PCA人脸识别GUI的python代码

以下是使用PCA进行人脸识别的Python GUI代码：

import tkinter as tk
from tkinter import filedialog
import cv2
import numpy as np

class App(tk.Tk):
    def __init__(self):
        super().__init__()
        self.title("PCA Face Recognition")

        # 创建选择文件按钮
        self.file_button = tk.Button(self, text="选择图像文件", command=self.select_file)
        self.file_button.pack(pady=10)

        # 创建显示图像的画布
        self.canvas = tk.Canvas(self, width=400, height=400)
        self.canvas.pack(pady=10)

        # 创建识别按钮
        self.recognize_button = tk.Button(self, text="识别人脸", command=self.recognize_face)
        self.recognize_button.pack(pady=10)

        # 加载人脸图像数据
        self.faces = np.load("faces.npy")

        # 计算平均人脸和特征向量
        self.mean_face = np.mean(self.faces, axis=0)
        self.feature_vectors = self.faces - self.mean_face

        # 计算协方差矩阵和特征值/特征向量
        cov_matrix = np.cov(self.feature_vectors.T)
        eig_vals, eig_vecs = np.linalg.eig(cov_matrix)

        # 选择前k个特征向量
        k = 10
        self.eig_vecs_k = eig_vecs[:, :k]

    def select_file(self):
        # 打开文件选择对话框
        file_path = filedialog.askopenfilename(filetypes=[("Image Files", "*.jpg;*.jpeg;*.png;*.bmp")])

        # 显示选择的图像
        img = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (400, 400))
        img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
        img = cv2.transpose(img)
        img = cv2.flip(img, 1)
        self.img = img
        self.display_image(img)

    def display_image(self, img):
        # 显示图像到画布
        img_tk = self.get_image_tk(img)
        self.canvas.create_image(0, 0, anchor="nw", image=img_tk)

    def get_image_tk(self, img):
        # 转换图像格式为tkinter可用的格式
        height, width, channels = img.shape
        img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img_pil = Image.fromarray(img_rgb)
        img_tk = ImageTk.PhotoImage(img_pil)
        return img_tk

    def recognize_face(self):
        # 将选择的图像转换成特征向量
        img_gray = cv2.cvtColor(self.img, cv2.COLOR_RGB2GRAY)
        img_gray_flatten = img_gray.flatten()
        img_feature_vector = img_gray_flatten - self.mean_face
        img_feature_vector = img_feature_vector.reshape(1, -1)

        # 计算特征向量的投影
        img_projected = np.dot(img_feature_vector, self.eig_vecs_k)

        # 计算所有人脸图像的投影
        faces_projected = np.dot(self.feature_vectors, self.eig_vecs_k)

        # 计算与选择的图像最近的人脸图像
        min_distance = np.inf
        min_index = -1
        for i in range(len(faces_projected)):
            distance = np.linalg.norm(faces_projected[i] - img_projected)
            if distance < min_distance:
                min_distance = distance
                min_index = i

        # 显示识别结果
        recognized_face = self.faces[min_index]
        recognized_face = recognized_face.reshape((400, 400))
        recognized_face = cv2.cvtColor(recognized_face, cv2.COLOR_GRAY2RGB)
        recognized_face = cv2.transpose(recognized_face)
        recognized_face = cv2.flip(recognized_face, 1)
        self.display_image(recognized_face)

if __name__ == "__main__":
    app = App()
    app.mainloop()

该GUI程序使用PCA进行人脸识别，可以选择本地的人脸图像文件，然后对其进行识别并显示识别结果。在程序运行之前需要将人脸图像数据保存为`faces.npy`文件。