2 Star 6 Fork 2

ASouthernCat / yolo+OpenCV目标检测 QQ飞车手游赛车及弯道识别

加入 Gitee
与超过 1200万 开发者一起发现、参与优秀开源项目,私有仓库也完全免费 :)
免费加入
克隆/下载
贡献代码
同步代码
取消
提示: 由于 Git 不支持空文件夾,创建文件夹后会生成空的 .keep 文件
Loading...
README
MulanPSL-2.0

yolo+OpenCV目标检测 QQ飞车手游赛车及弯道识别

原文链接:https://gitee.com/asoutherncat/qqspeedyolo/blob/master/README.md GitHub:https://github.com/ASouthernCat/yolo-OpenCV-qqspeed-Object-Detection

介绍

keras_yolov3+OpenCV目标检测,可识别QQ飞车手游内赛车和弯道,支持图片和视频的检测与输出。本视觉小白的初次尝试,经过种种困难后,终于训练出了自己标注的目标检测模型,实属不易,在此记录一下训练的整体步骤,还有很多不足,欢迎学习交流。视频效果展示请移步b站:https://space.bilibili.com/480493680

环境配置

  1. 系统:Windows11/10
  2. python3.7.8
  3. 包(Package) Keras 2.2.4 Pillow 5.1.0 opencv-contrib-python 4.6.0.66 numpy 1.21.6 protobuf 3.20.1 tensorboard 1.13.1 tensorflow 1.13.1

整体代码结构

  • 整体代码结构:

    输入图片说明

训练过程记录

  • 成功啦!!!!

  • 过程回顾:

  • 打标很重要:labelimg 安装很简单,在terminal窗口pip install labelimg即可,装好后,直接输入labelimg就能启动 输入图片说明 输入图片说明

  • 打标看看就会了,具体也可以参考这篇博文:https://blog.csdn.net/qq_45504119/article/details/105033492

  • 输入图片说明

  • 数据集目录结构:

    输入图片说明

  • 如果是视频,也需打标,用下面这个程序可以转换为一张张图片:video_img_tool.py

    输入图片说明

  • 打完标后的文件夹里内容如下:

    输入图片说明

    输入图片说明

  • 打完标后,运行voc.py 在ImageSets/Main文件夹生成4个txt文件:

    输入图片说明

  • 接着修改、运行voc_annotation.py

    输入图片说明

  • 修改classes为自己打标时所设置的标签

    输入图片说明

  • 运行完后生成这3个文件:

    输入图片说明

  • 到此,数据集就整理好了

  • 准备训练!!!

  • 准备好权重文件yolov3.weights,官网下载地址:https://pjreddie.com/media/files/yolov3.weights

  • 先将其转为Keras适用的h5文件:python convert.py yolov3.cfg yolov3.weights model_data/yolo_weights.h5

  • 注意:yolov3.cfg在这里先不用修改;h5文件名命名为yolo_weights.h5,因为后面的训练程序中要用到,默认为这个。

  • 接着voc_classes.txt 和 yolo_anchors.txt 的内容:

    输入图片说明

  • 改成自己的标签名就行

  • yolo_anchors.txt 的值用kmeans.py可生成:

  • yolo中anchor值的解释 | https://zhen8838.github.io/2019/03/12/yolo-anchor/#:~:text=yolo%E4%B8%ADanchor%E5%80%BC%E7%9A%84%E8%A7%A3%E9%87%8A anchor

    输入图片说明

  • k=9,对应生成yolo_anchors.txt;k=6,对应生成tiny_yolo_anchors.txt,这里cluster_number设置成9就行。同时运行窗口也会有打印输出,若没有写入文件,可参照原文件内容格式自行复制修改。

  • 好了,训练!!!

  • 检查一下train.py 里要用到的文件是否配齐。

  • log_dir是生成权重文件的路径,最终的成果就存在这里!

    输入图片说明

  • 运行train.py

  • 也可以在pycharm里运行,在小黑窗运行感觉高级些[doge]

    输入图片说明

  • 训练中:

    输入图片说明

  • 训练有两个阶段,第二阶段最耗时间:

    输入图片说明

  • 训练完成啦!!!!!!

  • 耗费7个小时,我的数据集只有200来张,电脑配置:小新AIR15 R7_4800U。

    输入图片说明

  • log/000文件夹里的trained_weights_final.h5文件就是我们要的。yeah!

    输入图片说明

  • 测试一下看能不能用:

  • 命令:python yolo_video.py --image

    输入图片说明

  • 好耶!!! 功夫没白费

  • 官方的视频保存有问题,尝试过多次,但结果一直是文件损坏,没找到解决办法,所以用了其它的程序来写(利用OpenCV),但是又得把生成的trained_weights_final.h5权重文件转为weights格式:

  • h5_weights转换:

    输入图片说明

  • voc_classers.txt、yolo_anchors.txt的内容均修改为和训练时用到的对应文件内容相同,而yolov3.cfg还需修改的东西较多,为减少差错,用keras-yolo3-master的yolov3.cfg,复制过来后,修改3个yolo层的内容:

  • 大概在607、693、780行。

  • 修改filters、anchors、classes,

  • filters=3*(classes+5),我这里classes为2,即标签种类数为2,所以filters为21,anchors也是用训练时生成的值。3层都要同样地修改。

    输入图片说明 https://blog.csdn.net/weixin_45392081/article/details/106933516

  • 程序主要是修改一下权重文件的路径

    输入图片说明

  • 检查无误后,运行check_weight.py。OK,生成成功。

    输入图片说明

  • 使用:

  • detection_yolo_video.py

    输入图片说明

  • pathIn为所要检测的视频路径,pathOut为检测结果输出路径,后3个均为上一步h5转weights时用到的,内容都一致,能对应上就行。

  • 运行,等待检测完成。

    输入图片说明

    输入图片说明

  • 大功告成,非常nice! https://space.bilibili.com/480493680

参考的大佬博文

  1. https://blog.csdn.net/qq_45504119/article/details/105052478
  2. https://www.cnblogs.com/yssgxxy/p/13949536.html
  3. https://blog.csdn.net/weixin_44747240/article/details/104084273
  4. https://blog.csdn.net/weixin_45392081/article/details/106933516

参与贡献

  1. Fork 本仓库
  2. 新建 Feat_xxx 分支
  3. 提交代码
  4. 新建 Pull Request

特技

  1. 使用 Readme_XXX.md 来支持不同的语言,例如 Readme_en.md, Readme_zh.md
  2. Gitee 官方博客 blog.gitee.com
  3. 你可以 https://gitee.com/explore 这个地址来了解 Gitee 上的优秀开源项目
  4. GVP 全称是 Gitee 最有价值开源项目,是综合评定出的优秀开源项目
  5. Gitee 官方提供的使用手册 https://gitee.com/help
  6. Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 https://gitee.com/gitee-stars/
木兰宽松许可证, 第2版 木兰宽松许可证, 第2版 2020年1月 http://license.coscl.org.cn/MulanPSL2 您对“软件”的复制、使用、修改及分发受木兰宽松许可证,第2版(“本许可证”)的如下条款的约束: 0. 定义 “软件”是指由“贡献”构成的许可在“本许可证”下的程序和相关文档的集合。 “贡献”是指由任一“贡献者”许可在“本许可证”下的受版权法保护的作品。 “贡献者”是指将受版权法保护的作品许可在“本许可证”下的自然人或“法人实体”。 “法人实体”是指提交贡献的机构及其“关联实体”。 “关联实体”是指,对“本许可证”下的行为方而言,控制、受控制或与其共同受控制的机构,此处的控制是指有受控方或共同受控方至少50%直接或间接的投票权、资金或其他有价证券。 1. 授予版权许可 每个“贡献者”根据“本许可证”授予您永久性的、全球性的、免费的、非独占的、不可撤销的版权许可,您可以复制、使用、修改、分发其“贡献”,不论修改与否。 2. 授予专利许可 每个“贡献者”根据“本许可证”授予您永久性的、全球性的、免费的、非独占的、不可撤销的(根据本条规定撤销除外)专利许可,供您制造、委托制造、使用、许诺销售、销售、进口其“贡献”或以其他方式转移其“贡献”。前述专利许可仅限于“贡献者”现在或将来拥有或控制的其“贡献”本身或其“贡献”与许可“贡献”时的“软件”结合而将必然会侵犯的专利权利要求,不包括对“贡献”的修改或包含“贡献”的其他结合。如果您或您的“关联实体”直接或间接地,就“软件”或其中的“贡献”对任何人发起专利侵权诉讼(包括反诉或交叉诉讼)或其他专利维权行动,指控其侵犯专利权,则“本许可证”授予您对“软件”的专利许可自您提起诉讼或发起维权行动之日终止。 3. 无商标许可 “本许可证”不提供对“贡献者”的商品名称、商标、服务标志或产品名称的商标许可,但您为满足第4条规定的声明义务而必须使用除外。 4. 分发限制 您可以在任何媒介中将“软件”以源程序形式或可执行形式重新分发,不论修改与否,但您必须向接收者提供“本许可证”的副本,并保留“软件”中的版权、商标、专利及免责声明。 5. 免责声明与责任限制 “软件”及其中的“贡献”在提供时不带任何明示或默示的担保。在任何情况下,“贡献者”或版权所有者不对任何人因使用“软件”或其中的“贡献”而引发的任何直接或间接损失承担责任,不论因何种原因导致或者基于何种法律理论,即使其曾被建议有此种损失的可能性。 6. 语言 “本许可证”以中英文双语表述,中英文版本具有同等法律效力。如果中英文版本存在任何冲突不一致,以中文版为准。 条款结束 如何将木兰宽松许可证,第2版,应用到您的软件 如果您希望将木兰宽松许可证,第2版,应用到您的新软件,为了方便接收者查阅,建议您完成如下三步: 1, 请您补充如下声明中的空白,包括软件名、软件的首次发表年份以及您作为版权人的名字; 2, 请您在软件包的一级目录下创建以“LICENSE”为名的文件,将整个许可证文本放入该文件中; 3, 请将如下声明文本放入每个源文件的头部注释中。 Copyright (c) [Year] [name of copyright holder] [Software Name] is licensed under Mulan PSL v2. You can use this software according to the terms and conditions of the Mulan PSL v2. You may obtain a copy of Mulan PSL v2 at: http://license.coscl.org.cn/MulanPSL2 THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. See the Mulan PSL v2 for more details. Mulan Permissive Software License,Version 2 Mulan Permissive Software License,Version 2 (Mulan PSL v2) January 2020 http://license.coscl.org.cn/MulanPSL2 Your reproduction, use, modification and distribution of the Software shall be subject to Mulan PSL v2 (this License) with the following terms and conditions: 0. Definition Software means the program and related documents which are licensed under this License and comprise all Contribution(s). Contribution means the copyrightable work licensed by a particular Contributor under this License. Contributor means the Individual or Legal Entity who licenses its copyrightable work under this License. Legal Entity means the entity making a Contribution and all its Affiliates. Affiliates means entities that control, are controlled by, or are under common control with the acting entity under this License, ‘control’ means direct or indirect ownership of at least fifty percent (50%) of the voting power, capital or other securities of controlled or commonly controlled entity. 1. Grant of Copyright License Subject to the terms and conditions of this License, each Contributor hereby grants to you a perpetual, worldwide, royalty-free, non-exclusive, irrevocable copyright license to reproduce, use, modify, or distribute its Contribution, with modification or not. 2. Grant of Patent License Subject to the terms and conditions of this License, each Contributor hereby grants to you a perpetual, worldwide, royalty-free, non-exclusive, irrevocable (except for revocation under this Section) patent license to make, have made, use, offer for sale, sell, import or otherwise transfer its Contribution, where such patent license is only limited to the patent claims owned or controlled by such Contributor now or in future which will be necessarily infringed by its Contribution alone, or by combination of the Contribution with the Software to which the Contribution was contributed. The patent license shall not apply to any modification of the Contribution, and any other combination which includes the Contribution. If you or your Affiliates directly or indirectly institute patent litigation (including a cross claim or counterclaim in a litigation) or other patent enforcement activities against any individual or entity by alleging that the Software or any Contribution in it infringes patents, then any patent license granted to you under this License for the Software shall terminate as of the date such litigation or activity is filed or taken. 3. No Trademark License No trademark license is granted to use the trade names, trademarks, service marks, or product names of Contributor, except as required to fulfill notice requirements in Section 4. 4. Distribution Restriction You may distribute the Software in any medium with or without modification, whether in source or executable forms, provided that you provide recipients with a copy of this License and retain copyright, patent, trademark and disclaimer statements in the Software. 5. Disclaimer of Warranty and Limitation of Liability THE SOFTWARE AND CONTRIBUTION IN IT ARE PROVIDED WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL ANY CONTRIBUTOR OR COPYRIGHT HOLDER BE LIABLE TO YOU FOR ANY DAMAGES, INCLUDING, BUT NOT LIMITED TO ANY DIRECT, OR INDIRECT, SPECIAL OR CONSEQUENTIAL DAMAGES ARISING FROM YOUR USE OR INABILITY TO USE THE SOFTWARE OR THE CONTRIBUTION IN IT, NO MATTER HOW IT’S CAUSED OR BASED ON WHICH LEGAL THEORY, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 6. Language THIS LICENSE IS WRITTEN IN BOTH CHINESE AND ENGLISH, AND THE CHINESE VERSION AND ENGLISH VERSION SHALL HAVE THE SAME LEGAL EFFECT. IN THE CASE OF DIVERGENCE BETWEEN THE CHINESE AND ENGLISH VERSIONS, THE CHINESE VERSION SHALL PREVAIL. END OF THE TERMS AND CONDITIONS How to Apply the Mulan Permissive Software License,Version 2 (Mulan PSL v2) to Your Software To apply the Mulan PSL v2 to your work, for easy identification by recipients, you are suggested to complete following three steps: i Fill in the blanks in following statement, including insert your software name, the year of the first publication of your software, and your name identified as the copyright owner; ii Create a file named “LICENSE” which contains the whole context of this License in the first directory of your software package; iii Attach the statement to the appropriate annotated syntax at the beginning of each source file. Copyright (c) [Year] [name of copyright holder] [Software Name] is licensed under Mulan PSL v2. You can use this software according to the terms and conditions of the Mulan PSL v2. You may obtain a copy of Mulan PSL v2 at: http://license.coscl.org.cn/MulanPSL2 THIS SOFTWARE IS PROVIDED ON AN "AS IS" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO NON-INFRINGEMENT, MERCHANTABILITY OR FIT FOR A PARTICULAR PURPOSE. See the Mulan PSL v2 for more details.

简介

keras_yolov3+OpenCV目标检测,可识别QQ飞车手游内赛车和弯道,支持图片和视频的检测与输出。本视觉小白的初次尝试,经过种种困难后,终于训练出了自己标注的目标检测模型,实属不易,在此记录一下训练的整体步骤,还有很多不足,欢迎学习交流。 展开 收起
Python
MulanPSL-2.0
取消

发行版

暂无发行版

贡献者

全部

近期动态

加载更多
不能加载更多了
Python
1
https://gitee.com/asoutherncat/qqspeedyolo.git
git@gitee.com:asoutherncat/qqspeedyolo.git
asoutherncat
qqspeedyolo
yolo+OpenCV目标检测 QQ飞车手游赛车及弯道识别
master

搜索帮助