keras实现简单性别识别（二分类问题）

keras实现简单性别识别（二分类问题）

第一步：准备好需要的库

• tensorflow  1.4.0
• h5py 2.7.0
• hdf5 1.8.15.1
• Keras     2.0.8
• opencv-python     3.3.0
• numpy    1.13.3+mkl
• 所需要的人脸检测模块
• mtcnn和opencv
• https://pan.baidu.com/s/1rhP7mcnAtiojhk8eiLroEw

第二步：准备数据集：

将性别不同的图片按照不同的分类放到不同的文件夹内。

数据集

https://pan.baidu.com/s/1_f36Gw4PWztUXZWH_jLWcw

 import shutil

 # 读取文件中图片信息根据性别分类图片到对应目录中
 dirroot = "D:\\Users\\a\\Pictures\\adience"
 f = open(dirroot+"\\fold_frontal_3_data.txt","r")
 i = 0

 for line in f.readlines():
     line = line.split()
     dir = line[0]

     imgName = "landmark_aligned_face."+ line[2] +'.'+ line[1]
     if i > 0:
         if line[5]== "f":
             print("female")
             shutil.copy(dirroot+'\\faces\\'+dir+'\\'+imgName, "D:\\pycode\\learn\\data\\validation\\"+imgName)
         #     移动图片到female目录
         elif line[5]=="m":
             print("male")
             shutil.copy(dirroot+'\\faces\\'+dir+'\\'+imgName, "D:\\pycode\\learn\\data\\validation\\"+imgName)
         #     移动图片到male目录
         else:
             print("N")
             # 未识别男女
     i += 1
 f.close()

使用ImageDataGenerator，来对图片进行归一化和随机旋转。使用flow_from_directory，来自动产生图片标签生成器。

 class Dataset(object):

     def __init__(self):
         self.train = None
         self.valid = None

     def read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE):
         train_datagen = ImageDataGenerator(
             rescale=1. / 255,
             horizontal_flip=True)

         test_datagen = ImageDataGenerator(rescale=1. / 255)

         train_generator = train_datagen.flow_from_directory(
             train_data_dir,
             target_size=(img_rows, img_cols),
             batch_size=batch_size,
             class_mode='binary')

         validation_generator = test_datagen.flow_from_directory(
             validation_data_dir,
             target_size=(img_rows, img_cols),
             batch_size=batch_size,
             class_mode='binary')

         self.train = train_generator
         self.valid = validation_generator

第三部：网络

 class Model(object):

     def __init__(self):
         self.model = Sequential()
         self.model.add(Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE,IMAGE_SIZE,3)))
         self.model.add(Activation('relu'))
         self.model.add(MaxPooling2D(pool_size=(2, 2)))

         self.model.add(Conv2D(32, (3, 3)))
         self.model.add(Activation('relu'))
         self.model.add(MaxPooling2D(pool_size=(2, 2)))

         self.model.add(Conv2D(64, (3, 3)))
         self.model.add(Activation('relu'))
         self.model.add(MaxPooling2D(pool_size=(2, 2)))

         self.model.add(Conv2D(64, (3, 3)))
         self.model.add(Activation('relu'))
         self.model.add(MaxPooling2D(pool_size=(2, 2)))

         self.model.add(Flatten())
         self.model.add(Dense(64))
         self.model.add(Activation('relu'))
         self.model.add(Dropout(0.85))
         self.model.add(Dense(1))
         self.model.add(Activation('sigmoid'))

     def train(self, dataset, batch_size=batch_size, nb_epoch=epochs):

         self.model.compile(loss='binary_crossentropy',
                       optimizer='adam',
                       metrics=['accuracy'])
         self.model.fit_generator(dataset.train,
                                  steps_per_epoch=nb_train_samples // batch_size,
                                  epochs=epochs,
                                  validation_data=dataset.valid,
                                  validation_steps=nb_validation_samples//batch_size)

     def save(self, file_path=FILE_PATH):
         print('Model Saved.')
         self.model.save_weights(file_path)

     def load(self, file_path=FILE_PATH):
         print('Model Loaded.')
         self.model.load_weights(file_path)

     def predict(self, image):
         # 预测样本分类
         img = image.resize((1, IMAGE_SIZE, IMAGE_SIZE, 3))
         img = image.astype('float32')
         img /= 255

         #归一化
         result = self.model.predict(img)
         print(result)
         # 概率
         result = self.model.predict_classes(img)
         print(result)
         # 0/1

         return result[0]

     def evaluate(self, dataset):
         # 测试样本准确率
         score = self.model.evaluate_generator(dataset.valid,steps=2)
         print("样本准确率%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))

第四部：主程序

 if __name__ == '__main__':
     dataset = Dataset()
     dataset.read()

     model = Model()
     model.load()
     model.train(dataset)
     model.evaluate(dataset)
     model.save()

第五步：识别程序

opencv检测模块版

 #!/usr/bin/env python
 """
 从摄像头中获取图像实时监测
 """
 import numpy as np
 import cv2
 from GenderTrain import Model

 def detect(img, cascade):
     """
     检测图像是否含有人脸部分
     :param img: 待检测帧图像
     :param cascade: 面部对象检测器
     :return: 面部图像标记
     """
     rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
                                      flags=cv2.CASCADE_SCALE_IMAGE)
     if len(rects) == 0:
         return []
     rects[:,2:] += rects[:,:2]
     return rects

 def draw_rects(img, rects, color):
     """
     根据图像标记人脸区域与性别
     :param img:
     :param rects:
     :param color:
     :return:
     """
     for x, y, w, h in rects:
         face = img[x:x+w,y:y+h]
         face = cv2.resize(face,(224,224))
         if gender.predict(face)==1:
             text = "Male"
         else:
             text = "Female"
         cv2.rectangle(img, (x, y), (w, h), color, 2)
         cv2.putText(img, text, (x, h), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 255, 255), lineType=cv2.LINE_AA)

 if __name__ == '__main__':
     haar__cascade_path = "D:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_default.xml"

     cascade = cv2.CascadeClassifier( haar__cascade_path)
     cam = cv2.VideoCapture(0)
     # 获取摄像头视频
     gender = Model()
     gender.load()
     # 加载性别模型
     while True:
         ret, img = cam.read()
         # 读取帧图像
         rects = detect(img, cascade)
         print(rects)
         vis = img.copy()
         draw_rects(vis, rects, (0, 255, 0))
         cv2.imshow('Gender', vis)
         if cv2.waitKey(5) == 27:
             break
     cv2.destroyAllWindows()

MTCNN检测版

"""
从摄像头中获取图像实时监测
"""
import PIL
import numpy as np
import detect_face
import tensorflow as tf
import cv2
from GenderTrain import Model

with tf.Graph().as_default():
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
    sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
    with sess.as_default():
        pnet, rnet, onet = detect_face.create_mtcnn(sess,
                                                    'E:\\pycode\\real-time-deep-face-recognition-master\\20170512-110547')
minsize = 20  # minimum size of face
threshold = [0.6, 0.7, 0.7]  # three steps's threshold
factor = 0.709  # scale factor
margin = 44
frame_interval = 3
batch_size = 1000
image_size = 182
input_image_size = 160

def draw_rects(img, rects, color):
    """
    根据图像标记人脸区域与性别
    :param img:
    :param rects:
    :param color:
    :return:
    """
    for x, y, w, h in rects:
        face = img[x:x+w,y:y+h]
        face = cv2.resize(face,(224,224))
        if gender.predict(face)==1:
            text = "Male"
        else:
            text = "Female"
        cv2.rectangle(img, (x, y), (w, h), color, 2)
        cv2.putText(img, text, (x, h), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 255, 255), lineType=cv2.LINE_AA)

if __name__ == '__main__':
    cam = cv2.VideoCapture(0)
    # 获取摄像头视频
    gender = Model()
    gender.load()
    # 加载性别模型
    while True:
        ret, img = cam.read()
        # 读取帧图像
        bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
        # 读取帧图像
        for face_position in bounding_boxes:
            face_position = face_position.astype(int)
            print(face_position[0:4])
            rects = [[face_position[0], face_position[1], face_position[2], face_position[3]]]
            vis = img.copy()
            draw_rects(vis, rects, (255, 255, 255))
            cv2.imshow('Gender', vis)
        if cv2.waitKey(5) == 27:
            break
    cv2.destroyAllWindows()

完全版

import os
import random
import cv2
import numpy as np
from tensorflow.contrib.keras.api.keras.preprocessing.image import ImageDataGenerator,img_to_array
from tensorflow.contrib.keras.api.keras.models import Sequential
from tensorflow.contrib.keras.api.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.contrib.keras.api.keras.layers import Conv2D, MaxPooling2D
from tensorflow.contrib.keras.api.keras.optimizers import SGD

IMAGE_SIZE = 182
# 训练图片大小
epochs = 150#原来是50
# 遍历次数
batch_size = 32
# 批量大小
nb_train_samples = 512*2
# 训练样本总数
nb_validation_samples = 128*2
# 测试样本总数
train_data_dir = 'D:\\code\\learn\\data_sex\\train_data\\'
validation_data_dir = 'D:\\data_sex\\test_data\\'
# 样本图片所在路径
FILE_PATH = 'Gender_new.h5'
# 模型存放路径
class Dataset(object):

    def __init__(self):
        self.train = None
        self.valid = None

    def read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE):
        train_datagen = ImageDataGenerator(
            rescale=1. / 255,
            horizontal_flip=True)

        test_datagen = ImageDataGenerator(rescale=1. / 255)

        train_generator = train_datagen.flow_from_directory(
            train_data_dir,
            target_size=(img_rows, img_cols),
            batch_size=batch_size,
            class_mode='binary')

        validation_generator = test_datagen.flow_from_directory(
            validation_data_dir,
            target_size=(img_rows, img_cols),
            batch_size=batch_size,
            class_mode='binary')

        self.train = train_generator
        self.valid = validation_generator

class Model(object):

    def __init__(self):
        self.model = Sequential()
        self.model.add(Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE,IMAGE_SIZE,3)))
        self.model.add(Activation('relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))

        self.model.add(Conv2D(32, (3, 3)))
        self.model.add(Activation('relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))

        self.model.add(Conv2D(64, (3, 3)))
        self.model.add(Activation('relu'))
        self.model.add(MaxPooling2D(pool_size=(2, 2)))

        self.model.add(Flatten())
        self.model.add(Dense(64))
        self.model.add(Activation('relu'))
        self.model.add(Dropout(0.5))
        self.model.add(Dense(1))
        self.model.add(Activation('sigmoid'))

    def train(self, dataset, batch_size=batch_size, nb_epoch=epochs):

        self.model.compile(loss='binary_crossentropy',
                      optimizer='adam',
                      metrics=['accuracy'])
        self.model.fit_generator(dataset.train,
                                 steps_per_epoch=nb_train_samples // batch_size,
                                 epochs=epochs,
                                 validation_data=dataset.valid,
                                 validation_steps=nb_validation_samples//batch_size)

    def save(self, file_path=FILE_PATH):
        print('Model Saved.')
        self.model.save_weights(file_path)

    def load(self, file_path=FILE_PATH):
        print('Model Loaded.')
        self.model.load_weights(file_path)

    def predict(self, image):
        # 预测样本分类
        img = image.resize((1, IMAGE_SIZE, IMAGE_SIZE, 3))
        img = image.astype('float32')
        img /= 255

        #归一化
        result = self.model.predict(img)
        print(result)
        # 概率
        result = self.model.predict_classes(img)
        print(result)
        # 0/1

        return result[0]

    def evaluate(self, dataset):
        # 测试样本准确率
        score = self.model.evaluate_generator(dataset.valid,steps=2)
        print("样本准确率%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))

if __name__ == '__main__':
    dataset = Dataset()
    dataset.read()

    model = Model()
    model.load()
    model.train(dataset)
    model.evaluate(dataset)
    model.save()