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diff --git a/runs/detect/train30/results.csv b/runs/detect/train30/results.csv
index 863d0e3..50f6f60 100644
--- a/runs/detect/train30/results.csv
+++ b/runs/detect/train30/results.csv
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diff --git a/runs/detect/train31/args.yaml b/runs/detect/train31/args.yaml
new file mode 100644
index 0000000..799289c
--- /dev/null
+++ b/runs/detect/train31/args.yaml
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+task: detect
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+data: data.yaml
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diff --git a/runs/detect/train31/results.csv b/runs/detect/train31/results.csv
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--- /dev/null
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diff --git a/src/data.py b/src/data.py
index 3dfab1c..e7d451d 100644
--- a/src/data.py
+++ b/src/data.py
@@ -4,24 +4,13 @@ import os
 from PIL import Image,ImageEnhance
 from PIL import Image
 import os
-
-# 指定文件夹路径
-folder_path = 'your_folder_path'
-
-# 遍历文件夹中的所有文件
+folder_path = 'C:/workspace/le-yolo/data/images/train'
 for filename in os.listdir(folder_path):
-    # 检查文件是否是图片（这里以常见的图片格式为例）
     if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
-        # 打开图片
         img_path = os.path.join(folder_path, filename)
         img = Image.open(img_path)
-
-        # 转换为灰度图像
         gray_img = img.convert('L')
-
-        # 保存灰度图像（可以保存到原文件夹或指定的新文件夹）
-        # 这里以在原文件夹保存为例，文件名不变，只是修改了内容
-        gray_img.save(img_path)
-
-        print(f"已将 {filename} 转换为灰度图像并保存")
-
+        new_filename = os.path.splitext(filename)[0] + '_gray.jpg'  # 修改为新文件名，如 "image_gray.jpg"
+        new_img_path = os.path.join(folder_path, new_filename)
+        gray_img.save(new_img_path)
+        print(f"已将 {filename} 转换为灰度图像并保存")
\ No newline at end of file
diff --git a/src/data.yaml b/src/data.yaml
index 3cabfd0..7b1c8a9 100644
--- a/src/data.yaml
+++ b/src/data.yaml
@@ -1,6 +1,6 @@
 path: C:\workspace\le-yolo\data
-train: images/train
-val: images/test
-test: images/val
+train: images/test
+val: images/val
+test: images/train
 nc: 1
 names: [ 'person' ]
\ No newline at end of file
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diff --git a/src/pic.py b/src/pic.py
index 8f65343..bb2f44f 100644
--- a/src/pic.py
+++ b/src/pic.py
@@ -1,88 +1,29 @@
-import os
+from ultralytics import YOLO
 import cv2
-def get_class_folder(catagetory, class_id, base_folder):
-    # 根据类别ID创建文件夹路径
-    class_folder = os.path.join(base_folder, catagetory[int(class_id)])
-    if not os.path.exists(class_folder):
-        os.makedirs(class_folder)
-    return class_folder
-
-def crop_and_save(image_path, label, class_folder):
-    # 读取图片
-    img = cv2.imread(image_path)
-    height, width = img.shape[:2]
-
-    # 解析标签
-    parts = label.strip().split()
-
-    x_center = float(parts[1]) * width
-    y_center = float(parts[2]) * height
-    img_width = float(parts[3]) * width
-    img_height = float(parts[4]) * height
-
-    # 计算左上角坐标
-    x1 = int(x_center - (img_width / 2))
-    y1 = int(y_center - (img_height / 2))
-
-    # 确保裁剪区域在图片范围内
-    x1 = max(0, x1)
-    y1 = max(0, y1)
-    x2 = min(width, x1 + img_width)
-    y2 = min(height, y1 + img_height)
-
-    # 裁剪图片
-    cropped_img = img[int(y1):int(y2), int(x1):int(x2)]
-
-    # 保存裁剪后的图片，文件名为数字递增
-    file_number = 1
-    file_path = os.path.join(class_folder, f"{file_number}.jpg")
-    while os.path.exists(file_path):
-        file_number += 1
-        file_path = os.path.join(class_folder, f"{file_number}.jpg")
-
-    cv2.imwrite(file_path, cropped_img)
-
-
-def main():
-    root = r"D:deskAdd_Building_20240616IR"
-    images_folder = os.path.join(root, 'images')
-    labels_folder = os.path.join(root, 'labels')
-    cropped_images_base_folder = os.path.join(root, 'cropped_images')
-    class_path = os.path.join(labels_folder, r"classes.txt")
-    category = {}
-    with open(class_path, 'r', encoding='utf-8') as f:
-        cls = f.readlines()
-        for i, c in enumerate(cls):
-            category[i] = c.strip()
-
-    # 确保裁剪图片的基文件夹存在
-    if not os.path.exists(cropped_images_base_folder):
-        os.makedirs(cropped_images_base_folder)
-
-    # 遍历images文件夹中的所有图片
-    for image_filename in os.listdir(images_folder):
-        if image_filename.lower().endswith(('.png', '.jpg', '.jpeg')):
-            # 构建图片和标签的完整路径
-            image_path = os.path.join(images_folder, image_filename)
-            label_path = os.path.join(labels_folder, os.path.splitext(image_filename)[0] + '.txt')
-
-            # 确保标签文件存在
-            if os.path.exists(label_path):
-                # 读取标签文件
-                with open(label_path, 'r') as file:
-                    labels = file.readlines()
-
-                # 遍历每个标签
-                for label in labels:
-                    # 获取类别ID
-                    class_id = int(label.strip().split()[0])
-
-                    # 获取类别文件夹
-                    class_folder = get_class_folder(category, class_id, cropped_images_base_folder)
-
-                    # 裁剪并保存图片
-                    crop_and_save(image_path, label, class_folder)
-
-
-if __name__ == '__main__':
-    main()
\ No newline at end of file
+import os
+model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\best.pt")
+target_class = 0
+cap = cv2.VideoCapture('../res/1.mp4')
+if not os.path.exists('detected_objects'):
+    os.makedirs('detected_objects')
+while cap.isOpened():
+    success, frame = cap.read()
+    if not success:
+        break
+    results = model.predict(frame)
+    for result in results:
+        boxes = result.boxes
+        for box in boxes:
+            cls_id = box.cls.item()
+            if cls_id == target_class:
+                x1, y1, x2, y2 = map(int, box.xyxy[0].tolist())
+                detected_object = frame[y1:y2, x1:x2]
+                save_path = os.path.join('detected_objects', f'detected_object_{target_class}_{cap.get(cv2.CAP_PROP_POS_FRAMES)}.jpg')
+                cv2.imwrite(save_path, detected_object)
+                print(f"截图保存至：{save_path}")
+    annotated_frame = results[0].plot()
+    cv2.imshow('YOLOv8 Detection', annotated_frame)
+    if cv2.waitKey(1) & 0xFF == ord(' '):
+        break
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/src/test.py b/src/test.py
index b1a14f1..1875b89 100644
--- a/src/test.py
+++ b/src/test.py
@@ -1,3 +1,3 @@
 from ultralytics import YOLO
-model = YOLO(r"C:\workspace\le-yolo\runs\detect\train28\weights\best.pt")
-results = model.predict("../res/4.mp4", show=True, save=True)
+model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\best.pt")
+results = model.predict("../res/3.mp4", show=True, save=True)
diff --git a/src/train.py b/src/train.py
index c7abe9b..e52ae7c 100644
--- a/src/train.py
+++ b/src/train.py
@@ -1,5 +1,5 @@
 from ultralytics import YOLO
-model = YOLO(r"C:\workspace\le-yolo\runs\detect\train28\weights\last.pt")
-model.train(data="data.yaml", epochs=500,  batch=8, device='cpu',  imgsz=640)
+model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\last.pt")
+model.train(data="data.yaml", epochs=100,  batch=8, device='cpu',  imgsz=640)
 model.val()
 print('训练完成')
\ No newline at end of file