FAILURE MAP
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FA-79266 / Image orientation metadata / Open access

Letterbox offsets use stored instead of displayed size · case 01

Taps on rotated photos land a band away from where the user touched.

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

ROOT CAUSE

The centring offsets subtract the stored extents although the view shows the displayed extents.

VERIFIED REPAIR

Centre using the displayed width and height.

Unsuccessful approach: Integer-dividing the offset drops the half pixel of centring.

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 >= 5 else (w, h)
    s = min(Fraction(vw, dw), Fraction(vh, dh))
    ox = (vw - w * s) / 2
    oy = (vh - h * 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 = [[[([20, 22], [9, 8], 8, [11, 3]), [7, 4]], [([13, 26], [7, 5], 8, [9, 3]), None], [([27, 18], [5, 7], 7, [4, 11]), [1, 6]], [([40, 25], [5, 4], 8, [32, 13]), None], [([35, 16], [9, 2], 5, [3, 11]), None], [([13, 34], [5, 3], 1, [10, 31]), None], [([17, 19], [3, 9], 1, [7, 11]), [0, 5]], [([19, 36], [5, 7], 8, [17, 11]), None]], [[([14, 32], [7, 4], 7, [8, 32]), None], [([31, 20], [5, 2], 2, [3, 3]), None], [([31, 31], [8, 9], 5, [30, 8]), [1, 8]], [([40, 21], [2, 9], 3, [13, 12]), None], [([25, 26], [2, 9], 6, [18, 0]), None], [([21, 19], [8, 6], 3, [19, 2]), [0, 5]], [([17, 18], [6, 7], 3, [1, 6]), [5, 4]], [([25, 40], [2, 8], 8, [22, 21]), [0, 7]]], [[([17, 21], [6, 3], 5, [7, 3]), [0, 1]], [([27, 16], [6, 6], 3, [5, 8]), None], [([30, 35], [3, 5], 5, [25, 8]), None], [([17, 38], [4, 3], 4, [2, 16]), [0, 2]], [([35, 17], [7, 6], 1, [31, 5]), None], [([29, 33], [4, 5], 5, [5, 29]), None], [([37, 32], [8, 3], 3, [9, 25]), None], [([22, 15], [4, 6], 7, [16, 2]), [3, 1]]], [[([17, 29], [5, 8], 5, [14, 8]), None], [([23, 39], [2, 4], 0, [11, 20]), [0, 2]], [([17, 11], [6, 3], 5, [11, 0]), [0, 2]], [([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], [([16, 11], [3, 6], 6, [5, 3]), [0, 4]]], [[([18, 21], [2, 4], 6, [13, 6]), [0, 1]], [([11, 36], [4, 4], 6, [3, 15]), [0, 2]], [([38, 33], [8, 5], 6, [29, 25]), [6, 0]], [([29, 20], [4, 2], 6, [24, 13]), None], [([22, 31], [8, 9], 5, [14, 4]), None], [([15, 21], [6, 7], 7, [6, 5]), [5, 4]], [([36, 16], [8, 2], 3, [32, 16]), None], [([24, 8], [4, 7], 8, [16, 3]), [2, 5]]]]
labels = ["regression: letterbox offset extent", "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: letterbox offset extent 0[8, 4][7, 4]Failed
repair trap 1NoneNonePassed
combined fault 2None[1, 6]Failed
control 3NoneNonePassed
control 4NoneNonePassed
boundary 5NoneNonePassed
boundary 6[0, 5][0, 5]Passed
control 7[4, 5]NoneFailed

SHA-256 / 79af807ea7709b14f4f9fbf154e8b263902747cbaac052837a8a0d176b8c2cf6

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 = [[[([20, 22], [9, 8], 8, [11, 3]), [7, 4]], [([13, 26], [7, 5], 8, [9, 3]), None], [([27, 18], [5, 7], 7, [4, 11]), [1, 6]], [([40, 25], [5, 4], 8, [32, 13]), None], [([35, 16], [9, 2], 5, [3, 11]), None], [([13, 34], [5, 3], 1, [10, 31]), None], [([17, 19], [3, 9], 1, [7, 11]), [0, 5]], [([19, 36], [5, 7], 8, [17, 11]), None]], [[([14, 32], [7, 4], 7, [8, 32]), None], [([31, 20], [5, 2], 2, [3, 3]), None], [([31, 31], [8, 9], 5, [30, 8]), [1, 8]], [([40, 21], [2, 9], 3, [13, 12]), None], [([25, 26], [2, 9], 6, [18, 0]), None], [([21, 19], [8, 6], 3, [19, 2]), [0, 5]], [([17, 18], [6, 7], 3, [1, 6]), [5, 4]], [([25, 40], [2, 8], 8, [22, 21]), [0, 7]]], [[([17, 21], [6, 3], 5, [7, 3]), [0, 1]], [([27, 16], [6, 6], 3, [5, 8]), None], [([30, 35], [3, 5], 5, [25, 8]), None], [([17, 38], [4, 3], 4, [2, 16]), [0, 2]], [([35, 17], [7, 6], 1, [31, 5]), None], [([29, 33], [4, 5], 5, [5, 29]), None], [([37, 32], [8, 3], 3, [9, 25]), None], [([22, 15], [4, 6], 7, [16, 2]), [3, 1]]], [[([17, 29], [5, 8], 5, [14, 8]), None], [([23, 39], [2, 4], 0, [11, 20]), [0, 2]], [([17, 11], [6, 3], 5, [11, 0]), [0, 2]], [([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], [([16, 11], [3, 6], 6, [5, 3]), [0, 4]]], [[([18, 21], [2, 4], 6, [13, 6]), [0, 1]], [([11, 36], [4, 4], 6, [3, 15]), [0, 2]], [([38, 33], [8, 5], 6, [29, 25]), [6, 0]], [([29, 20], [4, 2], 6, [24, 13]), None], [([22, 31], [8, 9], 5, [14, 4]), None], [([15, 21], [6, 7], 7, [6, 5]), [5, 4]], [([36, 16], [8, 2], 3, [32, 16]), None], [([24, 8], [4, 7], 8, [16, 3]), [2, 5]]]]
labels = ["regression: letterbox offset extent", "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: letterbox offset extent 0[7, 4][7, 4]Passed
repair trap 1[6, 3]NoneFailed
combined fault 2[1, 5][1, 6]Failed
control 3NoneNonePassed
control 4NoneNonePassed
boundary 5NoneNonePassed
boundary 6[0, 5][0, 5]Passed
control 7[4, 6]NoneFailed

SHA-256 / 547e8029c1b8279a803b9b7b2835486727bc6340d5028f8ea68022921b6af27b

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 = [[[([20, 22], [9, 8], 8, [11, 3]), [7, 4]], [([13, 26], [7, 5], 8, [9, 3]), None], [([27, 18], [5, 7], 7, [4, 11]), [1, 6]], [([40, 25], [5, 4], 8, [32, 13]), None], [([35, 16], [9, 2], 5, [3, 11]), None], [([13, 34], [5, 3], 1, [10, 31]), None], [([17, 19], [3, 9], 1, [7, 11]), [0, 5]], [([19, 36], [5, 7], 8, [17, 11]), None]], [[([14, 32], [7, 4], 7, [8, 32]), None], [([31, 20], [5, 2], 2, [3, 3]), None], [([31, 31], [8, 9], 5, [30, 8]), [1, 8]], [([40, 21], [2, 9], 3, [13, 12]), None], [([25, 26], [2, 9], 6, [18, 0]), None], [([21, 19], [8, 6], 3, [19, 2]), [0, 5]], [([17, 18], [6, 7], 3, [1, 6]), [5, 4]], [([25, 40], [2, 8], 8, [22, 21]), [0, 7]]], [[([17, 21], [6, 3], 5, [7, 3]), [0, 1]], [([27, 16], [6, 6], 3, [5, 8]), None], [([30, 35], [3, 5], 5, [25, 8]), None], [([17, 38], [4, 3], 4, [2, 16]), [0, 2]], [([35, 17], [7, 6], 1, [31, 5]), None], [([29, 33], [4, 5], 5, [5, 29]), None], [([37, 32], [8, 3], 3, [9, 25]), None], [([22, 15], [4, 6], 7, [16, 2]), [3, 1]]], [[([17, 29], [5, 8], 5, [14, 8]), None], [([23, 39], [2, 4], 0, [11, 20]), [0, 2]], [([17, 11], [6, 3], 5, [11, 0]), [0, 2]], [([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], [([16, 11], [3, 6], 6, [5, 3]), [0, 4]]], [[([18, 21], [2, 4], 6, [13, 6]), [0, 1]], [([11, 36], [4, 4], 6, [3, 15]), [0, 2]], [([38, 33], [8, 5], 6, [29, 25]), [6, 0]], [([29, 20], [4, 2], 6, [24, 13]), None], [([22, 31], [8, 9], 5, [14, 4]), None], [([15, 21], [6, 7], 7, [6, 5]), [5, 4]], [([36, 16], [8, 2], 3, [32, 16]), None], [([24, 8], [4, 7], 8, [16, 3]), [2, 5]]]]
labels = ["regression: letterbox offset extent", "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: letterbox offset extent 0[7, 4][7, 4]Passed
repair trap 1NoneNonePassed
combined fault 2[1, 6][1, 6]Passed
control 3NoneNonePassed
control 4NoneNonePassed
boundary 5NoneNonePassed
boundary 6[0, 5][0, 5]Passed
control 7NoneNonePassed

SHA-256 / bb1ba4ad012bfd914e1d8b5f4fd00911e5b940ace46fd826a854bccdc311ae65

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 2026-09-29T14:49:43.050901+00:00.

Case digest / 5ba4067027ffddbe9e192010c80cfa45b6183336aa56a73cb969f323bd8232a7