转载请注明：https://blog.csdn.net/wyx100/article/details/80428424
效果展示
未完待续。。。
环境配置
win7sp1
python 3.6.3
dlib 19.7.0
tensorflow 1.3.0rc0
keras 2.1.5
opencv-python 3.4.1+contrib
pillow 4.2.1
numpy 1.14.1+mkl
numpy 1.12.1
软件下载地址：https://www.lfd.uci.edu/~gohlke/pythonlibs/
项目实现步骤
1.整理人脸图片（格式：jpg，150x150）
每个人物100张（80张训练（train），20张验证（validation））
data
train
fsm
0.jpg
1.jpg
。。。
79.jpg
gje
qyy
validation
fsm
80.jpg
81.jpg
。。。
99.jpg
gje
qyy
2.训练CNN（tensorflow、keras）模型
3.基于2训练的model（dlib检测人脸）识别视频某张图片中的人脸，并标记姓名
1）打开视频，截取一帧图片
2）检测1）中图片的人脸（1张或多张），未检测到人脸则结束本次循环
3）基于2训练的model识别图片中的人脸，并标记姓名
4）输出框出人脸并标记姓名的图片。
代码
1. model_cnn_train.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
batch_size = 32 # 训练时每个批次的样本数 训练样本数/批次样本数 = 批次数（每个周期）
# num_classes = 10
num_classes = 3 # 3类别
# epochs = 100
epochs = 50 # 训练50周期，训练集所有样本（数据、记录）参与训练一次为一个周期
# data_augmentation = True
# num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_face_trained_model.h5'
img_w = 150
img_h = 150
# LossHistory类，保存loss和acc
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = {'batch':[], 'epoch':[]}
self.accuracy = {'batch':[], 'epoch':[]}
self.val_loss = {'batch':[], 'epoch':[]}
self.val_acc = {'batch':[], 'epoch':[]}
def on_batch_end(self, batch, logs={}):
self.losses['batch'].append(logs.get('loss'))
self.accuracy['batch'].append(logs.get('acc'))
self.val_loss['batch'].append(logs.get('val_loss'))
self.val_acc['batch'].append(logs.get('val_acc'))
def on_epoch_end(self, batch, logs={}):
self.losses['epoch'].append(logs.get('loss'))
self.accuracy['epoch'].append(logs.get('acc'))
self.val_loss['epoch'].append(logs.get('val_loss'))
self.val_acc['epoch'].append(logs.get('val_acc'))
def loss_plot(self, loss_type):
iters = range(len(self.losses[loss_type]))
plt.figure()
# acc
plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')
# loss
plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
if loss_type == 'epoch':
# val_acc
plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
# val_loss
plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
plt.grid(True)
plt.xlabel(loss_type)
plt.ylabel('acc-loss')
plt.legend(loc="upper right")
plt.show()
'''
# The data, shuffled and split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
'''
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
# input_shape=x_train.shape[1:]))
input_shape=(150, 150, 3))) # 输入数据是图片转换的矩阵格式，150（行）x 150（列） x 3 （通道）（每个像素点3个单位，分别表示RGB(红绿蓝)）
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
# x_train = x_train.astype('float32')
# x_test = x_test.astype('float32')
# x_train /= 255
# x_test /= 255
# 创建history实例
history = LossHistory()
'''
if not data_augmentation:
print('Not using data augmentation.')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4)
'''
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1. / 255)
# 训练样本初始化处理:长宽调整，批次大小调整，数据打乱排序（shuffle=True），分类区分（binary：2分类、categorical：多分类）
train_generator = train_datagen.flow_from_directory(
'./data/train', # 本例，提供80 x 3 = 240 个训练样本
target_size=(img_w, img_h), # 图片格式调整为 150x150
batch_size=batch_size,
shuffle=True,
class_mode='categorical') # matt，多分类
validation_generator = test_datagen.flow_from_directory(
'./data/validation',# 本例，提供20 x 3 = 60 个验证样本
target_size=(img_w, img_h),
batch_size=batch_size,
shuffle=True,
class_mode='categorical') # matt，多分类
# 模型适配生成
model.fit_generator(
train_generator, # 训练集
samples_per_epoch=2400, # 训练集总样本数，如果提供样本数量不够，则调整图片（翻转、平移等）补足数量（本例，该函数补充2400-240个样本）
nb_epoch=epochs,
validation_data=validation_generator, # 验证集
nb_val_samples=800, # 验证集总样本数，如果提供样本数量不够，则调整图片（翻转、平移等）补足数量（本例，该函数补充800-60个样本）
callbacks=[history]) # 回调函数，绘制批次（epoch）和精确度（acc）关系图表函数
# Save model and weights
if not os.path.isdir(save_dir): # 没有save_dir对应目录则建立
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# 显示批次（epoch）和精确度（acc）关系图表
history.loss_plot('epoch')
# 模型结构图
from keras.utils import plot_model
plot_model(model, to_file='model.png', show_shapes=True)
# Score trained model.
# scores = model.evaluate(x_test, y_test, verbose=1)
# print('Test loss:', scores[0])
# print('Test accuracy:', scores[1])
根据keras2.1.6中example（点击下载）下cifar10_cnn.py修改
英文文档
https://pypi.org/project/Keras/
https://pypi.org/project/Keras/#files
https://github.com/keras-team/keras.git （代码下载）
中文文档
http://keras-cn.readthedocs.io/en/latest/
'''Train a simple deep CNN on the CIFAR10 small images dataset.
It gets to 75% validation accuracy in 25 epochs, and 79% after 50 epochs.
(it's still underfitting at that point, though).
'''
from __future__ import print_function
import keras
from keras.datasets import cifar10
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Conv2D, MaxPooling2D
import os
batch_size = 32
num_classes = 10
epochs = 100
data_augmentation = True
num_predictions = 20
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_cifar10_trained_model.h5'
# The data, split between train and test sets:
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='same',
input_shape=x_train.shape[1:]))
model.add(Activation('relu'))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), padding='same'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
# initiate RMSprop optimizer
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)
# Let's train the model using RMSprop
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
if not data_augmentation:
print('Not using data augmentation.')
model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
validation_data=(x_test, y_test),
shuffle=True)
else:
print('Using real-time data augmentation.')
# This will do preprocessing and realtime data augmentation:
datagen = ImageDataGenerator(
featurewise_center=False, # set input mean to 0 over the dataset
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=0, # randomly rotate images in the range (degrees, 0 to 180)
width_shift_range=0.1, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.1, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False) # randomly flip images
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4)
# Save model and weights
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
6个周期可以达到99%的准确率。
2. video_face_sign.py
通过模型、样本质量、样本数量、样本多样性调整可优化实际识别效果。
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import numpy as np
import sys
import time
import cv2
import dlib
from keras.preprocessing import image as imagekeras
from keras.models import load_model
from PIL import Image, ImageDraw, ImageFont
size = 150
save_dir = os.path.join(os.getcwd(), 'saved_models')
model_name = 'keras_face_trained_model.h5'
# 类别编码转换为中文名称返回
def return_name(codelist):
names = ['樊胜美', '关雎尔', '邱莹莹']
for it in range(0, len(codelist), 1):
if int(codelist[it]) == 1.0:
return names[it]
# 类别编码转换为英文名称返回
def return_name_en(codelist):
names = ['fsm', 'gje', 'qyy']
for it in range(0, len(codelist), 1):
if int(codelist[it]) == 1.0:
return names[it]
# 区分和标记视频中截图的人脸
def face_rec():
global image_ouput
model = load_model(os.path.join(save_dir, model_name))
camera = cv2.VideoCapture("2.mp4") # 视频
# camera = cv2.VideoCapture(0) # 摄像头
while (True):
read, img = camera.read()
try:
# 未截取视频图片结束本次循环
if not (type(img) is np.ndarray):
continue
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # 图片转为灰度图
except:
print("Unexpected error:", sys.exc_info()[0])
break
# 使用detector进行人脸检测
# 使用dlib自带的frontal_face_detector作为我们的特征提取器
detector = dlib.get_frontal_face_detector()
dets = detector(gray_img, 1) # 提取截图中所有人脸
facelist = []
for i, d in enumerate(dets): # 依次区分截图中的人脸
x1 = d.top() if d.top() > 0 else 0
y1 = d.bottom() if d.bottom() > 0 else 0
x2 = d.left() if d.left() > 0 else 0
y2 = d.right() if d.right() > 0 else 0
img = cv2.rectangle(img, (x2, x1), (y2, y1), (255, 0, 0), 2) # 人脸画框
face = img[x1:y1, x2:y2]
face = cv2.resize(face, (size, size))
x_input = np.expand_dims(face, axis=0)
prey = model.predict(x_input)
print(prey, 'prey')
facelist.append([d, return_name(prey[0])]) # 存储一张图中多张人脸坐标和标记（姓名）
cv2_im = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # cv2和PIL中颜色的hex码的储存顺序不同
pil_im = Image.fromarray(cv2_im)
draw = ImageDraw.Draw(pil_im) # 括号中为需要打印的cqanvas，这里就是在图片上直接打印
font = ImageFont.truetype("simhei.ttf", 20, encoding="utf-8") # 第一个参数为字体文件路径，第二个为字体大小
try:
for i in facelist:
# 人脸标记写入图片，第一个参数为打印的坐标，第二个为打印的文本，第三个为字体颜色，第四个为字体
draw.text((i[0].left() + int((i[0].right() - i[0].left()) / 2 - len(i[1]) * 10), i[0].top() - 20), i[1],
(255, 0, 0), font=font)
except:
print("Unexpected error:", sys.exc_info()[0])
continue
# PIL图片转换为cv2图片
cv2_char_img = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
# 显示图片
cv2.imshow("camera", cv2_char_img)
if cv2.waitKey(1) & 0xff == ord("q"):
break
cv2.destroyAllWindows()
if __name__ == "__main__":
face_rec()
模型训练epoch-acc-loss图
模型结构图
完整项目下载
为方便没积分童鞋，请加企鹅，共享文件夹。
包括：代码、数据集合（图片）、已生成model、安装库文件等。
https://github.com/gbusr/ML/tree/master/facecnn
详细讲解
