文章目錄

前言
一、專案結構
二、資料集準備
三、網路模型
四、具體代碼
- 1.get_face.py
- 2.find_iu.py
- 3.spider_iu.py
- 4.file_deal.py
- 5.network.py
- 4.utils.py
總結

前言

出于興趣和對IU的喜愛，筆者花了幾天嘗試用爬蟲從百度圖片獲取韓國藝人的人物圖片并下載，利用深度神經網路從其中識別出IU（李知恩），并將IU的人物圖片保存至檔案夾，但是限于GPU性能無法使用太復雜的網路，最后識別的效果不佳，若讀者有興趣，也可以去替換一下網路，改善一下資料集，嘗試提升識別性能，
專案環境：python3.6

一、專案結構

在這里插入圖片描述

二、資料集準備

資料集準備分為兩步：1、獲取圖片，2、提取人臉，
1、獲取圖片
首先可以利用爬蟲，從百度圖片上批量下載圖片，但注意下載資料集所用的關鍵詞不要和之后識別任務的關鍵詞太接近，否則若有圖片重合，就會產生“識別得很準”的錯覺，下面的程式為爬蟲部分，在name.txt檔案中寫好要搜索的關鍵詞，即可使用，

# 爬蟲部分，存放到 name + ‘檔案’
    #############################################################################################
    if GET_PIC == 1:

        headers = {
            'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',
            'Connection': 'keep-alive',
            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:60.0) Gecko/20100101 Firefox/60.0',
            'Upgrade-Insecure-Requests': '1'
        }

        A = requests.Session()
        A.headers = headers


        tm = int(input('請輸入每類圖片的下載數量 '))
        numPicture = tm
        line_list = []
        with open('./name.txt', encoding='utf-8') as file:
            line_list = [k.strip() for k in file.readlines()]  # 用 strip()移除末尾的空格

        for word in line_list:
            url = 'https://image.baidu.com/search/flip?tn=baiduimage&ie=utf-8&word=' + word + '&pn='
            tot = Find(url, A)
            Recommend = recommend(url)  # 記錄相關推薦
            print('經過檢測%s類圖片共有%d張' % (word, tot))
            file = word + '檔案'
            y = os.path.exists(file)
            if y == 1:
                print('該檔案已存在，無需創建')
            else:
                os.mkdir(file)
            t = 0
            tmp = url
            while t < numPicture:
                try:
                    url = tmp + str(t)
                    # result = requests.get(url, timeout=10)
                    # 這里搞了下
                    result = A.get(url, timeout=10, allow_redirects=False)
                    print(url)
                except error.HTTPError as e:
                    print('網路錯誤，請調整網路后重試')
                    t = t + 60
                else:
                    dowmloadPicture(result.text, word)
                    t = t + 60
            numPicture = numPicture + tm

        print('當前搜索結束，開始提取人臉')
    #############################################################################################

下載圖片時要注意區分，將IU的圖片放在一個檔案夾下，Other的放在另一檔案夾下，訓練集和測驗集都要如此，如下圖所示：
在這里插入圖片描述
每個檔案夾內都是下圖形式：

在IU檔案夾內圖片如下所示：

對于檔案夾內檔案的命名，可以利用以下這段程式，按順序重命名，

import os

raw_train_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/train/IU/'
raw_train_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/train/Other/'
raw_test_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/test/IU/'
raw_test_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/test/Other/'
raw_roots = [raw_train_root_1, raw_train_root_2, raw_test_root_1, raw_test_root_2]

for path in raw_roots:

    # 獲取該目錄下所有檔案，存入串列中
    fileList = os.listdir(path)

    n = 0
    for i in fileList:
        # 設定舊檔案名（就是路徑+檔案名）
        oldname = path + os.sep + fileList[n]  # os.sep添加系統分隔符

        # 設定新檔案名
        newname = path + os.sep + str(n) + '.JPG'

        os.rename(oldname, newname)  # 用os模塊中的rename方法對檔案改名
        print(oldname, '======>', newname)

        n += 1

2、提取人臉

提取人臉需要用到一個人臉識別庫face_recognition庫，face_recognition庫的下載步驟參考：https://blog.csdn.net/qq_34020487/article/details/101871752
主要有三步，可以直接在anaconda的命令列界面復制使用：

pip install CMake -i https://pypi.douban.com/simple
pip install dlib==19.7.0 -i https://pypi.douban.com/simple
pip install face_recognition -i https://pypi.douban.com/simple

筆者已嘗試，確實可用，
使用下述的函式就可以獲得一張圖片對應的人臉，回傳值就是人臉圖片，

# 找到圖片中的人臉
#############################################################################################
def find_face(path):
    # Load the jpg file into a numpy array
    image = face_recognition.load_image_file(path)

    # Find all the faces in the image using the default HOG-based model.
    # This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
    # See also: find_faces_in_picture_cnn.py
    face_locations = face_recognition.face_locations(image) # 可以選擇 model="cnn"
    if len(face_locations) == 0:
        return None
    else:
        for face_location in face_locations:
            # Print the location of each face in this image
            top, right, bottom, left = face_location
            # You can access the actual face itself like this:
            face_image = image[top:bottom, left:right]
            pil_image = Image.fromarray(face_image)
            return pil_image
#############################################################################################

對資料集進行操作之后，就可以獲得處理后的人臉圖片，之所以不用人物圖訓練，而是提取出人臉后再進行訓練，是考慮到人物影像中干擾因素太多，且經過試驗后發現識別的效果非常差，于是加入這個提取人臉的環節，對資料集的操作代碼如下：

# 將訓練集和測驗集中的raw圖片處理，提取出人臉圖片
#############################################################################################
if __name__ == '__main__':  # 主函式入口

    raw_train_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/train/IU/'
    raw_train_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/train/Other/'
    raw_test_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/test/IU/'
    raw_test_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/test/Other/'
    raw_roots = [raw_train_root_1, raw_train_root_2, raw_test_root_1, raw_test_root_2]

    img_raw_train_1 = os.listdir(raw_train_root_1)
    img_raw_train_2 = os.listdir(raw_train_root_2)
    img_raw_test_1 = os.listdir(raw_test_root_1)
    img_raw_test_2 = os.listdir(raw_test_root_2)
    img_raws = [img_raw_train_1, img_raw_train_2, img_raw_test_1, img_raw_test_2]

    new_path_train_1 = 'E:/Table/學習資料集/find_iu/data/processed/train/IU/'
    new_path_train_2 = 'E:/Table/學習資料集/find_iu/data/processed/train/Other/'
    new_path_test_1 = 'E:/Table/學習資料集/find_iu/data/processed/test/IU/'
    new_path_test_2 = 'E:/Table/學習資料集/find_iu/data/processed/test/Other/'
    new_paths = [new_path_train_1, new_path_train_2, new_path_test_1, new_path_test_2]


    for raw_root, img_raw, new_path in zip(raw_roots, img_raws, new_paths):
        n = 0
        for i in range(len(img_raw)):
            try:
                img = Image.open(raw_root + img_raw[i])
            except:
                print('a file error, continue')
                continue
            else:
                img_train = find_face(raw_root + img_raw[i])
                if img_train == None:
                    continue
                else:
                    # img_train.save(new_path + '%d.JPG'%n)
                    # print(raw_root + img_raw[i])

                    n += 1
        print('在%d張圖片中，共找到%d張臉' % (len(img_raw), n))
#############################################################################################

處理前的圖片資料均存放在raw檔案夾中，處理后的存放在processed檔案夾中，如下圖：
在這里插入圖片描述
兩個檔案夾的內部結構完全一樣：

在這里插入圖片描述

三、網路模型

1、影像處理

將圖片裁剪為112×92大小，使用RGB影像，（這里試過用灰度影像，但好像效果不會更好，就放棄了），在對圖片進行歸一化處理，

data_transform = transforms.Compose([
        # transforms.Grayscale(num_output_channels=1),  # 彩色影像轉灰度影像num_output_channels默認1
        transforms.Resize(112),
        transforms.CenterCrop((112, 92)),  # 中心裁剪為112*92
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
        # transforms.Normalize(mean=0.5, std=0.5)
    ])

使用孿生神經網路(Siamese Network)

class SiameNetwork(nn.Module):
    def __init__(self):
        super(SiameNetwork, self).__init__()

        # input: h=112, w=92

        self.conv1 = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels=3,  # 輸入單通道
                            out_channels=16,  # 16個3*3卷積核
                            kernel_size=3,  # 卷積核尺寸
                            stride=2,  # 卷積核滑動步長, 1的話圖片大小不變，2的話會大小會變為(h/2)*(w/2)
                            padding=1),  # 邊緣填充大小，如果要保持原大小，kernel_size//2
            torch.nn.BatchNorm2d(16),  # 標準化，前面卷積后有16個圖層
            torch.nn.ReLU()  # 激活函式
        )  # output: h=56, w=46
        self.conv2 = torch.nn.Sequential(
            torch.nn.Conv2d(16, 32, 3, 2, 1),
            torch.nn.BatchNorm2d(32),
            torch.nn.ReLU()
        )  # output: h=28, w=23
        self.conv3 = torch.nn.Sequential(
            torch.nn.Conv2d(32, 64, 3, 2, 1),
            torch.nn.BatchNorm2d(64),
            torch.nn.ReLU()
        )  # output: h=14, w=12
        self.conv4 = torch.nn.Sequential(
            torch.nn.Conv2d(64, 64, 2, 2, 0),
            torch.nn.BatchNorm2d(64),
            torch.nn.ReLU()
        )  # output: h=7, w=6
        self.mlp1 = torch.nn.Linear(7 * 6 * 64, 100)  # 需要計算conv4的輸出尺寸，每次卷積的輸出尺寸(size - kernal + 2*padding)/stride + 1
        self.mlp2 = torch.nn.Linear(100, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.mlp1(x.view(x.size(0), -1))  # view展平
        x = self.mlp2(x)
        return x

四、具體代碼

1.get_face.py

from PIL import Image
import face_recognition
import os

# 找到圖片中的人臉
#############################################################################################
def find_face(path):
    # Load the jpg file into a numpy array
    image = face_recognition.load_image_file(path)

    # Find all the faces in the image using the default HOG-based model.
    # This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
    # See also: find_faces_in_picture_cnn.py
    face_locations = face_recognition.face_locations(image) # 可以選擇 model="cnn"
    if len(face_locations) == 0:
        return None
    else:
        for face_location in face_locations:
            # Print the location of each face in this image
            top, right, bottom, left = face_location
            # You can access the actual face itself like this:
            face_image = image[top:bottom, left:right]
            pil_image = Image.fromarray(face_image)
            return pil_image
#############################################################################################

# 將訓練集和測驗集中的raw圖片處理，提取出人臉圖片
#############################################################################################
if __name__ == '__main__':  # 主函式入口

    raw_train_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/train/IU/'
    raw_train_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/train/Other/'
    raw_test_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/test/IU/'
    raw_test_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/test/Other/'
    raw_roots = [raw_train_root_1, raw_train_root_2, raw_test_root_1, raw_test_root_2]

    img_raw_train_1 = os.listdir(raw_train_root_1)
    img_raw_train_2 = os.listdir(raw_train_root_2)
    img_raw_test_1 = os.listdir(raw_test_root_1)
    img_raw_test_2 = os.listdir(raw_test_root_2)
    img_raws = [img_raw_train_1, img_raw_train_2, img_raw_test_1, img_raw_test_2]

    new_path_train_1 = 'E:/Table/學習資料集/find_iu/data/processed/train/IU/'
    new_path_train_2 = 'E:/Table/學習資料集/find_iu/data/processed/train/Other/'
    new_path_test_1 = 'E:/Table/學習資料集/find_iu/data/processed/test/IU/'
    new_path_test_2 = 'E:/Table/學習資料集/find_iu/data/processed/test/Other/'
    new_paths = [new_path_train_1, new_path_train_2, new_path_test_1, new_path_test_2]


    for raw_root, img_raw, new_path in zip(raw_roots, img_raws, new_paths):
        n = 0
        for i in range(len(img_raw)):
            try:
                img = Image.open(raw_root + img_raw[i])
            except:
                print('a file error, continue')
                continue
            else:
                img_train = find_face(raw_root + img_raw[i])
                if img_train == None:
                    continue
                else:
                    # img_train.save(new_path + '%d.JPG'%n)
                    # print(raw_root + img_raw[i])

                    n += 1
        print('在%d張圖片中，共找到%d張臉' % (len(img_raw), n))
#############################################################################################

2.find_iu.py

import torch
import torchvision
import torch.nn as nn
from torch.autograd import Variable
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import cv2   #opencv庫，用于圖片可視化
import numpy as np
import os
from utils import draw_result
from network import SiameNetwork
from get_face import find_face


if __name__ == '__main__':  # 主函式入口
    # 設定引數
    #############################################################################################
    path = 'E:/Table/學習資料集/find_iu/result/'    # 存放和生成結果的路徑標志
    epochs = 20       #訓練周期
    BATCH_SIZE = 16    #批量樣本大小
    NUM_WORKERS = 0
    #############################################################################################




    # 資料處理
    #############################################################################################
    data_transform = transforms.Compose([
        # transforms.Grayscale(num_output_channels=1),  # 彩色影像轉灰度影像num_output_channels默認1
        transforms.Resize(112),
        transforms.CenterCrop((112, 92)),  # 中心裁剪為112*92
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
        # transforms.Normalize(mean=0.5, std=0.5)
    ])

    train_dataset = datasets.ImageFolder(root = r'E:/Table/學習資料集/find_iu/data/processed/train',
                                         transform = data_transform)
    test_dataset = datasets.ImageFolder(root = r'E:/Table/學習資料集/find_iu/data/processed/test',
                                         transform = data_transform)

    train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS)
    test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS)

    image, labels = next(iter(train_loader))       #資料可視化

    img = torchvision.utils.make_grid(image, nrow = 10)
    img = img.numpy().transpose(1, 2, 0)
    cv2.imshow('img', img)    #展示影像
    cv2.waitKey(0)  #按下任一按鍵后開始作業

    print("data ready!")
    #############################################################################################

    #配置設備、損失函式和優化器
    #############################################################################################
    device = torch.device('cuda')
    model = SiameNetwork().to(device)
    cost = torch.nn.CrossEntropyLoss()        #定義損失函式，使用交叉熵
    optimizer = torch.optim.Adam(model.parameters(), lr=0.0008, weight_decay=0.001)            #Adam優化器
    print("device ready!")
    #############################################################################################

    #訓練程序，訓練周期由epochs決定
    #############################################################################################

    draw_epoch = []   #記錄訓練階段
    draw_loss = []    #記錄訓練損失，用于繪制
    draw_train_acc = []   #記錄訓練準確度，用于繪制
    draw_val_loss = []   #記錄測驗損失，用于繪制
    draw_val_acc = []  # 記錄測驗準確度，用于繪制
    for epoch in range(epochs):
        #訓練程序
        sum_loss = 0.0
        sum_val_loss = 0.0
        train_correct = 0
        test_correct = 0
        for data in train_loader:
            inputs,labels = data
            inputs,labels = Variable(inputs).cuda(),Variable(labels).cuda()
            optimizer.zero_grad()        #將上一batch梯度清零
            outputs = model(inputs)
            loss = cost(outputs, labels)
            loss.backward()             #反向傳播
            optimizer.step()

            _, id = torch.max(outputs.data, 1)
            sum_loss += loss.data
            train_correct += torch.sum(id == labels.data)

        for data in test_loader:              # 模型測驗
            inputs,labels = data
            inputs,labels = Variable(inputs).cuda(),Variable(labels).cuda()
            outputs = model(inputs)
            val_loss = cost(outputs, labels)
            _,id = torch.max(outputs.data, 1)
            sum_val_loss += val_loss.data
            test_correct += torch.sum(id == labels.data)


        print('[%d,%d] train loss:%.03f      train acc:%.03f%%'
              %(epoch + 1, epochs, sum_loss / len(train_loader), (100 * train_correct / len(train_dataset))))
        print('        val loss:%.03f        val acc:%.03f%%'
              %(sum_val_loss / len(test_loader), (100 * test_correct / len(test_dataset))))


        draw_epoch.append(epoch+1)       # 用于后續畫圖的資料
        draw_loss.append(sum_loss / len(train_loader))
        draw_train_acc.append(100 * train_correct / len(train_dataset))
        draw_val_loss.append(sum_val_loss / len(test_loader))
        draw_val_acc.append(100 * test_correct / len(test_dataset))

        np.savetxt('%s/train_loss.txt'%(path), draw_loss, fmt="%.3f")        # 保存損失資料
        np.savetxt('%s/train_acc.txt'%(path), draw_train_acc, fmt="%.3f")  # 保存準確率資料
        np.savetxt('%s/val_loss.txt'%(path), draw_val_loss, fmt="%.3f")     # 保存損失資料
        np.savetxt('%s/val_acc.txt'%(path), draw_val_acc, fmt="%.3f")  # 保存準確率資料

    print("train ready!")
    #############################################################################################

    #資料可視化
    #############################################################################################
    draw_result(draw_epoch, path)   # 繪圖函式

    print("draw ready!")
    #############################################################################################

    #模型的存盤和載入
    #############################################################################################
    torch.save(model.state_dict(), "parameter.pkl") #save

    print("save ready!")
    #############################################################################################

3.spider_iu.py

import re
import requests
from urllib import error
from bs4 import BeautifulSoup
import os
import torch
from torch.autograd import Variable
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
from network import SiameNetwork
from utils import cv_imread
import cv2
from PIL import Image
import shutil
from get_face import find_face

# 設定引數
#############################################################################################
GET_PIC = 0    # 1 執行這步，0 不執行
GET_FACE = 0
GET_IU = 1
#############################################################################################
num = 0
numPicture = 0
file = ''
List = []

# 爬蟲所用函式
#############################################################################################
def Find(url, A):
    global List
    print('正在檢測圖片總數，請稍等.....')
    t = 0
    i = 1
    s = 0
    while t < 1000:
        Url = url + str(t)
        try:
            # 這里搞了下
            Result = A.get(Url, timeout=7, allow_redirects=False)
        except BaseException:
            t = t + 60
            continue
        else:
            result = Result.text
            pic_url = re.findall('"objURL":"(.*?)",', result, re.S)  # 先利用正則運算式找到圖片url
            s += len(pic_url)
            if len(pic_url) == 0:
                break
            else:
                List.append(pic_url)
                t = t + 60
    return s


def recommend(url):
    Re = []
    try:
        html = requests.get(url, allow_redirects=False)
    except error.HTTPError as e:
        return
    else:
        html.encoding = 'utf-8'
        bsObj = BeautifulSoup(html.text, 'html.parser')
        div = bsObj.find('div', id='topRS')
        if div is not None:
            listA = div.findAll('a')
            for i in listA:
                if i is not None:
                    Re.append(i.get_text())
        return Re


def dowmloadPicture(html, keyword):
    global num
    # t =0
    pic_url = re.findall('"objURL":"(.*?)",', html, re.S)  # 先利用正則運算式找到圖片url
    print('找到關鍵詞:' + keyword + '的圖片，即將開始下載圖片...')
    for each in pic_url:
        print('正在下載第' + str(num + 1) + '張圖片，圖片地址:' + str(each))
        try:
            if each is not None:
                pic = requests.get(each, timeout=7)
            else:
                continue
        except BaseException:
            print('錯誤，當前圖片無法下載')
            continue
        else:
            string = file + r'\\' + keyword + '_' + str(num) + '.jpg'
            fp = open(string, 'wb')
            fp.write(pic.content)
            fp.close()
            num += 1
        if num >= numPicture:
            return
#############################################################################################



if __name__ == '__main__':  # 主函式入口

    # 爬蟲部分，存放到 name + ‘檔案’
    #############################################################################################
    if GET_PIC == 1:

        headers = {
            'Accept-Language': 'zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2',
            'Connection': 'keep-alive',
            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:60.0) Gecko/20100101 Firefox/60.0',
            'Upgrade-Insecure-Requests': '1'
        }

        A = requests.Session()
        A.headers = headers


        tm = int(input('請輸入每類圖片的下載數量 '))
        numPicture = tm
        line_list = []
        with open('./name.txt', encoding='utf-8') as file:
            line_list = [k.strip() for k in file.readlines()]  # 用 strip()移除末尾的空格

        for word in line_list:
            url = 'https://image.baidu.com/search/flip?tn=baiduimage&ie=utf-8&word=' + word + '&pn='
            tot = Find(url, A)
            Recommend = recommend(url)  # 記錄相關推薦
            print('經過檢測%s類圖片共有%d張' % (word, tot))
            file = word + '檔案'
            y = os.path.exists(file)
            if y == 1:
                print('該檔案已存在，無需創建')
            else:
                os.mkdir(file)
            t = 0
            tmp = url
            while t < numPicture:
                try:
                    url = tmp + str(t)
                    # result = requests.get(url, timeout=10)
                    # 這里搞了下
                    result = A.get(url, timeout=10, allow_redirects=False)
                    print(url)
                except error.HTTPError as e:
                    print('網路錯誤，請調整網路后重試')
                    t = t + 60
                else:
                    dowmloadPicture(result.text, word)
                    t = t + 60
            numPicture = numPicture + tm

        print('當前搜索結束，開始提取人臉')
    #############################################################################################

    # 將訓練集和測驗集中的raw圖片處理，提取出人臉圖片,從file+'檔案'到‘待分辨人臉’
    ############################################################################################
    if GET_FACE == 1:
        if GET_PIC == 0:
            file = '韓國女藝人檔案'
        raw_root = 'E:/Table/學習資料集/find_iu/'+ file + '/'
        img_raw = os.listdir(raw_root)
        new_path = 'E:/Table/學習資料集/find_iu/待分辨人臉/'

        n = 0
        for i in range(len(img_raw)):
            try:
                img = Image.open(raw_root + img_raw[i])
            except:
                print('a file error, continue')
                continue
            else:
                img_train = find_face(raw_root + img_raw[i])
                if img_train == None:
                    continue
                else:
                    img_train.save(new_path + '%d.JPG' % n)
                    print(raw_root + img_raw[i])
                    n += 1
        print('在%d張圖片中，共找到%d張臉' % (len(img_raw), n))
        print('提取人臉結束，開始尋找IU')
    #############################################################################################

    # 開始判別，從’待分辨人臉‘中找出IU存放到’IU_pic‘
    #############################################################################################
    if GET_IU == 1:

        data_transform = transforms.Compose([
            # transforms.Grayscale(num_output_channels=1),  # 彩色影像轉灰度影像num_output_channels默認1
            transforms.Resize(112),
            transforms.CenterCrop((112, 92)),  # 中心裁剪為112*92
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5],std=[0.5, 0.5, 0.5])
            # transforms.Normalize(mean=0.5, std=0.5)
        ])

        device = torch.device('cuda')
        model = SiameNetwork().to(device)
        model.load_state_dict(torch.load('parameter.pkl'))  # load
        model.eval()

        judge_root = 'E:/Table/學習資料集/find_iu/待分辨人臉/'
        img_judge = os.listdir(judge_root)
        new_path = 'E:/Table/學習資料集/find_iu/IU_pic/'

        result = []
        n = 0
        for i in range(len(img_judge)):
            try:
                img = Image.open(judge_root + img_judge[i])
            except:
                print('a file error, continue')
                continue
            else:
                img = img.convert('RGB')
                print(judge_root + img_judge[i])
                input = data_transform(img)
                input = input.unsqueeze(0)  # 這里經過轉換后輸出的input格式是[C,H,W],網路輸入還需要增加一維批量大小B
                # 增加一維，輸出的img格式為[1,C,H,W]
                input = Variable(input.cuda())
                output = model(input)  # 將圖片輸入網路得到輸出
                _, id = torch.max(output.data, 1)   # 0是IU,1是其他
                if id.item() == 0:
                    shutil.copy(judge_root + img_judge[i], new_path)
                    n += 1

        print('/n在%d張圖片中，共找到%d張IU的圖片'%(len(img_judge), n))
    #############################################################################################

4.file_deal.py

import os

raw_train_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/train/IU/'
raw_train_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/train/Other/'
raw_test_root_1 = 'E:/Table/學習資料集/find_iu/data/raw/test/IU/'
raw_test_root_2 = 'E:/Table/學習資料集/find_iu/data/raw/test/Other/'
raw_roots = [raw_train_root_1, raw_train_root_2, raw_test_root_1, raw_test_root_2]

for path in raw_roots:

    # 獲取該目錄下所有檔案，存入串列中
    fileList = os.listdir(path)

    n = 0
    for i in fileList:
        # 設定舊檔案名（就是路徑+檔案名）
        oldname = path + os.sep + fileList[n]  # os.sep添加系統分隔符

        # 設定新檔案名
        newname = path + os.sep + str(n) + '.JPG'

        os.rename(oldname, newname)  # 用os模塊中的rename方法對檔案改名
        print(oldname, '======>', newname)

        n += 1

5.network.py

import torch
import torch.nn as nn

class SiameNetwork(nn.Module):
    def __init__(self):
        super(SiameNetwork, self).__init__()

        # input: h=112, w=92

        self.conv1 = torch.nn.Sequential(
            torch.nn.Conv2d(in_channels=3,  # 輸入單通道
                            out_channels=16,  # 16個3*3卷積核
                            kernel_size=3,  # 卷積核尺寸
                            stride=2,  # 卷積核滑動步長, 1的話圖片大小不變，2的話會大小會變為(h/2)*(w/2)
                            padding=1),  # 邊緣填充大小，如果要保持原大小，kernel_size//2
            torch.nn.BatchNorm2d(16),  # 標準化，前面卷積后有16個圖層
            torch.nn.ReLU()  # 激活函式
        )  # output: h=56, w=46
        self.conv2 = torch.nn.Sequential(
            torch.nn.Conv2d(16, 32, 3, 2, 1),
            torch.nn.BatchNorm2d(32),
            torch.nn.ReLU()
        )  # output: h=28, w=23
        self.conv3 = torch.nn.Sequential(
            torch.nn.Conv2d(32, 64, 3, 2, 1),
            torch.nn.BatchNorm2d(64),
            torch.nn.ReLU()
        )  # output: h=14, w=12
        self.conv4 = torch.nn.Sequential(
            torch.nn.Conv2d(64, 64, 2, 2, 0),
            torch.nn.BatchNorm2d(64),
            torch.nn.ReLU()
        )  # output: h=7, w=6
        self.mlp1 = torch.nn.Linear(7 * 6 * 64, 100)  # 需要計算conv4的輸出尺寸，每次卷積的輸出尺寸(size - kernal + 2*padding)/stride + 1
        self.mlp2 = torch.nn.Linear(100, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.mlp1(x.view(x.size(0), -1))  # view展平
        x = self.mlp2(x)
        return x

4.utils.py

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import cv2

# 繪制訓練、測驗的損失、準確度
#############################################################################################
def draw_result(draw_epoch, path):

    show_loss = np.loadtxt('%s/train_loss.txt' % (path))   # 讀取txt檔案，不同優化器的損失
    show_train_acc = np.loadtxt('%s/train_acc.txt' % (path))  # 讀取不同模型的準確度
    show_val_loss = np.loadtxt('%s/val_loss.txt' % (path))  # 讀取txt檔案，不同優化器的損失
    show_val_acc = np.loadtxt('%s/val_acc.txt' % (path))  # 讀取不同模型的準確度

    mpl.rc('font',family='Times New Roman', weight='semibold', size=9)  # 設定matplotlib中所有繪圖風格的設定
    font1 = {'weight' : 'semibold', 'size' : 11}  #設定文字風格

    fig = plt.figure(figsize = (7,5))    #figsize是圖片的大小`

    ax1 = fig.add_subplot(2, 2, 1)       # ax1是子圖的名字
    ax1.plot(draw_epoch, show_loss,color = 'red', label = u'AdaPID', linewidth =1.0)
    ax1.legend()   #顯示圖例
    ax1.set_title('Training Loss', font1)
    ax1.set_xlabel(u'Epoch', font1)

    ax2 = fig.add_subplot(2, 2, 2)
    ax2.plot(draw_epoch, show_val_loss,color = 'red', label = u'Adam', linewidth =1.0)
    ax2.legend()   #顯示圖例
    ax2.set_title('Validation Loss', font1)
    ax2.set_xlabel(u'Epoch', font1)

    ax3 = fig.add_subplot(2, 2, 3)
    ax3.plot(draw_epoch, show_train_acc,color = 'red', label = u'Adam', linewidth =1.0)
    ax3.legend()   #顯示圖例
    ax3.set_title('Training Accuracy', font1)
    ax3.set_xlabel(u'Epoch', font1)

    ax4 = fig.add_subplot(2, 2, 4)
    ax4.plot(draw_epoch, show_val_acc,color = 'red', label = u'Adam', linewidth =1.0)
    ax4.legend()   #顯示圖例
    ax4.set_title('Validation Accuracy', font1)
    ax4.set_xlabel(u'Epoch', font1)

    plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=0.45) # hspace為子圖上下間距
    plt.savefig('%s/show_curve.jpg' % (path), dpi=300)
#############################################################################################

# 用于解決cv.imread不能讀取中文路徑的問題
#############################################################################################
def cv_imread(filePath):
    # 核心就是下面這句，一般直接用這句就行，直接把圖片轉為mat資料
    cv_img = cv2.imdecode(np.fromfile(filePath, dtype=np.uint8), -1)
    # imdecode讀取的是rgb，如果后續需要opencv處理的話，需要轉換成bgr，轉換后圖片顏色會變化
    # cv_img=cv2.cvtColor(cv_img,cv2.COLOR_RGB2BGR)
    return cv_img
#############################################################################################

總結

總體而言，這是一個新人的興趣之作，雖然失敗了但是做的程序還蠻有趣的，主要是程序中可以看到iu，哈哈，

轉載請註明出處，本文鏈接：https://www.uj5u.com/houduan/276192.html

標籤：python

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人臉識別IU（李知恩）