基于tensorflow、CNN网络识别花卉的种类（图像识别）

基于tensorflow、CNN网络识别花卉的种类

这是一个图像识别项目，基于 tensorflow，现有的 CNN 网络可以识别四种花的种类。适合新手对使用 tensorflow进行一个完整的图像识别过程有一个大致轮廓。项目包括对数据集的处理，从硬盘读取数据，CNN 网络的定义，训练过程，还实现了一个 GUI界面用于使用训练好的网络。

Notice:本项目完全开源，需要源码关注我，再私信我哦

1、环境工具支持

1. 安装 Anaconda
2. 导入环境 environment.yamlconda env update -f=environment.yaml

2、运行方法

• git clone 这个项目;
• 解压 input_data.rar 到你喜欢的目录;
• 修改 train.py 中;(如下修改）

 train_dir = 'D:/ML/flower/input_data'  # 训练样本的读入路径
logs_train_dir = 'D:/ML/flower/save'  # logs存储路径

为你本机的目录。

• 运行 train.py 开始训练。
• 训练完成后，修改 test.py 中的logs_train_dir = 'D:/ML/flower/save/'为你的目录。
• 运行 test.py 或者 gui.py 查看结果。

3、运行UI界面结果

gui.py运行界面：

4、项目源码模块化介绍（需要源码关注我，并私信我）

主界面文件（gui.py）：
主要包含控件的设计，很简单，没有用到其他库

class HelloFrame(wx.Frame):

    def __init__(self,*args,**kw):
        super(HelloFrame,self).__init__(*args,**kw)

        pnl = wx.Panel(self)

        self.pnl = pnl
        st = wx.StaticText(pnl, label="花朵识别", pos=(200, 0))
        font = st.GetFont()
        font.PointSize += 10
        font = font.Bold()
        st.SetFont(font)

        # 选择图像文件按钮
        btn = wx.Button(pnl, -1, "select")
        btn.Bind(wx.EVT_BUTTON, self.OnSelect)

        self.makeMenuBar()

        self.CreateStatusBar()
        self.SetStatusText("Welcome to flower world")

    def makeMenuBar(self):
        fileMenu = wx.Menu()
        helloItem = fileMenu.Append(-1, "&Hello...\tCtrl-H",
                                    "Help string shown in status bar for this menu item")
        fileMenu.AppendSeparator()

        exitItem = fileMenu.Append(wx.ID_EXIT)
        helpMenu = wx.Menu()
        aboutItem = helpMenu.Append(wx.ID_ABOUT)

        menuBar = wx.MenuBar()
        menuBar.Append(fileMenu, "&File")
        menuBar.Append(helpMenu, "Help")

        self.SetMenuBar(menuBar)

        self.Bind(wx.EVT_MENU, self.OnHello, helloItem)
        self.Bind(wx.EVT_MENU, self.OnExit, exitItem)
        self.Bind(wx.EVT_MENU, self.OnAbout, aboutItem)

    def OnExit(self, event):
        self.Close(True)

    def OnHello(self, event):
        wx.MessageBox("Hello again from wxPython")

    def OnAbout(self, event):
        """Display an About Dialog"""
        wx.MessageBox("This is a wxPython Hello World sample",
                      "About Hello World 2",
                      wx.OK | wx.ICON_INFORMATION)

    def OnSelect(self, event):
        wildcard = "image source(*.jpg)|*.jpg|" \
                   "Compile Python(*.pyc)|*.pyc|" \
                   "All file(*.*)|*.*"
        dialog = wx.FileDialog(None, "Choose a file", os.getcwd(),
                               "", wildcard, wx.ID_OPEN)
        if dialog.ShowModal() == wx.ID_OK:
            print(dialog.GetPath())
            img = Image.open(dialog.GetPath())
            imag = img.resize([64, 64])
            image = np.array(imag)
            result = evaluate_one_image(image)
            result_text = wx.StaticText(self.pnl, label=result, pos=(320, 0))
            font = result_text.GetFont()
            font.PointSize += 8
            result_text.SetFont(font)
            self.initimage(name= dialog.GetPath())

    # 生成图片控件
    def initimage(self, name):
        imageShow = wx.Image(name, wx.BITMAP_TYPE_ANY)
        sb = wx.StaticBitmap(self.pnl, -1, imageShow.ConvertToBitmap(), pos=(0,30), size=(600,400))
        return sb


if __name__ == '__main__':

    app = wx.App()
    frm = HelloFrame(None, title='flower wolrd', size=(1000,600))
    frm.Show()
    app.MainLoop()

将原始图片转换成需要的大小，并将其保存（creat record.py):
这里就不做详细介绍了，具体解释看源码注释，注释里面写的很详细

# 原始图片的存储位置
orig_picture = 'D:/ML/flower/flower_photos/'

# 生成图片的存储位置
gen_picture = 'D:/ML/flower/input_data/'

# 需要的识别类型
classes = {'dandelion', 'roses', 'sunflowers','tulips'}

# 样本总数
num_samples = 4000


# 制作TFRecords数据
def create_record():
    writer = tf.python_io.TFRecordWriter("flower_train.tfrecords")
    for index, name in enumerate(classes):
        class_path = orig_picture + "/" + name + "/"
        for img_name in os.listdir(class_path):
            img_path = class_path + img_name
            img = Image.open(img_path)
            img = img.resize((64, 64))  # 设置需要转换的图片大小
            img_raw = img.tobytes()  # 将图片转化为原生bytes
            print(index, img_raw)
            example = tf.train.Example(
                features=tf.train.Features(feature={
                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
                    'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
                }))
            writer.write(example.SerializeToString())
    writer.close()


# =======================================================================================
def read_and_decode(filename):
    # 创建文件队列,不限读取的数量
    filename_queue = tf.train.string_input_producer([filename])
    # create a reader from file queue
    reader = tf.TFRecordReader()
    # reader从文件队列中读入一个序列化的样本
    _, serialized_example = reader.read(filename_queue)
    # get feature from serialized example
    # 解析符号化的样本
    features = tf.parse_single_example(
        serialized_example,
        features={
            'label': tf.FixedLenFeature([], tf.int64),
            'img_raw': tf.FixedLenFeature([], tf.string)
        })
    label = features['label']
    img = features['img_raw']
    img = tf.decode_raw(img, tf.uint8)
    img = tf.reshape(img, [64, 64, 3])
    # img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
    label = tf.cast(label, tf.int32)
    return img, label


# =======================================================================================
if __name__ == '__main__':
    create_record()
    batch = read_and_decode('flower_train.tfrecords')
    init_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())

    with tf.Session() as sess:  # 开始一个会话
        sess.run(init_op)
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(coord=coord)

        for i in range(num_samples):
            example, lab = sess.run(batch)  # 在会话中取出image和label
            img = Image.fromarray(example, 'RGB')  # 这里Image是之前提到的
            img.save(gen_picture + '/' + str(i) + 'samples' + str(lab) + '.jpg')  # 存下图片;注意cwd后边加上‘/’
            print(example, lab)
        coord.request_stop()
        coord.join(threads)
        sess.close()

生成图片路径和标签的List，Batch：
这里用源码结构图来呈现：

1. 生成图片路径和标签的List
   • 获取所有的图片路径名，存放到对应的列表中，同时贴上标签，存放到label列表中
   • 对生成的图片路径和标签List做打乱处理
   • 利用shuffle打乱顺序
   • 将所有的img和lab转换成list
   • 将所得List分为两部分，一部分用来训练tra，一部分用来测试valratio是测试集的比例
2. 生成Batch
   • 将上面生成的List传入get_batch() ，转换类型，产生一个输入队列queue，因为img和lab是分开的，所以使用tf.train.slice_input_producer()，然后用tf.read_file()从队列中读取图像
   • image_W, image_H：设置好固定的图像高度和宽度设置
   • batch_size：每个batch要放多少张图片
   • capacity：一个队列最大多少
   • 将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等
   • 数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮
   • 重新排列label，行数为[batch_size]

# ============================================================================
# -----------------生成图片路径和标签的List------------------------------------

train_dir = 'D:/ML/flower/input_data'

roses = []
label_roses = []
tulips = []
label_tulips = []
dandelion = []
label_dandelion = []
sunflowers = []
label_sunflowers = []


# step1：获取所有的图片路径名，存放到
# 对应的列表中，同时贴上标签，存放到label列表中。
def get_files(file_dir, ratio):
    for file in os.listdir(file_dir + '/roses'):
        roses.append(file_dir + '/roses' + '/' + file)
        label_roses.append(0)
    for file in os.listdir(file_dir + '/tulips'):
        tulips.append(file_dir + '/tulips' + '/' + file)
        label_tulips.append(1)
    for file in os.listdir(file_dir + '/dandelion'):
        dandelion.append(file_dir + '/dandelion' + '/' + file)
        label_dandelion.append(2)
    for file in os.listdir(file_dir + '/sunflowers'):
        sunflowers.append(file_dir + '/sunflowers' + '/' + file)
        label_sunflowers.append(3)

    # step2：对生成的图片路径和标签List做打乱处理
    image_list = np.hstack((roses, tulips, dandelion, sunflowers))
    label_list = np.hstack((label_roses, label_tulips, label_dandelion, label_sunflowers))

    # 利用shuffle打乱顺序
    temp = np.array([image_list, label_list])
    temp = temp.transpose()
    np.random.shuffle(temp)

    # 从打乱的temp中再取出list（img和lab）
    # image_list = list(temp[:, 0])
    # label_list = list(temp[:, 1])
    # label_list = [int(i) for i in label_list]
    # return image_list, label_list

    # 将所有的img和lab转换成list
    all_image_list = list(temp[:, 0])
    all_label_list = list(temp[:, 1])

    # 将所得List分为两部分，一部分用来训练tra，一部分用来测试val
    # ratio是测试集的比例
    n_sample = len(all_label_list)
    n_val = int(math.ceil(n_sample * ratio))  # 测试样本数
    n_train = n_sample - n_val  # 训练样本数

    tra_images = all_image_list[0:n_train]
    tra_labels = all_label_list[0:n_train]
    tra_labels = [int(float(i)) for i in tra_labels]
    val_images = all_image_list[n_train:-1]
    val_labels = all_label_list[n_train:-1]
    val_labels = [int(float(i)) for i in val_labels]

    return tra_images, tra_labels, val_images, val_labels


# ---------------------------------------------------------------------------
# --------------------生成Batch----------------------------------------------

# step1：将上面生成的List传入get_batch() ，转换类型，产生一个输入队列queue，因为img和lab
# 是分开的，所以使用tf.train.slice_input_producer()，然后用tf.read_file()从队列中读取图像
#   image_W, image_H, ：设置好固定的图像高度和宽度
#   设置batch_size：每个batch要放多少张图片
#   capacity：一个队列最大多少
def get_batch(image, label, image_W, image_H, batch_size, capacity):
    # 转换类型
    image = tf.cast(image, tf.string)
    label = tf.cast(label, tf.int32)

    # make an input queue
    input_queue = tf.train.slice_input_producer([image, label])

    label = input_queue[1]
    image_contents = tf.read_file(input_queue[0])  # read img from a queue

    # step2：将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等。
    image = tf.image.decode_jpeg(image_contents, channels=3)

    # step3：数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮。
    image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
    image = tf.image.per_image_standardization(image)

    # step4：生成batch
    # image_batch: 4D tensor [batch_size, width, height, 3],dtype=tf.float32
    # label_batch: 1D tensor [batch_size], dtype=tf.int32
    image_batch, label_batch = tf.train.batch([image, label],
                                              batch_size=batch_size,
                                              num_threads=32,
                                              capacity=capacity)
    # 重新排列label，行数为[batch_size]
    label_batch = tf.reshape(label_batch, [batch_size])
    image_batch = tf.cast(image_batch, tf.float32)
    return image_batch, label_batch

CNN网络结构的定义（model.py):
这里主要运用tensorflow库进行定义，不懂源码的可以看一下我的注释

# 网络结构定义
# 输入参数：images，image batch、4D tensor、tf.float32、[batch_size, width, height, channels]
# 返回参数：logits, float、 [batch_size, n_classes]
def inference(images, batch_size, n_classes):
    # 一个简单的卷积神经网络，卷积+池化层x2，全连接层x2，最后一个softmax层做分类。
    # 卷积层1
    # 64个3x3的卷积核（3通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu()
    with tf.variable_scope('conv1') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=1.0, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]),
                             name='biases', dtype=tf.float32)

        conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv1 = tf.nn.relu(pre_activation, name=scope.name)

    # 池化层1
    # 3x3最大池化，步长strides为2，池化后执行lrn()操作，局部响应归一化，对训练有利。
    with tf.variable_scope('pooling1_lrn') as scope:
        pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1')
        norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1')

    # 卷积层2
    # 16个3x3的卷积核（16通道），padding=’SAME’，表示padding后卷积的图与原图尺寸一致，激活函数relu()
    with tf.variable_scope('conv2') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 16], stddev=0.1, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[16]),
                             name='biases', dtype=tf.float32)

        conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME')
        pre_activation = tf.nn.bias_add(conv, biases)
        conv2 = tf.nn.relu(pre_activation, name='conv2')

    # 池化层2
    # 3x3最大池化，步长strides为2，池化后执行lrn()操作，
    # pool2 and norm2
    with tf.variable_scope('pooling2_lrn') as scope:
        norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2')
        pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2')

    # 全连接层3
    # 128个神经元，将之前pool层的输出reshape成一行，激活函数relu()
    with tf.variable_scope('local3') as scope:
        reshape = tf.reshape(pool2, shape=[batch_size, -1])
        dim = reshape.get_shape()[1].value
        weights = tf.Variable(tf.truncated_normal(shape=[dim, 128], stddev=0.005, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),
                             name='biases', dtype=tf.float32)

        local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)

    # 全连接层4
    # 128个神经元，激活函数relu()
    with tf.variable_scope('local4') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[128, 128], stddev=0.005, dtype=tf.float32),
                              name='weights', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]),
                             name='biases', dtype=tf.float32)

        local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4')

    # dropout层
    #    with tf.variable_scope('dropout') as scope:
    #        drop_out = tf.nn.dropout(local4, 0.8)

    # Softmax回归层
    # 将前面的FC层输出，做一个线性回归，计算出每一类的得分
    with tf.variable_scope('softmax_linear') as scope:
        weights = tf.Variable(tf.truncated_normal(shape=[128, n_classes], stddev=0.005, dtype=tf.float32),
                              name='softmax_linear', dtype=tf.float32)

        biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[n_classes]),
                             name='biases', dtype=tf.float32)

        softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear')

    return softmax_linear


# -----------------------------------------------------------------------------
# loss计算
# 传入参数：logits，网络计算输出值。labels，真实值，在这里是0或者1
# 返回参数：loss，损失值
def losses(logits, labels):
    with tf.variable_scope('loss') as scope:
        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels,
                                                                       name='xentropy_per_example')
        loss = tf.reduce_mean(cross_entropy, name='loss')
        tf.summary.scalar(scope.name + '/loss', loss)
    return loss


# --------------------------------------------------------------------------
# loss损失值优化
# 输入参数：loss。learning_rate，学习速率。
# 返回参数：train_op，训练op，这个参数要输入sess.run中让模型去训练。
def trainning(loss, learning_rate):
    with tf.name_scope('optimizer'):
        optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
        global_step = tf.Variable(0, name='global_step', trainable=False)
        train_op = optimizer.minimize(loss, global_step=global_step)
    return train_op


# -----------------------------------------------------------------------
# 评价/准确率计算
# 输入参数：logits，网络计算值。labels，标签，也就是真实值，在这里是0或者1。
# 返回参数：accuracy，当前step的平均准确率，也就是在这些batch中多少张图片被正确分类了。
def evaluation(logits, labels):
    with tf.variable_scope('accuracy') as scope:
        correct = tf.nn.in_top_k(logits, labels, 1)
        correct = tf.cast(correct, tf.float16)
        accuracy = tf.reduce_mean(correct)
        tf.summary.scalar(scope.name + '/accuracy', accuracy)
    return accuracy

训练模块（train.py):
这里只针对四种花进行分类（时间有限，只准备了四种花的数据）

# 变量声明
N_CLASSES = 4  # 四种花类型
IMG_W = 64  # resize图像，太大的话训练时间久
IMG_H = 64
BATCH_SIZE = 20
CAPACITY = 200
MAX_STEP = 10000  # 一般大于10K
learning_rate = 0.0001  # 一般小于0.0001

# 获取批次batch
train_dir = 'D:/ML/flower/input_data'  # 训练样本的读入路径
logs_train_dir = 'D:/ML/flower/save'  # logs存储路径

# train, train_label = input_data.get_files(train_dir)
train, train_label, val, val_label = input_data.get_files(train_dir, 0.3)
# 训练数据及标签
train_batch, train_label_batch = input_data.get_batch(train, train_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)
# 测试数据及标签
val_batch, val_label_batch = input_data.get_batch(val, val_label, IMG_W, IMG_H, BATCH_SIZE, CAPACITY)

# 训练操作定义
train_logits = model.inference(train_batch, BATCH_SIZE, N_CLASSES)
train_loss = model.losses(train_logits, train_label_batch)
train_op = model.trainning(train_loss, learning_rate)
train_acc = model.evaluation(train_logits, train_label_batch)

# 测试操作定义
test_logits = model.inference(val_batch, BATCH_SIZE, N_CLASSES)
test_loss = model.losses(test_logits, val_label_batch)
test_acc = model.evaluation(test_logits, val_label_batch)

# 这个是log汇总记录
summary_op = tf.summary.merge_all()

# 产生一个会话
sess = tf.Session()
# 产生一个writer来写log文件
train_writer = tf.summary.FileWriter(logs_train_dir, sess.graph)
# val_writer = tf.summary.FileWriter(logs_test_dir, sess.graph)
# 产生一个saver来存储训练好的模型
saver = tf.train.Saver()
# 所有节点初始化
sess.run(tf.global_variables_initializer())
# 队列监控
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)

# 进行batch的训练
try:
    # 执行MAX_STEP步的训练，一步一个batch
    for step in np.arange(MAX_STEP):
        if coord.should_stop():
            break
        _, tra_loss, tra_acc = sess.run([train_op, train_loss, train_acc])

        # 每隔50步打印一次当前的loss以及acc，同时记录log，写入writer
        if step % 10 == 0:
            print('Step %d, train loss = %.2f, train accuracy = %.2f%%' % (step, tra_loss, tra_acc * 100.0))
            summary_str = sess.run(summary_op)
            train_writer.add_summary(summary_str, step)
        # 每隔100步，保存一次训练好的模型
        if (step + 1) == MAX_STEP:
            checkpoint_path = os.path.join(logs_train_dir, 'model.ckpt')
            saver.save(sess, checkpoint_path, global_step=step)

except tf.errors.OutOfRangeError:
    print('Done training -- epoch limit reached')

finally:
    coord.request_stop()

测试模块（test.py):
通过输入指定的图像数据到模型中，进行简单测试（源码中含有注释）

# 获取一张图片
def get_one_image(train):
    # 输入参数：train,训练图片的路径
    # 返回参数：image，从训练图片中随机抽取一张图片
    n = len(train)
    ind = np.random.randint(0, n)
    img_dir = train[ind]  # 随机选择测试的图片

    img = Image.open(img_dir)
    plt.imshow(img)
    plt.show()
    image = np.array(img)
    return image


# 测试图片
def evaluate_one_image(image_array):
    with tf.Graph().as_default():
        BATCH_SIZE = 1
        N_CLASSES = 4

        image = tf.cast(image_array, tf.float32)
        image = tf.image.per_image_standardization(image)
        image = tf.reshape(image, [1, 64, 64, 3])

        logit = model.inference(image, BATCH_SIZE, N_CLASSES)

        logit = tf.nn.softmax(logit)

        x = tf.placeholder(tf.float32, shape=[64, 64, 3])

        # you need to change the directories to yours.
        logs_train_dir = 'D:/ML/flower/save/'

        saver = tf.train.Saver()

        with tf.Session() as sess:

            print("Reading checkpoints...")
            ckpt = tf.train.get_checkpoint_state(logs_train_dir)
            if ckpt and ckpt.model_checkpoint_path:
                global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
                saver.restore(sess, ckpt.model_checkpoint_path)
                print('Loading success, global_step is %s' % global_step)
            else:
                print('No checkpoint file found')

            prediction = sess.run(logit, feed_dict={x: image_array})
            max_index = np.argmax(prediction)
            if max_index == 0:
                result = ('这是玫瑰花的可能性为： %.6f' % prediction[:, 0])
            elif max_index == 1:
                result = ('这是郁金香的可能性为： %.6f' % prediction[:, 1])
            elif max_index == 2:
                result = ('这是蒲公英的可能性为： %.6f' % prediction[:, 2])
            else:
                result = ('这是这是向日葵的可能性为： %.6f' % prediction[:, 3])
            return result


# ------------------------------------------------------------------------

if __name__ == '__main__':
    img = Image.open('D:/ML/flower/flower_photos/roses/12240303_80d87f77a3_n.jpg')
    plt.imshow(img)
    plt.show()
    imag = img.resize([64, 64])
    image = np.array(imag)
    evaluate_one_image(image)

至此主要源码部分就讲解完毕了，还包括其他的训练数据集，就不讲解了。
需要源码的同志们请关注，再私信我（本人看到私信一定及时回复）
创作不易，大家且行且珍惜！！！！！！