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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

文章目录

  • 基于tensorflow、CNN网络识别花卉的种类
      • 1、环境工具支持
      • 2、运行方法
      • 3、运行UI界面结果
      • 4、项目源码模块化介绍(需要源码关注我,并私信我)

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 = pnlst = wx.StaticText(pnl, label="花朵识别", pos=(200, 0))font = st.GetFont()font.PointSize += 10font = 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 += 8result_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 sbif __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_nameimg = 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bytesprint(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 queuereader = 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) - 0.5label = 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labelimg = 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listall_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 queueinput_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.int32image_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 norm2with 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].valueweights = 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batchfor 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writerif 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 = 1N_CLASSES = 4image = 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。
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本文标签: 基于tensorflowCNN网络识别花卉的种类(图像识别)