(YOLO) Yolo v8 샘플 코드

YOLO v8 설치

사전 설치

• Conda 가상환경 등의 python 환경에서 진행

패키지 설치

pip install ultralytics
pip install opencv-python

YOLO v8 샘플 코드

sample_code.py

# author: msk
import cv2
import argparse
from ultralytics import YOLO
 
if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description='Yolo v8 Test'
    )
    parser.add_argument('flag', help= 'video file path', nargs= '?', const= 1, default= 'None')
    
    args = parser.parse_args()
    flag = args.flag
 
    if flag == 'None':
        # webcam 사용시
        cap = cv2.VideoCapture(0)
    else:
        # 동영상 불러오기
        video_path = flag
        cap = cv2.VideoCapture(video_path)
 
    
    # Load the YOLOv8
    model = YOLO('yolov8n.pt')
    msec = 1
    stop_trigger = False
 
	# for Save
    fcc = cv2.VideoWriter_fourcc(*'MP4V')
    out = cv2.VideoWriter('result.mp4', fcc, 30, (1280, 720))
 
    # Loop through the video frames
    while cap.isOpened():
        # Read a frame from the video
        success, frame = cap.read()
 
        if success:
            # 매 프레임 inference
            results = model(frame)
 
            # Visualization
            annotated_frame = results[0].plot()
 
            # For print on terminal
            print("[Class with bbox-points(x, y)]")
            for r in results:
                boxes = r.boxes.xyxy
                cls = r.boxes.cls
                conf = r.boxes.conf
                cls_dict = r.names
                for box, cls_number, conf in zip(boxes, cls, conf):
                    conf_number = float(conf.item())
                    cls_number_int = int(cls_number.item())
                    cls_name = cls_dict[cls_number_int]
                    x1, y1, x2, y2 = box
                    x1_int = int(x1.item())
                    y1_int = int(y1.item())
                    x2_int = int(x2.item())
                    y2_int = int(y2.item())
                    print("{0} - pt1:({1}, {2}), pt2:({3}, {4})" .format(cls_name, x1_int, y1_int, x2_int, y2_int))
 
            # Display
            out.write(annotated_frame)
            cv2.imshow("YOLOv8 Inference", annotated_frame)
 
            key = cv2.waitKey(msec) & 0xFF
            # input esc
            if key == 27:
                break
            # input spacebar
            elif key == 32 and stop_trigger == False:
                msec = 0
                stop_trigger = True
            elif key == 32 and stop_trigger == True:
                msec = 1
                stop_trigger = False
        else:
            break
    
    cap.release()
    out.release()
    cv2.destroyAllWindows()
 

실행

샘플 코드 실행

아래 명령 프롬포트를 통해 실행

# 웹캠 모드
(yolov8) python sample_code.py
 
# 동영상 모드
(yolov8) python sample_code.py sample_video.mp4

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DeepLearningObjectDetection yolo