This document discusses using OpenCV to read gauge values. It describes using Canny edge detection and Hough transformations to detect lines in an image of a gauge. The Hough transform is applied to the edge detected image to find 11 lines representing the gauge. The inclination and speed values are then calculated from the lines' rho and theta parameters. While OpenCV image processing works well for this, preprocessing must be chosen carefully and detection errors can occur.

1. Reading Gauge Values With OpenCV
Imgproc
Oleksii Kuchuk

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⚫ dnn
⚫ ml
⚫ flann
⚫ video
⚫ ...
⚫ imgproc

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imgproc
⚫ Filtering
⚫ Color conversion
⚫ Histograms
⚫ Rotations and affine
transformations
⚫ Canny edge detection
⚫ Hough transformations

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Canny edge detector
https://commons.wikimedia.org/wiki/File:Valve_monochrome_canny_(6).PNG
https://commons.wikimedia.org/wiki/File:Valve_original_(1).PNG

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Hough transformation

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Hough transformation
1. ”huff”
(rough)
2. ”hock”

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https://docs.opencv.org/3.4/d9/db0/tutorial_hough_lines.html
𝑦
= 𝑘𝑥 + 𝑏
𝛼 − 𝜃
=
𝜋
2

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https://docs.opencv.org/3.4/d9/db0/tutorial_hough_lines.html

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Step 2
Edge detection

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Step 3
Hough transformation

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Lines found: 11
Rho: 552 Theta: 1.36136
Rho: 548 Theta: 1.27409
Rho: 549 Theta: 1.37881
Rho: 546 Theta: 1.29154
Rho: 556 Theta: 1.3439
Rho: 550 Theta: 1.25664
Rho: 542 Theta: 1.309
Rho: 547 Theta: 1.39626
Rho: 558 Theta: 1.32645
Rho: 544 Theta: 1.39626
Rho: 539 Theta: 1.32645

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Needle inclination: 17.1136
Speed: 11.2905

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Conclusion
⚫ OpenCV imgproc does a good job
⚫ Image preprocessing must be chosen individually
⚫ Detection errors can occur

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Q&A