diff --git a/runs/pose/predict/video.avi b/runs/pose/predict/video.avi
new file mode 100644
index 0000000..98e226d
Binary files /dev/null and b/runs/pose/predict/video.avi differ
diff --git a/runs/pose/predict2/test.avi b/runs/pose/predict2/test.avi
new file mode 100644
index 0000000..d0cb08d
Binary files /dev/null and b/runs/pose/predict2/test.avi differ
diff --git a/src/data.py b/src/data.py
index e7d451d..b6bea77 100644
--- a/src/data.py
+++ b/src/data.py
@@ -4,6 +4,7 @@ import os
 from PIL import Image,ImageEnhance
 from PIL import Image
 import os
+# 将彩色图片转为灰度图片并保存
 folder_path = 'C:/workspace/le-yolo/data/images/train'
 for filename in os.listdir(folder_path):
     if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.gif')):
diff --git a/src/labels.py b/src/labels.py
index 05d20d7..0866b14 100644
--- a/src/labels.py
+++ b/src/labels.py
@@ -3,6 +3,8 @@ from ultralytics import YOLO
 import os
 from video import video_to_pic
 
+#  用训练好的模型来进行模型自动标注
+
 class Yolov8Detect():
     def __init__(self, weights):
         cuda = True if torch.cuda.is_available() else False
diff --git a/src/pic.py b/src/pic.py
index fc745e5..5b0add3 100644
--- a/src/pic.py
+++ b/src/pic.py
@@ -1,6 +1,7 @@
 from ultralytics import YOLO
 import cv2
 import os
+# 截取目标图片 并保存
 # model = YOLO("yolov8n.pt")
 model = YOLO(r"C:\workspace\le-yolo\runs\detect\train40\weights\best.pt")
 target_class = 0
diff --git a/src/pose.py b/src/pose.py
new file mode 100644
index 0000000..e09cd37
--- /dev/null
+++ b/src/pose.py
@@ -0,0 +1,12 @@
+from ultralytics import YOLO
+import time
+# 关键点检测
+start_time = time.time()
+model = YOLO('yolov8n-pose.pt')
+#  预测
+results = model.predict(source='../res/test.mp4', show=True, save=True)
+print('预测完成')
+end_time  = time.time()
+run_time = end_time - start_time
+min = run_time // 60
+print('运行时间：', min, '分钟')
\ No newline at end of file
diff --git a/src/pose.yaml b/src/pose.yaml
new file mode 100644
index 0000000..cea9b13
--- /dev/null
+++ b/src/pose.yaml
@@ -0,0 +1,11 @@
+path: /home/le/le-yolo/data
+train: images/train
+val: images/val
+test: images/test
+
+# 关键点坐标 13个关键点，每个关键点有3个坐标[x,y,v]
+kpt_shape: [13, 3]
+
+# 翻转索引
+flip_idx: [5, 4, 3, 2, 1, 0, 8, 9, 6, 7]
+names: [ 'person' ]
\ No newline at end of file
diff --git a/src/pose_show.py b/src/pose_show.py
new file mode 100644
index 0000000..6d94406
--- /dev/null
+++ b/src/pose_show.py
@@ -0,0 +1,59 @@
+import cv2
+import cvzone
+import numpy as np
+from ultralytics import YOLO
+
+model = YOLO("yolov8n-pose.pt")
+
+vide_opath = '../res/test.mp4'
+cap = cv2.VideoCapture(vide_opath)
+# cap = cv2.VideoCapture(0)  # 打开摄像头
+
+# 连接顺序 （0,1) 0-1
+# 17个关键点 鼻子 眼睛 耳朵 肩膀 手肘 手腕 胯 膝盖 脚腕
+connections = [
+    (3, 1), (1, 0), (0, 2), (2, 4), (1, 2), (4, 6), (3, 5),
+    (5, 6), (5, 7), (7, 9),
+    (6, 8), (8, 10),
+    (11, 12), (11, 13), (13, 15),
+    (12, 14), (14, 16),
+    (5, 11), (6, 12)
+]
+
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        cap = cv2.VideoCapture("video.MOV")
+        continue
+    frame = cv2.resize(frame, (640, 480))
+    width, height = frame.shape[:2]
+    # 创建一个空白的图像  绘制目标关键点
+    blank_image = np.zeros((width, height, 3), dtype=np.uint8)
+    results = model(frame)
+    # 绘制关键点
+    for keypoints in results[0].keypoints.data:
+        keypoints = keypoints.cpu().numpy()
+        for i, keypoint in enumerate(keypoints):
+            x, y, confidence = keypoint
+            if confidence > 0.7:
+                cv2.circle(blank_image, (int(x), int(y)), radius=5, color=(0, 250, 50), thickness=1)
+                cv2.putText(blank_image, f'{i}', (int(x), int(y) - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255),
+                            1, cv2.LINE_AA)
+    #  连线 绘制骨架
+    for part_a, part_b in connections:
+        if keypoints.any():
+            x1, y1, conf1 = keypoints[part_a]
+            x2, y2, conf2 = keypoints[part_b]
+            # 置信度  大于0.5 连线
+            if conf1 > 0.5 and conf2 > 0.5:
+                cv2.line(blank_image, (int(x1), int(y1)), (int(x2), int(y2)), (50, 250, 250), thickness=2)
+    frame = results[0].plot(labels=False, conf=False, boxes=False)
+    # output = cvzone.stackImages([frame, blank_image], cols=2, scale=0.80)
+    # cv2.imshow("并排绘制", output)
+    cv2.imshow("叠加图像", frame)
+    # 按空格提前结束循环
+    if cv2.waitKey(1) & 0xFF == ord(' '):
+        break
+
+cap.release()
+cv2.destroyAllWindows()
\ No newline at end of file
diff --git a/src/train.py b/src/train.py
index b58fd84..a011784 100644
--- a/src/train.py
+++ b/src/train.py
@@ -3,9 +3,10 @@ import time
 start_time = time.time()
 model = YOLO("yolov8n.pt")
 # model = YOLO(r"C:\workspace\le-yolo\runs\detect\train40\weights\last.pt")
-model.train(data="data.yaml", epochs=100,  batch=8, device=0,  imgsz=640, augment = True)
+# CPU：device='cpu'  GPU：device=0
+model.train(data="data.yaml", epochs=100,  batch=8, device=0,  imgsz=640, augment = True)  # augment=True 数据增强
 # model.train(data="data.yaml", epochs=100,  batch=8, device='cpu',  imgsz=640, augment = True, lr = 0.001,wight_decay  = 0.0005 )
-# model.val()
+# model.val()  # 验证
 print('训练完成')
 end_time  = time.time()
 run_time = end_time - start_time
diff --git a/src/video.py b/src/video.py
index fd560fc..97f2f5c 100644
--- a/src/video.py
+++ b/src/video.py
@@ -1,4 +1,5 @@
 import cv2
+# 视频抽帧 并保存
 def video_to_pic(vide_opath):
     # vide_opath = 'C:/workspace/le-yolo/res/6.mp4'
     video = cv2.VideoCapture(vide_opath)
@@ -7,8 +8,8 @@ def video_to_pic(vide_opath):
         ret, frame = video.read()
     else:
         ret = False
-    timeF = 10
-    filepath = 'C:/pic/test_'
+    timeF = 10  # 帧数
+    filepath = 'C:/pic/test_'   #  保存图片的路径
     while ret:
         ret, frame = video.read()
         if num % timeF == 0:
diff --git a/src/yolov8n-pose.pt b/src/yolov8n-pose.pt
new file mode 100644
index 0000000..6ad23ab
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