人脸识别对于人类来说很简单，如果对于机器，如何才能构建一个人脸识别器。
主要步骤如下：
（1） 定义标签编码。在输入训练数据中，标签用单词表示，但我们需要数字来训练系统。
（2） 从每幅图中提取ROI属性值和标签编码器。
（3） 加载人脸级联文件。
（4） 生成局部二值模式直方图人脸识别器。
（5） 用训练集数据训练人脸识别器。
（6） 加载测试数据所在路径，将批量数据读取到内存，用人脸级联文件来检测器确定人脸的位置。
（7） 对于每个人的脸ROI，运行人脸识别器，将标签转换为单词。
（8） 直接在图片打上分类文字，并展示。
（9） 关闭cv2。
源代码如下：
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):
print("[label_word]",[label_word])
print("self.le.transform([label_word])[0]",self.le.transform([label_word])[0])
print("int(self.le.transform([label_word])[0])",int(self.le.transform([label_word])[0]))
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(faceCascade,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])
print("label_words",label_words)
print("filepath",filepath)
# Initialize variables
images = []
le = LabelEncoder()
le.encode_labels(label_words)
print("le",le)
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
# print(faces)
for (x, y, w, h) in faces:
images.append(image[y:y+h, x:x+w])
labels.append(le.word_to_num(name))
print("x,y,w,h",x,y,w,h,"filepath=",filepath)
print("labels=",labels)
return images, labels, le
class my_face_reconginizer:
def __init__(self,cascade_path = r"cascade_files\haarcascade_frontalface_alt.xml",
path_train = r'faces_dataset\train',path_test = r'faces_dataset\test'):
self.faceCascade = cv2.CascadeClassifier(cascade_path) #人脸级联文件。
self.path_train=path_train
self.path_test=path_test
def Recongizer_train(self):
self.images, self.labels, self.le = get_images_and_labels(self.faceCascade,self.path_train)
self.recognizer = cv2.face.LBPHFaceRecognizer_create()
print( "\nTraining...")
self.recognizer.train(self.images, np.array(self.labels))
def Recongizer_Predict(self):
# Test the recognizer on unknown images
print('\nPerforming prediction on test images...')
stop_flag = False
for root, dirs, files in os.walk(self.path_test):
for filename in (x for x in files if x.endswith('.jpg')):
filepath = os.path.join(root, filename)
# Read the image
predict_image = cv2.imread(filepath, 0)
print("predict_image",predict_image)
print("filepath",filepath)
print("type(predict_image)",type(predict_image))
print("shape(predict_image)",np.shape(predict_image))
# Detect faces
faces = self.faceCascade.detectMultiScale(predict_image, 1.1,
2, minSize=(100,100))
print("faces",faces)
# Iterate through face rectangles
for (x, y, w, h) in faces:
# Predict the output
predicted_index, conf = self.recognizer.predict(
predict_image[y:y+h, x:x+w])
# Convert to word label
predicted_person = self.le.num_to_word(predicted_index)
# Overlay text on the output image and display it
cv2.putText(predict_image, 'Prediction: ' + predicted_person,
(10,60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255),3)
cv2.imshow("Recognizing face", predict_image)
c = cv2.waitKey(0)
if c == 27:
stop_flag = True
break
return 0
if stop_flag:
break
test1=my_face_reconginizer()
test1.Recongizer_train()
test1.Recongizer_Predict()