Synthetic DPM Code Generator (v1.1.0)

A Windows GUI utility for generating synthetic training datasets and YOLO-compatible annotations (ABB/OBB) for Data Matrix / Direct Part Marking (DPM) style patterns. It simulates industrial needle-peening marks and surface defects.

Changelog from v1.0.2: Boosting mode for Stage-2 classifier training (logs/features.csv), strike-force defect, version bump.

Downstream inference / training project that uses this generator’s data:
olesha-ai/yolox-dmc-inference

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Technical Status & Security Notice

• Distribution: This repository distributes only a pre-compiled Windows portable binary (generate_dmc.exe) via the Releases tab.
• Source Code: The source code is currently private as it is part of a broader proprietary research framework.
• VirusTotal Result: 6 / 69 flags (Heuristic / ML False Positives)

Why are there 6 flags? (False Positives Explanation)

The executable is compiled natively using the Nim programming language. Nim binaries without a commercial extended validation (EV) digital certificate regularly trigger automated Machine Learning (ML) heuristic rules in specific scanners:

1. Microsoft (Trojan:Win32/Wacatac.C!ml): The !ml suffix explicitly indicates a Machine Learning automated guess, not a signature match. Windows Defender flags new, unsigned independent binaries by default.
2. CrowdStrike Falcon / Symantec / Elastic: These are static AI-driven enterprise scanners flagging the high-performance compiler structure of Nim as "suspicious" due to lack of global reputation metadata.
3. Clean Verdicts: Industry-standard engines including Kaspersky, BitDefender, ESET-NOD32, Avast, and Malwarebytes flag the file as completely clean.

If you prefer not to run independent closed-source binaries directly on your host machine, we recommend running the application within an isolated Windows Sandbox or a dedicated Virtual Machine (VM).

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Interface & Output Samples

Note: 1.png shows the control layout and preview. 2.png is an exported grayscale JPEG with defect simulation applied.

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Core Functionality

What it does

• DPM Layout Logic: Procedural geometric patterns modeled after ECC200 Data Matrix codes (fixed L-frame + randomized filling dots). It does not encode a real serial number bitstream.
• YOLO-Ready Export: Saves 600×200 grayscale JPEGs with matching .txt labels (OBB or ABB).
• Defect Modeling: Squash, tilt, jitter, missing dots, strike-force variation, and small/big points. Class 1 (Bad) applies two different defects at once.
• Boosting (optional): When the Boosting checkbox is enabled, each saved sample also appends a feature row for training a second-stage classifier (LightGBM / tabular model). See below.

Package Properties

• Portable: Single .exe. No installer, no registry changes.
• Storage: Creates images/ next to the exe on first save.
• Boosting log: Creates logs/features.csv (append-only) when Boosting is on.

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Dual Rendering Modes

• Mode A — Pure Synthetic (default on launch)
  No assets required. Procedural gray steel fill, light scratch noise, vector circles. Background and dot gray levels randomize on each Generate.

• Mode B — Photo Compositing

  • BG… — folder of steel / surface photos (.jpg, .jpeg, .png, .bmp)
  • Dots… — folder of single needle-impact sprites (transparent PNG recommended)

  Folder choices are session-only; next launch starts again in Mode A.

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Boosting / Stage-2 features

Optional checkbox in the GUI. Off by default.

When Boosting is checked and you press Save, for every exported image the tool also:

1. Converts the render to grayscale
2. Letterboxes to 500×144
3. Applies CLAHE (tile 8, clip 40)
4. Extracts 23 LBP / gradient features (same layout as the inference Stage-2 pipeline)
5. Appends one line to <exe_dir>/logs/features.csv

CSV format (no header, ; separator, 6 decimal places):

class;f0;f1;…;f22

class is 0 (Good) or 1 (Bad). This file is meant for training the second model used in yolox-dmc-inference (cat_model / LightGBM path).

JPEG + YOLO labels are still written as usual; Boosting only adds the CSV side channel.

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Annotation & Output Specs

Export directory: <exe_dir>/images/

• Good: Good_etch_<timestamp>_<index>.jpg + .txt
• Bad: Bad_etch_<timestamp>_<index>.jpg + .txt

Label syntax (normalized 0…1, 6 decimals)

• OBB: class x1 y1 x2 y2 x3 y3 x4 y4
• ABB: class cx cy w h

Preview uses a yellow box for guidance only — it is not burned into saved images.

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Configuration Controls

Preview scale is fixed at 5×.

• Generate — new pattern
• Save — batch export (progress on the button: n/N)
• Class — 0 Good / 1 Bad (Bad = two-defect combo)
• Count — 1 / 50 / 100 / 500
• Format — OBB (default) or ABB
• Boosting — append Stage-2 features to logs/features.csv
• Folder… — save directory (default: images next to the exe)
• BG… / Dots… — Mode B asset folders

Window title: olesha-ai — Needle Etching on Steel · Version 1.1.0

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Technical Requirements

• OS: Windows 10 / 11 x64
• CPU: AVX2 (Intel Haswell / AMD Zen 1 or newer, roughly 2017+)

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Asset Prompts (promts/)

Starter prompt texts for building your own Mode B textures in diffusion tools (Midjourney, Stable Diffusion, etc.):

• promts/promtFON.txt — steel backgrounds
• promts/promtDot.txt — needle dots (white background helps PNG cutout)

These are examples only — not a full asset pack. Generate images yourself, then point BG… / Dots… at your folders.

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Related

• Inference / PoC that trains on this generator’s synthetic data: yolox-dmc-inference

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Terms of Use

Distributed strictly for educational, academic, and non-commercial personal research. Commercial use, commercial model training on generated datasets, and redistribution of the binary are prohibited under LICENSE.md.