Implementing Image Classification on Android Phones Using PaddleMobile
Your project has covered the complete process of image prediction using PaddleMobile, including model downloading, loading, image preprocessing, and result display. The following are some supplementary explanations for the code and steps: ### Supplementary Explanations #### 1. **Environment Preparation** Ensure the necessary dependencies are installed in the environment where this project will be run: - Install Android Studio. - Configure the Android development environment (Java or Kotlin). - Ensure your device or emulator has an internet connection to download the required models and resources.
Read MoreNotes on "My PaddlePaddle Learning Journey" (14) —— Migrating PaddlePaddle to Android Devices
This article provides a detailed introduction to integrating a trained PaddlePaddle model into an Android application, including steps such as building the PaddleMobile library, using JNI technology in an Android project to call C++ code, and converting images into the input format acceptable by PaddlePaddle for prediction. The following is a summary and supplementary explanation of the article's content: 1. **Environment Preparation**: Ensure your development environment has installed the necessary tools, including Android Studio, Pad
Read MoreDynamic Permission Request for Single or Multiple Permissions in Android
This article details the methods for handling permission requests in Android applications. Specifically, it is divided into several parts: 1. **Application for a Single Permission**: - First, it demonstrates how to check and request a single permission (such as using the camera, writing to external storage, etc.). - Check if the current permission is granted; if not, add it to the list. - If the list is not empty, call the `ActivityCompat.requestPermissions()` method to request these permissions.
Read MoreInstallation of TensorFlow
This article provides a detailed introduction to the specific steps of model training and prediction using TensorFlow locally, with special emphasis on how to install and configure TensorFlow through Docker containers to ensure the stability and portability of the development environment. The main contents include the following aspects: 1. **Installing TensorFlow Dependencies**: First, it is necessary to install a specific version of Python, pip, and a virtual environment. A specified version (such as 3.5) is recommended to avoid compatibility issues. 2. **Simplifying Installation Using Docker Containers**
Read MoreImplementing Read-Write Splitting with Mycat Middleware for One-Master-One-Slave and Two-Master-Two-Slave Topologies
This document provides a detailed introduction to how to build a MySQL master-slave cluster using Mycat, along with testing for load balancing and high availability. The following is a summary of the main content: ### I. Environment Preparation 1. **Install the Server**: MyCat, version 2.0, has been installed. 2. **Install the Client**: MySQL-8.0.17 has been installed. 3. **Configuration File Check**: The configurations of `conf/mycat-server.xml` and `schema-mysql.sql` were checked and confirmed.
Read MoreImplementing MySQL Database Master-Slave Replication
This document provides a detailed introduction to how to configure Master-Slave Replication in MySQL database, accompanied by configuration steps and simple test cases. The key points summarized are as follows: ### Configuration Steps #### 1. Select servers as master and slave - Choose one MySQL server as the master and another as the slave. #### 2. Setup on the master server - First, add the following configurations to the master's `/etc/my.cnf` or `my.ini` configuration file:
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