笑脸数据集和口罩数据集

灰太狼 2023-02-25 07:15 189阅读 0赞

### 文章目录 ###

*  掌握笑脸数据集(genki4k)正负样本的划分、模型训练和测试的过程(至少包括SVM、CNN
 *  训练数据集
 *  需要导入的库：
 *  对数据集设置训练测试集、正负样本路径
 *  定义打印出训练集和测试集的正负样本尺寸函数
 *  输入函数（函数包括对模型的训练，模型优化、和模型保存、以及模型精度）
 *  摄像头采集人脸：
 *  口罩数据集

# 掌握笑脸数据集(genki4k)正负样本的划分、模型训练和测试的过程(至少包括SVM、CNN #

# 训练数据集 #

导入keras：  
![在这里插入图片描述][20200710213744911.png]

# 需要导入的库： #

import keras
    import os, shutil
    from keras import layers
    from keras import models
    from keras import optimizers
    from keras.preprocessing.image import ImageDataGenerator
    import matplotlib.pyplot as plt
    ————————————————

效果：  
![在这里插入图片描述][20200710214538694.png]

# 对数据集设置训练测试集、正负样本路径 #

train_dir='./smile/train'
    train_smiles_dir='./smile/train/smile'
    train_unsmiles_dir='./smile/train/unsmile'
    test_dir='./smile/test'
    test_smiles_dir='./smile/test/smile'
    test_unsmiles_dir='./smile/test/unsmile'

![在这里插入图片描述][20200710221132636.png]

# 定义打印出训练集和测试集的正负样本尺寸函数 #

![在这里插入图片描述][20200710222426666.png]\# 定义构建小型卷积网络并进行数据集预处理函数

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70]

# 输入函数（函数包括对模型的训练，模型优化、和模型保存、以及模型精度） #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 1]

整体函数运行之后，得到如下：  
训练模型精度及损失：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 2]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 3]  
在本地文件生成了：  
![在这里插入图片描述][20200711001609729.png]

# 摄像头采集人脸： #

#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 Image
    model = load_model('./smile.h5')
    detector = dlib.get_frontal_face_detector()
    video=cv2.VideoCapture(0)
    font = cv2.FONT_HERSHEY_SIMPLEX
    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='smile'
                else:
                    result='unsmile'
                cv2.putText(img, result, (left,top), font, 2, (0, 255, 0), 2, cv2.LINE_AA)
            cv2.imshow('myself', img)
    while video.isOpened():
        res, img_rd = video.read()
        if not res:
            break
        rec(img_rd)
        if cv2.waitKey(5) & 0xFF == ord('q'):
            break
    video.release()
    cv2.destroyAllWindows()

实现结果：

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 4]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 5]

# 口罩数据集 #

通过如下python代码，修改正样本数据集的像素为20x20：

#修改正样本像素
    import pandas as pd
    import cv2
    for n in range(1000,1606):#代表正数据集中开始和结束照片的数字
        path='F:\\mask\\have_mask\\'+str(n)+'.jpg'
        # 读取图片
        img = cv2.imread(path)
        img=cv2.resize(img,(20,20)) #修改样本像素为20x20
        cv2.imwrite('F:\\mask\\have_mask\\' + str(n) + '.jpg', img)
        n += 1

同样的，修改负样本的像素为60x60：

#修改负样本像素
    import pandas as pd
    import cv2
    for n in range(10000,11790):#代表负样本数据集中开始和结束照片的数字
        path='F:\\mask\\no_mask\\'+str(n)+'.jpg'
        # 读取图片
        img = cv2.imread(path)
        img=cv2.resize(img,(60,60)) #修改样本像素为60x60
        cv2.imwrite('F:\\mask\\no_mask\\' + str(n) + '.jpg', img)
        n += 1

需要通过对txt文档进行预处理，向have\_mask.txt文件没行中加入 1 0 0 20 20

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 6]  
负样本文件预处理 没行目录结尾加入 1 0 0 60 60：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 7]  
双击traincascade.bat文件，进行人脸口罩模型训练  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 8]

最后生成了如下：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 9]  
xml文件则生成：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 10]  
代码:

import cv2
    detector= cv2.CascadeClassifier('C:/Users\zys/Anaconda3/Lib/site-packages/cv2\data/haarcascade_frontalface_default.xml')
    mask_detector=cv2.CascadeClassifier('F:/mask/xml/cascade.xml')
    cap = cv2.VideoCapture(0)
    while True:
        ret, img = cap.read()
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        faces = detector.detectMultiScale(gray, 1.1, 3)
        for (x, y, w, h) in faces:
            #参数分别为 图片、左上角坐标，右下角坐标，颜色，厚度
            face=img[y:y+h,x:x+w]  # 裁剪坐标为[y0:y1, x0:x1]
            mask_face=mask_detector.detectMultiScale(gray, 1.1, 5)
            for (x2,y2,w2,h2) in mask_face:
                cv2.rectangle(img, (x2, y2), (x2 + w2, y2 + h2), (0, 0, 255), 2)
        cv2.imshow('mask', img)
        cv2.waitKey(3)
    cap.release()
    cv2.destroyAllWindows()

运行结果：  
戴口罩识别了出来：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 11]  
没戴口罩，识别不出来：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 12]  
参考地址：

https://blog.csdn.net/cj151525/article/details/104984897

https://blog.csdn.net/qq\_42451251/article/details/107057643

https://blog.csdn.net/weixin\_44436677/article/details/107203449?%3E

[20200710213744911.png]: /images/20230209/68bdf932470b451c9df4e63d0693517e.png
[20200710214538694.png]: https://img-blog.csdnimg.cn/20200710214538694.png
[20200710221132636.png]: https://img-blog.csdnimg.cn/20200710221132636.png
[20200710222426666.png]: https://img-blog.csdnimg.cn/20200710222426666.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70]: https://img-blog.csdnimg.cn/20200710222544556.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20200711000610441.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 3]: https://img-blog.csdnimg.cn/20200711000625909.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
[20200711001609729.png]: https://img-blog.csdnimg.cn/20200711001609729.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI_size_16_color_FFFFFF_t_70 5]: https://img-blog.csdnimg.cn/20200711013329483.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L0RlZmluaXRlTXI=,size_16,color_FFFFFF,t_70
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