README.md

metadata

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

./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

./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.