---
title: Crack Detection And Measurement
emoji: 🧱
colorFrom: gray
colorTo: red
sdk: docker
app_port: 7860
pinned: false
---

# Crack Detection & Measurement

Upload a photo of a wall; the app detects the crack, measures its width and
length in real centimetres, and shows the segmented result.

> The block above is Hugging Face Spaces configuration. It is ignored when
> running locally and tells the Space to build from the `Dockerfile`.

## Pipeline

1. **Scale & rectification (ArUco)** — a printed ArUco marker in the photo
   gives four points with known real-world coordinates. The wall plane's
   homography is recovered and the image is warped to a fronto-parallel view
   where **1 pixel = a fixed known mm**. This corrects camera angle and fixes
   scale. See `aruco_scale.py`.
2. **Segmentation (ML)** — `best_crack_model.pth`, a ResNet34-Unet, *locates*
   the crack and produces a binary mask. See `crack_pipeline.py`.
3. **Measurement (computer vision)** — within the ML-detected region, a
   black-tophat operator (local intensity contrast) plus Otsu thresholding
   re-segments the crack tightly on its actual dark pixels, not the model's
   wider "crack zone". Width is then sampled along the crack centreline.
   See `cv_crack.py`.

Reported: **max width**, **min width** (both excluding the crack's tapering
tips), and **total length**. The refined crack mask is shown as the
segmented image.

## Print the marker

```bash
./venv/bin/python make_marker.py     # -> aruco_marker_60mm_A4.pdf
```

Print at **100% / Actual Size** (no scaling); verify the black square is
exactly **6.0 cm**. Tape it flat on the wall next to the crack and photograph
wall + marker together. The app serves the PDF at `/marker`.

## Run

```bash
./run.sh
```

Creates the virtualenv on first run, then serves at
<http://127.0.0.1:5001> (port 5000 is taken by macOS AirPlay). Override with
`PORT=8080 ./venv/bin/python app.py`.

## Files

| File                   | Purpose                                          |
|------------------------|--------------------------------------------------|
| `aruco_scale.py`       | ArUco detection, plane rectification, mm scaling |
| `crack_pipeline.py`    | Model loading, ML segmentation, pipeline, overlay|
| `cv_crack.py`          | Intensity-profile crack-width measurement        |
| `make_marker.py`       | Generates the printable marker PDF               |
| `app.py`               | Flask server (`/`, `/analyze`, `/marker`, `/health`) |
| `templates/index.html` | Upload UI and results view                       |
| `best_crack_model.pth` | Trained segmentation weights                     |

## API

`POST /analyze` — multipart form, field `image`. Returns JSON: base64 PNG
`overlay`, a `measurements` object (`summary` / `summary_mm` with max width,
min width, total length), and an `aruco` object describing marker detection.

## Deploying

The app is Flask + PyTorch (CPU). For a public deployment:

- Replace the Flask dev server with a production server (`gunicorn`).
- Pin dependency versions in `requirements.txt`.
- It needs ~1.5–2 GB RAM (PyTorch + the 98 MB model).
- Good hosts: **Hugging Face Spaces** (free, built for ML demos), or a Docker
  container on Fly.io / Render / Railway.