How to Crop and Save Detected Object with YOLOv8

YOLOv8 is a popular object detection model that can be used to detect and localize objects in images and videos. In this tutorial, we will learn how to crop and save the detected object using YOLOv8, OpenCV, and Python.

Step 1: Install Required Libraries

First, you need to install the necessary libraries for this project. You can install YOLOv8, OpenCV, and other dependencies using the following commands:


pip install yolov8 opencv-python


Step 2: Load YOLOv8 Model

Next, you need to load the YOLOv8 model and configure it for object detection. You can do this by importing the necessary libraries and loading the pre-trained YOLOv8 model. Here’s an example code snippet:


import cv2
import numpy as np
from yolov5.v8 import YOLOv8

yolo = YOLOv8(weights='yolov8.weights', cfg='yolov8.cfg')


Step 3: Detect Objects in an Image

Now, you can use the loaded YOLOv8 model to detect objects in an image. To do this, you need to read the image using OpenCV and pass it through the YOLOv8 model. The model will return the bounding boxes and class labels of the detected objects. Here’s an example code snippet:


image = cv2.imread('image.jpg')
boxes, labels = yolo.detect(image)


Step 4: Crop and Save Detected Objects

Finally, you can crop and save the detected objects by iterating through the bounding boxes and cropping the corresponding regions in the image. Here’s an example code snippet that shows how to crop and save the detected objects:


for i, box in enumerate(boxes):
x, y, w, h = box
crop_img = image[y:y+h, x:x+w]
cv2.imwrite(f'object_{i}.jpg', crop_img)


By following these steps, you can easily crop and save the detected objects using YOLOv8, OpenCV, and Python. This can be useful for various computer vision applications, such as object recognition and tracking. Have fun experimenting with YOLOv8 and exploring the possibilities of computer vision!