|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/build-complete-webcam-security-camera-python-opencv-pyqt/
课程评论：没有评论
课程名称：使用 Python、OpenCV & PyQt 构建完整的网络摄像头安全摄像头 课程概述： 本课程将教授如何使用 PyQt 库和 Qt Designer 创建一个带有精美用户界面的网络摄像头安全摄像头项目。 主要学习内容： 1. **安装与配置：** * 学习安装 Python、PyQt5、PyQt5-tools 和 OpenCV。 * 安装并配置 VS Code 用于 Python 编程。 2. **设计用户界面：** * 使用 Qt Designer 设计具有美观界面的安全摄像头应用。 * 掌握 QPushButton、QLabel、QSlider 等基础控件的使用。 * 学习使用样式表美化控件。 * 实现按钮悬停效果和 QLabel 动态图片更换。 3. **摄像头捕获与显示：** * 使用 OpenCV (cv2) 库实现摄像头功能，捕获图像。 * 在窗口中显示捕获的摄像头画面。 4. **图像处理：** * 将图像转换为所需格式以识别轮廓。 * 学习使用 OpenCV 将图像灰度化。 * 应用高斯模糊（Gaussian Blur）平滑图像。 * 使用 OpenCV 的膨胀（Dilate）操作。 * 利用 OpenCV 收集图像中的所有轮廓。 5. **物体检测：** * 检测轮廓面积大于 5000 的区域。 * 使用 OpenCV 在检测到的物体周围绘制绿色矩形框。 6. **显示捕获的物体：** * 将捕获并标记的物体保存为图像文件。 * 在 PyQt 窗口的 QLabel 中显示保存的图像，以便识别物体即使其已离开摄像头区域。 通过完成此项目，您将深入了解 OpenCV 库的众多基础功能，掌握 Qt Designer 的基本控件运用，并学会如何通过 Python 代码控制 GUI 控件。
Hello StudentsWelcome to the course "Build Complete Webcam Security Camera Python OpenCv & Pyqt"You will learn how to create beautiful user interface to the project using Pyqt Library and the Qt Designer.1. Installation and configurationFirst we are going to install the required software to start our project from the internet. Learn to install Python, pyqt5, pyqt5-tools and opencv library. Then you are going to learn how to install the vs code and configure vs code to python programming through this course.2. Design the user interfaceThen we are going to design the beautiful user interface using Qt Designer. In this interface we are going to use basic controls like QPushButton, QLabel, QSlider and how to use style sheets to the controls to look good. Then you will learn how to provide the hover effects to the QPushButtons and how to dynamically change the change the images in the qlabels.3. Camera Capture and display in windowThen we are going to implement the camera using cv2 library and capture the images in the camera. Then we show the captured images in the cv2 window.4. Image processingThen we will convert the images to our required formats to identify contours in the images. We will first convert the images to grayscale image using opencv. Then we will blur the images using gaussian blur in opencv python. Then we are going to dilate images using opencv. Then we are going to collect all the contours in the images using opencv python.5. Object DetectionThen will find the contour area greater than 5000 and draw rectangle using cv2 library for the captured objects. This shows the captured objects in green colour to identify easily. 6. Display captured objectsThen we are going to save the captured objects in a image file. The captured image file is then displayed in a qlabel in the pyqt window. This is used to identify the object even if the object passes the cam area.By doing this project you will learn lot of basic functions in opencv library and how to use basic controls using qt designer and how to process the GUI controls using python code.Thank you for your interest in this course...I will see you in the course.
