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 cv2
import 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 img
output_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cap
cap.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 cv
import mediapipe as mp
import  tqdm
import time
import  matplotlib.pyplot as plt
def 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_utils
img=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solution
mp_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-python
import cv2
import 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cap
cap.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solution
mp_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

def 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cap
cap.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()