这个问题是关于使用Python OpenCV进行人脸识别，无摄像头输入，有准备好的人脸照片文件夹，，给出python代码

当然可以，以下是基于OpenCV和LBPH算法的Python代码示例，用于从准备好的人脸照片文件夹中识别人脸：

import cv2
import os
import numpy as np

# 准备好的人脸照片文件夹路径
face_folder_path = "path/to/faces/folder"

# LBPH算法参数
radius = 1
neighbors = 8
grid_x = 8
grid_y = 8
threshold = 100

# 初始化LBPH算法
recognizer = cv2.face.LBPHFaceRecognizer_create(radius, neighbors, grid_x, grid_y, threshold)

# 收集人脸数据和标签
faces = []
labels = []
for label, name in enumerate(os.listdir(face_folder_path)):
    for file_name in os.listdir(os.path.join(face_folder_path, name)):
        image_path = os.path.join(face_folder_path, name, file_name)
        image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
        faces.append(image)
        labels.append(label)

# 训练LBPH算法
recognizer.train(faces, np.array(labels))

# 加载待识别的图片，进行人脸检测和特征提取
test_image_path = "path/to/test/image"
test_image = cv2.imread(test_image_path, cv2.IMREAD_GRAYSCALE)
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(test_image, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# 遍历检测到的人脸，进行识别
for (x, y, w, h) in faces:
    roi = test_image[y:y+h, x:x+w]
    label, confidence = recognizer.predict(roi)

    # 显示识别结果
    if confidence < threshold:
        name = os.listdir(face_folder_path)[label]
    else:
        name = "unknown"
    cv2.rectangle(test_image, (x, y), (x+w, y+h), (0, 255, 0), 2)
    cv2.putText(test_image, name, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# 显示识别结果
cv2.imshow("Face Recognition", test_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

您需要将`face_folder_path`和`test_image_path`替换成您自己的路径，并下载适用于您的OpenCV版本的Haar特征级联分类器（例如`haarcascade_frontalface_default.xml`），并将其与您的代码放在同一个目录中。