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ps:为之后的opencv项目做准备
代码显示及注释
import cv2 import mediapipe as mp import time cap = cv2.VideoCapture(0) #
调用摄像头 mpHands = mp.solutions.hands # 调用手部模型 hands = mpHands.Hands() # 使用默认的函式 #
设置样式 mpDraw = mp.solutions.drawing_utils # 点的样式 red 粗度 5px handLmsStyle =
mpDraw.DrawingSpec(color=(0, 0, 255), thickness=5) # 线的样式 green cudu 5px
handConStyle = mpDraw.DrawingSpec(color=(0, 255, 0), thickness=5) pTime = 0
cTime = 0 while True: ret, img = cap.read() # 查看摄像头读取的图像 if ret: imgRGB =
cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # 将图片转为灰度图 result = hands.process(imgRGB)
# 图片预测出来的一个结果 #print(result.multi_hand_landmarks) imgHeight = img.shape[0]
imgWidth = img.shape[1] if result.multi_hand_landmarks: # 如果检测到手 for handLms in
result.multi_hand_landmarks: # 将手的图片在整个检测到的手的位置列表中一一遍历 # 参数：图片 点 线 点的样式 线的样式
mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS, handLmsStyle,
handConStyle) cTime = time.time() fps = 1/(cTime-pTime) # 1秒/一张图片的显示时间=1秒显示多少帧
pTime = cTime # 将fps输出到图片上 cv2.putText(img, f"FPS :{int(fps)}", (30, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 3) # 显示图片 cv2.imshow('img', img) #
当输入英文字符q退出循环 if cv2.waitKey(1) == ord('q'): break
效果展示