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keras实现简单性别识别(二分类问题)

简介: 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。
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keras实现简单性别识别(二分类问题)

第一步:准备好需要的库

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

 

 1 import shutil
 2 
 3 # 读取文件中图片信息根据性别分类图片到对应目录中
 4 dirroot = "D:\\Users\\a\\Pictures\\adience"
 5 f = open(dirroot+"\\fold_frontal_3_data.txt","r")
 6 i = 0
 7 
 8 for line in f.readlines():
 9     line = line.split()
10     dir = line[0]
11 
12     imgName = "landmark_aligned_face."+ line[2] +'.'+ line[1]
13     if i > 0:
14         if line[5]== "f":
15             print("female")
16             shutil.copy(dirroot+'\\faces\\'+dir+'\\'+imgName, "D:\\pycode\\learn\\data\\validation\\"+imgName)
17         #     移动图片到female目录
18         elif line[5]=="m":
19             print("male")
20             shutil.copy(dirroot+'\\faces\\'+dir+'\\'+imgName, "D:\\pycode\\learn\\data\\validation\\"+imgName)
21         #     移动图片到male目录
22         else:
23             print("N")
24             # 未识别男女
25     i += 1
26 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。

 1 class Dataset(object):
 2 
 3     def __init__(self):
 4         self.train = None
 5         self.valid = None
 6 
 7 
 8     def read(self, img_rows=IMAGE_SIZE, img_cols=IMAGE_SIZE):
 9         train_datagen = ImageDataGenerator(
10             rescale=1. / 255,
11             horizontal_flip=True)
12 
13         test_datagen = ImageDataGenerator(rescale=1. / 255)
14 
15         train_generator = train_datagen.flow_from_directory(
16             train_data_dir,
17             target_size=(img_rows, img_cols),
18             batch_size=batch_size,
19             class_mode='binary')
20 
21         validation_generator = test_datagen.flow_from_directory(
22             validation_data_dir,
23             target_size=(img_rows, img_cols),
24             batch_size=batch_size,
25             class_mode='binary')
26 
27         self.train = train_generator
28         self.valid = validation_generator

第三部:网络

 1 class Model(object):
 2 
 3 
 4 
 5     def __init__(self):
 6         self.model = Sequential()
 7         self.model.add(Conv2D(32, (3, 3), input_shape=(IMAGE_SIZE,IMAGE_SIZE,3)))
 8         self.model.add(Activation('relu'))
 9         self.model.add(MaxPooling2D(pool_size=(2, 2)))
10 
11         self.model.add(Conv2D(32, (3, 3)))
12         self.model.add(Activation('relu'))
13         self.model.add(MaxPooling2D(pool_size=(2, 2)))
14 
15         self.model.add(Conv2D(64, (3, 3)))
16         self.model.add(Activation('relu'))
17         self.model.add(MaxPooling2D(pool_size=(2, 2)))
18 
19         self.model.add(Conv2D(64, (3, 3)))
20         self.model.add(Activation('relu'))
21         self.model.add(MaxPooling2D(pool_size=(2, 2)))
22 
23         self.model.add(Flatten())
24         self.model.add(Dense(64))
25         self.model.add(Activation('relu'))
26         self.model.add(Dropout(0.85))
27         self.model.add(Dense(1))
28         self.model.add(Activation('sigmoid'))
29 
30 
31     def train(self, dataset, batch_size=batch_size, nb_epoch=epochs):
32 
33         self.model.compile(loss='binary_crossentropy',
34                       optimizer='adam',
35                       metrics=['accuracy'])
36         self.model.fit_generator(dataset.train,
37                                  steps_per_epoch=nb_train_samples // batch_size,
38                                  epochs=epochs,
39                                  validation_data=dataset.valid,
40                                  validation_steps=nb_validation_samples//batch_size)
41 
42 
43     def save(self, file_path=FILE_PATH):
44         print('Model Saved.')
45         self.model.save_weights(file_path)
46 
47     def load(self, file_path=FILE_PATH):
48         print('Model Loaded.')
49         self.model.load_weights(file_path)
50 
51     def predict(self, image):
52         # 预测样本分类
53         img = image.resize((1, IMAGE_SIZE, IMAGE_SIZE, 3))
54         img = image.astype('float32')
55         img /= 255
56 
57         #归一化
58         result = self.model.predict(img)
59         print(result)
60         # 概率
61         result = self.model.predict_classes(img)
62         print(result)
63         # 0/1
64 
65         return result[0]
66 
67     def evaluate(self, dataset):
68         # 测试样本准确率
69         score = self.model.evaluate_generator(dataset.valid,steps=2)
70         print("样本准确率%s: %.2f%%" % (self.model.metrics_names[1], score[1] * 100))

第四部:主程序

 1 if __name__ == '__main__':
 2     dataset = Dataset()
 3     dataset.read()
 4 
 5 
 6     model = Model()
 7     model.load()
 8     model.train(dataset)
 9     model.evaluate(dataset)
10     model.save()

第五步:识别程序

opencv检测模块版

 1 #!/usr/bin/env python
 2 """
 3 从摄像头中获取图像实时监测
 4 """
 5 import numpy as np
 6 import cv2
 7 from GenderTrain import Model
 8 
 9 
10 def detect(img, cascade):
11     """
12     检测图像是否含有人脸部分
13     :param img: 待检测帧图像
14     :param cascade: 面部对象检测器
15     :return: 面部图像标记
16     """
17     rects = cascade.detectMultiScale(img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30),
18                                      flags=cv2.CASCADE_SCALE_IMAGE)
19     if len(rects) == 0:
20         return []
21     rects[:,2:] += rects[:,:2]
22     return rects
23 
24 
25 def draw_rects(img, rects, color):
26     """
27     根据图像标记人脸区域与性别
28     :param img: 
29     :param rects: 
30     :param color: 
31     :return: 
32     """
33     for x, y, w, h in rects:
34         face = img[x:x+w,y:y+h]
35         face = cv2.resize(face,(224,224))
36         if gender.predict(face)==1:
37             text = "Male"
38         else:
39             text = "Female"
40         cv2.rectangle(img, (x, y), (w, h), color, 2)
41         cv2.putText(img, text, (x, h), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 255, 255), lineType=cv2.LINE_AA)
42 
43 
44 if __name__ == '__main__':
45     haar__cascade_path = "D:\\opencv\\sources\\data\\haarcascades\\haarcascade_frontalface_default.xml"
46 
47     cascade = cv2.CascadeClassifier( haar__cascade_path)
48     cam = cv2.VideoCapture(0)
49     # 获取摄像头视频
50     gender = Model()
51     gender.load()
52     # 加载性别模型
53     while True:
54         ret, img = cam.read()
55         # 读取帧图像
56         rects = detect(img, cascade)
57         print(rects)
58         vis = img.copy()
59         draw_rects(vis, rects, (0, 255, 0))
60         cv2.imshow('Gender', vis)
61         if cv2.waitKey(5) == 27:
62             break
63     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()

 

 

 

 

 

 

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