How to Measure Object Orientation and Angle with Computer Vision

Mostafa Ibrahim’s tutorial describes a Roboflow computer-vision workflow for measuring an object’s tilt or orientation by segmenting it with an RF-DETR instance segmentation model and applying principal component analysis (PCA) to the coordinates of its mask pixels. PCA identifies the object’s dominant axis, which is converted into an angle representing deviation from vertical; the workflow then overlays that value on the image and sends a Slack alert when it exceeds a configurable threshold. Demonstrated on bottles, fixture-mounted parts, and utility poles, the approach can be reused across object types with appropriate training data and class filtering for multi-class models. The article reports strong bottle-segmentation metrics and explains why PCA can be more robust to irregular mask edges than rectangle- or polygon-fitting methods, while noting practical limitations including 180-degree directional ambiguity, symmetrical shapes, mask noise, occlusion, and camera-perspective distortion. It also outlines deployment through hosted or local Roboflow inference using image uploads, cameras, or RTSP streams, and notes that Roboflow Agent can construct a similar workflow from a natural-language prompt.

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