I cropped the images in my yolo dataset by drawing roi. The picture was created only within the roi frame whose coordinates were given. When I want to perform object detection with Yolo within this ROI framework, it tags outside of the ROI. How can I perform object detection only in the selected area with ROI?

The code for drawing roi and creating new directories is as follows;

import os
import cv2
import numpy as np
import yaml

x1, y1, x2, y2 = 250, 229, 738, 511  # Örnek koordinatlar

input_base_path = 'yolo_converted'
output_base_path = 'roi'

input_images_train_path = os.path.join(input_base_path, 'images/train')
input_images_val_path = os.path.join(input_base_path, 'images/val')
input_labels_train_path = os.path.join(input_base_path, 'labels/train')
input_labels_val_path = os.path.join(input_base_path, 'labels/val')

output_images_train_path = os.path.join(output_base_path, 'images/train')
output_images_val_path = os.path.join(output_base_path, 'images/val')
output_labels_train_path = os.path.join(output_base_path, 'labels/train')
output_labels_val_path = os.path.join(output_base_path, 'labels/val')

os.makedirs(output_images_train_path, exist_ok=True)
os.makedirs(output_images_val_path, exist_ok=True)
os.makedirs(output_labels_train_path, exist_ok=True)
os.makedirs(output_labels_val_path, exist_ok=True)

def apply_roi(image_path, label_path, output_image_path, output_label_path, points):
    image = cv2.imread(image_path)
    if image is None:
        print(f"Hata: Görüntü okunamadı {image_path}")
        return

    mask = np.zeros(image.shape[:2], dtype=np.uint8)
    cv2.fillPoly(mask, [np.array(points)], 255)
    masked_image = cv2.bitwise_and(image, image, mask=mask)

    cv2.imwrite(output_image_path, masked_image)
    print(f"Görüntü kaydedildi: {output_image_path}")

    new_label_lines = []
    if os.path.exists(label_path):
        with open(label_path, 'r') as label_file:
            lines = label_file.readlines()
            for line in lines:
                cls, x, y, w, h = map(float, line.strip().split())
                abs_x, abs_y = x * image.shape[1], y * image.shape[0]
                abs_w, abs_h = w * image.shape[1], h * image.shape[0]

                box_x1 = abs_x - abs_w / 2
                box_y1 = abs_y - abs_h / 2
                box_x2 = abs_x + abs_w / 2
                box_y2 = abs_y + abs_h / 2

                if (x1 <= box_x1 <= x2 and y1 <= box_y1 <= y2 and
                    x1 <= box_x2 <= x2 and y1 <= box_y2 <= y2):
                    new_x = (abs_x - x1) / (x2 - x1)
                    new_y = (abs_y - y1) / (y2 - y1)
                    new_w = abs_w / (x2 - x1)
                    new_h = abs_h / (y2 - y1)
                    new_label_lines.append(f"{cls} {new_x:.6f} {new_y:.6f} {new_w:.6f} {new_h:.6f}n")

        with open(output_label_path, 'w') as new_label_file:
            new_label_file.writelines(new_label_lines)
        print(f"Etiket kaydedildi: {output_label_path}")
    else:
        print(f"Uyarı: Etiket dosyası bulunamadı {label_path}")

def process_folder(input_images_path, input_labels_path, output_images_path, output_labels_path, points):
    for file_name in os.listdir(input_images_path):
        image_path = os.path.join(input_images_path, file_name)
        label_path = os.path.join(input_labels_path, os.path.splitext(file_name)[0] + '.txt')
        output_image_path = os.path.join(output_images_path, file_name)
        output_label_path = os.path.join(output_labels_path, os.path.splitext(file_name)[0] + '.txt')

        if os.path.isfile(image_path):
            print(f"İşleniyor: {image_path}")
            apply_roi(image_path, label_path, output_image_path, output_label_path, points)
        else:
            print(f"Uyarı: Geçersiz dosya {file_name}")

points = [(250, 253), (330, 229), (738, 444), (601, 511)]

print("Train klasörü işleniyor...")
process_folder(input_images_train_path, input_labels_train_path, output_images_train_path, output_labels_train_path, points)
print("Val klasörü işleniyor...")
process_folder(input_images_val_path, input_labels_val_path, output_images_val_path, output_labels_val_path, points)

train_yaml_path = os.path.join(input_base_path, 'train.yaml')
with open(train_yaml_path, 'r') as file:
    train_yaml_data = yaml.safe_load(file)

base_path = '/home/ubuntu/yolo/folders/performance_metrics/'
train_yaml_data['train'] = os.path.join(base_path, output_images_train_path)
train_yaml_data['val'] = os.path.join(base_path, output_images_val_path)

output_yaml_path = os.path.join(output_base_path, 'train.yaml')
with open(output_yaml_path, 'w') as yaml_file:
    yaml.dump(train_yaml_data, yaml_file)

The training code is as follows;

from ultralytics import YOLO
model = YOLO('yolov8n.pt')  # YOLOv8 kullanıyorsanız 'yolov8n.pt' veya YOLOv5 kullanıyorsanız 'yolov5s.pt'
results = model.train(data='roi/train.yaml', epochs=20, imgsz=640, batch=4)

When I train according to these codes, it labels other than ROI.