深度学习应用2——果蔬分类应用

注意：使用tensorflow2.3以上的版本

参考网址：https://blog.csdn.net/ECHOSON/article/details/117600329

通过爬取百度的图片，准备数据集，会保存在data文件中

import requests
import re
import os

headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36'}
name = input('请输入要爬取的图片类别：')
num = 0
num_1 = 0
num_2 = 0
x = input('请输入要爬取的图片数量？（1等于60张图片，2等于120张图片）：')
list_1 = []
for i in range(int(x)):
    name_1 = os.getcwd()
    name_2 = os.path.join(name_1, 'data/' + name)
    url = 'https://image.baidu.com/search/flip?tn=baiduimage&ie=utf-8&word=' + name + '&pn=' + str(i * 30)
    res = requests.get(url, headers=headers)
    htlm_1 = res.content.decode()
    a = re.findall('"objURL":"(.*?)",', htlm_1)
    if not os.path.exists(name_2):
        os.makedirs(name_2)
    for b in a:
        try:
            b_1 = re.findall('https:(.*?)&', b)
            b_2 = ''.join(b_1)
            if b_2 not in list_1:
                num = num + 1
                img = requests.get(b)
                f = open(os.path.join(name_1, 'data/' + name, name + str(num) + '.jpg'), 'ab')
                print('---------正在下载第' + str(num) + '张图片----------')
                f.write(img.content)
                f.close()
                list_1.append(b_2)
            elif b_2 in list_1:
                num_1 = num_1 + 1
                continue
        except Exception as e:
            print('---------第' + str(num) + '张图片无法下载----------')
            num_2 = num_2 + 1
            continue

print('下载完成,总共下载{}张,成功下载:{}张,重复下载:{}张,下载失败:{}张'.format(num + num_1 + num_2, num, num_1, num_2))

划分数据集

# 作者: 宋老狗
import os
import random
from shutil import copy2


def data_set_split(src_data_folder, target_data_folder, train_scale=0.8, val_scale=0.0, test_scale=0.2):
    '''
    读取源数据文件夹，生成划分好的文件夹，分为trian、val、test三个文件夹进行
    :param src_data_folder: 源文件夹 E:/biye/gogogo/note_book/torch_note/data/utils_test/data_split/src_data
    :param target_data_folder: 目标文件夹 E:/biye/gogogo/note_book/torch_note/data/utils_test/data_split/target_data
    :param train_scale: 训练集比例
    :param val_scale: 验证集比例
    :param test_scale: 测试集比例
    :return:
    '''
    print("开始数据集划分")
    class_names = os.listdir(src_data_folder)
    # 在目标目录下创建文件夹
    split_names = ['train', 'val', 'test']
    for split_name in split_names:
        split_path = os.path.join(target_data_folder, split_name)
        if os.path.isdir(split_path):
            pass
        else:
            os.mkdir(split_path)
        # 然后在split_path的目录下创建类别文件夹
        for class_name in class_names:
            class_split_path = os.path.join(split_path, class_name)
            if os.path.isdir(class_split_path):
                pass
            else:
                os.mkdir(class_split_path)

    # 按照比例划分数据集，并进行数据图片的复制
    # 首先进行分类遍历
    for class_name in class_names:
        current_class_data_path = os.path.join(src_data_folder, class_name)
        current_all_data = os.listdir(current_class_data_path)
        current_data_length = len(current_all_data)
        current_data_index_list = list(range(current_data_length))
        random.shuffle(current_data_index_list)

        train_folder = os.path.join(os.path.join(target_data_folder, 'train'), class_name)
        val_folder = os.path.join(os.path.join(target_data_folder, 'val'), class_name)
        test_folder = os.path.join(os.path.join(target_data_folder, 'test'), class_name)
        train_stop_flag = current_data_length * train_scale
        val_stop_flag = current_data_length * (train_scale + val_scale)
        current_idx = 0
        train_num = 0
        val_num = 0
        test_num = 0
        for i in current_data_index_list:
            src_img_path = os.path.join(current_class_data_path, current_all_data[i])
            if current_idx <= train_stop_flag:
                copy2(src_img_path, train_folder)
                # print("{}复制到了{}".format(src_img_path, train_folder))
                train_num = train_num + 1
            elif (current_idx > train_stop_flag) and (current_idx <= val_stop_flag):
                copy2(src_img_path, val_folder)
                # print("{}复制到了{}".format(src_img_path, val_folder))
                val_num = val_num + 1
            else:
                copy2(src_img_path, test_folder)
                # print("{}复制到了{}".format(src_img_path, test_folder))
                test_num = test_num + 1

            current_idx = current_idx + 1

        print("*********************************{}*************************************".format(class_name))
        print(
            "{}类按照{}：{}：{}的比例划分完成，一共{}张图片".format(class_name, train_scale, val_scale, test_scale, current_data_length))
        print("训练集{}：{}张".format(train_folder, train_num))
        print("验证集{}：{}张".format(val_folder, val_num))
        print("测试集{}：{}张".format(test_folder, test_num))


if __name__ == '__main__':
    src_data_folder = "D:/python-project/untitled/data/"  # todo 原始数据集目录
    target_data_folder = "D:/python-project/untitled/split_data/"  # todo 数据集分割之后存放的目录
    data_set_split(src_data_folder, target_data_folder)

训练数据集

# -*- coding: utf-8 -*-
# @Time    : 2021/6/17 20:29
# @Author  : dejahu
# @Email   : [email protected]
# @File    : train_cnn.py
# @Software: PyCharm
# @Brief   : cnn模型训练代码，训练的代码会保存在models目录下，折线图会保存在results目录下

import tensorflow as tf
import matplotlib.pyplot as plt
from time import *



# 数据集加载函数，指明数据集的位置并统一处理为imgheight*imgwidth的大小，同时设置batch
def data_load(data_dir, test_data_dir, img_height, img_width, batch_size):
    # 加载训练集
    train_ds = tf.keras.preprocessing.image_dataset_from_directory(
        data_dir,
        label_mode='categorical',
        seed=123,
        image_size=(img_height, img_width),
        batch_size=batch_size)
    # 加载测试集
    val_ds = tf.keras.preprocessing.image_dataset_from_directory(
        test_data_dir,
        label_mode='categorical',
        seed=123,
        image_size=(img_height, img_width),
        batch_size=batch_size)
    class_names = train_ds.class_names
    # 返回处理之后的训练集、验证集和类名
    return train_ds, val_ds, class_names


# 构建CNN模型
def model_load(IMG_SHAPE=(224, 224, 3), class_num=12):
    # 搭建模型
    model = tf.keras.models.Sequential([
        # 对模型做归一化的处理，将0-255之间的数字统一处理到0到1之间
        tf.keras.layers.experimental.preprocessing.Rescaling(1. / 255, input_shape=IMG_SHAPE),
        # 卷积层，该卷积层的输出为32个通道，卷积核的大小是3*3，激活函数为relu
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu'),
        # 添加池化层，池化的kernel大小是2*2
        tf.keras.layers.MaxPooling2D(2, 2),
        # Add another convolution
        # 卷积层，输出为64个通道，卷积核大小为3*3，激活函数为relu
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        # 池化层，最大池化，对2*2的区域进行池化操作
        tf.keras.layers.MaxPooling2D(2, 2),
        # 将二维的输出转化为一维
        tf.keras.layers.Flatten(),
        # The same 128 dense layers, and 10 output layers as in the pre-convolution example:
        tf.keras.layers.Dense(128, activation='relu'),
        # 通过softmax函数将模型输出为类名长度的神经元上，激活函数采用softmax对应概率值
        tf.keras.layers.Dense(class_num, activation='softmax')
    ])
    # 输出模型信息
    model.summary()
    # 指明模型的训练参数，优化器为sgd优化器，损失函数为交叉熵损失函数
    model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
    # 返回模型
    return model


# 展示训练过程的曲线
def show_loss_acc(history):
    # 从history中提取模型训练集和验证集准确率信息和误差信息
    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']
    loss = history.history['loss']
    val_loss = history.history['val_loss']

    # 按照上下结构将图画输出
    plt.figure(figsize=(8, 8))
    plt.subplot(2, 1, 1)
    plt.plot(acc, label='Training Accuracy')
    plt.plot(val_acc, label='Validation Accuracy')
    plt.legend(loc='lower right')
    plt.ylabel('Accuracy')
    plt.ylim([min(plt.ylim()), 1])
    plt.title('Training and Validation Accuracy')

    plt.subplot(2, 1, 2)
    plt.plot(loss, label='Training Loss')
    plt.plot(val_loss, label='Validation Loss')
    plt.legend(loc='upper right')
    plt.ylabel('Cross Entropy')
    plt.title('Training and Validation Loss')
    plt.xlabel('epoch')
    plt.savefig('D:/python-project/untitled/split_data/results_cnn.png', dpi=100)


def train(epochs):
    # 开始训练，记录开始时间
    begin_time = time()
    # todo 加载数据集， 修改为你的数据集的路径
    train_ds, val_ds, class_names = data_load("D:/python-project/untitled/split_data/train",
                                              "D:/python-project/untitled/split_data/test", 224, 224, 16)
    print(class_names)
    # 加载模型
    model = model_load(class_num=len(class_names))
    # 指明训练的轮数epoch，开始训练
    history = model.fit(train_ds, validation_data=val_ds, epochs=epochs)
    # todo 保存模型， 修改为你要保存的模型的名称
    model.save("models/cnn_fv.h5")
    # 记录结束时间
    end_time = time()
    run_time = end_time - begin_time
    print('该循环程序运行时间：', run_time, "s")  # 该循环程序运行时间： 1.4201874732
    # 绘制模型训练过程图
    show_loss_acc(history)


if __name__ == '__main__':
    train(epochs=15)

通过界面调用模型对新图片进行识别

# -*- coding: utf-8 -*-
# @Time    : 2021/6/17 20:29
# @Author  : dejahu
# @Email   : [email protected]
# @File    : window.py
# @Software: PyCharm
# @Brief   : 图形化界面

import tensorflow as tf
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
import sys
import cv2
from PIL import Image
import numpy as np
import shutil


class MainWindow(QTabWidget):
    # 初始化
    def __init__(self):
        super().__init__()
        self.setWindowIcon(QIcon('images/logo.png'))
        self.setWindowTitle('果蔬识别系统')  # todo 修改系统名称
        # 模型初始化
        self.model = tf.keras.models.load_model("models/cnn_fv.h5")  # todo 修改模型名称
        self.to_predict_name = "images/tim9.jpeg"  # todo 修改初始图片，这个图片要放在images目录下
       #'土豆', '圣女果', '大白菜', '大葱', '梨', '胡萝卜', '芒果', '苹果', '西红柿', '韭菜', '香蕉', '黄瓜']  # todo 修改类名，这个数组在模型训练的开始会输出
        self.class_names = ['苹果','香蕉']
        self.resize(900, 700)
        self.initUI()

    # 界面初始化，设置界面布局
    def initUI(self):
        main_widget = QWidget()
        main_layout = QHBoxLayout()
        font = QFont('楷体', 15)

        # 主页面，设置组件并在组件放在布局上
        left_widget = QWidget()
        left_layout = QVBoxLayout()
        img_title = QLabel("样本")
        img_title.setFont(font)
        img_title.setAlignment(Qt.AlignCenter)
        self.img_label = QLabel()
        img_init = cv2.imread(self.to_predict_name)
        h, w, c = img_init.shape
        scale = 400 / h
        img_show = cv2.resize(img_init, (0, 0), fx=scale, fy=scale)
        cv2.imwrite("images/show.png", img_show)
        img_init = cv2.resize(img_init, (224, 224))
        cv2.imwrite('images/target.png', img_init)
        self.img_label.setPixmap(QPixmap("images/show.png"))
        left_layout.addWidget(img_title)
        left_layout.addWidget(self.img_label, 1, Qt.AlignCenter)
        left_widget.setLayout(left_layout)
        right_widget = QWidget()
        right_layout = QVBoxLayout()
        btn_change = QPushButton(" 上传图片 ")
        btn_change.clicked.connect(self.change_img)
        btn_change.setFont(font)
        btn_predict = QPushButton(" 开始识别 ")
        btn_predict.setFont(font)
        btn_predict.clicked.connect(self.predict_img)
        label_result = QLabel(' 果蔬名称 ')
        self.result = QLabel("等待识别")
        label_result.setFont(QFont('楷体', 16))
        self.result.setFont(QFont('楷体', 24))
        right_layout.addStretch()
        right_layout.addWidget(label_result, 0, Qt.AlignCenter)
        right_layout.addStretch()
        right_layout.addWidget(self.result, 0, Qt.AlignCenter)
        right_layout.addStretch()
        right_layout.addStretch()
        right_layout.addWidget(btn_change)
        right_layout.addWidget(btn_predict)
        right_layout.addStretch()
        right_widget.setLayout(right_layout)
        main_layout.addWidget(left_widget)
        main_layout.addWidget(right_widget)
        main_widget.setLayout(main_layout)

        # 关于页面，设置组件并把组件放在布局上
        about_widget = QWidget()
        about_layout = QVBoxLayout()
        about_title = QLabel('欢迎使用果蔬识别系统')  # todo 修改欢迎词语
        about_title.setFont(QFont('楷体', 18))
        about_title.setAlignment(Qt.AlignCenter)
        about_img = QLabel()
        about_img.setPixmap(QPixmap('images/bj.jpg'))
        about_img.setAlignment(Qt.AlignCenter)
        label_super = QLabel("作者：dejahu")  # todo 更换作者信息
        label_super.setFont(QFont('楷体', 12))
        # label_super.setOpenExternalLinks(True)
        label_super.setAlignment(Qt.AlignRight)
        about_layout.addWidget(about_title)
        about_layout.addStretch()
        about_layout.addWidget(about_img)
        about_layout.addStretch()
        about_layout.addWidget(label_super)
        about_widget.setLayout(about_layout)

        # 添加注释
        self.addTab(main_widget, '主页')
        self.addTab(about_widget, '关于')
        self.setTabIcon(0, QIcon('images/主页面.png'))
        self.setTabIcon(1, QIcon('images/关于.png'))

    # 上传并显示图片
    def change_img(self):
        openfile_name = QFileDialog.getOpenFileName(self, 'chose files', '',
                                                    'Image files(*.jpg *.png *jpeg)')  # 打开文件选择框选择文件
        img_name = openfile_name[0]  # 获取图片名称
        if img_name == '':
            pass
        else:
            target_image_name = "images/tmp_up." + img_name.split(".")[-1]  # 将图片移动到当前目录
            shutil.copy(img_name, target_image_name)
            self.to_predict_name = target_image_name
            img_init = cv2.imread(self.to_predict_name)  # 打开图片
            h, w, c = img_init.shape
            scale = 400 / h
            img_show = cv2.resize(img_init, (0, 0), fx=scale, fy=scale)  # 将图片的大小统一调整到400的高，方便界面显示
            cv2.imwrite("images/show.png", img_show)
            img_init = cv2.resize(img_init, (224, 224))  # 将图片大小调整到224*224用于模型推理
            cv2.imwrite('images/target.png', img_init)
            self.img_label.setPixmap(QPixmap("images/show.png"))
            self.result.setText("等待识别")

    # 预测图片
    def predict_img(self):
        img = Image.open('images/target.png')  # 读取图片
        img = np.asarray(img)  # 将图片转化为numpy的数组
        outputs = self.model.predict(img.reshape(1, 224, 224, 3))  # 将图片输入模型得到结果
        result_index = int(np.argmax(outputs))
        result = self.class_names[result_index]  # 获得对应的水果名称
        self.result.setText(result)  # 在界面上做显示

    # 界面关闭事件，询问用户是否关闭
    def closeEvent(self, event):
        reply = QMessageBox.question(self,
                                     '退出',
                                     "是否要退出程序？",
                                     QMessageBox.Yes | QMessageBox.No,
                                     QMessageBox.No)
        if reply == QMessageBox.Yes:
            self.close()
            event.accept()
        else:
            event.ignore()


if __name__ == "__main__":
    app = QApplication(sys.argv)
    x = MainWindow()
    x.show()
    sys.exit(app.exec_())