一文了解如何使用Python+OpenCV构建手部跟踪系统

OpenCV 是一个用于计算机视觉应用程序的库。在 OpenCV 的帮助下,我们可以构建大量实时运行更好的应用程序。主要用于图像和视频处理。可以在此处获取有关 OpenCV 的更多信息

除了 OpenCV,我们将使用 MediaPipe 库。MediaPipeMediaPipe是一个主要用于构建音频、视频或任何时间序列数据的框架。在 MediaPipe 框架的帮助下,我们可以为不同的媒体处理功能构建管道。MediaPipe 的一些主要应用。多手追踪人脸检测对象检测和跟踪Objection:3D 对象检测和跟踪AutoFlip:自动视频裁剪管道等。

手地标模型

MediaPipe 使用单次手掌检测模型,一旦完成,它会对检测到的手部区域中的 21 个 3D 手掌坐标执行精确的关键点定位。MediaPipe 管道使用多个模型,例如,从完整图像返回定向手边界框的手掌检测模型。裁剪后的图像区域被馈送到由手掌检测器定义的手部标志模型,并返回高保真 3D 手部关键点。现在让我们实现手部跟踪模型。安装所需的模块–> pip install opencv-python–> pip install mediapipe首先,让我们检查网络摄像头的工作情况。import cv2import timecap = cv2．VideoCapture(0)pTime = 0while True: success, img = cap．read() imgRGB = cv2．cvtColor(img, cv2．COLOR_BGR2RGB) cTime = time．time() fps = 1 / (cTime - pTime) pTime = cTime cv2．putText(img, f'FPS:{int(fps)}', (20, 70), cv2．FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) cv2．imshow("Test", img)cv2．waitKey(1)

如果任何网络摄像头连接到你的 PC,上面的代码将弹出一个窗口,并在输出窗口的左上角显示每秒帧数 (fps)。现在让我们开始实施。导入所需的模块并初始化所需的变量。import cv2import mediapipe as mpimport timecap = cv2．VideoCapture(0)mpHands = mp．solutions．handshands = mpHands．Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0．5, min_tracking_confidence=0．5)mpDraw = mp．solutions．drawing_utilspTime = 0cTime = 0在上面这段代码中,我们在mp．solutions．hand 中声明了一个名为“hands”的对象来检测手部,默认情况下,查看类“ Hands() ”内部,要检测的手部数量设置为2、最小检测置信度设置为0．5,最小跟踪置信度设置为0．5。我们将使用mpDraw绘制关键点。现在让我们编写一个 while 循环来执行我们的代码。while True: success, img = cap．read() imgRGB = cv2．cvtColor(img, cv2．COLOR_BGR2RGB) results = hands．process(imgRGB) #print(results．multi_hand_landmarks) if results．multi_hand_landmarks: for handLms in results．multi_hand_landmarks: for id, lm in enumerate(handLms．landmark): #print(id,lm) h, w, c = img．shape cx, cy = int(lm．x *w), int(lm．y*h) #if id ==0: cv2．circle(img, (cx,cy), 3, (255,0,255), cv2．FILLED) mpDraw．draw_landmarks(img, handLms, mpHands．HAND_CONNECTIONS) cTime = time．time() fps = 1/(cTime-pTime) pTime = cTime cv2．putText(img,str(int(fps)), (10,70), cv2．FONT_HERSHEY_PLAIN, 3, (255,0,255), 3) cv2．imshow("Image", img) cv2．waitKey(1)在上面的代码中,我们从网络摄像头读取帧并将图像转换为 RGB。然后我们在“ hands．process()” 函数的帮助下检测帧中的手。一旦检测到手,我们将找到关键点,然后使用cv2．circle突出显示关键点中的点,并使用mpDraw．draw_landmarks连接关键点。整个代码如下import cv2import mediapipe as mpimport timecap = cv2．VideoCapture(0)mpHands = mp．solutions．handshands = mpHands．Hands(static_image_mode=False, max_num_hands=2, min_detection_confidence=0．5, min_tracking_confidence=0．5)mpDraw = mp．solutions．drawing_utilspTime = 0cTime = 0while True: success, img = cap．read() imgRGB = cv2．cvtColor(img, cv2．COLOR_BGR2RGB) results = hands．process(imgRGB) #print(results．multi_hand_landmarks) if results．multi_hand_landmarks: for handLms in results．multi_hand_landmarks: for id, lm in enumerate(handLms．landmark): #print(id,lm) h, w, c = img．shape cx, cy = int(lm．x *w), int(lm．y*h) #if id ==0: cv2．circle(img, (cx,cy), 3, (255,0,255), cv2．FILLED) mpDraw．draw_landmarks(img, handLms, mpHands．HAND_CONNECTIONS) cTime = time．time() fps = 1/(cTime-pTime) pTime = cTime cv2．putText(img,str(int(fps)), (10,70), cv2．FONT_HERSHEY_PLAIN, 3, (255,0,255), 3) cv2．imshow("Image", img) cv2．waitKey(1)输出是:

手部追踪模型输出现在让我们创建一个手部跟踪模块,以便我们可以在其他项目中使用它。创建一个新的 python 文件,首先让我们创建一个名为handDetector的类,其中有两个成员函数,名为findHands 和findPosition。函数findHands将接受一个 RGB 图像并检测帧中的手并定位关键点,绘制地标,函数findPosition 将给出手的位置和 id。然后是我们初始化模块的 main 函数,我们还编写了一个 while 循环来运行模型。你可以在此处将此设置或模块导入到任何其他相关项目作品中。整个代码如下import cv2import mediapipe as mpimport timeclass handDetector(): def __init__(self, mode = False, maxHands = 2, detectionCon = 0．5, trackCon = 0．5): self．mode = mode self．maxHands = maxHands self．detectionCon = detectionCon self．trackCon = trackCon self．mpHands = mp．solutions．hands self．hands = self．mpHands．Hands(self．mode, self．maxHands, self．detectionCon, self．trackCon) self．mpDraw = mp．solutions．drawing_utils def findHands(self,img, draw = True): imgRGB = cv2．cvtColor(img, cv2．COLOR_BGR2RGB) self．results = self．hands．process(imgRGB) # print(results．multi_hand_landmarks) if self．results．multi_hand_landmarks: for handLms in self．results．multi_hand_landmarks: if draw: self．mpDraw．draw_landmarks(img, handLms, self．mpHands．HAND_CONNECTIONS) return img def findPosition(self, img, handNo = 0, draw = True): lmlist = [] if self．results．multi_hand_landmarks: myHand = self．results．multi_hand_landmarks[handNo] for id, lm in enumerate(myHand．landmark): h, w, c = img．shape cx, cy = int(lm．x * w), int(lm．y * h) lmlist．append([id, cx, cy]) if draw: cv2．circle(img, (cx, cy), 3, (255, 0, 255), cv2．FILLED) return lmlistdef main(): pTime = 0 cTime = 0 cap = cv2．VideoCapture(0) detector = handDetector() while True: success, img = cap．read() img = detector．findHands(img) lmlist = detector．findPosition(img) if len(lmlist) != 0: print(lmlist[4]) cTime = time．time() fps = 1 / (cTime - pTime) pTime = cTime cv2．putText(img, str(int(fps)), (10, 70), cv2．FONT_HERSHEY_PLAIN, 3, (255, 0, 255), 3) cv2．imshow("Image", img) cv2．waitKey(1)if __name__ == "__main__": main()输出将与上面显示的相同,以及被跟踪的手的位置。