自学教程：OpenCV+MediaPipe实现手部关键点识别

import matplotlib.pyplot as plt# 使用ipython的魔法方法，将绘制出的图像直接嵌入在notebook单元格中import cv2# 定义可视化图像函数def look_img(img):    '''opencv读入图像格式为BGR，matplotlib可视化格式为RGB，因此需将BGR转RGB'''    img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)    plt.imshow(img_RGB)    plt.show()#调用摄像头拍照time.sleep(2) # 运行本代码后两秒拍照# 获取摄像头，0为电脑默认摄像头，1为外接摄像头cap = cv2.VideoCapture(0)# 从摄像头捕获一帧画面success, image = cap.read()# 关闭摄像头cap.release()# 关闭图像窗口cv2.destroyAllWindows()cv2.imwrite('photo.jpg', image)#调用摄像头拍视频import cv2import time# 定义逐帧处理函数，可不进行任何处理，直接将摄像头捕获的画面写入视频帧def process_frame(img):    return imgoutput_name = 'record_video.mp4'# 获取摄像头，传入0表示获取系统默认摄像头cap = cv2.VideoCapture(0)# 打开capcap.open(0)frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH), cap.get(cv2.CAP_PROP_FRAME_HEIGHT))fourcc = cv2.VideoWriter_fourcc(*'mp4v')fps = cap.get(cv2.CAP_PROP_FPS)out = cv2.VideoWriter(output_name, fourcc, fps, (int(frame_size[0]), int(frame_size[1])))# 无限循环，直到break被触发while cap.isOpened():    # 获取画面    success, frame = cap.read()    if not success:        break    # 对捕获的帧进行图像处理    frame = process_frame(frame)    ## 将帧写入视频文件中    out.write(frame)    # 展示处理后的三通道图像    cv2.imshow('press q to break', frame)    if cv2.waitKey(1) in [ord('q'), 27]:  # 按键盘上的q或esc退出（在英文输入法下）        break# 关闭图像窗口cv2.destroyAllWindows()out.release()# 关闭摄像头cap.release()print('视频已保存', output_name)

import cv2 as cvimport mediapipe as mpimport  tqdmimport timeimport  matplotlib.pyplot as pltdef look_img(img):    img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)    plt.imshow(img_RGB)    plt.show()# 手部关键点检测模型mp_hand=mp.solutions.hands# 导入模型hands=mp_hand.Hands(static_image_mode=False,                    max_num_hands=5,                    min_detection_confidence=0.3,                    min_tracking_confidence=0.3                    )# 导入绘图函数mpDraw=mp.solutions.drawing_utilsimg=cv.imread('hand2.png')# look_img(img)img_RGB=cv.cvtColor(img,cv.COLOR_BGR2RGB)results=hands.process(img_RGB)if results.multi_hand_landmarks:    for hand_idx in range(len(results.multi_hand_landmarks)):        hand_21=results.multi_hand_landmarks[hand_idx]        mpDraw.draw_landmarks(img, hand_21, mp_hand.HAND_CONNECTIONS)  # 可视化look_img(img)cv.imwrite('hands2.jpg',img)# 在三维坐标系中可视化索引为0的手mpDraw.plot_landmarks(results.multi_hand_landmarks[0], mp_

import cv2# mediapipe人工智能工具包import mediapipe as mp# 进度条库from tqdm import tqdm# 时间库import time# 导入模型# 导入solutionmp_hands = mp.solutions.hands# 导入模型hands = mp_hands.Hands(static_image_mode=False,        # 是静态图片还是连续视频帧                       max_num_hands=2,                # 最多检测几只手                       min_detection_confidence=0.7,   # 置信度阈值                       min_tracking_confidence=0.5)    # 追踪阈值# 导入绘图函数mpDraw = mp.solutions.drawing_utils# 处理单帧函数# 处理帧函数def process_frame(img):    # 水平镜像翻转图像，使图中左右手与真实左右手对应    # 参数 1：水平翻转，0：竖直翻转，-1：水平和竖直都翻转    img = cv2.flip(img, 1)    # BGR转RGB    img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)    # 将RGB图像输入模型，获取预测结果    results = hands.process(img_RGB)    if results.multi_hand_landmarks:  # 如果有检测到手        # 遍历每一只检测出的手        for hand_idx in range(len(results.multi_hand_landmarks)):            hand_21 = results.multi_hand_landmarks[hand_idx]  # 获取该手的所有关键点坐标            mpDraw.draw_landmarks(img, hand_21, mp_hands.HAND_CONNECTIONS)  # 可视化        # 在三维坐标系中可视化索引为0的手        # mpDraw.plot_landmarks(results.multi_hand_landmarks[0], mp_hands.HAND_CONNECTIONS)    return img# 导入opencv-pythonimport cv2import time# 获取摄像头，传入0表示获取系统默认摄像头cap = cv2.VideoCapture(1)# 打开capcap.open(0)# 无限循环，直到break被触发while cap.isOpened():    # 获取画面    success, frame = cap.read()    if not success:        print('Error')        break    ## !!!处理帧函数    frame = process_frame(frame)    # 展示处理后的三通道图像    cv2.imshow('my_window', frame)    if cv2.waitKey(1) in [ord('q'), 27]:  # 按键盘上的q或esc退出（在英文输入法下）        break# 关闭摄像头cap.release()# 关闭图像窗口cv2.destroyAllWindows()

import cv2# mediapipe人工智能工具包import mediapipe as mp# 进度条库from tqdm import tqdm# 时间库import time# 导入solutionmp_hands = mp.solutions.hands# 导入模型hands = mp_hands.Hands(static_image_mode=False,        # 是静态图片还是连续视频帧                       max_num_hands=2,                # 最多检测几只手                       min_detection_confidence=0.7,   # 置信度阈值                       min_tracking_confidence=0.5)    # 追踪阈值# 导入绘图函数mpDraw = mp.solutions.drawing_utilsdef process_frame(img):    # 记录该帧开始处理的时间    start_time = time.time()    # 获取图像宽高    h, w = img.shape[0], img.shape[1]    # 水平镜像翻转图像，使图中左右手与真实左右手对应    # 参数 1：水平翻转，0：竖直翻转，-1：水平和竖直都翻转    img = cv2.flip(img, 1)    # BGR转RGB    img_RGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)    # 将RGB图像输入模型，获取预测结果    results = hands.process(img_RGB)    if results.multi_hand_landmarks:  # 如果有检测到手        handness_str = ''        index_finger_tip_str = ''        for hand_idx in range(len(results.multi_hand_landmarks)):            # 获取该手的21个关键点坐标            hand_21 = results.multi_hand_landmarks[hand_idx]            # 可视化关键点及骨架连线            mpDraw.draw_landmarks(img, hand_21, mp_hands.HAND_CONNECTIONS)            # 记录左右手信息            temp_handness = results.multi_handedness[hand_idx].classification[0].label            handness_str += '{}:{} '.format(hand_idx, temp_handness)            # 获取手腕根部深度坐标            cz0 = hand_21.landmark[0].z            for i in range(21):  # 遍历该手的21个关键点                # 获取3D坐标                cx = int(hand_21.landmark[i].x * w)                cy = int(hand_21.landmark[i].y * h)                cz = hand_21.landmark[i].z                depth_z = cz0 - cz                # 用圆的半径反映深度大小                radius = max(int(6 * (1 + depth_z * 5)), 0)                if i == 0:  # 手腕                    img = cv2.circle(img, (cx, cy), radius, (0, 0, 255), -1)                if i == 8:  # 食指指尖                    img = cv2.circle(img, (cx, cy), radius, (193, 182, 255), -1)                    # 将相对于手腕的深度距离显示在画面中                    index_finger_tip_str += '{}:{:.2f} '.format(hand_idx, depth_z)                if i in [1, 5, 9, 13, 17]:  # 指根                    img = cv2.circle(img, (cx, cy), radius, (16, 144, 247), -1)                if i in [2, 6, 10, 14, 18]:  # 第一指节                    img = cv2.circle(img, (cx, cy), radius, (1, 240, 255), -1)                if i in [3, 7, 11, 15, 19]:  # 第二指节                    img = cv2.circle(img, (cx, cy), radius, (140, 47, 240), -1)                if i in [4, 12, 16, 20]:  # 指尖（除食指指尖）                    img = cv2.circle(img, (cx, cy), radius, (223, 155, 60), -1)        scaler = 1        img = cv2.putText(img, handness_str, (25 * scaler, 100 * scaler), cv2.FONT_HERSHEY_SIMPLEX, 1.25 * scaler,                          (255, 0, 255), 2 * scaler)        img = cv2.putText(img, index_finger_tip_str, (25 * scaler, 150 * scaler), cv2.FONT_HERSHEY_SIMPLEX,                          1.25 * scaler, (255, 0, 255), 2 * scaler)        # 记录该帧处理完毕的时间        end_time = time.time()        # 计算每秒处理图像帧数FPS        FPS = 1 / (end_time - start_time)        # 在图像上写FPS数值，参数依次为：图片，添加的文字，左上角坐标，字体，字体大小，颜色，字体粗细        img = cv2.putText(img, 'FPS  ' + str(int(FPS)), (25 * scaler, 50 * scaler), cv2.FONT_HERSHEY_SIMPLEX,                          1.25 * scaler, (255, 0, 255), 2 * scaler)    return img# 获取摄像头，传入0表示获取系统默认摄像头cap = cv2.VideoCapture(0)# 打开capcap.open(0)# 无限循环，直到break被触发while cap.isOpened():    # 获取画面    success, frame = cap.read()    if not success:        break    frame = process_frame(frame)    # 展示处理后的三通道图像    cv2.imshow('my_window', frame)    if cv2.waitKey(1) in [ord('q'), 27]:  # 按键盘上的q或esc退出（在英文输入法下）        break# 关闭摄像头cap.release()# 关闭图像窗口cv2.destroyAllWindows()