使用Python+OpenCV实现鼠标画框的代码，供大家参考，具体内容如下 # -*-coding: utf-8 -*- @Project: IntelligentManufacture @File : user_interaction.py @Author : panjq @E-mail : pan_jinquan@163.com @Date : 2019-02-21 15:03:18 # -*- coding: utf-8 -*- import cv2 from utils import image_processing import numpy as np

使用OpenCV完美迷宫求解器 该项目旨在找到完美迷宫的解决方案，完美迷宫的定义是从迷宫中的任何点到任何其他点只有一条路径的迷宫。 另外，迷宫没有难以接近的部分，没有圆形路径，也没有开放区域。 迷宫图像在浅色背景上应具有深色的墙壁。 为了生成迷宫，已经使用了在线迷宫生成器工具。 使用的工具 OpenCV3.4 Python 3.6 任务分析 OpenCV OpenCV（开源计算机视觉库）是一个开源计算机视觉和机器学习软件库。 OpenCV构建旨在为计算机视觉应用程序提供通用的基础结构，并加速在商业产品中使用机器感知。 该库具有2500多种优化算法，其中包括一整套经典和最新的计算机视觉和机器学习算法。 OpenCV应用领域包括： 面部识别系统 手势识别 人机交互（HCI） 移动机器人 运动理解 对象识别 等高线 轮廓可以简单地解释为连接具有相同颜色或强度的所有连续点（沿边界）的曲线。

answer_sheet_scan 使用python3+opencv3实现的一些识别答题卡的例子 由于工作需要，最近在研究关于如何通过程序识别答题卡的客观题的答案，之前虽然接触过python，但对于计算机视觉这一块却完全是一个陌生的领域，经过各种调研，发现网上大多数的例子都是采用的OpenCV这个开源库来做的，OpenCV是计算机视觉领域的处理的一个非常优秀的开源库，原生由C++编写，也提供了各个主流编程语言的接口支持，这里选择python完全是因为python在计算机科学领域有着压倒性的优势和生态系统，所以使用它毫无疑问，最快上手的方式莫过于直接阅读网上已有的例子或者轮子了，通过阅读源码以问题驱动的方式来学习和研究某一项技术是比较高效的一种方式。 答题卡识别例子 博客相关 （1） （2） （3） （4） （5） 我的公众号(woshigcs) 有问题可关注我的公众号留言咨询

我使用了opencv来录取人脸,使用了txt文本来保存人脸数据来提高第二次和以后的识别速度.

基于python+opencv实现国内车牌识别系统源码(带GUI界面+PPT报告+项目说明)数字图像处理课设.zip 数字图像处理课程设计作业 Python3+OpenCV 使用tkinter搭建界面 tmp/文件夹是数字图像处理过程 chepai/文件夹是车牌图片 pic/文件夹是程序界面图 PPT答辩报告 Python opencv 车牌识别 【备注】主要针对正在做毕设的同学和需要项目实战的深度学习cv图像识别模式识别方向学习者。 也可作为课程设计、期末大作业。包含：项目源码\项目操作说明等，该项目可直接作为毕设使用。 也可以用来学习、参考、借鉴。

Build real-world computer vision applications and develop cool demos using OpenCV for Python, About This Book, Learn how to apply complex visual effects to images using geometric transformations and image filtersExtract features from an image and use them to develop advanced applicationsBuild algorithms to help you understand the image content and perform visual searches, Who This Book Is For, This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on., What You Will Learn, Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like imageDetect and track various body parts such as the face, nose, eyes, ears, and mouthStitch multiple images of a scene together to create a panoramic imageMake an object disappear from an imageIdentify different shapes, segment an image, and track an object in a live videoRecognize an object in an image and build a visual search engineReconstruct a 3D map from imagesBuild an augmented reality application, In Detail, Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel., This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications., This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples., The book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation., Style and approach, This is a conversational-style book filled with hands-on examples that are really easy to understand. Each topic is explained very clearly and is followed by a programmatic implementation so that the concept is solidified. Each topic contributes to something bigger in the following chapters, which helps you understand how to piece things together to build something big and complex.

基于OpenCV和Python的课堂考勤系统的设计与实现.pdf

线结构光扫描激光条纹提取-opencv+python-附件资源

基于opencv实现多张图像的全景拼接，有效避免鬼影现象，可直接运行。

本人在此项目拥有的功能：根据图片识别图片中的车牌号、GUI界面设计、导出识别数据到Excel文件中的功能基础上，添加了运用opencv摄像头实时识别车牌的功能；与传统识别方法不同的是，将opencv摄像头识别与此项目结合，可以极大的提高识别的效率和准确率。同时，本人优化了原项目的识别模块的函数，也极大的优化了识别的速度和效率，希望大家多多交流，多多学习！！！！！