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所在平台: Udemy |
课程主页: https://www.udemy.com/course/computer-vision-face-mask-detection-with-deep-learning/
课程评论：没有评论
课程名称：面罩识别：基于深度学习的桌面应用程序 课程概述：在这门课程中，您将开发一个面罩识别的项目。课程的前提是掌握OpenCV图像处理。我们将分为几个部分进行详细讲解： 第一部分：项目设置 - 安装Python及所需依赖库。 第二部分：数据预处理 - 收集图像，并从中提取面部。 - 对图像进行标注（目标输出）。 - 进行RGB均值减法图像处理。 第三部分：开发深度学习模型 - 使用自己构建的深度学习模型进行面部识别训练。 - 采用卷积神经网络（CNN）进行模型评估。 第四部分：使用CNN模型进行预测 - 将上述所有内容整合到一起。 第五部分：PyQT基础 - 学习PyQT，开发桌面应用程序。 课程的开始将是安装Python及其库，介绍图像处理技术及其数学概念。我们将进行必要的图像分析和预处理步骤，完成一个基于OpenCV和深度神经网络的面部检测小项目。接下来，将进入项目的第一阶段——面部身份识别，使用深度神经网络从图像中提取特征，并训练不同的深度学习模型，如卷积神经网络。课程还将涵盖模型选择和超参数调优。一旦深度学习模型准备就绪，我们将编写代码进行CNN模型的预测。最后，开发桌面应用程序并实现实时视频流预测。 赶快开始这门课程，使用机器学习和Python开发您自己的计算机视觉Flask桌面应用程序项目，并将其部署到云端！
Project that you will be Developing:Prerequisite of Project: OpenCVImage Processing with OpenCVSection -0: Setting Up ProjectInstall PythonInstall DependenciesSection -1: Data PreprocessingGather ImagesExtract Faces only from ImagesLabeling (Target output) ImagesData PreprocessingRGB mean subtraction imageSection - 2: Develop Deep Learning ModelTraining Face Recognition with OWN Deep Learning Model.Convolutional Neural NetworkModel EvaluationSection - 3: Prediction with CNN Model1. Putting All togetherSection - 4: PyQT BasicsSection -5: PyQt based Desktop ApplicationOverview:I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks.With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Deep learning models like Convolutional Neural Network. I will teach you the model selection and hyperparameter tuning for face recognition modelsOnce our Deep learning model is ready, will we move to Section-3, and write the code for preforming predictions with CNN model. Finally, we will develop the desktop application and make prediction to live video streaming. What are you waiting for? Start the course develop your own Computer Vision Flask Desktop Application Project using Machine Learning, Python and Deploy it in Cloud with your own hands.
