA-256 / 42581596716b252c52cae2cafebf8f2a3bb221f3e5343f3b25e5785724b868ac

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(view, size, tag, tap):
    vw, vh = view
    w, h = size
    tx, ty = tap
    if tag not in range(1, 9):
        tag = 1
    dw, dh = (h, w) if tag > 5 else (w, h)
    s = min(Fraction(vw, dw), Fraction(vh, dh))
    ox = (vw - dw * s) / 2
    oy = (vh - dh * s) / 2
    dx = (tx - ox) / s
    dy = (ty - oy) / s
    if dx < 0 or dy < 0 or dx >= dw or dy >= dh:
        return None
    px, py = math.floor(dx), math.floor(dy)
    if tag == 1:
        return [px, py]
    if tag == 2:
        return [w - 1 - px, py]
    if tag == 3:
        return [w - 1 - px, h - 1 - py]
    if tag == 4:
        return [px, h - 1 - py]
    if tag == 5:
        return [py, px]
    if tag == 6:
        return [py, h - 1 - px]
    if tag == 7:
        return [w - 1 - py, h - 1 - px]
    return [w - 1 - py, px]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[([35, 16], [9, 2], 5, [3, 11]), None], [([29, 25], [7, 6], 5, [23, 24]), [6, 5]], [([40, 25], [5, 4], 8, [32, 13]), None], [([13, 34], [5, 3], 1, [10, 31]), None], [([17, 19], [3, 9], 1, [7, 11]), [0, 5]], [([13, 26], [7, 5], 8, [9, 3]), None], [([22, 31], [4, 9], 8, [16, 29]), None], [([8, 10], [4, 8], 5, [4, 8]), None]], [[([26, 22], [9, 7], 7, [0, 13]), None], [([24, 14], [5, 8], 5, [7, 8]), [2, 2]], [([16, 30], [4, 2], 5, [9, 29]), [3, 1]], [([40, 21], [2, 9], 3, [13, 12]), None], [([26, 29], [2, 4], 6, [11, 12]), [0, 2]], [([25, 26], [2, 9], 6, [18, 0]), None], [([21, 19], [8, 6], 3, [19, 2]), [0, 5]], [([40, 35], [8, 6], 5, [27, 26]), [5, 4]]], [[([17, 21], [6, 3], 5, [7, 3]), [0, 1]], [([39, 28], [2, 4], 5, [14, 12]), [0, 1]], [([31, 21], [7, 5], 5, [2, 18]), None], [([11, 19], [8, 2], 8, [8, 11]), None], [([17, 38], [4, 3], 4, [2, 16]), [0, 2]], [([35, 17], [7, 6], 1, [31, 5]), None], [([37, 32], [8, 3], 3, [9, 25]), None], [([29, 33], [4, 5], 5, [5, 29]), None]], [[([27, 39], [2, 3], 7, [8, 5]), None], [([31, 38], [2, 6], 5, [4, 16]), [0, 0]], [([27, 31], [6, 9], 5, [15, 23]), [5, 5]], [([22, 29], [9, 2], 3, [8, 26]), None], [([35, 16], [3, 9], 2, [10, 1]), None], [([39, 33], [9, 7], 1, [6, 23]), [1, 5]], [([20, 23], [3, 9], 0, [20, 12]), None], [([17, 29], [5, 8], 5, [14, 8]), None]], [[([26, 16], [5, 6], 7, [22, 12]), [1, 0]], [([31, 9], [5, 9], 5, [10, 8]), [4, 1]], [([22, 21], [2, 4], 5, [18, 6]), [0, 3]], [([29, 20], [4, 2], 6, [24, 13]), None], [([29, 29], [7, 9], 8, [12, 2]), None], [([36, 16], [8, 2], 3, [32, 16]), None], [([31, 20], [7, 7], 6, [7, 16]), [5, 6]], [([15, 21], [6, 7], 7, [6, 5]), [5, 4]]]]
labels = ["regression: displayed axis swap", "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 fixtureActualExpectedOutcome
regression: displayed axis swap 0[1, 0]NoneFailed
repair trap 1[5, 5][6, 5]Failed
combined fault 2NoneNonePassed
control 3NoneNonePassed
control 4[0, 5][0, 5]Passed
boundary 5NoneNonePassed
boundary 6NoneNonePassed
control 7[6, 2]NoneFailed

SHA-256 / f4c04279e48d781decd2d36845be68d0394f5802179e5681962584a9b2fb28e7

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
import math
from fractions import Fraction
N = 1
observations = []
def solve(view, size, tag, tap):
    vw, vh = view
    w, h = size
    tx, ty = tap
    if tag not in range(1, 9):
        tag = 1
    dw, dh = (h, w) if tag >= 5 else (w, h)
    s = min(Fraction(vw, dw), Fraction(vh, dh))
    ox = (vw - dw * s) / 2
    oy = (vh - dh * s) / 2
    dx = (tx - ox) / s
    dy = (ty - oy) / s
    if dx < 0 or dy < 0 or dx >= dw or dy >= dh:
        return None
    px, py = math.floor(dx), math.floor(dy)
    if tag == 1:
        return [px, py]
    if tag == 2:
        return [w - 1 - px, py]
    if tag == 3:
        return [w - 1 - px, h - 1 - py]
    if tag == 4:
        return [px, h - 1 - py]
    if tag == 5:
        return [py, px]
    if tag == 6:
        return [py, h - 1 - px]
    if tag == 7:
        return [w - 1 - py, h - 1 - px]
    return [w - 1 - py, px]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[([35, 16], [9, 2], 5, [3, 11]), None], [([29, 25], [7, 6], 5, [23, 24]), [6, 5]], [([40, 25], [5, 4], 8, [32, 13]), None], [([13, 34], [5, 3], 1, [10, 31]), None], [([17, 19], [3, 9], 1, [7, 11]), [0, 5]], [([13, 26], [7, 5], 8, [9, 3]), None], [([22, 31], [4, 9], 8, [16, 29]), None], [([8, 10], [4, 8], 5, [4, 8]), None]], [[([26, 22], [9, 7], 7, [0, 13]), None], [([24, 14], [5, 8], 5, [7, 8]), [2, 2]], [([16, 30], [4, 2], 5, [9, 29]), [3, 1]], [([40, 21], [2, 9], 3, [13, 12]), None], [([26, 29], [2, 4], 6, [11, 12]), [0, 2]], [([25, 26], [2, 9], 6, [18, 0]), None], [([21, 19], [8, 6], 3, [19, 2]), [0, 5]], [([40, 35], [8, 6], 5, [27, 26]), [5, 4]]], [[([17, 21], [6, 3], 5, [7, 3]), [0, 1]], [([39, 28], [2, 4], 5, [14, 12]), [0, 1]], [([31, 21], [7, 5], 5, [2, 18]), None], [([11, 19], [8, 2], 8, [8, 11]), None], [([17, 38], [4, 3], 4, [2, 16]), [0, 2]], [([35, 17], [7, 6], 1, [31, 5]), None], [([37, 32], [8, 3], 3, [9, 25]), None], [([29, 33], [4, 5], 5, [5, 29]), None]], [[([27, 39], [2, 3], 7, [8, 5]), None], [([31, 38], [2, 6], 5, [4, 16]), [0, 0]], [([27, 31], [6, 9], 5, [15, 23]), [5, 5]], [([22, 29], [9, 2], 3, [8, 26]), None], [([35, 16], [3, 9], 2, [10, 1]), None], [([39, 33], [9, 7], 1, [6, 23]), [1, 5]], [([20, 23], [3, 9], 0, [20, 12]), None], [([17, 29], [5, 8], 5, [14, 8]), None]], [[([26, 16], [5, 6], 7, [22, 12]), [1, 0]], [([31, 9], [5, 9], 5, [10, 8]), [4, 1]], [([22, 21], [2, 4], 5, [18, 6]), [0, 3]], [([29, 20], [4, 2], 6, [24, 13]), None], [([29, 29], [7, 9], 8, [12, 2]), None], [([36, 16], [8, 2], 3, [32, 16]), None], [([31, 20], [7, 7], 6, [7, 16]), [5, 6]], [([15, 21], [6, 7], 7, [6, 5]), [5, 4]]]]
labels = ["regression: displayed axis swap", "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 fixtureActualExpectedOutcome
regression: displayed axis swap 0NoneNonePassed
repair trap 1[6, 5][6, 5]Passed
combined fault 2NoneNonePassed
control 3NoneNonePassed
control 4[0, 5][0, 5]Passed
boundary 5NoneNonePassed
boundary 6NoneNonePassed
control 7NoneNonePassed

SHA-256 / ed6d07ad9372c26166a73f3e75010464f1192b491a8120eaeee324e8127f8f0e

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 2026-09-29T14:49:43.159131+00:00.

Case digest / 4422ddb83ca7fab2073103c06150ac665e7affe144b0bf340418eaf35a481623