局部二值模式直方图创建人脸识别器

先把代码搁这，纪录一下，可以直接运行摄像头检测：

import os

import cv2
import numpy as np
from sklearn import preprocessing

# Class to handle tasks related to label encoding
class LabelEncoder(object):
    # Method to encode labels from words to numbers
    def encode_labels(self, label_words):
        self.le = preprocessing.LabelEncoder()
        self.le.fit(label_words)

    # Convert input label from word to number
    def word_to_num(self, label_word):
        return int(self.le.transform([label_word])[0])

    # Convert input label from number to word
    def num_to_word(self, label_num):
        return self.le.inverse_transform([label_num])[0]

# Extract images and labels from input path
def get_images_and_labels(input_path):
    label_words = []

    # Iterate through the input path and append files
    for root, dirs, files in os.walk(input_path):
        for filename in (x for x in files if x.endswith('.jpg')):
            filepath = os.path.join(root, filename)
            label_words.append(filepath.split('\\')[-2]) 
            
    # Initialize variables
    images = []
    le = LabelEncoder()
    le.encode_labels(label_words)
    labels = []

    # Parse the input directory
    for root, dirs, files in os.walk(input_path):
        for filename in (x for x in files if x.endswith('.jpg')):
            filepath = os.path.join(root, filename)

            # Read the image in grayscale format
            image = cv2.imread(filepath, 0) 

            # Extract the label
            name = filepath.split('\\')[-2]
                
            # Perform face detection
            faces = faceCascade.detectMultiScale(image, 1.1, 2, minSize=(100,100))

            # Iterate through face rectangles
            for (x, y, w, h) in faces:
                images.append(image[y:y+h, x:x+w])
                labels.append(le.word_to_num(name))

    return images, labels, le

if __name__=='__main__':
    cascade_path =r'C:\Users\Administrator\Desktop\face\cascade_files\haarcascade_frontalface_alt.xml'
    path_train = r'C:\Users\Administrator\Desktop\face\train'
   
    
    # Load face cascade file
    faceCascade = cv2.CascadeClassifier(cascade_path)

    # Initialize Local Binary Patterns Histogram face recognizer
    recognizer = cv2.face.LBPHFaceRecognizer_create()

    # Extract images, labels, and label encoder from training dataset
    images, labels, le = get_images_and_labels(path_train)

    # Train the face recognizer 
    print( "\nTraining...")
    recognizer.train(images, np.array(labels))
    recognizer.save(r'C:\Users\Administrator\Desktop\face\train\a.xml')
    # Test the recognizer on unknown images
    print( '\nPerforming prediction on test images...')
    cap = cv2.VideoCapture(0)

# Define the scaling factor


# Loop until you hit the Esc key
    while True:
    # Capture the current frame and resize it
        ret, frame = cap.read()
        frame1=cv2.resize(frame, None, fx=0.5, fy=0.5,interpolation=cv2.INTER_AREA)
        gray = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
    #predict_image = cv2.resize(frame, None, fx=scaling_factor, fy=scaling_factor,interpolation=cv2.INTER_AREA)
        faces = faceCascade.detectMultiScale(gray, 1.1,2, minSize=(100,100))
    # Convert to grayscale
    #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Run the face detector on the grayscale image
    #face_rects = face_cascade.detectMultiScale(gray, 1.3, 5)

    # Draw rectangles on the image
        for (x, y, w, h) in faces:
                # Predict the output
            predicted_index, conf = recognizer.predict( gray[y:y+h, x:x+w])

                # Convert to word label
            predicted_person = le.num_to_word(predicted_index)

                # Overlay text on the output image and display it
            cv2.putText(frame1, predicted_person,   (10,60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255,255,255), 3)
        cv2.imshow("Recognizing face", frame1)
    
        
        c = cv2.waitKey(5)
        if c == 27:
            break
    # Check if Esc key has been pressed


# Release the video capture object and close all windows
cap.release()
cv2.destroyAllWindows()
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`