YOLOv8 Object Detection with Flask

Object detection is a computer vision task that involves identifying and locating objects in an image or video. YOLOv8 is a state-of-the-art object detection model that is known for its accuracy and speed. In this article, we will explore how to use YOLOv8 with Flask to create an object detection web application.

Setting up the environment

Before we can create our object detection web application, we need to set up our environment. We will need to install Python, Flask, and other dependencies. Additionally, we will need to download the YOLOv8 model and its weights. Once our environment is set up, we can start building our web application.

Creating the Flask app

Flask is a lightweight web framework for Python. We will use Flask to create the backend of our object detection web application. We will create routes for uploading an image, processing the image with YOLOv8, and displaying the detected objects. We will also need to create a template for the frontend of our web application.

Code snippet:
“`python
from flask import Flask, render_template, request
import yolo_detection

app = Flask(__name__)

@app.route(‘/’)
def index():
return render_template(‘index.html’)

@app.route(‘/upload’, methods=[‘POST’])
def upload():
if request.method == ‘POST’:
# Get the uploaded image
image = request.files[‘image’]
# Process the image with YOLOv8
detected_objects = yolo_detection.detect_objects(image)
# Display the detected objects
return render_template(‘results.html’, detected_objects=detected_objects)
“`

Using YOLOv8 for object detection

YOLOv8 is a deep learning model that is capable of detecting objects in images and videos. We will use YOLOv8 to process the uploaded images and identify the objects within them. YOLOv8 is known for its high accuracy and speed, making it an ideal choice for our object detection web application.

Displaying the results

Once the uploaded image has been processed with YOLOv8, we will display the detected objects on the frontend of our web application. We will create a results template that will display the image with bounding boxes around the detected objects. Additionally, we will display the names of the detected objects and their confidence scores.

Conclusion

In this article, we have explored how to use YOLOv8 with Flask to create an object detection web application. By combining the power of YOLOv8 with the simplicity of Flask, we have created a user-friendly and efficient web application for detecting objects in images. With this knowledge, you can now create your own object detection web applications and explore the possibilities of computer vision.