MareArts Computer Vision Study.: Yolo V7 vs V8

5/02/2023

Yolo V7 vs V8

V7 vs V8 comparison

https://youtu.be/k1dOZFcLOek

https://youtu.be/tpOGDclq7KY

https://youtu.be/u5qxN2ACEP4

https://youtu.be/85SH08jN4dY

This is a comparison video between yolo v7 and v8.

Here is information for each version

Yolo V7
- Github : https://github.com/WongKinYiu/yolov7
- Model : yolov7x.pt
Yolo V8
- Github : https://github.com/ultralytics/ultralytics
- Model : yolov8x.pt

Testing Computer :

Intel(R) Core(TM) i7-9800X CPU @ 3.80GHz
RTX 4090

Something might be useful code

yolo v8, video writer for detection result

import cv2
import time
from ultralytics import YOLO

def process_video(model, video_path, output_path):
    cap = cv2.VideoCapture(video_path)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = int(cap.get(cv2.CAP_PROP_FPS))

    # Create a VideoWriter object to save the annotated video
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_path, fourcc, fps, (width, height))

    while cap.isOpened():
        success, frame = cap.read()

        if success:
            start_time = time.time()
            results = model(frame)
            end_time = time.time()
            processing_time = end_time - start_time
            fps = 1/processing_time
            # Visualize the results on the frame
            annotated_frame = results[0].plot()
            
            # Display the processing time on the annotated frame
            cv2.putText(annotated_frame, f"Processing time: {processing_time:.4f} seconds / {fps:.4f} fps",
                        (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)

            # Write the annotated frame to the output video
            out.write(annotated_frame)

            # cv2.imshow("YOLOv8 Inference", annotated_frame)
            # if cv2.waitKey(1) & 0xFF == ord("q"):
            #     break
        else:
            break

    cap.release()
    out.release()

def main():
    # Load the YOLO model
    model = YOLO('yolov8x.pt')

    # List of video files
    video_paths = [
        "../video/videoplayback-1.mp4",
        "../video/videoplayback-2.mp4",
        "../video/videoplayback-3.mp4",
        "../video/videoplayback-4.mp4",
    ]

    # Loop through video files and process them
    for i, video_path in enumerate(video_paths):
        output_path = f"../video/yolo_88_output_{i+1}.mp4"
        process_video(model, video_path, output_path)

    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()