MediaPipe介绍与安装：

MediaPipe 优点

1) 支持各种平台和语言，如 IOS 、 Android 、 C++ 、 Python 、 JAVAScript 、 Coral 等。

2) 速度很快，模型基本可以做到实时运行。

3) 模型和代码能够实现很高的复用率。

MediaPipe 缺点

1) 对于移动端， MediaPipe 略显笨重，需要至少 10M 以上的空间。

2) 深度依赖于 Tensorflow ，若想更换成其他机器学习框架，需要更改大量代码。

3) 使用的是静态图，虽然有助于提高效率，但也会导致很难发现错误。

安装更新 APT 下载列表：

sudo apt update

sudo apt install python3-pip

python3 -m pip install --upgrade pip

文件下载：https://download.csdn.net/download/qq_64257614/88322416?spm=1001.2014.3001.5503

在jetson桌面将其拖进文件管理的home目录然后输入终端指令进行安装：

pip3 install mediapipe-0.8.5_cuda102-cp36-cp36m-linux_aarch64.whl

MediaPipe使用流程：

Mediapipe 人脸识别：

输入指令安装依赖包：

pip3 install dataclasses

编写Python程序：

import cv2
import mediapipe as mp
import timelast_time = 0
current_time = 0
fps = 0.0
def show_fps(img):global last_time, current_time, fpslast_time = current_timecurrent_time = time.time()new_fps = 1.0 / (current_time - last_time)if fps == 0.0:fps = new_fps if last_time != 0 else 0.0else:fps = new_fps * 0.2 + fps * 0.8fps_text = 'FPS: {:.2f}'.format(fps)cv2.putText(img, fps_text, (11, 20), cv2.FONT_HERSHEY_PLAIN, 1.0, (32, 32, 32), 4, cv2.LINE_AA)cv2.putText(img, fps_text, (10, 20), cv2.FONT_HERSHEY_PLAIN, 1.0, (240, 240, 240), 1, cv2.LINE_AA)return imgmp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils# For webcam input:
cap = cv2.VideoCapture(0)
with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection:while cap.isOpened():success, image = cap.read()if not success:print("Ignoring empty camera frame.")# If loading a video, use 'break' instead of 'continue'.continue# To improve performance, optionally mark the image as not writeable to# pass by reference.image.flags.writeable = Falseimage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)results = face_detection.process(image)# Draw the face detection annotations on the image.image.flags.writeable = Trueimage = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)if results.detections:for detection in results.detections:mp_drawing.draw_detection(image, detection)# Flip the image horizontally for a selfie-view display.image = show_fps(cv2.flip(image, 1))cv2.imshow('MediaPipe Face Detection', image)if cv2.waitKey(5) & 0xFF == 27:break
cap.release()

最后传输python文件,然后输入指令运行，注意放在文件夹中的需要使用cd命令进行目录的跳转

效果测试：

Mediapipe 手势识别：

 编写python程序：

import cv2
import mediapipe as mp
import numpy as np
import timelast_time = 0
current_time = 0
fps = 0.0
def show_fps(img):global last_time, current_time, fpslast_time = current_timecurrent_time = time.time()new_fps = 1.0 / (current_time - last_time)if fps == 0.0:fps = new_fps if last_time != 0 else 0.0else:fps = new_fps * 0.2 + fps * 0.8fps_text = 'FPS: {:.2f}'.format(fps)cv2.putText(img, fps_text, (11, 20), cv2.FONT_HERSHEY_PLAIN, 1.0, (32, 32, 32), 4, cv2.LINE_AA)cv2.putText(img, fps_text, (10, 20), cv2.FONT_HERSHEY_PLAIN, 1.0, (240, 240, 240), 1, cv2.LINE_AA)return imgdef distance(point_1, point_2):"""计算两个点间的距离:param point_1: 点1:param point_2: 点2:return: 两点间的距离"""return math.sqrt((point_1[0] - point_2[0]) ** 2 + (point_1[1] - point_2[1]) ** 2)def vector_2d_angle(v1, v2):"""计算两向量间的夹角 -pi ~ pi:param v1: 第一个向量:param v2: 第二个向量:return: 角度"""norm_v1_v2 = np.linalg.norm(v1) * np.linalg.norm(v2)cos = v1.dot(v2) / (norm_v1_v2)sin = np.cross(v1, v2) / (norm_v1_v2)angle = np.degrees(np.arctan2(sin, cos))return angledef get_hand_landmarks(img_size, landmarks):"""将landmarks从medipipe的归一化输出转为像素坐标:param img: 像素坐标对应的图片:param landmarks: 归一化的关键点:return:"""w, h = img_sizelandmarks = [(lm.x * w, lm.y * h) for lm in landmarks]return np.array(landmarks)def hand_angle(landmarks):"""计算各个手指的弯曲角度:param landmarks: 手部关键点:return: 各个手指的角度"""angle_list = []# thumb 大拇指angle_ = vector_2d_angle(landmarks[3] - landmarks[4], landmarks[0] - landmarks[2])angle_list.append(angle_)# index 食指angle_ = vector_2d_angle(landmarks[0] - landmarks[6], landmarks[7] - landmarks[8])angle_list.append(angle_)# middle 中指angle_ = vector_2d_angle(landmarks[0] - landmarks[10], landmarks[11] - landmarks[12])angle_list.append(angle_)# ring 无名指angle_ = vector_2d_angle(landmarks[0] - landmarks[14], landmarks[15] - landmarks[16])angle_list.append(angle_)# pink 小拇指angle_ = vector_2d_angle(landmarks[0] - landmarks[18], landmarks[19] - landmarks[20])angle_list.append(angle_)angle_list = [abs(a) for a in angle_list]return angle_listdef h_gesture(angle_list):"""通过二维特征确定手指所摆出的手势:param angle_list: 各个手指弯曲的角度:return : 手势名称字符串"""thr_angle = 65.thr_angle_thumb = 53.thr_angle_s = 49.gesture_str = "none"if (angle_list[0] > thr_angle_thumb) and (angle_list[1] > thr_angle) and (angle_list[2] > thr_angle) and (angle_list[3] > thr_angle) and (angle_list[4] > thr_angle):gesture_str = "fist"elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle_s) and (angle_list[2] > thr_angle) and (angle_list[3] > thr_angle) and (angle_list[4] > thr_angle):gesture_str = "gun"elif (angle_list[0] < thr_angle_s) and (angle_list[1] > thr_angle) and (angle_list[2] > thr_angle) and (angle_list[3] > thr_angle) and (angle_list[4] > thr_angle):gesture_str = "hand_heart"elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] > thr_angle) and (angle_list[3] > thr_angle) and (angle_list[4] > thr_angle):gesture_str = "one"elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and (angle_list[3] > thr_angle) and (angle_list[4] > thr_angle):gesture_str = "two"elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and (angle_list[3] < thr_angle_s) and (angle_list[4] > thr_angle):gesture_str = "three"elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] > thr_angle) and (angle_list[2] < thr_angle_s) and (angle_list[3] < thr_angle_s) and (angle_list[4] < thr_angle_s):gesture_str = "ok"elif (angle_list[0] > thr_angle_thumb) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and (angle_list[3] < thr_angle_s) and (angle_list[4] < thr_angle_s):gesture_str = "four"elif (angle_list[0] < thr_angle_s) and (angle_list[1] < thr_angle_s) and (angle_list[2] < thr_angle_s) and (angle_list[3] < thr_angle_s) and (angle_list[4] < thr_angle_s):gesture_str = "five"elif (angle_list[0] < thr_angle_s) and (angle_list[1] > thr_angle) and (angle_list[2] > thr_angle) and (angle_list[3] > thr_angle) and (angle_list[4] < thr_angle_s):gesture_str = "six"else:"none"return gesture_strmp_drawing = mp.solutions.drawing_utils
mp_hands = mp.solutions.hands# For webcam input:
cap = cv2.VideoCapture(0)
with mp_hands.Hands(min_detection_confidence=0.5,min_tracking_confidence=0.5) as hands:while cap.isOpened():success, image = cap.read()if not success:print("Ignoring empty camera frame.")# If loading a video, use 'break' instead of 'continue'.continue# Flip the image horizontally for a later selfie-view display, and convert# the BGR image to RGB.image = cv2.cvtColor(cv2.flip(image, 1), cv2.COLOR_BGR2RGB)# To improve performance, optionally mark the image as not writeable to# pass by reference.image.flags.writeable = Falseresults = hands.process(image)# Draw the hand annotations on the image.image.flags.writeable = Trueimage = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)gesture = "none"if results.multi_hand_landmarks:for hand_landmarks in results.multi_hand_landmarks:mp_drawing.draw_landmarks(image, hand_landmarks, mp_hands.HAND_CONNECTIONS)landmarks = get_hand_landmarks((image.shape[1], image.shape[0]), hand_landmarks.landmark)angle_list = hand_angle(landmarks)gesture = h_gesture(angle_list)if gesture != "none":break;image = show_fps(cv2.flip(image, 1))cv2.putText(image, gesture, (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 1.5, (255, 0, 0), 4)cv2.imshow('MediaPipe Hands', image)if cv2.waitKey(5) & 0xFF == 27:break
cap.release()

 效果测试：