Chapter 2 Pose Estimation

Author: Diraw

Chapter 2 Pose Estimation/PoseModule.py

@@ -6,21 +6,44 @@
 
 # 姿势检测类
 class poseDetector:
-    def __init__(
-        self, mode=False, upBody=False, smooth=True, detectionCon=0.5, trackCon=0.5
-    ):
-        # 初始化参数
-        self.mode = mode  # 静态图像模式
-        self.upBody = upBody  # 是否只检测上半身
-        self.smooth = smooth  # 平滑处理
-        self.detectionCon = detectionCon  # 检测置信度
-        self.trackCon = trackCon  # 跟踪置信度
-        self.mpDraw = mp.solutions.drawing_utils  # Mediapipe 绘图工具
-        self.mpPose = mp.solutions.pose  # Mediapipe 姿势检测模块
+    # 以下为老版本代码，部分参数已经弃用
+    # def __init__(
+    #     self, mode=False, upBody=False, smooth=True, detectionCon=0.5, trackCon=0.5
+    # ):
+    #     # 初始化参数
+    #     self.mode = mode  # 静态图像模式
+    #     self.upBody = upBody  # 是否只检测上半身
+    #     self.smooth = smooth  # 平滑处理
+    #     self.detectionCon = detectionCon  # 检测置信度
+    #     self.trackCon = trackCon  # 跟踪置信度
+    #     self.mpDraw = mp.solutions.drawing_utils  # Mediapipe 绘图工具
+    #     self.mpPose = mp.solutions.pose  # Mediapipe 姿势检测模块
+    #     self.pose = self.mpPose.Pose(
+    #         self.mode, self.upBody, self.smooth, self.detectionCon, self.trackCon
+    #     )
+
+    def __init__(self, static_image_mode=False, model_complexity=1, enable_segmentation=False, min_detection_confidence=0.5, min_tracking_confidence=0.5):
+        # 初始化姿势检测器的参数
+        self.static_image_mode = static_image_mode  # 是否处理静态图像，False 表示处理视频流
+        self.model_complexity = model_complexity  # 模型复杂度，0、1 或 2，越高越准确但更慢
+        self.enable_segmentation = enable_segmentation  # 是否启用分割功能
+        self.min_detection_confidence = min_detection_confidence  # 姿势检测的最小置信度
+        self.min_tracking_confidence = min_tracking_confidence  # 姿势跟踪的最小置信度
+
+        # 设置 MediaPipe 工具
+        self.mpDraw = mp.solutions.drawing_utils  # MediaPipe 绘图工具
+        self.mpPose = mp.solutions.pose  # MediaPipe 姿势估计模块
+
+        # 创建姿势估计对象
         self.pose = self.mpPose.Pose(
-            self.mode, self.upBody, self.smooth, self.detectionCon, self.trackCon
+            static_image_mode=self.static_image_mode,  # 静态图像模式设置
+            model_complexity=self.model_complexity,  # 模型复杂度设置
+            enable_segmentation=self.enable_segmentation,  # 分割功能设置
+            min_detection_confidence=self.min_detection_confidence,  # 检测置信度阈值
+            min_tracking_confidence=self.min_tracking_confidence  # 跟踪置信度阈值
         )
 
+
     def findPose(self, img, draw=True):
         # 将图像从 BGR 转换为 RGB
         imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
@@ -52,12 +75,20 @@ def findAngle(self, img, p1, p2, p3, draw=True):
         x1, y1 = self.lmList[p1][1:]
         x2, y2 = self.lmList[p2][1:]
         x3, y3 = self.lmList[p3][1:]
+
+        # x1, y1 = self.lmList[p1][1], self.lmList[p1][2]
+        # x2, y2 = self.lmList[p2][1], self.lmList[p2][2]
+        # x3, y3 = self.lmList[p3][1], self.lmList[p3][2]
+
         # 计算角度
         angle = math.degrees(
             math.atan2(y3 - y2, x3 - x2) - math.atan2(y1 - y2, x1 - x2)
         )
+        # 两个向量 (x1-x2, y1-y2), (x3-x2, y3-y2)
+        # 使用 atan2 计算每个向量与 x 轴的角度
         if angle < 0:
             angle += 360
+
         # 绘制
         if draw:
             cv2.line(img, (x1, y1), (x2, y2), (255, 255, 255), 3)
@@ -82,12 +113,20 @@ def findAngle(self, img, p1, p2, p3, draw=True):
 
 # 主函数
 def main():
-    cap = cv2.VideoCapture("PoseVideos/1.mp4")  # 打开视频文件
+    cap = cv2.VideoCapture(
+        "E:\\Advance Computer Vision with Python\\Chapter 2 Pose Estimation\\PoseVideos\\5.mp4"
+    )  # 打开视频文件
     pTime = 0  # 前一帧时间
     detector = poseDetector()  # 创建姿势检测器
     while True:
         success, img = cap.read()  # 读取视频帧
+        if not cap.isOpened():
+            print("Error: Could not open video.")
+            return
         img = detector.findPose(img)  # 检测姿势
+        if not success:
+            print("Failed to read frame")
+            break
         lmList = detector.findPosition(img, draw=False)  # 获取关键点位置
         if len(lmList) != 0:
             print(lmList[14])  # 打印关键点信息
@@ -98,6 +137,7 @@ def main():
         cv2.putText(
             img, str(int(fps)), (70, 50), cv2.FONT_HERSHEY_PLAIN, 3, (255, 0, 0), 3
         )
+        cv2.namedWindow("Image", cv2.WINDOW_NORMAL)  # 创建可调整大小的窗口
         cv2.imshow("Image", img)  # 显示图像
         cv2.waitKey(1)  # 等待按键以显示下一帧
 

Chapter 2 Pose Estimation/PoseVideos/5.mp4

@@ -2,4 +2,5 @@ index,video download link
 1,https://www.pexels.com/zh-cn/video/4608975/
 2,https://www.pexels.com/zh-cn/video/8224597/
 3,https://www.pexels.com/zh-cn/video/7989824/
-4,https://www.pexels.com/zh-cn/video/6603299/
+4,https://www.pexels.com/zh-cn/video/6603299/
+5,https://www.pexels.com/zh-cn/video/5752496/

Chapter 2 Pose Estimation/PoseVideos/video download link.csv

@@ -1,7 +1,9 @@
 import cv2
 import time
 import PoseModule as pm
-cap = cv2.VideoCapture('PoseVideos/9.mp4')
+cap = cv2.VideoCapture(
+    "E:\\Advance Computer Vision with Python\\Chapter 2 Pose Estimation\\PoseVideos\\5.mp4"
+)
 pTime = 0
 detector = pm.poseDetector()
 while True:
@@ -16,5 +18,6 @@
     pTime = cTime
     cv2.putText(img, str(int(fps)), (70, 50), cv2.FONT_HERSHEY_PLAIN, 3,
                 (255, 0, 0), 3)
+    cv2.namedWindow("Image", cv2.WINDOW_NORMAL)  # 创建可调整大小的窗口
     cv2.imshow("Image", img)
-    cv2.waitKey(1)
+    cv2.waitKey(1)

Chapter 2 Pose Estimation/ProjectExample.py

@@ -4,4 +4,10 @@
 
 `pose = mpPose.Pose()` 创建一个姿势检测对象，用于处理图像并检测人体姿势
 
-`results = pose.process(imgRGB)` 处理图像，检测姿势
+`results = pose.process(imgRGB)` 处理图像，检测姿势
+
+# 注意事项
+
+用vscode执行代码时，请让终端在`Advance Computer Vision with Python`文件夹下（即vs打开这个整体的文件夹），以及视频路径请使用**绝对路径**，否则，将可能出现一些莫名其妙的报错
+
+比如你让终端在`Chapter 2 Pose Estimation`文件夹下运行，opencv的GUI就要报错，**日怪得很**

Chapter 2 Pose Estimation/README.md

@@ -0,0 +1 @@
+E:\\Advance Computer Vision with Python\\Chapter 2 Pose Estimation\\PoseVideos\\3.mp4