一、数据预处理

实验数据来自genki4k

提取含有完整人脸的图片

def init_file():num = 0bar = tqdm(os.listdir(read_path))for file_name in bar:bar.desc = "预处理图片: "# a图片的全路径img_path = (read_path + "/" + file_name)# 读入的图片的路径中含非英文img = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)# 获取图片的宽高img_shape = img.shapeimg_height = img_shape[0]img_width = img_shape[1]# 用来存储生成的单张人脸的路径# dlib检测dets = detector(img, 1)for k, d in enumerate(dets):if len(dets) > 1:continuenum += 1# 计算矩形大小# (x,y), (宽度width, 高度height)# pos_start = tuple([d.left(), d.top()])# pos_end = tuple([d.right(), d.bottom()])# 计算矩形框大小height = d.bottom() - d.top()width = d.right() - d.left()# 根据人脸大小生成空的图像img_blank = np.zeros((height, width, 3), np.uint8)for i in range(height):if d.top() + i >= img_height:  # 防止越界continuefor j in range(width):if d.left() + j >= img_width:  # 防止越界continueimg_blank[i][j] = img[d.top() + i][d.left() + j]img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.INTER_CUBIC)# 保存图片cv2.imencode('.jpg', img_blank)[1].tofile(save_path + "/" + "file" + str(num) + ".jpg")logging.info("一共", len(os.listdir(read_path)), "个样本")logging.info("有效样本", num)

二、训练模型

创建模型

# 创建网络
def create_model():model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Flatten())model.add(layers.Dropout(0.5))model.add(layers.Dense(512, activation='relu'))model.add(layers.Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])return model

训练模型

# 训练模型
def train_model(model):# 归一化处理train_datagen = ImageDataGenerator(rescale=1. / 255,rotation_range=40,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True, )test_datagen = ImageDataGenerator(rescale=1. / 255)train_generator = train_datagen.flow_from_directory(# This is the target directorytrain_dir,# All images will be resized to 150x150target_size=(150, 150),batch_size=32,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=32,class_mode='binary')history = model.fit_generator(train_generator,steps_per_epoch=60,epochs=12,validation_data=validation_generator,validation_steps=30)# 保存模型save_path = "../output/model"if not os.path.exists(save_path):os.makedirs(save_path)model.save(save_path + "/smileDetect.h5")return history

训练结果

准确率

丢失率

训练过程

三、预测

通过读取摄像头内容进行预测

def rec(img):gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)dets = detector(gray, 1)if dets is not None:for face in dets:left = face.left()top = face.top()right = face.right()bottom = face.bottom()cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2)img1 = cv2.resize(img[top:bottom, left:right], dsize=(150, 150))img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)img1 = np.array(img1) / 255.img_tensor = img1.reshape(-1, 150, 150, 3)prediction = model.predict(img_tensor)if prediction[0][0] > 0.5:result = 'unsmile'else:result = 'smile'cv2.putText(img, result, (left, top), font, 2, (0, 255, 0), 2, cv2.LINE_AA)cv2.imshow('Video', img)while video.isOpened():res, img_rd = video.read()if not res:breakrec(img_rd)if cv2.waitKey(1) & 0xFF == ord('q'):break

效果

四、源代码

pretreatment.py

import dlib  # 人脸识别的库dlib
import numpy as np  # 数据处理的库numpy
import cv2  # 图像处理的库OpenCv
import os
import shutil
from tqdm import tqdm
import logging# dlib预测器
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('../resources/shape_predictor_68_face_landmarks.dat')
# 原图片路径
read_path = "../resources/genki4k/files"
# 提取人脸存储路径
save_path = "../output/genki4k/files"
if not os.path.exists(save_path):os.makedirs(save_path)# 新的数据集
data_dir = '../resources/data'
if not os.path.exists(data_dir):os.makedirs(data_dir)# 训练集
train_dir = data_dir + "/train"
if not os.path.exists(train_dir):os.makedirs(train_dir)
# 验证集
validation_dir = os.path.join(data_dir, 'validation')
if not os.path.exists(validation_dir):os.makedirs(validation_dir)
# 测试集
test_dir = os.path.join(data_dir, 'test')
if not os.path.exists(test_dir):os.makedirs(test_dir)# 初始化训练数据
def init_data(file_list):# 如果不存在文件夹则新建for file_path in file_list:if not os.path.exists(file_path):os.makedirs(file_path)# 存在则清空里面所有数据else:for i in os.listdir(file_path):path = os.path.join(file_path, i)if os.path.isfile(path):os.remove(path)def init_file():num = 0bar = tqdm(os.listdir(read_path))for file_name in bar:bar.desc = "预处理图片: "# a图片的全路径img_path = (read_path + "/" + file_name)# 读入的图片的路径中含非英文img = cv2.imdecode(np.fromfile(img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)# 获取图片的宽高img_shape = img.shapeimg_height = img_shape[0]img_width = img_shape[1]# 用来存储生成的单张人脸的路径# dlib检测dets = detector(img, 1)for k, d in enumerate(dets):if len(dets) > 1:continuenum += 1# 计算矩形大小# (x,y), (宽度width, 高度height)# pos_start = tuple([d.left(), d.top()])# pos_end = tuple([d.right(), d.bottom()])# 计算矩形框大小height = d.bottom() - d.top()width = d.right() - d.left()# 根据人脸大小生成空的图像img_blank = np.zeros((height, width, 3), np.uint8)for i in range(height):if d.top() + i >= img_height:  # 防止越界continuefor j in range(width):if d.left() + j >= img_width:  # 防止越界continueimg_blank[i][j] = img[d.top() + i][d.left() + j]img_blank = cv2.resize(img_blank, (200, 200), interpolation=cv2.INTER_CUBIC)# 保存图片cv2.imencode('.jpg', img_blank)[1].tofile(save_path + "/" + "file" + str(num) + ".jpg")logging.info("一共", len(os.listdir(read_path)), "个样本")logging.info("有效样本", num)# 划分数据集
def divide_data(file_path, message, begin, end):files = ['file{}.jpg'.format(i) for i in range(begin, end)]bar = tqdm(files)bar.desc = messagefor file in bar:src = os.path.join(save_path, file)dst = os.path.join(file_path, file)shutil.copyfile(src, dst)if __name__ == "__main__":init_file()positive_train_dir = os.path.join(train_dir, 'smile')negative_train_dir = os.path.join(train_dir, 'unSmile')positive_validation_dir = os.path.join(validation_dir, 'smile')negative_validation_dir = os.path.join(validation_dir, 'unSmile')positive_test_dir = os.path.join(test_dir, 'smile')negative_test_dir = os.path.join(test_dir, 'unSmile')file_list = [positive_train_dir, positive_validation_dir, positive_test_dir,negative_train_dir, negative_validation_dir, negative_test_dir]init_data(file_list)divide_data(positive_train_dir, "划分训练集正样本", 1, 1001)divide_data(negative_train_dir, "划分训练集负样本", 2200, 3200)divide_data(positive_validation_dir, "划分验证集正样本", 1000, 1500)divide_data(negative_validation_dir, "划分验证集负样本", 3000, 3500)divide_data(positive_test_dir, "划分测试集正样本", 1500, 2000)divide_data(negative_test_dir, "划分测试集负样本", 2800, 3500)

train.py

import os
from keras import layers
from keras import models
from tensorflow import optimizers
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGeneratortrain_dir = "../resources/data/train"
validation_dir = "../resources/data/validation"# 创建网络
def create_model():model = models.Sequential()model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(150, 150, 3)))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(64, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Conv2D(128, (3, 3), activation='relu'))model.add(layers.MaxPooling2D((2, 2)))model.add(layers.Flatten())model.add(layers.Dropout(0.5))model.add(layers.Dense(512, activation='relu'))model.add(layers.Dense(1, activation='sigmoid'))model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])return model# 训练模型
def train_model(model):# 归一化处理train_datagen = ImageDataGenerator(rescale=1. / 255,rotation_range=40,width_shift_range=0.2,height_shift_range=0.2,shear_range=0.2,zoom_range=0.2,horizontal_flip=True, )test_datagen = ImageDataGenerator(rescale=1. / 255)train_generator = train_datagen.flow_from_directory(# This is the target directorytrain_dir,# All images will be resized to 150x150target_size=(150, 150),batch_size=32,# Since we use binary_crossentropy loss, we need binary labelsclass_mode='binary')validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(150, 150),batch_size=32,class_mode='binary')history = model.fit_generator(train_generator,steps_per_epoch=60,epochs=12,validation_data=validation_generator,validation_steps=30)# 保存模型save_path = "../output/model"if not os.path.exists(save_path):os.makedirs(save_path)model.save(save_path + "/smileDetect.h5")return history# 展示训练结果
def show_results(history):# 数据增强过后的训练集与验证集的精确度与损失度的图形acc = history.history['acc']val_acc = history.history['val_acc']loss = history.history['loss']val_loss = history.history['val_loss']# 绘制结果epochs = range(len(acc))plt.plot(epochs, acc, 'bo', label='Training acc')plt.plot(epochs, val_acc, 'b', label='Validation acc')plt.title('Training and validation accuracy')plt.legend()plt.figure()plt.plot(epochs, loss, 'bo', label='Training loss')plt.plot(epochs, val_loss, 'b', label='Validation loss')plt.title('Training and validation loss')plt.legend()plt.show()if __name__ == "__main__":model = create_model()history = train_model(model)show_results(history)

predict.py

import os
from keras import layers
from keras import models
from tensorflow import optimizers
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGeneratortrain_dir = "../resources/data/train"
validation_dir = "../resources/data/validation"# 创建网络
# 检测视频或者摄像头中的人脸
import cv2
from keras.preprocessing import image
from keras.models import load_model
import numpy as np
import dlib
from PIL import Imagemodel = load_model('../output/model/smileDetect.h5')
detector = dlib.get_frontal_face_detector()
video = cv2.VideoCapture(0)
font = cv2.FONT_HERSHEY_SIMPLEXdef rec(img):gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)dets = detector(gray, 1)if dets is not None:for face in dets:left = face.left()top = face.top()right = face.right()bottom = face.bottom()cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0), 2)img1 = cv2.resize(img[top:bottom, left:right], dsize=(150, 150))img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)img1 = np.array(img1) / 255.img_tensor = img1.reshape(-1, 150, 150, 3)prediction = model.predict(img_tensor)if prediction[0][0] > 0.5:result = 'unsmile'else:result = 'smile'cv2.putText(img, result, (left, top), font, 2, (0, 255, 0), 2, cv2.LINE_AA)cv2.imshow('Video', img)while video.isOpened():res, img_rd = video.read()if not res:breakrec(img_rd)if cv2.waitKey(1) & 0xFF == ord('q'):break
video.release()
cv2.destroyAllWindows()

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Python基于keras训练简单微笑识别

文章目录

一、数据预处理

二、训练模型

创建模型

训练模型

训练结果

三、预测

效果

四、源代码

pretreatment.py

train.py

predict.py

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