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()

• make 2 video to side by side

Combine Two Videos Side by Side with OpenCV python

Thank you! 😺

yolo data coordinate format, draw rectangle by cv2

yolo v5 data coordinate format

 

ex)

str_v = "32 0.262 0.7878 0.314 0.385"

#read image

cvmat = cv2.imread(img_path)

#get height, width

h,w,_ = cvmat.shape

#extract x1, y1 <- center, width, height

x1 = int( float(str_v.split(' ')[1]) * w )

y1 = int( float(str_v.split(' ')[2]) * h )

xw = int( float(str_v.split(' ')[3]) * w /2)

yw = int( float(str_v.split(' ')[4]) * h /2)

#make x1,y1, x2,y2

start_point = (x1 - xw, y1 - yw )

end_point = (x1 + xw, y1 + yw )

#draw rectangle

cvmat = cv2.rectangle(cvmat, start_point, end_point, (255, 0, 0), 2)

Thank you.

https://study.marearts.com