From 0d561bed8772c28da8f49cd0b16f503c93b0f77d Mon Sep 17 00:00:00 2001
From: skal <pascal.massimino@gmail.com>
Date: Tue, 24 Mar 2026 23:14:13 +0100
Subject: add more samples + tool html

---
 .../training/dataset/simple/sample_000/albedo.png  | Bin 0 -> 2894133 bytes
 .../training/dataset/simple/sample_000/depth.png   | Bin 0 -> 2654 bytes
 .../training/dataset/simple/sample_000/matid.png   | Bin 0 -> 1368 bytes
 .../training/dataset/simple/sample_000/normal.png  | Bin 0 -> 6897 bytes
 .../training/dataset/simple/sample_000/shadow.png  | Bin 0 -> 3907 bytes
 .../training/dataset/simple/sample_000/target.png  | Bin 0 -> 1704763 bytes
 .../training/dataset/simple/sample_000/transp.png  | Bin 0 -> 1368 bytes
 .../training/dataset/simple/sample_001/albedo.png  | Bin 3267671 -> 1665978 bytes
 .../training/dataset/simple/sample_001/depth.png   | Bin 433 -> 1582 bytes
 .../training/dataset/simple/sample_001/matid.png   | Bin 433 -> 832 bytes
 .../training/dataset/simple/sample_001/normal.png  | Bin 494 -> 3965 bytes
 .../training/dataset/simple/sample_001/shadow.png  | Bin 948 -> 2356 bytes
 .../training/dataset/simple/sample_001/target.png  | Bin 1594636 -> 1274711 bytes
 .../training/dataset/simple/sample_001/transp.png  | Bin 433 -> 832 bytes
 .../training/dataset/simple/sample_002/albedo.png  | Bin 1910125 -> 2449144 bytes
 .../training/dataset/simple/sample_002/depth.png   | Bin 385 -> 2664 bytes
 .../training/dataset/simple/sample_002/matid.png   | Bin 388 -> 1373 bytes
 .../training/dataset/simple/sample_002/normal.png  | Bin 449 -> 6979 bytes
 .../training/dataset/simple/sample_002/shadow.png  | Bin 705 -> 3953 bytes
 .../training/dataset/simple/sample_002/target.png  | Bin 936612 -> 1808188 bytes
 .../training/dataset/simple/sample_002/transp.png  | Bin 388 -> 1373 bytes
 .../training/dataset/simple/sample_003/albedo.png  | Bin 712863 -> 1881372 bytes
 .../training/dataset/simple/sample_003/depth.png   | Bin 944 -> 2010 bytes
 .../training/dataset/simple/sample_003/matid.png   | Bin 512 -> 1045 bytes
 .../training/dataset/simple/sample_003/normal.png  | Bin 2978 -> 5128 bytes
 .../training/dataset/simple/sample_003/shadow.png  | Bin 1918 -> 3254 bytes
 .../training/dataset/simple/sample_003/target.png  | Bin 386534 -> 917739 bytes
 .../training/dataset/simple/sample_003/transp.png  | Bin 512 -> 1045 bytes
 .../training/dataset/simple/sample_004/albedo.png  | Bin 1245962 -> 715310 bytes
 .../training/dataset/simple/sample_004/depth.png   | Bin 7389 -> 940 bytes
 .../training/dataset/simple/sample_004/matid.png   | Bin 3735 -> 510 bytes
 .../training/dataset/simple/sample_004/normal.png  | Bin 15342 -> 2978 bytes
 .../training/dataset/simple/sample_004/shadow.png  | Bin 7738 -> 1918 bytes
 .../training/dataset/simple/sample_004/target.png  | Bin 1803485 -> 337663 bytes
 .../training/dataset/simple/sample_004/transp.png  | Bin 3735 -> 510 bytes
 .../training/dataset/simple/sample_005/albedo.png  | Bin 1187567 -> 1212746 bytes
 .../training/dataset/simple/sample_005/depth.png   | Bin 1632 -> 5568 bytes
 .../training/dataset/simple/sample_005/matid.png   | Bin 857 -> 2824 bytes
 .../training/dataset/simple/sample_005/normal.png  | Bin 4590 -> 11587 bytes
 .../training/dataset/simple/sample_005/shadow.png  | Bin 3137 -> 6244 bytes
 .../training/dataset/simple/sample_005/target.png  | Bin 1054621 -> 1505231 bytes
 .../training/dataset/simple/sample_005/transp.png  | Bin 857 -> 2824 bytes
 .../training/dataset/simple/sample_006/albedo.png  | Bin 2059642 -> 1211680 bytes
 .../training/dataset/simple/sample_006/depth.png   | Bin 390 -> 1626 bytes
 .../training/dataset/simple/sample_006/matid.png   | Bin 390 -> 854 bytes
 .../training/dataset/simple/sample_006/normal.png  | Bin 451 -> 4287 bytes
 .../training/dataset/simple/sample_006/shadow.png  | Bin 713 -> 3129 bytes
 .../training/dataset/simple/sample_006/target.png  | Bin 1617556 -> 963050 bytes
 .../training/dataset/simple/sample_006/transp.png  | Bin 390 -> 854 bytes
 .../training/dataset/simple/sample_007/albedo.png  | Bin 2915020 -> 1882632 bytes
 .../training/dataset/simple/sample_007/depth.png   | Bin 454 -> 2079 bytes
 .../training/dataset/simple/sample_007/matid.png   | Bin 454 -> 1080 bytes
 .../training/dataset/simple/sample_007/normal.png  | Bin 515 -> 5323 bytes
 .../training/dataset/simple/sample_007/shadow.png  | Bin 1033 -> 3174 bytes
 .../training/dataset/simple/sample_007/target.png  | Bin 3084795 -> 1643278 bytes
 .../training/dataset/simple/sample_007/transp.png  | Bin 454 -> 1080 bytes
 .../training/dataset/simple/sample_008/albedo.png  | Bin 0 -> 2463328 bytes
 .../training/dataset/simple/sample_008/depth.png   | Bin 0 -> 2799 bytes
 .../training/dataset/simple/sample_008/matid.png   | Bin 0 -> 1440 bytes
 .../training/dataset/simple/sample_008/normal.png  | Bin 0 -> 7565 bytes
 .../training/dataset/simple/sample_008/shadow.png  | Bin 0 -> 4496 bytes
 .../training/dataset/simple/sample_008/target.png  | Bin 0 -> 2550834 bytes
 .../training/dataset/simple/sample_008/transp.png  | Bin 0 -> 1440 bytes
 .../training/dataset/simple/sample_009/albedo.png  | Bin 0 -> 1761742 bytes
 .../training/dataset/simple/sample_009/depth.png   | Bin 0 -> 2691 bytes
 .../training/dataset/simple/sample_009/matid.png   | Bin 0 -> 1386 bytes
 .../training/dataset/simple/sample_009/normal.png  | Bin 0 -> 7025 bytes
 .../training/dataset/simple/sample_009/shadow.png  | Bin 0 -> 3979 bytes
 .../training/dataset/simple/sample_009/target.png  | Bin 0 -> 1140399 bytes
 .../training/dataset/simple/sample_009/transp.png  | Bin 0 -> 1386 bytes
 .../training/dataset/simple/sample_010/albedo.png  | Bin 0 -> 925190 bytes
 .../training/dataset/simple/sample_010/depth.png   | Bin 0 -> 1519 bytes
 .../training/dataset/simple/sample_010/matid.png   | Bin 0 -> 800 bytes
 .../training/dataset/simple/sample_010/normal.png  | Bin 0 -> 3801 bytes
 .../training/dataset/simple/sample_010/shadow.png  | Bin 0 -> 2260 bytes
 .../training/dataset/simple/sample_010/target.png  | Bin 0 -> 636870 bytes
 .../training/dataset/simple/sample_010/transp.png  | Bin 0 -> 800 bytes
 cnn_v3/training/go.sh                              |  13 +
 cnn_v3/training/input/photo1.jpg                   | Bin 5813575 -> 742550 bytes
 cnn_v3/training/input/photo10.jpg                  | Bin 0 -> 423215 bytes
 cnn_v3/training/input/photo11.jpg                  | Bin 0 -> 588431 bytes
 cnn_v3/training/input/photo2.jpg                   | Bin 3328126 -> 494566 bytes
 cnn_v3/training/input/photo3.jpg                   | Bin 191081 -> 197458 bytes
 cnn_v3/training/input/photo4.jpg                   | Bin 286046 -> 267311 bytes
 cnn_v3/training/input/photo5.jpg                   | Bin 194497 -> 264340 bytes
 cnn_v3/training/input/photo6.jpg                   | Bin 341559 -> 462015 bytes
 cnn_v3/training/input/photo7.jpg                   | Bin 3328126 -> 635181 bytes
 cnn_v3/training/input/photo8.jpg                   | Bin 0 -> 406649 bytes
 cnn_v3/training/input/photo9.jpg                   | Bin 0 -> 233190 bytes
 cnn_v3/training/target_1/photo10_out.png           | Bin 0 -> 1261727 bytes
 cnn_v3/training/target_1/photo11_out.png           | Bin 0 -> 1774086 bytes
 cnn_v3/training/target_1/photo1_out.png            | Bin 4038843 -> 1684950 bytes
 cnn_v3/training/target_1/photo2_out.png            | Bin 2875939 -> 908040 bytes
 cnn_v3/training/target_1/photo3_out.png            | Bin 3250140 -> 334148 bytes
 cnn_v3/training/target_1/photo4_out.png            | Bin 718912 -> 1467590 bytes
 cnn_v3/training/target_1/photo5_out.png            | Bin 3438962 -> 934590 bytes
 cnn_v3/training/target_1/photo6_out.png            | Bin 3238587 -> 1717812 bytes
 cnn_v3/training/target_1/photo7_out.png            | Bin 2865857 -> 2532029 bytes
 cnn_v3/training/target_1/photo8_out.png            | Bin 0 -> 1124391 bytes
 cnn_v3/training/target_1/photo9_out.png            | Bin 0 -> 631696 bytes
 tools/adjust.html                                  | 570 ++++++++-------------
 101 files changed, 226 insertions(+), 357 deletions(-)
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/albedo.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/depth.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/matid.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/normal.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/shadow.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/target.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_000/transp.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/albedo.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/depth.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/matid.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/normal.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/shadow.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/target.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_008/transp.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/albedo.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/depth.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/matid.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/normal.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/shadow.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/target.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_009/transp.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/albedo.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/depth.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/matid.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/normal.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/shadow.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/target.png
 create mode 100644 cnn_v3/training/dataset/simple/sample_010/transp.png
 create mode 100755 cnn_v3/training/go.sh
 create mode 100644 cnn_v3/training/input/photo10.jpg
 create mode 100644 cnn_v3/training/input/photo11.jpg
 create mode 100644 cnn_v3/training/input/photo8.jpg
 create mode 100644 cnn_v3/training/input/photo9.jpg
 create mode 100644 cnn_v3/training/target_1/photo10_out.png
 create mode 100644 cnn_v3/training/target_1/photo11_out.png
 create mode 100644 cnn_v3/training/target_1/photo8_out.png
 create mode 100644 cnn_v3/training/target_1/photo9_out.png

diff --git a/cnn_v3/training/dataset/simple/sample_000/albedo.png b/cnn_v3/training/dataset/simple/sample_000/albedo.png
new file mode 100644
index 0000000..6656f52
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diff --git a/cnn_v3/training/dataset/simple/sample_000/depth.png b/cnn_v3/training/dataset/simple/sample_000/depth.png
new file mode 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_000/matid.png b/cnn_v3/training/dataset/simple/sample_000/matid.png
new file mode 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_000/normal.png b/cnn_v3/training/dataset/simple/sample_000/normal.png
new file mode 100644
index 0000000..00030c6
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diff --git a/cnn_v3/training/dataset/simple/sample_000/shadow.png b/cnn_v3/training/dataset/simple/sample_000/shadow.png
new file mode 100644
index 0000000..f2d5e70
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diff --git a/cnn_v3/training/dataset/simple/sample_000/target.png b/cnn_v3/training/dataset/simple/sample_000/target.png
new file mode 100644
index 0000000..6f407ef
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diff --git a/cnn_v3/training/dataset/simple/sample_000/transp.png b/cnn_v3/training/dataset/simple/sample_000/transp.png
new file mode 100644
index 0000000..7428ad7
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diff --git a/cnn_v3/training/dataset/simple/sample_001/albedo.png b/cnn_v3/training/dataset/simple/sample_001/albedo.png
index aa60e27..ee43d7c 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_001/depth.png b/cnn_v3/training/dataset/simple/sample_001/depth.png
index 16e97cb..8f289bd 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_001/matid.png b/cnn_v3/training/dataset/simple/sample_001/matid.png
index 88cb3de..1ff8a13 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_001/normal.png b/cnn_v3/training/dataset/simple/sample_001/normal.png
index d60bd34..0822f2d 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_001/shadow.png b/cnn_v3/training/dataset/simple/sample_001/shadow.png
index 07873e5..0d2de99 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_001/target.png b/cnn_v3/training/dataset/simple/sample_001/target.png
index 74e3705..9f3b32d 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_001/transp.png b/cnn_v3/training/dataset/simple/sample_001/transp.png
index 6aaa9b9..1ff8a13 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/albedo.png b/cnn_v3/training/dataset/simple/sample_002/albedo.png
index eabbccf..462f2ff 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/depth.png b/cnn_v3/training/dataset/simple/sample_002/depth.png
index 40f9214..a337f7b 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/matid.png b/cnn_v3/training/dataset/simple/sample_002/matid.png
index 3ff1922..0c987bd 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/normal.png b/cnn_v3/training/dataset/simple/sample_002/normal.png
index ddff986..cfd0c0d 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/shadow.png b/cnn_v3/training/dataset/simple/sample_002/shadow.png
index 797d48e..056130a 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/target.png b/cnn_v3/training/dataset/simple/sample_002/target.png
index 3bec403..2e23781 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_002/transp.png b/cnn_v3/training/dataset/simple/sample_002/transp.png
index 492437f..0c987bd 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/albedo.png b/cnn_v3/training/dataset/simple/sample_003/albedo.png
index 0cdd4df..bd18f05 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/depth.png b/cnn_v3/training/dataset/simple/sample_003/depth.png
index 87e66a5..2436c91 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/matid.png b/cnn_v3/training/dataset/simple/sample_003/matid.png
index af70ff3..44a5c21 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/normal.png b/cnn_v3/training/dataset/simple/sample_003/normal.png
index 206f2cf..5b1a79e 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/shadow.png b/cnn_v3/training/dataset/simple/sample_003/shadow.png
index e523baa..87a125e 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/target.png b/cnn_v3/training/dataset/simple/sample_003/target.png
index 5dd34d2..b9d15a2 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_003/transp.png b/cnn_v3/training/dataset/simple/sample_003/transp.png
index af70ff3..44a5c21 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/albedo.png b/cnn_v3/training/dataset/simple/sample_004/albedo.png
index a1b817d..59dab7c 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/depth.png b/cnn_v3/training/dataset/simple/sample_004/depth.png
index 083c4a3..99ce9a3 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/matid.png b/cnn_v3/training/dataset/simple/sample_004/matid.png
index f14721f..3cac678 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/normal.png b/cnn_v3/training/dataset/simple/sample_004/normal.png
index 0694ac1..fd611b4 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/shadow.png b/cnn_v3/training/dataset/simple/sample_004/shadow.png
index 9b6a5e0..bceee38 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/target.png b/cnn_v3/training/dataset/simple/sample_004/target.png
index c098192..61d312f 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_004/transp.png b/cnn_v3/training/dataset/simple/sample_004/transp.png
index f14721f..3cac678 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/albedo.png b/cnn_v3/training/dataset/simple/sample_005/albedo.png
index 5dfada7..087270c 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/depth.png b/cnn_v3/training/dataset/simple/sample_005/depth.png
index db65c11..7ed07de 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/matid.png b/cnn_v3/training/dataset/simple/sample_005/matid.png
index f098c4d..6c1fea2 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/normal.png b/cnn_v3/training/dataset/simple/sample_005/normal.png
index 40305ec..e7589f1 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/shadow.png b/cnn_v3/training/dataset/simple/sample_005/shadow.png
index 8e270d3..6b63afa 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/target.png b/cnn_v3/training/dataset/simple/sample_005/target.png
index 976a080..228acb3 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_005/transp.png b/cnn_v3/training/dataset/simple/sample_005/transp.png
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diff --git a/cnn_v3/training/dataset/simple/sample_006/albedo.png b/cnn_v3/training/dataset/simple/sample_006/albedo.png
index 0de8932..43bf608 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_006/depth.png b/cnn_v3/training/dataset/simple/sample_006/depth.png
index 9379478..378f379 100644
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index 5888a55..3878358 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_006/shadow.png b/cnn_v3/training/dataset/simple/sample_006/shadow.png
index c92a2e9..835abed 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_006/target.png b/cnn_v3/training/dataset/simple/sample_006/target.png
index 6107560..33f72cb 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_006/transp.png b/cnn_v3/training/dataset/simple/sample_006/transp.png
index 32ff022..355cd72 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_007/albedo.png b/cnn_v3/training/dataset/simple/sample_007/albedo.png
index f8884bb..3bd2364 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_007/depth.png b/cnn_v3/training/dataset/simple/sample_007/depth.png
index 9bbc913..47e766f 100644
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diff --git a/cnn_v3/training/dataset/simple/sample_007/matid.png b/cnn_v3/training/dataset/simple/sample_007/matid.png
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diff --git a/cnn_v3/training/dataset/simple/sample_007/target.png b/cnn_v3/training/dataset/simple/sample_007/target.png
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index 0000000..77c88b4
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diff --git a/cnn_v3/training/dataset/simple/sample_010/target.png b/cnn_v3/training/dataset/simple/sample_010/target.png
new file mode 100644
index 0000000..c2a4b22
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diff --git a/cnn_v3/training/dataset/simple/sample_010/transp.png b/cnn_v3/training/dataset/simple/sample_010/transp.png
new file mode 100644
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diff --git a/cnn_v3/training/go.sh b/cnn_v3/training/go.sh
new file mode 100755
index 0000000..31ac655
--- /dev/null
+++ b/cnn_v3/training/go.sh
@@ -0,0 +1,13 @@
+#!/bin/sh
+
+i=0
+for f in input/*.jpg; do
+    name=$(basename "${f%.jpg}")
+    convert ${f} ${name}.png
+    index=$(printf "%03d" "$i")
+    echo "processing ${name} ${index}"
+    python3 pack_photo_sample.py --photo "${name}.png" \
+        --target target_1/${name}_out.png \
+        --output dataset/simple/sample_${index}/
+    ((i++))
+done
diff --git a/cnn_v3/training/input/photo1.jpg b/cnn_v3/training/input/photo1.jpg
index 1c1c2b2..7427ad2 100644
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diff --git a/cnn_v3/training/input/photo10.jpg b/cnn_v3/training/input/photo10.jpg
new file mode 100644
index 0000000..09d3cb3
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diff --git a/cnn_v3/training/input/photo11.jpg b/cnn_v3/training/input/photo11.jpg
new file mode 100644
index 0000000..6b8ebf1
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diff --git a/cnn_v3/training/input/photo2.jpg b/cnn_v3/training/input/photo2.jpg
index a662fa8..4723943 100644
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diff --git a/cnn_v3/training/input/photo3.jpg b/cnn_v3/training/input/photo3.jpg
index 0342d04..f6f5c07 100644
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diff --git a/cnn_v3/training/input/photo4.jpg b/cnn_v3/training/input/photo4.jpg
index 5035993..01cd3d7 100644
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diff --git a/cnn_v3/training/input/photo5.jpg b/cnn_v3/training/input/photo5.jpg
index c71f388..2db699b 100644
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diff --git a/cnn_v3/training/input/photo6.jpg b/cnn_v3/training/input/photo6.jpg
index 68af708..0467933 100644
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diff --git a/cnn_v3/training/input/photo7.jpg b/cnn_v3/training/input/photo7.jpg
index a662fa8..e9021bf 100644
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diff --git a/cnn_v3/training/input/photo8.jpg b/cnn_v3/training/input/photo8.jpg
new file mode 100644
index 0000000..e15ed7d
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diff --git a/cnn_v3/training/input/photo9.jpg b/cnn_v3/training/input/photo9.jpg
new file mode 100644
index 0000000..cdd5dae
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diff --git a/cnn_v3/training/target_1/photo10_out.png b/cnn_v3/training/target_1/photo10_out.png
new file mode 100644
index 0000000..f9688db
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diff --git a/cnn_v3/training/target_1/photo11_out.png b/cnn_v3/training/target_1/photo11_out.png
new file mode 100644
index 0000000..082514b
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diff --git a/cnn_v3/training/target_1/photo1_out.png b/cnn_v3/training/target_1/photo1_out.png
index 36a36a7..2434c16 100644
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diff --git a/cnn_v3/training/target_1/photo2_out.png b/cnn_v3/training/target_1/photo2_out.png
index 5398232..912649e 100644
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diff --git a/cnn_v3/training/target_1/photo3_out.png b/cnn_v3/training/target_1/photo3_out.png
index 81c0a86..e2f6e58 100644
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diff --git a/cnn_v3/training/target_1/photo4_out.png b/cnn_v3/training/target_1/photo4_out.png
index 56f6274..81f3482 100644
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diff --git a/cnn_v3/training/target_1/photo5_out.png b/cnn_v3/training/target_1/photo5_out.png
index b530fc3..0177d36 100644
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diff --git a/cnn_v3/training/target_1/photo6_out.png b/cnn_v3/training/target_1/photo6_out.png
index e4e1197..8ad3b60 100644
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diff --git a/cnn_v3/training/target_1/photo7_out.png b/cnn_v3/training/target_1/photo7_out.png
index b0a76bd..d1320c8 100644
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diff --git a/cnn_v3/training/target_1/photo8_out.png b/cnn_v3/training/target_1/photo8_out.png
new file mode 100644
index 0000000..4213006
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diff --git a/cnn_v3/training/target_1/photo9_out.png b/cnn_v3/training/target_1/photo9_out.png
new file mode 100644
index 0000000..504cadf
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diff --git a/tools/adjust.html b/tools/adjust.html
index 3b5b243..429bffc 100644
--- a/tools/adjust.html
+++ b/tools/adjust.html
@@ -1,419 +1,275 @@
-<!-- tools for https://colorifyai.art/photo-to-sketch/#playground in 'Ink Sketch' mode -->
-
 <!DOCTYPE html>
 <html>
 <head>
 <meta charset="UTF-8">
-<title>Align & Optimize</title>
+<title>Align & Optimize - Explicit Fix</title>
 <style>
-body{font-family:sans-serif;text-align:center;background:#f5f5f5}
-canvas{border:1px solid #ccc;margin-top:10px;cursor:grab}
-textarea{width:90%;height:90px;margin:10px;font-family:monospace;font-size:12px}
-button{margin:5px}
-button:disabled{opacity:0.5}
-#status{font-size:12px;color:#666;height:16px}
+    body { font-family: sans-serif; text-align: center; background: #f5f5f5; margin: 20px; }
+    canvas { border: 1px solid #333; background: #fff; cursor: grab; box-shadow: 0 4px 10px rgba(0,0,0,0.1); }
+    .panel { background: white; padding: 20px; border-radius: 8px; display: inline-block; border: 1px solid #ccc; margin-bottom: 10px; }
+    textarea { width: 90%; height: 100px; font-family: monospace; font-size: 11px; margin-top: 10px; }
+    button { padding: 10px 15px; margin: 5px; cursor: pointer; font-weight: bold; }
+    #status { color: red; height: 20px; font-weight: bold; }
 </style>
 </head>
 <body>
 
-<h3>Align & Optimize</h3>
-
-<textarea id="cmd"></textarea><br>
-<button id="copy">Copy commands</button><br>
-
-<input type="file" id="f1">
-<input type="file" id="f2"><br><br>
-
-<button id="opt">Optimize</button>
-<button id="optZoom">Optimize Zoom</button>
-<button id="crop">Crop</button>
-<button id="dl" disabled>Download</button>
-<br>
-Iterations: <input type="range" id="iterSlider" min="2" max="30" value="8" style="vertical-align:middle;width:120px">
-<span id="iterVal">8</span>
+<h3>Align & Optimize (Asymmetric)</h3>
+
+<div class="panel">
+    <input type="file" id="f1" accept="image/*">
+    <input type="file" id="f2" accept="image/*"><br><br>
+    
+    <button id="opt">Optimize All</button>
+    <button id="optZoom">Optimize Zoom (Space)</button>
+    <button id="crop">Prepare Crop</button>
+    <button id="dl" disabled>Download Images</button>
+    <br><br>
+    Iterations: <input type="range" id="iterSlider" min="5" max="50" value="15">
+    <span id="iterVal">15</span>
+</div>
 
-<div>MSE: <span id="score">-</span> &nbsp; Overlap: <span id="overlap">-</span></div>
 <div id="status"></div>
+<div>MSE: <span id="score">-</span> | Overlap: <span id="overlap">-</span></div>
 
-<canvas id="cv"></canvas>
+<canvas id="cv"></canvas><br>
+
+<textarea id="cmd" readonly></textarea><br>
+<button id="copy">Copy ImageMagick Commands</button>
 
 <script>
 const canvas = document.getElementById('cv');
-function ctx() { return canvas.getContext('2d'); }
+const ctx = canvas.getContext('2d');
 const scoreEl = document.getElementById('score');
 const overlapEl = document.getElementById('overlap');
 const statusEl = document.getElementById('status');
 const cmdEl = document.getElementById('cmd');
 
 let i1 = new Image(), i2 = new Image(), l1 = 0, l2 = 0;
-let name1 = "image1.png", name2 = "image2.png";
-
+let name1 = "img1.png", name2 = "img2.png";
 let ox = 0, oy = 0, sx = 1, sy = 1;
 let mx = 0, my = 0, drag = 0, lx = 0, ly = 0;
-
-let out1, out2;
-let pyr1 = [], pyr2 = [];
+let pyr1 = [], pyr2 = [], out1, out2;
 const LEVELS = 4;
-
 let optimizing = false;
 
-// --- pyramid ---
-// FIX: clamp x+1 within row to avoid cross-row bleed
+// --- Pyramid logic ---
 function buildPyramid(img) {
-  let levels = [];
-  let cv = document.createElement('canvas');
-  cv.width = img.width; cv.height = img.height;
-  let x = cv.getContext('2d');
-  x.drawImage(img, 0, 0);
-
-  let d = x.getImageData(0, 0, img.width, img.height).data;
-  let g = new Float32Array(img.width * img.height);
-  for (let i = 0, j = 0; i < d.length; i += 4, j++)
-    g[j] = 0.299 * d[i] + 0.587 * d[i+1] + 0.114 * d[i+2];
-  levels.push({ w: img.width, h: img.height, data: g });
-
-  for (let l = 1; l < LEVELS; l++) {
-    let p = levels[l-1];
-    let w2 = Math.floor(p.w / 2), h2 = Math.floor(p.h / 2);
-    let g2 = new Float32Array(w2 * h2);
-    for (let y = 0; y < h2; y++) {
-      for (let xi = 0; xi < w2; xi++) {
-        // clamp xi+1 to stay within the row (p.w-1)
-        let x0 = 2 * xi, x1 = Math.min(2 * xi + 1, p.w - 1);
-        let y0 = 2 * y,  y1 = Math.min(2 * y  + 1, p.h - 1);
-        g2[y * w2 + xi] = (
-          p.data[y0 * p.w + x0] + p.data[y0 * p.w + x1] +
-          p.data[y1 * p.w + x0] + p.data[y1 * p.w + x1]
-        ) * 0.25;
-      }
+    let levels = [];
+    let c = document.createElement('canvas');
+    c.width = img.width; c.height = img.height;
+    let x = c.getContext('2d');
+    x.drawImage(img, 0, 0);
+    let d = x.getImageData(0, 0, img.width, img.height).data;
+    let g = new Float32Array(img.width * img.height);
+    for (let i = 0, j = 0; i < d.length; i += 4, j++)
+        g[j] = 0.299 * d[i] + 0.587 * d[i+1] + 0.114 * d[i+2];
+    levels.push({ w: img.width, h: img.height, data: g });
+
+    for (let l = 1; l < LEVELS; l++) {
+        let p = levels[l-1], w2 = Math.floor(p.w / 2), h2 = Math.floor(p.h / 2);
+        let g2 = new Float32Array(w2 * h2);
+        for (let y = 0; y < h2; y++) {
+            for (let xi = 0; xi < w2; xi++) {
+                let x0 = 2 * xi, x1 = Math.min(2 * xi + 1, p.w - 1);
+                let y0 = 2 * y,  y1 = Math.min(2 * y  + 1, p.h - 1);
+                g2[y * w2 + xi] = (p.data[y0 * p.w + x0] + p.data[y0 * p.w + x1] +
+                                   p.data[y1 * p.w + x0] + p.data[y1 * p.w + x1]) * 0.25;
+            }
+        }
+        levels.push({ w: w2, h: h2, data: g2 });
     }
-    levels.push({ w: w2, h: h2, data: g2 });
-  }
-  return levels;
+    return levels;
 }
 
-// --- bilinear sample with bounds check ---
-// FIX: return NaN for out-of-bounds so caller can skip; clamp indices correctly
 function sample(img, x, y) {
-  if (x < 0 || y < 0 || x >= img.w || y >= img.h) return NaN;
-  let x0 = Math.floor(x), y0 = Math.floor(y);
-  let x1 = Math.min(x0 + 1, img.w - 1);   // FIX: was img.w (off-by-one)
-  let y1 = Math.min(y0 + 1, img.h - 1);   // FIX: was img.h (off-by-one)
-  let dx = x - x0, dy = y - y0;
-  let i00 = img.data[y0 * img.w + x0];
-  let i10 = img.data[y0 * img.w + x1];
-  let i01 = img.data[y1 * img.w + x0];
-  let i11 = img.data[y1 * img.w + x1];
-  return (i00 * (1-dx) + i10 * dx) * (1-dy) + (i01 * (1-dx) + i11 * dx) * dy;
+    if (x < 0 || y < 0 || x >= img.w || y >= img.h) return NaN;
+    let x0 = Math.floor(x), y0 = Math.floor(y), x1 = Math.min(x0 + 1, img.w - 1), y1 = Math.min(y0 + 1, img.h - 1);
+    let dx = x - x0, dy = y - y0;
+    return (img.data[y0 * img.w + x0] * (1-dx) + img.data[y0 * img.w + x1] * dx) * (1-dy) + 
+           (img.data[y1 * img.w + x0] * (1-dx) + img.data[y1 * img.w + x1] * dx) * dy;
 }
 
-// --- MSE over overlapping region only ---
-// FIX: compute the intersection of A's rect and the mapped B rect in A-space,
-//      then only accumulate pixels where B is defined.
-function computeMSELevel(level) {
-  if (!pyr1.length || !pyr2.length) return Infinity;
-  let A = pyr1[level], B = pyr2[level];
-  let sF = 1 / (2 ** level);
-
-  // B's bounding box in A-pixel space at this pyramid level
-  let tox = ox * sF, toy = oy * sF;
-  let tsx = sx, tsy = sy;
-
-  // B covers [tox, tox + B.w*tsx] x [toy, toy + B.h*tsy] in A-space
-  let bx0 = tox, bx1 = tox + B.w * tsx;
-  let by0 = toy, by1 = toy + B.h * tsy;
-
-  // Intersect with A's extent [0, A.w] x [0, A.h]
-  let ix0 = Math.max(0, Math.ceil(bx0));
-  let iy0 = Math.max(0, Math.ceil(by0));
-  let ix1 = Math.min(A.w, Math.floor(bx1));
-  let iy1 = Math.min(A.h, Math.floor(by1));
-
-  if (ix1 <= ix0 || iy1 <= iy0) return Infinity;
-
-  let mse = 0, n = 0;
-  for (let y = iy0; y < iy1; y++) {
-    for (let x = ix0; x < ix1; x++) {
-      let bx = (x - tox) / tsx;
-      let by = (y - toy) / tsy;
-      let bVal = sample(B, bx, by);
-      if (isNaN(bVal)) continue;
-      let d = A.data[y * A.w + x] - bVal;
-      mse += d * d;
-      n++;
+function computeMSE(level, tx, ty, tsx, tsy) {
+    if (!l1 || !l2) return Infinity;
+    let A = pyr1[level], B = pyr2[level], sF = 1 / (2**level);
+    let lox = tx * sF, loy = ty * sF;
+    let ix0 = Math.max(0, Math.ceil(lox)), iy0 = Math.max(0, Math.ceil(loy));
+    let ix1 = Math.min(A.w, Math.floor(lox + B.w * tsx)), iy1 = Math.min(A.h, Math.floor(loy + B.h * tsy));
+    if (ix1 <= ix0 || iy1 <= iy0) return Infinity;
+    let mse = 0, n = 0;
+    for (let y = iy0; y < iy1; y++) {
+        for (let x = ix0; x < ix1; x++) {
+            let v = sample(B, (x - lox) / tsx, (y - loy) / tsy);
+            if (isNaN(v)) continue;
+            let d = A.data[y * A.w + x] - v;
+            mse += d * d; n++;
+        }
     }
-  }
-
-  // Update overlap display (only at level 0)
-  if (level === 0) {
-    let pct = n / (A.w * A.h) * 100;
-    overlapEl.textContent = n > 0 ? pct.toFixed(1) + '%' : '0%';
-  }
-
-  return n ? mse / n : Infinity;
+    if (level === 0) overlapEl.textContent = n > 0 ? (n/(A.w*A.h)*100).toFixed(1) + '%' : '0%';
+    return n ? mse / n : Infinity;
 }
 
-function computeMSE() { return computeMSELevel(0); }
-
-// --- draw ---
 function draw() {
-  if (!l1) return;
-  const c = ctx();
-  c.clearRect(0, 0, canvas.width, canvas.height);
-  c.drawImage(i1, 0, 0);
-  if (l2) {
-    c.save();
-    c.globalAlpha = 0.5;
-    c.translate(ox, oy);
-    c.scale(sx, sy);
-    c.drawImage(i2, 0, 0);
-    c.restore();
-  }
-  scoreEl.textContent = computeMSE().toFixed(2);
-  updateCmd();
+    if (!l1) return;
+    ctx.clearRect(0, 0, canvas.width, canvas.height);
+    ctx.drawImage(i1, 0, 0);
+    if (l2) {
+        ctx.save(); ctx.globalAlpha = 0.5;
+        ctx.translate(ox, oy); ctx.scale(sx, sy);
+        ctx.drawImage(i2, 0, 0); ctx.restore();
+    }
+    scoreEl.textContent = computeMSE(0, ox, oy, sx, sy).toFixed(2);
+    updateCmd();
 }
 
-// --- load ---
-f1.onchange = e => {
-  let f = e.target.files[0]; if (!f) return;
-  name1 = f.name;
-  i1 = new Image();
-  i1.onload = () => {
-    l1 = 1;
-    canvas.width = i1.width; canvas.height = i1.height;
-    pyr1 = buildPyramid(i1);
-    draw();
-  };
-  i1.src = URL.createObjectURL(f);
-};
+// --- NEW WHEEL LOGIC (EXPLICIT) ---
+canvas.onwheel = function(e) {
+    if (!l2) return;
+    e.preventDefault(); // Stop browser zoom
+
+    // 1. Identify scroll direction and magnitude
+    // When Shift is pressed, many browsers move vertical scroll to deltaX
+    let delta = (Math.abs(e.deltaX) > Math.abs(e.deltaY)) ? e.deltaX : e.deltaY;
+    let factor = delta < 0 ? 1.05 : 0.95;
+
+    // 2. Identify the fixed point (anchor) in image coordinates
+    // We want the point in i2 that is currently under the mouse to stay there
+    let imagePointX = (e.offsetX - ox) / sx;
+    let imagePointY = (e.offsetY - oy) / sy;
+
+    // 3. Apply scaling based on modifiers
+    if (e.shiftKey) {
+        // Shift + Wheel -> SY ONLY
+        sy *= factor;
+    } else if (e.ctrlKey) {
+        // Ctrl + Wheel -> SX ONLY
+        sx *= factor;
+    } else {
+        // Wheel Only -> UNIFORM
+        sx *= factor;
+        sy *= factor;
+    }
+
+    // 4. Update offsets to maintain the anchor point
+    // Formula: NewOffset = MousePos - (ImagePoint * NewScale)
+    ox = e.offsetX - (imagePointX * sx);
+    oy = e.offsetY - (imagePointY * sy);
 
-f2.onchange = e => {
-  let f = e.target.files[0]; if (!f) return;
-  name2 = f.name;
-  i2 = new Image();
-  i2.onload = () => {
-    l2 = 1;
-    pyr2 = buildPyramid(i2);
-    ox = (canvas.width - i2.width) / 2;
-    oy = (canvas.height - i2.height) / 2;
     draw();
-  };
-  i2.src = URL.createObjectURL(f);
 };
 
-// --- mouse ---
+canvas.onmousedown = e => { drag = 1; lx = e.offsetX; ly = e.offsetY; canvas.style.cursor = "grabbing"; };
+canvas.onmouseup = canvas.onmouseleave = () => { drag = 0; canvas.style.cursor = "grab"; };
 canvas.onmousemove = e => {
-  mx = e.offsetX; my = e.offsetY;
-  if (!drag) return;
-  ox += (e.offsetX - lx);
-  oy += (e.offsetY - ly);
-  lx = e.offsetX; ly = e.offsetY;
-  draw();
-};
-canvas.onmousedown = e => {
-  drag = 1;
-  lx = e.offsetX; ly = e.offsetY;
-  canvas.style.cursor = "grabbing";
-};
-canvas.onmouseup = canvas.onmouseleave = () => {
-  drag = 0;
-  canvas.style.cursor = "grab";
+    mx = e.offsetX; my = e.offsetY;
+    if (drag) { ox += (mx - lx); oy += (my - ly); lx = mx; ly = my; draw(); }
 };
 
-// --- scroll to zoom (mouse-anchored) ---
-canvas.onwheel = e => {
-  if (!l2) return;
-  e.preventDefault();
-  let factor = e.deltaY < 0 ? 1.01 : 0.99;
-  let ix = (mx - ox) / sx;
-  let iy = (my - oy) / sy;
-  sx *= factor; sy *= factor;
-  ox = mx - ix * sx;
-  oy = my - iy * sy;
-  draw();
-};
-
-// --- keyboard ---
-document.addEventListener("keydown", e => {
-  // FIX: don't intercept keys when typing in textarea
-  if (document.activeElement === cmdEl) return;
-
-  if (!l2) return;
-  if (e.key.startsWith("Arrow")) e.preventDefault();
-  const PAN_STEP = 1;
-  const SCALE_STEP = 0.002;
-  if (e.shiftKey) {
-    let ix = (mx - ox) / sx;
-    let iy = (my - oy) / sy;
-    if (e.key === "ArrowRight") sx += SCALE_STEP;
-    if (e.key === "ArrowLeft")  sx -= SCALE_STEP;
-    if (e.key === "ArrowUp")    sy += SCALE_STEP;
-    if (e.key === "ArrowDown")  sy -= SCALE_STEP;
-    ox = mx - ix * sx;
-    oy = my - iy * sy;
-  } else {
-    if (e.key === "ArrowRight") ox += PAN_STEP;
-    if (e.key === "ArrowLeft")  ox -= PAN_STEP;
-    if (e.key === "ArrowUp")    oy -= PAN_STEP;
-    if (e.key === "ArrowDown")  oy += PAN_STEP;
-  }
-  draw();
-});
-
-// FIX: space only fires optimize when not in textarea
-document.addEventListener("keydown", e => {
-  if (document.activeElement === cmdEl) return;
-  if (e.code === "Space") {
-    e.preventDefault();
-    opt.click();
-  }
-});
-
-// --- slider ---
-const iterSlider = document.getElementById('iterSlider');
-const iterVal = document.getElementById('iterVal');
-iterSlider.oninput = () => iterVal.textContent = iterSlider.value;
-function numIters() { return parseInt(iterSlider.value); }
-
-// --- set busy state ---
-function setBusy(busy) {
-  optimizing = busy;
-  opt.disabled = busy;
-  optZoom.disabled = busy;
-  statusEl.textContent = busy ? "Optimizing…" : "";
+window.onkeydown = e => { if(e.code === "Space" && !optimizing) { e.preventDefault(); optZoom.click(); } };
+
+// --- Optimizers ---
+async function runOpt(mode) {
+    if (!l1 || !l2) return;
+    optimizing = true; statusEl.textContent = "OPTIMIZING...";
+    const iters = parseInt(iterSlider.value);
+    
+    // For Zoom optimization, we lock the anchor once at the current mouse position
+    const anchorX = (mx - ox) / sx;
+    const anchorY = (my - oy) / sy;
+
+    for (let l = LEVELS - 1; l >= 0; l--) {
+        let currentBest = { ox, oy, sx, sy, score: computeMSE(l, ox, oy, sx, sy) };
+        for (let i = 0; i < iters; i++) {
+            let stepO = (2 ** l) / (i + 1);
+            let stepF = 1 + 0.03 / (i + 1);
+
+            // Grid search around current parameters
+            for (let dx of (mode === 'all' ? [-1, 0, 1] : [0])) {
+                for (let dy of (mode === 'all' ? [-1, 0, 1] : [0])) {
+                    for (let dsx of [-1, 0, 1]) {
+                        for (let dsy of [-1, 0, 1]) {                            
+                            // Calculate test candidates
+                            let testSX = sx * Math.pow(stepF, dsx);
+                            let testSY = sy * Math.pow(stepF, dsy);
+                            let testOX, testOY;
+
+                            if (mode === 'zoom') {
+                                // Locked to mouse position
+                                testOX = mx - (anchorX * testSX);
+                                testOY = my - (anchorY * testSY);
+                            } else {
+                                // Free translation
+                                testOX = ox + (dx * stepO);
+                                testOY = oy + (dy * stepO);
+                            }
+
+                            if (testSX < 0.01 || testSY < 0.01) continue;
+
+                            let s = computeMSE(l, testOX, testOY, testSX, testSY);
+                            if (s < currentBest.score) {
+                                currentBest = { ox: testOX, oy: testOY, sx: testSX, sy: testSY, score: s };
+                            }
+                        }
+                    }
+                }
+            }
+            // Apply iteration results
+            ox = currentBest.ox; oy = currentBest.oy; sx = currentBest.sx; sy = currentBest.sy;
+            draw(); 
+            await new Promise(r => setTimeout(r, 0));
+        }
+    }
+    optimizing = false; statusEl.textContent = "";
 }
 
-// --- probe MSE without mutating globals ---
-// Temporarily sets ox/oy/sx/sy, evaluates, then restores.
-function probeMSE(tox, toy, tsx, tsy, level) {
-  let pox = ox, poy = oy, psx = sx, psy = sy;
-  ox = tox; oy = toy; sx = tsx; sy = tsy;
-  let s = computeMSELevel(level);
-  ox = pox; oy = poy; sx = psx; sy = psy;
-  return s;
-}
+opt.onclick = () => runOpt('all');
+optZoom.onclick = () => runOpt('zoom');
 
-// --- optimizer (translation + uniform scale) ---
-opt.onclick = async () => {
-  if (!l1 || !l2 || optimizing) return;
-  setBusy(true);
-  const ITERS = numIters();
-  for (let level = LEVELS - 1; level >= 0; level--) {
-    let best = { ox, oy, sx, sy, score: computeMSELevel(level) };
-    for (let iter = 0; iter < ITERS; iter++) {
-      let stepO = (2 ** level) / (iter + 1);
-      let stepS = 0.01 / (iter + 1);
-      for (let dx of [-1, 0, 1])
-      for (let dy of [-1, 0, 1])
-      for (let ds of [-1, 0, 1]) {
-        let tox = best.ox + dx * stepO;
-        let toy = best.oy + dy * stepO;
-        let ts  = best.sx + ds * stepS;
-        if (ts <= 0.01) continue;
-        // FIX: probe without touching globals
-        let s = probeMSE(tox, toy, ts, ts, level);
-        if (s < best.score)
-          best = { ox: tox, oy: toy, sx: ts, sy: ts, score: s };
-      }
-      // commit the best found this iteration
-      ox = best.ox; oy = best.oy; sx = best.sx; sy = best.sy;
-      draw();
-      await new Promise(r => setTimeout(r, 0));
-    }
-  }
-  setBusy(false);
+// --- I/O ---
+f1.onchange = e => {
+    let f = e.target.files[0]; if (!f) return;
+    name1 = f.name; i1 = new Image();
+    i1.onload = () => { l1 = 1; canvas.width = i1.width; canvas.height = i1.height; pyr1 = buildPyramid(i1); draw(); };
+    i1.src = URL.createObjectURL(f);
 };
-
-// --- optimize zoom (mouse-anchor invariant) ---
-optZoom.onclick = async () => {
-  if (!l1 || !l2 || optimizing) return;
-  setBusy(true);
-  const ITERS = numIters();
-  let ax = mx, ay = my;
-
-  for (let level = LEVELS - 1; level >= 0; level--) {
-    // best stores the full transform, not just sx
-    let best = { ox, oy, sx, sy, score: computeMSELevel(level) };
-
-    for (let iter = 0; iter < ITERS; iter++) {
-      let stepS = 0.02 / (iter + 1);
-
-      for (let ds of [-1, 0, 1]) {
-        let ts = best.sx + ds * stepS;
-        if (ts <= 0.01) continue;
-        // anchor invariant: point (ax,ay) in canvas == point (ix,iy) in image2
-        // must satisfy: ax = ox + ix*sx  =>  ix = (ax - ox) / sx
-        // after scale change to ts: tox = ax - ix*ts
-        // derive from COMMITTED globals (best.ox/sx), not mid-loop state
-        let ix = (ax - best.ox) / best.sx;
-        let iy = (ay - best.oy) / best.sy;
-        let tox = ax - ix * ts;
-        let toy = ay - iy * ts;
-        let s = probeMSE(tox, toy, ts, ts, level);
-        if (s < best.score) best = { ox: tox, oy: toy, sx: ts, sy: ts, score: s };
-      }
-
-      // commit best directly — no re-derivation needed
-      ox = best.ox; oy = best.oy; sx = best.sx; sy = best.sy;
-
-      draw();
-      await new Promise(r => setTimeout(r, 0));
-    }
-  }
-  setBusy(false);
+f2.onchange = e => {
+    let f = e.target.files[0]; if (!f) return;
+    name2 = f.name; i2 = new Image();
+    i2.onload = () => { l2 = 1; pyr2 = buildPyramid(i2); ox = (canvas.width-i2.width)/2; oy = (canvas.height-i2.height)/2; draw(); };
+    i2.src = URL.createObjectURL(f);
 };
 
-// --- crop ---
 crop.onclick = () => {
-  let x2 = ox, y2 = oy, w2 = i2.width * sx, h2 = i2.height * sy;
-  let x0 = Math.max(0, x2), y0 = Math.max(0, y2);
-  let x1 = Math.min(canvas.width, x2 + w2), y1 = Math.min(canvas.height, y2 + h2);
-  let w = x1 - x0, h = y1 - y0;
-  if (w <= 0 || h <= 0) return alert("no overlap");
-
-  out1 = document.createElement('canvas');
-  out2 = document.createElement('canvas');
-  out1.width = out2.width = w; out1.height = out2.height = h;
-
-  out1.getContext('2d').drawImage(i1, x0, y0, w, h, 0, 0, w, h);
-  out2.getContext('2d').drawImage(
-    i2, (x0 - ox) / sx, (y0 - oy) / sy, w / sx, h / sy, 0, 0, w, h
-  );
-
-  dl.disabled = 0;
+    let x0 = Math.max(0, ox), y0 = Math.max(0, oy);
+    let w = Math.floor(Math.min(canvas.width, ox + i2.width * sx) - x0);
+    let h = Math.floor(Math.min(canvas.height, oy + i2.height * sy) - y0);
+    if (w <= 5 || h <= 5) return alert("Overlap too small!");
+    out1 = document.createElement('canvas'); out2 = document.createElement('canvas');
+    out1.width = out2.width = w; out1.height = out2.height = h;
+    out1.getContext('2d').drawImage(i1, x0, y0, w, h, 0, 0, w, h);
+    out2.getContext('2d').drawImage(i2, (x0 - ox)/sx, (y0 - oy)/sy, w/sx, h/sy, 0, 0, w, h);
+    document.getElementById('dl').disabled = false;
 };
 
-// --- download ---
-dl.onclick = () => {
-  let save = (cv, n) => {
-    let a = document.createElement('a');
-    a.download = n + '.clipped.png';
-    a.href = cv.toDataURL();
-    a.click();
-  };
-  save(out1, 'image1');
-  save(out2, 'image2');
+document.getElementById('dl').onclick = () => {
+    const s = (c, n) => { let a = document.createElement('a'); a.download = n; a.href = c.toDataURL(); a.click(); };
+    s(out1, "clipped_" + name1); s(out2, "clipped_" + name2);
 };
 
-// --- copy ---
-copy.onclick = () => navigator.clipboard.writeText(cmdEl.value);
-
-// --- ImageMagick command ---
 function updateCmd() {
-  if (!l1 || !l2) return;
-  let x2 = ox, y2 = oy, w2 = i2.width * sx, h2 = i2.height * sy;
-  let x0 = Math.max(0, x2), y0 = Math.max(0, y2);
-  let x1 = Math.min(canvas.width, x2 + w2), y1 = Math.min(canvas.height, y2 + h2);
-  let w = Math.floor(x1 - x0), h = Math.floor(y1 - y0);
-  if (w <= 0 || h <= 0) return;
-  let crop = `${w}x${h}+${Math.round(x0)}+${Math.round(y0)}`;
-  // AffineProjection: a,b,d,e,tx,ty  (uniform scale → a=e=sx)
-  let a = sx, b = 0, d = 0, e = sy, tx = ox, ty = oy;
-  cmdEl.value =
-`magick "${name1}" -crop ${crop} +repage "${name1}.clipped.png"
-magick "${name2}" -virtual-pixel black -distort AffineProjection "${a},${b},${d},${e},${tx},${ty}" -crop ${crop} +repage "${name2}.clipped.png"`;
+    if (!l1 || !l2) return;
+    let x0 = Math.max(0, ox), y0 = Math.max(0, oy);
+    let w = Math.floor(Math.min(canvas.width, ox + i2.width * sx) - x0);
+    let h = Math.floor(Math.min(canvas.height, oy + i2.height * sy) - y0);
+    if (w <= 0 || h <= 0) return;
+    let cr = `${w}x${h}+${Math.round(x0)}+${Math.round(y0)}`;
+    cmdEl.value = `magick "${name1}" -crop ${cr} +repage "clipped_${name1}"\n` +
+                  `magick "${name2}" -virtual-pixel black -distort AffineProjection "${sx.toFixed(6)},0,0,${sy.toFixed(6)},${ox.toFixed(2)},${oy.toFixed(2)}" -crop ${cr} +repage "clipped_${name2}"`;
 }
-</script>
 
+document.getElementById('copy').onclick = () => { cmdEl.select(); navigator.clipboard.writeText(cmdEl.value); };
+iterSlider.oninput = () => document.getElementById('iterVal').textContent = iterSlider.value;
+</script>
 </body>
 </html>
\ No newline at end of file
-- 
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