diff --git a/DPL/yolov5/mask.py b/DPL/yolov5/mask.py
new file mode 100644
index 0000000..51fcc12
--- /dev/null
+++ b/DPL/yolov5/mask.py
@@ -0,0 +1,12 @@
+from model import Model
+
+# 初始化 Model 对象，实例化时自动生成并保存掩膜
+model = Model(
+    image_folder="runs/detect/exp6",
+    label_folder="runs/detect/labels",
+    output_folder="datasets/mask"
+)
+
+# 读取某张图片对应的掩膜
+mask_array = model.get_mask_array("image1")  # 替换 "image1" 为你的图片名
+print(mask_array)
diff --git a/DPL/yolov5/model.py b/DPL/yolov5/model.py
new file mode 100644
index 0000000..e58a8b3
--- /dev/null
+++ b/DPL/yolov5/model.py
@@ -0,0 +1,122 @@
+import os
+import cv2
+import numpy as np
+import time
+
+class Model:
+    def __init__(self, image_folder, label_folder, output_folder, width_blocks=24, height_blocks=24):
+        """
+        初始化 Model 类，并自动生成并保存掩膜
+        :param image_folder: 输入图像文件夹路径
+        :param label_folder: YOLOv5标签文件夹路径
+        :param output_folder: 输出掩膜矩阵保存文件夹路径
+        :param width_blocks: 图像宽度分块数
+        :param height_blocks: 图像高度分块数
+        """
+        self.image_folder = image_folder
+        self.label_folder = label_folder
+        self.output_folder = output_folder
+        self.width_blocks = width_blocks
+        self.height_blocks = height_blocks
+
+        # 确保输出文件夹存在
+        if not os.path.exists(self.output_folder):
+            os.makedirs(self.output_folder)
+
+        # 自动处理并保存掩膜
+        self._process_and_save_masks()
+
+    def _read_yolov5_labels(self):
+        """
+        读取YOLOv5标签文件夹中的标签文件，提取每个框的位置。
+        :return: 返回一个字典，格式为 {image_name: [(x_center, y_center, width, height), ...]}
+        """
+        labels = {}
+        for filename in os.listdir(self.label_folder):
+            if filename.endswith('.txt'):
+                image_name = filename.replace('.txt', '')
+                file_path = os.path.join(self.label_folder, filename)
+
+                with open(file_path, 'r') as f:
+                    boxes = []
+                    for line in f:
+                        parts = line.strip().split()
+                        if len(parts) < 5:
+                            continue
+
+                        x_center = float(parts[1])
+                        y_center = float(parts[2])
+                        width = float(parts[3])
+                        height = float(parts[4])
+                        boxes.append([x_center, y_center, width, height])
+                    labels[image_name] = boxes
+        return labels
+
+    def _generate_mask(self, image_shape, boxes):
+        """
+        根据检测框信息生成掩膜，返回True和False的矩阵
+        :param image_shape: 图像的shape（height, width）
+        :param boxes: 检测框信息，格式为 [(x_center, y_center, width, height), ...]
+        :return: 掩膜矩阵
+        """
+        height, width = image_shape
+        mask = np.zeros((height, width), dtype=bool)
+
+        for box in boxes:
+            x_center, y_center, width_box, height_box = box
+            x1 = int((x_center - width_box / 2) * width)
+            y1 = int((y_center - height_box / 2) * height)
+            x2 = int((x_center + width_box / 2) * width)
+            y2 = int((y_center + height_box / 2) * height)
+
+            x1 = max(0, x1)
+            y1 = max(0, y1)
+            x2 = min(width, x2)
+            y2 = min(height, y2)
+
+            mask[y1:y2, x1:x2] = True
+
+        return mask
+
+    def _process_and_save_masks(self):
+        """
+        处理图像文件夹，生成掩膜并保存为True和False的矩阵
+        """
+        labels = self._read_yolov5_labels()
+
+        for filename in os.listdir(self.image_folder):
+            if filename.endswith(('.jpg', '.png', '.bmp')):
+                image_path = os.path.join(self.image_folder, filename)
+                image_name = filename.split('.')[0]
+
+                boxes = labels.get(image_name, [])
+                if not boxes:
+                    print(f"未找到检测框信息：{image_name}")
+                    continue
+
+                image = cv2.imread(image_path)
+                if image is None:
+                    print(f"无法读取图片: {image_path}")
+                    continue
+
+                height, width = image.shape[:2]
+                start_time = time.time()
+                mask = self._generate_mask((height, width), boxes)
+                processing_time = time.time() - start_time
+                print(f"处理图片 {image_name} 耗时: {processing_time:.4f}秒")
+
+                mask_filename = f"{image_name}_mask.npy"
+                mask_path = os.path.join(self.output_folder, mask_filename)
+                np.save(mask_path, mask)
+                print(f"保存掩膜: {mask_filename}")
+
+    def get_mask_array(self, image_name):
+        """
+        返回指定图片的掩膜数组
+        :param image_name: 图片名称（不带扩展名）
+        :return: 掩膜数组
+        """
+        mask_path = os.path.join(self.output_folder, f"{image_name}_mask.npy")
+        if not os.path.exists(mask_path):
+            raise FileNotFoundError(f"掩膜文件未找到: {mask_path}")
+        return np.load(mask_path)