请根据注释补全代码。其中X是一个列表，X的列表元素是以图片为列表元素的列表。最后保存X_process中的内容于一个文件夹中（这个文件夹里可以包含若干个二级文件夹）代码：X_processed = [] detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") for x_list in X: temp_X_processed = [] for x in x_list: # write the code to detect face in the image (x) using dlib facedetection library # write the code to crop the image (x) to keep only the face, resize the cropped image to 150x150 # write the code to convert the image (x) to grayscale # append the converted image into temp_X_processed # append temp_X_processed into X_processed

import dlib
import cv2
import os

X_processed = []
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")

for x_list in X:
    temp_X_processed = []
    for x in x_list:
        # detect face in the image (x) using dlib facedetection library
        gray = cv2.cvtColor(x, cv2.COLOR_BGR2GRAY)
        faces = detector(gray)
        
        # crop the image (x) to keep only the face, resize the cropped image to 150x150
        for face in faces:
            x, y, w, h = face.left(), face.top(), face.width(), face.height()
            cropped_face = cv2.resize(x[y:y+h, x:x+w], (150, 150))
            
            # convert the cropped image to grayscale
            gray_cropped_face = cv2.cvtColor(cropped_face, cv2.COLOR_BGR2GRAY)
            
            # append the converted image into temp_X_processed
            temp_X_processed.append(gray_cropped_face)
    
    # append temp_X_processed into X_processed
    X_processed.append(temp_X_processed)

# save X_processed into a folder
os.makedirs("processed_images", exist_ok=True)

for i, x_list in enumerate(X_processed):
    for j, x in enumerate(x_list):
        filename = f"processed_images/image_{i}_{j}.jpg"
        cv2.imwrite(filename, x)