人脸识别代码
作者：互联网
一、加载图片数据
from os import listdir
from os.path import isdir
from PIL import Image
from matplotlib import pyplot
from numpy import savez_compressed
from numpy import asarray
from mtcnn.mtcnn import MTCNN
def extract_face(filename, required_size=(160, 160)):
image = Image.open(filename)
image = image.convert('RGB')
pixels = asarray(image)
detector = MTCNN()
results = detector.detect_faces(pixels)
x1, y1, width, height = results[0]['box']
x1, y1 = abs(x1), abs(y1)
x2, y2 = x1 + width, y1 + height
face = pixels[y1:y2, x1:x2]
image = Image.fromarray(face)
image = image.resize(required_size)
face_array = asarray(image)
return face_array
def load_faces(directory):
faces = list()
for filename in listdir(directory):
path = directory + filename
face = extract_face(path)
faces.append(face)
return faces
def load_dataset(directory):
X, y = list(), list()
for subdir in listdir(directory):
path = directory + subdir + '/'
if not isdir(path):
continue
faces = load_faces(path)
labels = [subdir for _ in range(len(faces))]
print('>loaded %d examples for class: %s' % (len(faces), subdir))
X.extend(faces)
y.extend(labels)
return asarray(X), asarray(y)
trainX, trainy = load_dataset('5-celebrity-faces-dataset/train/')
print(trainX.shape, trainy.shape)
testX, testy = load_dataset('5-celebrity-faces-dataset/val/')
savez_compressed('5-celebrity-faces-dataset.npz', trainX, trainy, testX, testy)
二、提取图片特征
from numpy import load
from numpy import expand_dims
from numpy import asarray
from numpy import savez_compressed
from keras.models import load_model
def get_embedding(model, face_pixels):
face_pixels = face_pixels.astype('float32')
mean, std = face_pixels.mean(), face_pixels.std()
face_pixels = (face_pixels - mean) / std
samples = expand_dims(face_pixels, axis=0)
yhat = model.predict(samples)
return yhat[0]
data = load('5-celebrity-faces-dataset.npz')
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
print('Loaded: ', trainX.shape, trainy.shape, testX.shape, testy.shape)
model = load_model('facenet_keras.h5')
print('Loaded Model')
newTrainX = list()
for face_pixels in trainX:
embedding = get_embedding(model, face_pixels)
newTrainX.append(embedding)
newTrainX = asarray(newTrainX)
print(newTrainX.shape)
newTestX = list()
for face_pixels in testX:
embedding = get_embedding(model, face_pixels)
newTestX.append(embedding)
newTestX = asarray(newTestX)
print(newTestX.shape)
# save arrays to one file in compressed format
savez_compressed('5-celebrity-faces-embeddings.npz', newTrainX, trainy, newTestX, testy)
三、识别
from numpy import load
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
data = load('5-celebrity-faces-embeddings.npz')
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
print('Dataset: train=%d, test=%d' % (trainX.shape[0], testX.shape[0]))
# normalize input vectors
in_encoder = Normalizer(norm='l2')
trainX = in_encoder.transform(trainX)
testX = in_encoder.transform(testX)
# label encode targets
out_encoder = LabelEncoder()
out_encoder.fit(trainy)
trainy = out_encoder.transform(trainy)
testy = out_encoder.transform(testy)
# fit model
model = SVC(kernel='linear', probability=True)
model.fit(trainX, trainy)
# predict
yhat_train = model.predict(trainX)
yhat_test = model.predict(testX)
# score
score_train = accuracy_score(trainy, yhat_train)
score_test = accuracy_score(testy, yhat_test)
# summarize
print('Accuracy: train=%.3f, test=%.3f' % (score_train*100, score_test*100))
from random import choice
from numpy import load
from numpy import expand_dims
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVC
from matplotlib import pyplot
data = load('5-celebrity-faces-dataset.npz')
testX_faces = data['arr_2']
data = load('5-celebrity-faces-embeddings.npz')
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
in_encoder = Normalizer(norm='l2')
trainX = in_encoder.transform(trainX)
testX = in_encoder.transform(testX)
out_encoder = LabelEncoder()
out_encoder.fit(trainy)
trainy = out_encoder.transform(trainy)
testy = out_encoder.transform(testy)
model = SVC(kernel='linear', probability=True)
model.fit(trainX, trainy)
selection = choice([i for i in range(testX.shape[0])])
random_face_pixels = testX_faces[selection]
random_face_emb = testX[selection]
random_face_class = testy[selection]
random_face_name = out_encoder.inverse_transform([random_face_class])
samples = expand_dims(random_face_emb, axis=0)
yhat_class = model.predict(samples)
yhat_prob = model.predict_proba(samples)
class_index = yhat_class[0]
class_probability = yhat_prob[0,class_index] * 100
predict_names = out_encoder.inverse_transform(yhat_class)
print('Predicted: %s (%.3f)' % (predict_names[0], class_probability))
print('Expected: %s' % random_face_name[0])
pyplot.imshow(random_face_pixels)
title = '%s (%.3f)' % (predict_names[0], class_probability)
pyplot.title(title)
pyplot.show()
标签：人脸识别,testX,代码,face,faces,import,data,pixels 来源： https://www.cnblogs.com/mtgold/p/15330233.html