opencv - 技术栈

opencv实现人脸检测

一、摄像头人脸检测

python 复制代码

import cv2

# 加载预训练的人脸检测模型
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

# 打开默认摄像头（通常是 0）
cap = cv2.VideoCapture(0)

# 检查摄像头是否成功打开
if not cap.isOpened():
    print("无法打开摄像头")
    exit()

while True:
    # 逐帧读取摄像头视频
    ret, frame = cap.read()

    # 检查是否成功读取帧
    if not ret:
        print("无法读取帧")
        break

    # 将图像转换为灰度
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 使用人脸检测器检测人脸
    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # 在原始图像上绘制人脸方框
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # 在窗口中显示视频帧
    cv2.imshow("Face real-time detection", frame)

    # 按下 "q" 键退出循环
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

# 释放摄像头并关闭窗口
cap.release()
cv2.destroyAllWindows()

二、捕获屏幕人脸检测

python 复制代码

import cv2
import numpy as np
import pyautogui

# 创建窗体
cv2.namedWindow('Face Detection', cv2.WINDOW_NORMAL)

# 加载人脸检测器
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

while True:
    # 截取屏幕图像
    screenshot = pyautogui.screenshot()
    frame = cv2.cvtColor(np.array(screenshot), cv2.COLOR_RGB2BGR)

    # 在图像中检测人脸
    faces = face_cascade.detectMultiScale(frame, scaleFactor=1.1, minNeighbors=5, minSize=(20, 20))

    # 绘制方框
    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

    # 显示结果
    cv2.imshow('Face Detection', frame)

    # 检查是否按下'q'键退出循环
    key = cv2.waitKey(1)
    if key == ord('q') or key == 27:  # 'q'键或ESC键
        break

# 关闭窗口
cv2.destroyAllWindows()