Close YOLOv8 Video Detection Window by Keypress: Keep Image Detections Running
This article focuses on closing the video detection window using a keypress in YOLOv8, without stopping the entire code. With YOLOv8, you can perform real-time object detection in both images and videos. While YOLOv8 does not have a built-in feature to close the video detection window via keypress, it can be implemented through custom code.
YOLOv8 Overview
You Only Look Once (YOLO) is an object detection system that is known for its ability to recognize objects in real-time in both images and video streams. YOLOv8 is the latest version of the YOLO family, providing improved accuracy and performance compared to earlier YOLO versions.
Implementing Keypress Detection
To implement the keypress detection feature in YOLOv8, the following steps can be taken:
- Capture video frames using the OpenCV library
- Perform object detection using YOLOv8 on the captured frames
- Close the video detection window using a keypress
Step 1: Video Frame Capture
To capture video frames, OpenCV's VideoCapture class can be employed. The following code snippet shows how to initialize the VideoCapture object:
cap = cv2.VideoCapture(video_path)
The VideoCapture object can read video frames using the read() method. This method returns two values: a Boolean indicating success, and a NumPy array containing the video frame.
Step 2: Object Detection Using YOLOv8
Once the video frames are captured, YOLOv8 can be employed to detect objects. The YOLOv8 library provides the detect() function to perform object detection on an image.
outputs = yolo_model.detect(frame)
The detect() function returns a list of detections where each detection includes the class ID, confidence, and coordinates (x, y, width, height).
Step 3: Implementing Keypress Detection for Window Closure
The keypress detection can be implemented using OpenCV's waitKey() function. The waitKey() function waits for a keypress and returns the keycode if any. The following code snippet demonstrates how to listen for a keypress for window closure:
key = cv2.waitKey(1)
if key == ord('q'):
break
Here, the waitKey(1) function checks for a keypress every 1 millisecond. If the key pressed is 'q', the loop will be broken, and the video window will be closed.
Integrating Keypress Detection and YOLOv8
To integrate keypress detection and YOLOv8, the keypress detection code should be placed in the main loop of the video frame capture process. Here is the complete code:
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
outputs = yolo_model.detect(frame)
cv2.imshow('YOLOv8 Object Detection', frame)
key = cv2.waitKey(1)
if key == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
This article provided a detailed explanation of how to modify the YOLOv8 code for closing the video detection window using a keypress while maintaining image detections. Key points include video frame capture with OpenCV, YOLOv8 object detection, and integrating the keypress detection code. You can further explore YOLOv8 and related object detection frameworks through these references:
- YOLOv5 Repository < ```sql>span style="font-weight: normal;">— The repository contains YOLOv5, a close alternative to YOLOv8.
- PyTorch Vision Models < ```sql>span style="font-weight: normal;">— A collection of pre-trained models from PyTorch Vision, including YOLOv3.
- PyImageSearch: Running OpenCV Programs via Shell — A blog post demonstrating how to run OpenCV programs using a shell script.