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calculation_node's Introduction

小拍机器人计算节点

作者:钱宇辰

1 人脸检测方法比较

机器 检测方法 选项 分辨率 时间
i7-8550u Haar detector 640*480 300ms~700ms
-O3 320*240 100ms~200ms
libfacedetection 640*480 500ms~600ms
320*240 100ms~200ms
-O3 320*240 10ms~40ms

Haar检测子不具备旋转不变性,检测精度不高(实验中出现把插座口识别成人脸的情况),在低分辨率下难以识别出目标且速度较慢。

libfacedetection具有一定的旋转不变性,精度较高,在低分辨率下也运行良好,速度快,在-O3优化下可以满足实时性的要求。

2 构图

2.1 判断图片结构(找出图片的主要元素)

编号 方法 优点 缺点
1 opencv,canny找轮廓,配合霍夫线变换拟合直线 简单 结果对参数敏感,难以确定一组普适的参数来应付大部分场景
2 opencv,先使用距离变换函数把图片分为目标区域和背景区域,配合分水岭算法对图片进行分割,再对分割后的图像拟合线条 复杂,结果同样对参数敏感。同时,这是基于像素值对图片进行分割,分割结果不一定符合实际.在极端情况下(大纯色块)表现甚至不如方法1.
3 使用方法1寻找直线,若找不到,则缩小图片,继续法1。最多循环3次 简单,可以一定程度上消除局部纹路的干扰 同法1

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