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I Built a CLI That Detects 3D Print Defects from a Single Photo — No ML Required

#python #3dprinting #opensource #cv

A few weeks ago, I launched SupportSage (AI-optimized support structures) and FilamentDB (filament parameter database). Together they covered pre-print optimization: what settings to use and how to slice.

But the feedback loop was incomplete. You optimize, slice, print — and then what? How do you know if the print is actually good?

You eyeball it. Maybe post to r/3Dprinting and ask "what's wrong with my print?".

I wanted a programmatic answer. One CLI command, one photo.

Meet Printsight 🖨️👁️

pip install https://github.com/bossman-lab/printsight/releases/download/v0.1.0/printsight-0.1.0-py3-none-any.whl

printsight my_print.jpg
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The Three Detectable Defects

Printsight v0.1 detects three of the most common print quality issues, all with pure OpenCV — no ML, no training data, no GPU:

1. Stringing (Score 0.0–1.0)

Stringing happens when molten plastic oozes during travel moves, leaving thin wisps across your print.

Detection approach: A dual-strategy pipeline:

  • Canny edge detection → Hough Line Transform finds thin, non-horizontal line segments (stringing is rarely horizontal)
  • Adaptive threshold → morphological erosion subtracts the solid print body, leaving only thin features

Results from both methods are combined for robust detection.

# Simplified: edge-based + morphology-based stringing detection
edges = cv2.Canny(img_gray, 30, 100)
lines = cv2.HoughLinesP(edges, ...)  # stringing = thin, non-horizontal lines

thresh = cv2.adaptiveThreshold(img_gray, 255, ...)
eroded = cv2.erode(thresh, kernel)
thin_features = cv2.subtract(thresh, eroded)  # stringing = what remains
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2. Layer Quality (Score 0.0–1.0)

Layer issues include Z-banding, inconsistent extrusion, and layer shifts. These show up as irregularities in the horizontal layer pattern.

Detection approach:

  • Sobel X gradient isolates horizontal edges (layer boundaries)
  • Vertical projection sums each row's gradient → creates a "layer profile"
  • Peak analysis measures the regularity of layer spacing (coefficient of variation)
  • FFT analysis checks energy concentration — good layers have clean periodic signals 
grad_x = cv2.Sobel(img_gray, cv2.CV_64F, 1, 0, ksize=3)
row_profile = np.sum(np.abs(grad_x), axis=1)
# Regular peaks = good layers, irregular spacing = issues
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3. Warping (Score 0.0–1.0)

Warping occurs when print corners lift from the build plate due to thermal stress.

Detection approach:

  • Find the print contour (Otsu threshold)
  • Extract bottom-edge points (lower 20% of bounding box)
  • Line fitting → measure perpendicular deviations
  • High corner deviation = likely warping 
[vx, vy, cx, cy] = cv2.fitLine(bottom_points, cv2.DIST_L2, ...)
deviations = [abs(pt - center) × direction / |direction| for pt in bottom_points]
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Overall Score

The three sub-scores are weighted: Stringing 30% / Layer Quality 40% / Warping 30%.

Grade Score Meaning
Excellent 0.90+ Ready to ship
Good 0.75–0.89 Minor tweaks needed
Fair 0.50–0.74 Noticeable defects
Poor 0.25–0.49 Needs reprint
Failed <0.25 Major issues

The CLI exits with code 0 for Excellent/Good/Fair and 1 for Poor/Failed — making it usable in CI pipelines.

The Complete 3D Printing Toolkit

Printsight completes a three-part open-source workflow:

SupportSage ──► FilamentDB ──► Printsight
(slice optimize)  (settings)     (inspect)
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Tool Phase What it does Lines Downloads
SupportSage Pre-print Optimize support structures (~33% savings) ~1,200 5+
FilamentDB Pre-print Recommended print settings per filament ~700 —
Printsight Post-print Quality inspection from a photo ~500 —

All three share the same design philosophy:

  • Pure Python, minimal dependencies
  • CLI-first with JSON output
  • Zero ML — computer vision and geometry analysis only
  • Open source (MIT)

What's Next

v0.2 — Annotated output images (red circles on stringing, highlighted warped areas) so you can see exactly where the issues are.

v0.3 — HTML report generation with side-by-side defect visualization.

Try it: github.com/bossman-lab/printsight

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