I’m confident I can solve this project, but I need more data. I’ve found a small dataset (~100 images per class), but my models are quick to overfit and far from accurate. I’m working on a project where I need to classify the scenes of outdoor photographs into four distinct categories: cities, beaches, mountains, and forests. This is by far the best resource I’ve seen for deep learning. OK, let’s create /templates/index.html: return render_template('index.html', **templateData) if _name_ = "_main_": app.run(host='0.0.0.Hey Adrian, thanks for putting together Deep Learning for Computer Vision with Python. css) it’s a good option with bigger and more complex projects GEANY can be used to work with all project files at same time (.py. Can be Geany, “nano” at Terminal or the RPi Text Editor (LeafPad) that is located under “Accessories” Main Menu. You can use any Text editor to create your HTML file. So, we will create a file named index.html, that will be saved on /templates. This is, in fact, important, otherwise, you would complicate the Python Script putting all on it.Ĭreating an HTML file that will be located in the “template” sub-folder, we can use separate files with placeholders for spots where you want dynamic data to be inserted. Let’s sophisticated our “Hello World” application, creating an HTML template and a CSS file for styling our page. The application will be running unless you type +. On the first picture above you can see what will appear on your terminal. Now, run the above application program: sudo python3 helloWorld.py In my case: 10.0.1.27 Running the Application If you are not sure about your RPi IP address, run on your terminal: ifconfigĪt wlan0: section you will find it. Once this script is running from the command line at the terminal, the server starts to “listen” on port 80, reporting any errors: if _name_ = '_main_': app.run(debug=True, port=80, host='0.0.0.0') Raspberry PI IP Address: In this case, only send the text “Hello World!” to the client’s web browser thru “return” def index(): return "Hello Word"Ĥ. Run the index() function when someone accesses the root URL (‘/’) of the server. Create a Flask object called app: app = Flask(_name_)ģ. Load the Flask module into your Python script: from flask import FlaskĢ. Copy the “Hello Word” code below on your IDE and save it for example, as helloWorld.pyįrom flask import Flask app = Flask(_name_) def index(): return 'Hello world' if _name_ = '_main_': app.run(debug=True, port=80, host='0.0.0.0')ġ.Open your Python3 IDE, Thonny or Geany.Now, let’s create our first python WebServer with Flask: The final folder “tree”, will look like: /rpiWebServer /static /templates The Python WebServer Application Go to your newer created folder: cd rpiWebServerĪnd create the 2 new sub-folders: mkdir static On this folder, let’s create 2 other sub-folders: static for CSS and eventually JavaScript files and templates for HTML files (or more precisely, Jinja2 templates. There we will save our python files (application): /home/pi/Documents/Server The above command will create a folder named “Server”. The best when you start a new project is to create a folder where to have your files organized. Go to Terminal and enter: sudo apt-get install python3-flask The first thing to do is to install Flask on your Raspberry Pi. On this tutorial, we will use a Raspberry Pi as a local Web Server, where we will control via a simple webpage, 3 of its GPIOs programmed as outputs (acting as actuators) and monitor 2 of its GPIOs, programmed as inputs ( sensors). However, Flask supports extensions that can add application features as if they were implemented in Flask itself. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. Introductionįlask is called a micro framework because it does not require particular tools or libraries. With Flask, will be very simple to control Raspberry GPIOs over the internet.Īfter you read this tutorial, please give a visit to its continuation: From Data to Graph: a Web Jorney With Flask and SQLite 1. For this project here, we will use FLASK, a very simple and free microframework for Python. For example, on my tutorial: IoT - Controlling a Raspberry Pi Robot Over Internet With HTML and Shell Scripts Only, we have explored how to control a robot over the local network using the LIGHTTPD WebServer. Let’s create a simple WebServer to control things in your home. Python WebServer With Flask and Raspberry Pi
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