Principle:Tencent Ncnn Detection Visualization
| Knowledge Sources | |
|---|---|
| Domains | Computer_Vision, Visualization |
| Last Updated | 2026-02-09 00:00 GMT |
Overview
Process of rendering detection results (bounding boxes, class labels, confidence scores) onto an image for visual inspection and debugging.
Description
Detection visualization overlays the output of an object detection pipeline onto the original image. For each detected object, a colored bounding box rectangle is drawn around the predicted region, and a text label with the class name and confidence score is placed near the box. Different colors are typically assigned to different object classes for visual distinction.
Two approaches exist: using OpenCV drawing functions (cv::rectangle, cv::putText) for desktop/server applications, or using ncnn's built-in drawing functions (draw_rectangle_c3, draw_text_c3) for dependency-free rendering on embedded platforms.
Usage
Use detection visualization as the final step in a detection pipeline for debugging, demo, or display purposes. Choose between OpenCV drawing (richer features, anti-aliasing) and ncnn built-in drawing (no external dependencies, suitable for embedded).
Theoretical Basis
The visualization process is straightforward:
// Abstract visualization algorithm
for each detection in results:
color = class_colors[detection.label]
draw_rectangle(image, detection.bbox, color, thickness=2)
label = format("%s %.1f%%", class_name, detection.prob * 100)
draw_text(image, label, detection.bbox.top_left, color)