人脸识别 Python 代码：基于 OpenCV 和 PCA 的人脸检测与识别

import cv2
import os
import numpy as np
from sklearn.decomposition import PCA


def detect_face(img, face_cascade):
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)
    if len(faces) == 0:
        return None
    for (x, y, w, h) in faces:
        face = gray[y:y + w, x:x + h].astype(np.float32)
        face = cv2.resize(face, (150, 150))
        return face


def prepare_training_data(data_folder_path):
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
    dirs = os.listdir(data_folder_path)
    faces = []
    labels = []
    for dir_name in dirs:
        label = int(dir_name)
        subject_dir_path = data_folder_path + '/' + dir_name
        subject_images_names = os.listdir(subject_dir_path)

        for image_name in subject_images_names:
            image_path = subject_dir_path + '/' + image_name
            image = cv2.imread(image_path)

            if image is None:
                continue

            gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
            rect = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),flags=cv2.CASCADE_SCALE_IMAGE)
            for (x, y, w, h) in rect:
                face = gray[y:y + w, x:x + h].astype(np.float32)
                face = cv2.resize(face, (150, 150))
                if face is not None:
                   face_vector = np.reshape(face,(1,-1))[0]
                   faces.append(face_vector)
                   labels.append(label)

    faces= np.array(faces)
    labels= np.array(labels)

    return faces, labels


def preprocess_image(img, face_cascade, pca):
    face = detect_face(img, face_cascade)
    if face is not None:
        face_pca = pca.transform(face.reshape(1, -1))
        img_processed = cv2.cvtColor(face, cv2.COLOR_GRAY2RGB)
        return face_pca, img_processed
    else:
        return None, None


def main():
    cap = cv2.VideoCapture(0)
    stop = False
    face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
    pca = PCA(n_components=60, svd_solver='full')
    faces, labels = prepare_training_data('raw1')
    faces = pca.fit_transform(faces)

    while not stop:
        success, img = cap.read()
        face_pca, img_processed = preprocess_image(img, face_cascade, pca)
        if face_pca is not None:
            subjects = ['xu', 'LU', 'ZHOU']
            img1 = img_processed.copy()
            faces_cascade = face_cascade.detectMultiScale(img, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30),flags=cv2.CASCADE_SCALE_IMAGE)
            if len(faces_cascade) > 0:
                (x, y, w, h) = faces_cascade[0]
                cv2.rectangle(img1, (x, y), (x + w, y + h), (0, 255, 0), 2)
                min_d = 1000000000000
                c = -1
                for i, f in enumerate(faces):
                    d = ((face_pca - f) ** 2).sum()
                    if d < min_d:
                        min_d = d
                        c = labels[i]
                print(subjects[c])
            cv2.imshow('test', img1)
            cv2.putText(img1, subjects[c], (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (128, 128, 0), 2)
        c = cv2.waitKey(1)
        if c & 0xFF == ord('q'):
            stop = True
    cv2.destroyAllWindows()




if __name__ == '__main__':
    main()

可能的原因：

1. 摄像头没有正确连接或者没有打开。
2. 没有正确的人脸数据集，导致无法识别人脸。
3. 没有正确的分类器或者分类器没有被正确加载。
4. 图像处理过程中出现错误，导致无法显示图像。

建议：

1. 检查摄像头连接和打开状态。
2. 确保人脸数据集正确配置，并包含足够的训练样本。
3. 检查分类器的路径和加载方式，确保正确加载。
4. 检查代码逻辑，排除图像处理过程中可能出现的错误。
5. 尝试使用静态图像进行测试，以便更好地定位问题。