人脸识别Demo-优快云博客

本文链接：https://blog.youkuaiyun.com/irisMoon06/article/details/149323753
使用Python的OpenCV库实现人脸检测功能
import cv2
import numpy as np
from PIL import Image, ImageTk
import tkinter as tk
from tkinter import ttk, messagebox

class FaceRecognitionApp:
    def __init__(self, root):
        self.root = root
        self.root.title("人脸识别演示系统")
        self.root.geometry("900x650")
        self.root.configure(bg="#2c3e50")
        
        # 加载Haar级联分类器
        self.face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
        
        # 创建界面
        self.create_widgets()
        
        # 摄像头状态
        self.camera_active = False
        self.cap = None
        
        # 人脸统计
        self.face_count = 0
        self.max_faces_detected = 0
        
    def create_widgets(self):
        # 标题
        title_frame = tk.Frame(self.root, bg="#3498db", height=80)
        title_frame.pack(fill="x", padx=10, pady=10)
        
        title_label = tk.Label(title_frame, text="人脸识别演示系统", font=("Arial", 24, "bold"), 
                              fg="white", bg="#3498db", pady=15)
        title_label.pack()
        
        # 主内容区域
        main_frame = tk.Frame(self.root, bg="#2c3e50")
        main_frame.pack(fill="both", expand=True, padx=20, pady=10)
        
        # 左侧 - 摄像头画面
        left_frame = tk.Frame(main_frame, bg="#34495e")
        left_frame.pack(side="left", fill="both", expand=True, padx=(0, 10))
        
        self.camera_label = tk.Label(left_frame, bg="black", width=60, height=25)
        self.camera_label.pack(padx=10, pady=10)
        
        # 右侧 - 控制面板
        right_frame = tk.Frame(main_frame, bg="#34495e", width=300)
        right_frame.pack(side="right", fill="y", padx=(10, 0))
        
        # 控制面板标题
        control_title = tk.Label(right_frame, text="控制面板", font=("Arial", 16, "bold"), 
                               fg="white", bg="#3498db", width=20, pady=8)
        control_title.pack(fill="x", pady=(0, 15))
        
        # 按钮区域
        btn_frame = tk.Frame(right_frame, bg="#34495e")
        btn_frame.pack(fill="x", padx=15, pady=10)
        
        self.start_btn = tk.Button(btn_frame, text="启动摄像头", font=("Arial", 12), 
                                  bg="#2ecc71", fg="white", width=15, command=self.toggle_camera)
        self.start_btn.pack(pady=5)
        
        tk.Button(btn_frame, text="保存当前画面", font=("Arial", 12), 
                 bg="#9b59b6", fg="white", width=15, command=self.save_image).pack(pady=5)
        
        tk.Button(btn_frame, text="退出系统", font=("Arial", 12), 
                 bg="#e74c3c", fg="white", width=15, command=self.exit_app).pack(pady=5)
        
        # 统计信息
        stats_frame = tk.Frame(right_frame, bg="#34495e", relief="groove", bd=2)
        stats_frame.pack(fill="x", padx=15, pady=20)
        
        stats_title = tk.Label(stats_frame, text="检测统计", font=("Arial", 14, "bold"), 
                             fg="#3498db", bg="#34495e")
        stats_title.pack(pady=(10, 15))
        
        self.face_count_label = tk.Label(stats_frame, text="检测到人脸: 0", font=("Arial", 12), 
                                       fg="white", bg="#34495e")
        self.face_count_label.pack(pady=5)
        
        self.max_face_label = tk.Label(stats_frame, text="最大同时检测数: 0", font=("Arial", 12), 
                                      fg="white", bg="#34495e")
        self.max_face_label.pack(pady=5)
        
        # 信息区域
        info_frame = tk.Frame(right_frame, bg="#34495e", relief="groove", bd=2)
        info_frame.pack(fill="x", padx=15, pady=(0, 20))
        
        info_title = tk.Label(info_frame, text="系统信息", font=("Arial", 14, "bold"), 
                            fg="#3498db", bg="#34495e")
        info_title.pack(pady=(10, 5))
        
        info_text = tk.Label(info_frame, text="使用OpenCV Haar级联分类器进行人脸检测。\n\n"
                                           "该系统可以实时检测摄像头画面中的人脸。", 
                            font=("Arial", 10), fg="white", bg="#34495e", justify="left")
        info_text.pack(pady=5, padx=10)
        
        # 状态栏
        self.status_bar = tk.Label(self.root, text="就绪", bd=1, relief="sunken", anchor="w", 
                                 font=("Arial", 10), bg="#34495e", fg="white")
        self.status_bar.pack(side="bottom", fill="x", padx=10, pady=5)
    
    def toggle_camera(self):
        if not self.camera_active:
            # 启动摄像头
            self.cap = cv2.VideoCapture(0)
            if not self.cap.isOpened():
                messagebox.showerror("错误", "无法访问摄像头！")
                return
                
            self.camera_active = True
            self.start_btn.config(text="停止摄像头", bg="#e74c3c")
            self.status_bar.config(text="摄像头已启动 - 正在检测人脸...")
            self.update_camera()
        else:
            # 停止摄像头
            self.camera_active = False
            self.start_btn.config(text="启动摄像头", bg="#2ecc71")
            if self.cap:
                self.cap.release()
            self.status_bar.config(text="摄像头已停止")
    
    def update_camera(self):
        if self.camera_active:
            ret, frame = self.cap.read()
            if ret:
                # 转换颜色空间 BGR -> RGB
                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
                
                # 进行人脸检测
                gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
                faces = self.face_cascade.detectMultiScale(
                    gray, 
                    scaleFactor=1.1, 
                    minNeighbors=5, 
                    minSize=(30, 30)
                )
                
                # 更新人脸计数
                self.face_count = len(faces)
                if self.face_count > self.max_faces_detected:
                    self.max_faces_detected = self.face_count
                
                # 在检测到的人脸周围画矩形
                for (x, y, w, h) in faces:
                    cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
                    # 绘制人脸标签
                    cv2.putText(frame, f"Face", (x, y-10), 
                               cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
                
                # 更新统计信息
                self.face_count_label.config(text=f"检测到人脸: {self.face_count}")
                self.max_face_label.config(text=f"最大同时检测数: {self.max_faces_detected}")
                
                # 显示画面
                img = Image.fromarray(frame)
                img = img.resize((640, 480), Image.LANCZOS)
                img_tk = ImageTk.PhotoImage(image=img)
                self.camera_label.imgtk = img_tk
                self.camera_label.configure(image=img_tk)
            
            # 每10毫秒更新一次画面
            self.root.after(10, self.update_camera)
    
    def save_image(self):
        if not self.camera_active:
            messagebox.showwarning("警告", "请先启动摄像头！")
            return
            
        ret, frame = self.cap.read()
        if ret:
            # 保存当前帧
            cv2.imwrite("captured_face.jpg", frame)
            messagebox.showinfo("保存成功", "当前画面已保存为 'captured_face.jpg'")
            self.status_bar.config(text="当前画面已保存为 captured_face.jpg")
    
    def exit_app(self):
        if self.camera_active:
            self.camera_active = False
            if self.cap:
                self.cap.release()
        self.root.destroy()

if __name__ == "__main__":
    root = tk.Tk()
    app = FaceRecognitionApp(root)
    root.mainloop()