object_detection_script.py

import os
import cv2
from ultralytics import YOLO
from tqdm import tqdm

model = YOLO('yolov8n.pt')

def detect_cars_humans(image_path):
    image_cv = cv2.imread(image_path)
    
    # Perform object detection
    results = model(
        image_cv,
        classes=[0, 2, 7, 3, 5],
        iou=0.7,
        conf=0.65,
        show_labels=False,
        show_conf=False,
        boxes=True
    )

    if len(results[0].boxes.xyxy) == 0:
        return
    
    # Create the destination folder if it doesn't exist
    os.makedirs(r"D:\ascii\car_person\testing\Test_Purpose\Test1_results", exist_ok=True)
    
    # Save the annotated image in the results folder
    annotated_image_path = os.path.join(r"D:\ascii\car_person\testing\Test_Purpose\Test1_results", os.path.basename(image_path))
    cv2.imwrite(annotated_image_path, results[0].plot())

source_folder = r"D:\ascii\car_person\testing\Test_Purpose\Test1"
image_files = [f for f in os.listdir(source_folder) if f.endswith(".png") or f.endswith(".jpg")]

with tqdm(total=len(image_files), desc='Processing Images') as pbar:
    for filename in image_files:
        image_path = os.path.join(source_folder, filename)
        detect_cars_humans(image_path)
        pbar.update(1)