mediapipe手势识别步骤 |
| 时间：2025-03-30 11:10:00 来源：互联网 作者： |
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.b_algo .b_vlist2col.b_deep li{padding:0 0 12px}#b_results .b_algo .b_vlist2col.b_deep .deeplink_title_small{font-size:16px;line-height:20px;padding-top:4px;padding-bottom:1px}#b_results .b_algo .b_vlist2col.b_deep .deeplink_title_small :last-child{padding-bottom:10px}.b_deepdesk{padding-bottom:6px}.b_algo .b_deep h3{font-size:20px;line-height:24px}.b_algo .b_deep h3{padding-bottom:3px;line-height:1.2em}.b_deep p{display:-webkit-box;-webkit-box-orient:vertical;overflow:hidden;-webkit-line-clamp:2;height:40px;line-height:20px}【深度学习实战—12】：基于MediaPipe的手势识别-2024年10月22日 · 本文将通过 Mediapipe 检测出手部关键点，并通过对各种关键点的位置判别，以达到手势识别的目的。 本文将对如下 6 种手势进行判定识别： 计算两点之间的距离如 基于python+opencv+mediprocess ()是手势识别最核心的方法，通过调用这个方法，将窗口对象作为参 基于Mediapipe深度学习算 本文介绍了利用Mediapipe的深度学习算法开发的手势识别系统，该系统具有UI界 仅显示来自 blog.csdn.net 的更多内容请查看https://blog.csdn.net/qq_42856191/article/details/143128771
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}基于python+opencv+mediapipe实现手势识别详细讲解运行环境OpenCVMeidapipe配置实现手部的识别并标注python3.9.7 opencv-python4.6.0.66 mediapipe0.8.11 运行之前先要安装opencv-python、opencv-contrib-python、mediapipe 项目可能对版本的要求较为严格，安装不上的可以按我版本 这篇文章只介绍mediapipe的简单实现，拖拽和放大效果后续更新在blog.csdn.net上查看更多信息更多内容请查看https://blog.csdn.net/qq_63708623/article/details/126808531
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.b_imgcap_altitle p strong,.b_imgcap_altitle .b_factrow strong{color:#767676}#b_results .b_imgcap_altitle{line-height:22px}.b_hList img{display:block}.b_imagePair .inner img{display:block;border-radius:6px}.b_algo .vtv2 img{border-radius:0}.b_hList .cico{margin-bottom:10px}.b_title .b_imagePair>.inner,.b_vList>li>.b_imagePair>.inner,.b_hList .b_imagePair>.inner,.b_vPanel>div>.b_imagePair>.inner,.b_gridList .b_imagePair>.inner,.b_caption .b_imagePair>.inner,.b_imagePair>.inner>.b_footnote,.b_poleContent .b_imagePair>.inner{padding-bottom:0}.b_imagePair>.inner{padding-bottom:10px;float:left}.b_imagePair.reverse>.inner{float:right}.b_imagePair .b_imagePair:last-child:after{clear:none}.b_algo .b_title .b_imagePair{display:block}.b_imagePair.b_cTxtWithImg>*{vertical-align:middle;display:inline-block}.b_imagePair.b_cTxtWithImg>.inner{float:none;padding-right:10px}.b_imagePair.square_mp>.inner{width:80px}.b_imagePair.square_mp{padding-left:90px}.b_imagePair.square_mp>.inner{margin:2px 0 0 -90px}.b_imagePair.square_mp.reverse{padding-left:0;padding-right:90px}.b_imagePair.square_mp.reverse>.inner{margin:2px -90px 0 0}.b_imagePair.square_s>.inner{width:50px}.b_imagePair.square_s{padding-left:60px}.b_imagePair.square_s>.inner{margin:2px 0 0 -60px}.b_imagePair.square_s.reverse{padding-left:0;padding-right:60px}.b_imagePair.square_s.reverse>.inner{margin:2px -60px 0 0}.b_ci_image_overlay:hover{cursor:pointer}.b_greyBackgroundModal{display:none;position:fixed;left:0;top:0;width:0;height:0}51CTOMediaPipe手势识别_51CTO博客_mediapipe手势识 2024年8月8日 · 总结：MediaPipe手势检测与骨架提取模型识别相较传统方法更稳定，而且提供手指关节的3D坐标点，对于手势识别与进一步手势动作相关开发有很大帮助。更多内容请查看https://blog.51cto.com/whaosoft/11690219
oryoy.comhttps://www.oryoy.com/news/shi-yong-mediapipe-zai-python使用MediaPipe在Python中实现高效的手势识别与图像处理技术2024年10月29日 · 基本流程包括图像采集、预处理、特征提取和分类识别。 MediaPipe手势识别模块利用深度学习模型来检测手部的关键点，从而实现对复杂手势的准确识别。 在开始之前，我 更多内容请查看https://www.oryoy.com/news/shi-yong-mediapipe-zai-python-zhong-shi-xian-gao-xiao-de-shou-shi-shi-bie-yu-tu-xiang-chu-li-ji-shu.html
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腾讯云MediaPipe + OpenCV五分钟搞定手势识别-腾讯云 2023年8月21日 · MediaPipe介绍 这个是真的，首先需要从Google在2020年发布的mediapipe开发包说起，这个开发包集成了人脸、眼睛、虹膜、手势、姿态等各种landmark检测与跟踪算法。bkok.cn更多内容请查看https://cloud.tencent.com/developer/article/2315239
张生荣 文章将介绍如何使用 Python 利用 OpenCV图像捕捉，配合强大的 Mediapipe 库来实现 手势检测与识别；本系列后续还会 继续更新Mediapipe手势的各种衍生项目，还请多多关注！更多内容请查看https://www.zhangshengrong.com/p/3mNmRRK0Xj/
51CTOmediapipe手势识别demo_mob64ca1404ed65的技 3 天之前 · mediapipe手势识别demo，我们介绍了MediaPipeHolistic的基础知识，了解到MediaPipeHolistic分别利用MediaPipePose，MediaPipeFaceMesh和MediaPipeHands中的姿势，面部和手界标模型来生成总共543个界标（每 更多内容请查看https://blog.51cto.com/u_16213636/13662763
百家号【机器视觉】零基础Python+OpenCV+MediaPipe实现手势 2021年5月3日 · 我们利用Opencv的cv2.VideoCapture ()函数，获取视频对象，时间初始化为的是之后显示帧数而提前准备。更多内容请查看https://baijiahao.baidu.com/s?id=1698742742986863925
bytezonex.com技术博客实战| 用Python+OpenCV+MediaPipe搞定手势识别2023年9月19日 · 本教程将深入剖析如何使用 Python、OpenCV 和 MediaPipe 构建一个实时手势识别系统。 我们将利用 OpenCV 获取摄像头数据，借助 MediaPipe 检测手势，再通过 Python 更多内容请查看https://www.bytezonex.com/archives/fsrWmd2-.html
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