【python】TensorFlow框架下CNN神经网络的花卉识别系统
2024-06-08 20:45:56
提前说明一下，本文的CNN神经网络模型是参考网上诸多相关CNN图像分类大牛的博客修改的，在模型的基础上，用python的Flask框架搭载了一个web页面用来可视化展示。
第一步，爬取图片数据集
用python实现了一个非常简单的网络爬虫，对百度图片接口 http://image.baidu.com/search/acjson?tn=resultjson_com&ipn=rj&ct=201326592&is=&fp=result&queryWord=%E9%AB%98%E6%B8%85%E5%8A%A8%E6%BC%AB&cl=2&lm=-1&ie=utf-8&oe=utf-8&adpicid=&st=-1&z=&ic=0&word=%E4%BA%8C%E6%AC%A1%E5%85%83&s=&se=&tab=&width=&height=&face=0&istype=2&qc=&nc=1&fr=&pn=60&rn=30&gsm=1000000001e&1486375820481= 发送Http请求，返回Json串如下：
我们可以看到，data下的middleURL就是 我们想要的图片链接。于是，再向这个图片链接发请求，就可以获取到我们想要的图片了。代码如下：
# _*_ coding:utf-8 _*_
''''''
'''1.通过关键字进入图片界面2.加载图片queryWord:可爱图片word:可爱图片 pn:60gsm:3c
'''
import requests
import json
import time
import os#要修改的参数列表
queryWord=input('请输入您要搜索的图片：')
pn=0
gsm=str(hex(pn))[-2:]
timestrp=int(time.time()*1000)
#num表示照片数量
num=1
#while实现类似翻页功能，遍历所有图片信息
while True:#请求的urlurl='https://image.baidu.com/search/acjson?' \'tn=resultjson_com&ipn=rj&ct=201326592&' \'is=&fp=result&queryWord={0}&cl=2&lm=-1&ie=utf-8&' \'oe=utf-8&adpicid=&st=-1&z=&ic=0&word={0}&s=&se=' \'&tab=&width=&height=&face=0&istype=2&qc=&nc=1&fr=&pn={1}&rn=30&gsm={2}&{3}='#伪装头部header={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.119 Safari/537.36'}#解析为json()语句try:r_mus=requests.get(url.format(queryWord,pn,gsm,timestrp),headers=header).json()except BaseException as e:print("此处有错误%s"%e)print(r_mus)#遍历每一张图片信息for image in r_mus['data']:if image:#获取图片地址i_url=image['middleURL']#请求该地址r_img=requests.get(i_url,headers=header,stream=True).raw.read()print('正在读取第{}张图片'.format(num))num+=1time.sleep(0.7)#创建pictures目录if os.path.exists('data/other/'):passelse:os.mkdir('data/other/')#保存图片到文件夹pictureswith open('data/other/'+str(int(time.time()))+'.jpg','wb')as files:files.write(r_img)listNum = r_mus['listNum']if listNum>pn:pn+=30gsm = str(hex(pn))[-2:]time.sleep(5)else:break
第二步，训练模型
模型借鉴的网上大佬 的博客模型。数据使用了一部分自己的数据集，一部分开源的花卉数据集。对模型进行训练，把训练好的模型放在model文件夹下
具体代码如下：
from skimage import io,transform
import glob
import os
import tensorflow as tf
import numpy as np
import time
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"#读取花朵图片
def read_img(path):cate=[path+x for x in os.listdir(path) if os.path.isdir(path+x)]imgs=[]labels=[]for idx,folder in enumerate(cate):print('reading the dirs :%s' % (folder))for im in glob.glob(folder+'/*.jpg'):img=io.imread(im)img=transform.resize(img,(w,h))imgs.append(img)labels.append(idx)return np.asarray(imgs,np.float32),np.asarray(labels,np.int32)def inference(input_tensor, train, regularizer):with tf.variable_scope('layer1-conv1'):conv1_weights = tf.get_variable("weight",[5,5,3,32],initializer=tf.truncated_normal_initializer(stddev=0.1))conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))with tf.name_scope("layer2-pool1"):pool1 = tf.nn.max_pool(relu1, ksize = [1,2,2,1],strides=[1,2,2,1],padding="VALID")with tf.variable_scope("layer3-conv2"):conv2_weights = tf.get_variable("weight",[5,5,32,64],initializer=tf.truncated_normal_initializer(stddev=0.1))conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))with tf.name_scope("layer4-pool2"):pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')with tf.variable_scope("layer5-conv3"):conv3_weights = tf.get_variable("weight",[3,3,64,128],initializer=tf.truncated_normal_initializer(stddev=0.1))conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))with tf.name_scope("layer6-pool3"):pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')with tf.variable_scope("layer7-conv4"):conv4_weights = tf.get_variable("weight",[3,3,128,128],initializer=tf.truncated_normal_initializer(stddev=0.1))conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))with tf.name_scope("layer8-pool4"):pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')nodes = 6*6*128reshaped = tf.reshape(pool4,[-1,nodes])with tf.variable_scope('layer9-fc1'):fc1_weights = tf.get_variable("weight", [nodes, 1024],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)if train: fc1 = tf.nn.dropout(fc1, 0.5)with tf.variable_scope('layer10-fc2'):fc2_weights = tf.get_variable("weight", [1024, 512],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)if train: fc2 = tf.nn.dropout(fc2, 0.5)with tf.variable_scope('layer11-fc3'):fc3_weights = tf.get_variable("weight", [512, 5],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))logit = tf.matmul(fc2, fc3_weights) + fc3_biasesreturn logitdef minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):assert len(inputs) == len(targets)if shuffle:indices = np.arange(len(inputs))np.random.shuffle(indices)for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):if shuffle:excerpt = indices[start_idx:start_idx + batch_size]else:excerpt = slice(start_idx, start_idx + batch_size)yield inputs[excerpt], targets[excerpt]def semantic_alignment(src_feature, tgt_feature, src_label, tgt_label, num_classes=2):'''input:src_feature: feature from source domaintgt_feature: feature from target somainsrc_label: source label(one-hot encoding)tgt_label: target label(one-hot encoding)num_classes : the number of class(e.g., 2)output:semantic_loss : the semantic loss between domains.'''source_result = tf.argmax(src_label, 1) # source labeltarget_result = tf.argmax(tgt_label, 1) # target labelones = tf.ones_like(src_feature)#得到一个与源域数据格式一致的全1的张量print('ones',ones.shape)print('source_result', source_result.shape)print('target_result', target_result.shape)current_source_count = tf.unsorted_segment_sum(ones, source_result, num_classes)#计算出当前源域数据current_target_count = tf.unsorted_segment_sum(ones, target_result, num_classes)#计算出当前目标域数据current_positive_source_count = tf.maximum(current_source_count, tf.ones_like(current_source_count))#返回当前源域数据与之间的最大值current_positive_target_count = tf.maximum(current_target_count, tf.ones_like(current_target_count))#返回当前目标域数据与之间的最大值current_source_centroid = tf.divide(tf.unsorted_segment_sum(data=src_feature, segment_ids= \source_result, num_segments=num_classes), current_positive_source_count)current_target_centroid = tf.divide(tf.unsorted_segment_sum(data=tgt_feature, segment_ids= \target_result, num_segments=num_classes), current_positive_target_count)semantic_loss = tf.reduce_mean((tf.square(current_source_centroid - current_target_centroid)))return semantic_lossif __name__ == '__main__':# 数据集地址path = 'D:/python/workspace/flower/data/flowers/'# 模型保存地址model_path = 'D:/python/workspace/flower/model/model.ckpt'#测试集地址path1= 'D:/python/workspace/flower/test1/'# 将所有的图片resize成100*100w = 100h = 100c = 3data, label = read_img(path)newlabel=[]# 打乱顺序num_example = data.shape[0]arr = np.arange(num_example)np.random.shuffle(arr)data = data[arr]label = label[arr]# 将所有数据分为训练集和验证集ratio = 0.8s = np.int(num_example * ratio)x_train = data[:s]y_train = label[:s]x_val = data[s:]y_val = label[s:]# -----------------构建网络----------------------# 占位符x = tf.placeholder(tf.float32, shape=[None, w, h, c], name='x')y_ = tf.placeholder(tf.int32, shape=[None, ], name='y_')def inference(input_tensor, train, regularizer):with tf.variable_scope('layer1-conv1'):conv1_weights = tf.get_variable("weight", [5, 5, 3, 32],initializer=tf.truncated_normal_initializer(stddev=0.1))conv1_biases = tf.get_variable("bias", [32], initializer=tf.constant_initializer(0.0))conv1 = tf.nn.conv2d(input_tensor, conv1_weights, strides=[1, 1, 1, 1], padding='SAME')relu1 = tf.nn.relu(tf.nn.bias_add(conv1, conv1_biases))with tf.name_scope("layer2-pool1"):pool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")with tf.variable_scope("layer3-conv2"):conv2_weights = tf.get_variable("weight", [5, 5, 32, 64],initializer=tf.truncated_normal_initializer(stddev=0.1))conv2_biases = tf.get_variable("bias", [64], initializer=tf.constant_initializer(0.0))conv2 = tf.nn.conv2d(pool1, conv2_weights, strides=[1, 1, 1, 1], padding='SAME')relu2 = tf.nn.relu(tf.nn.bias_add(conv2, conv2_biases))with tf.name_scope("layer4-pool2"):pool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')with tf.variable_scope("layer5-conv3"):conv3_weights = tf.get_variable("weight", [3, 3, 64, 128],initializer=tf.truncated_normal_initializer(stddev=0.1))conv3_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))conv3 = tf.nn.conv2d(pool2, conv3_weights, strides=[1, 1, 1, 1], padding='SAME')relu3 = tf.nn.relu(tf.nn.bias_add(conv3, conv3_biases))with tf.name_scope("layer6-pool3"):pool3 = tf.nn.max_pool(relu3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')with tf.variable_scope("layer7-conv4"):conv4_weights = tf.get_variable("weight", [3, 3, 128, 128],initializer=tf.truncated_normal_initializer(stddev=0.1))conv4_biases = tf.get_variable("bias", [128], initializer=tf.constant_initializer(0.0))conv4 = tf.nn.conv2d(pool3, conv4_weights, strides=[1, 1, 1, 1], padding='SAME')relu4 = tf.nn.relu(tf.nn.bias_add(conv4, conv4_biases))with tf.name_scope("layer8-pool4"):pool4 = tf.nn.max_pool(relu4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')nodes = 6 * 6 * 128reshaped = tf.reshape(pool4, [-1, nodes])with tf.variable_scope('layer9-fc1'):fc1_weights = tf.get_variable("weight", [nodes, 1024],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc1_weights))fc1_biases = tf.get_variable("bias", [1024], initializer=tf.constant_initializer(0.1))fc1 = tf.nn.relu(tf.matmul(reshaped, fc1_weights) + fc1_biases)if train: fc1 = tf.nn.dropout(fc1, 0.5)with tf.variable_scope('layer10-fc2'):fc2_weights = tf.get_variable("weight", [1024, 512],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc2_weights))fc2_biases = tf.get_variable("bias", [512], initializer=tf.constant_initializer(0.1))fc2 = tf.nn.relu(tf.matmul(fc1, fc2_weights) + fc2_biases)if train: fc2 = tf.nn.dropout(fc2, 0.5)with tf.variable_scope('layer11-fc3'):fc3_weights = tf.get_variable("weight", [512, 5],initializer=tf.truncated_normal_initializer(stddev=0.1))if regularizer != None: tf.add_to_collection('losses', regularizer(fc3_weights))fc3_biases = tf.get_variable("bias", [5], initializer=tf.constant_initializer(0.1))logit = tf.matmul(fc2, fc3_weights) + fc3_biasesreturn logit# ---------------------------网络结束---------------------------regularizer = tf.contrib.layers.l2_regularizer(0.0001)logits = inference(x, False, regularizer)# (小处理)将logits乘以1赋值给logits_eval，定义name，方便在后续调用模型时通过tensor名字调用输出tensorb = tf.constant(value=1, dtype=tf.float32)logits_eval = tf.multiply(logits, b, name='logits_eval')loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y_)train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)correct_prediction = tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), y_)acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))# 定义一个函数，按批次取数据def minibatches(inputs=None, targets=None, batch_size=None, shuffle=False):assert len(inputs) == len(targets)if shuffle:indices = np.arange(len(inputs))np.random.shuffle(indices)for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):if shuffle:excerpt = indices[start_idx:start_idx + batch_size]else:excerpt = slice(start_idx, start_idx + batch_size)yield inputs[excerpt], targets[excerpt]# 训练和测试数据，可将n_epoch设置更大一些n_epoch = 10batch_size = 64saver = tf.train.Saver()sess = tf.Session()sess.run(tf.global_variables_initializer())for epoch in range(n_epoch):start_time = time.time()# trainingtrain_loss, train_acc, n_batch = 0, 0, 0for x_train_a, y_train_a in minibatches(x_train, y_train, batch_size, shuffle=True):_, err, ac = sess.run([train_op, loss, acc], feed_dict={x: x_train_a, y_: y_train_a})train_loss += err;train_acc += ac;n_batch += 1print("%d epoch" % epoch)print(" train loss: %f" % (np.sum(train_loss) / n_batch))print(" train acc: %f" % (np.sum(train_acc) / n_batch))# validationval_loss, val_acc, n_batch = 0, 0, 0for x_val_a, y_val_a in minibatches(x_val, y_val, batch_size, shuffle=False):err, ac = sess.run([loss, acc], feed_dict={x: x_val_a, y_: y_val_a})val_loss += err;val_acc += ac;n_batch += 1print(" validation loss: %f" % (np.sum(val_loss) / n_batch))print(" validation acc: %f" % (np.sum(val_acc) / n_batch))print("============================================================ ")saver.save(sess, model_path)sess.close()训练好后，我们可以看到model下已经有了具体的模型文件，代表训练成功。
训练成功后，我们用一个小程序来测试一下我们的模型
from skimage import io,transform
import tensorflow as tf
import numpy as nppath1 = "D:/python/workspace/flower/data/flowers/dandelion/8223968_6b51555d2f_n.jpg"
path2 = "D:/python/workspace/flower/data/other/1582514704.jpg"flower_dict = {0:'flower',1:'other'}w=100
h=100
c=3def read_one_image(path):img = io.imread(path)img = transform.resize(img,(w,h))return np.asarray(img)with tf.Session() as sess:data = []data1 = read_one_image(path1)data2 = read_one_image(path2)data.append(data1)data.append(data2)print(data1.shape)saver = tf.train.import_meta_graph('D:/python/workspace/flower/model1/model.ckpt.meta')saver.restore(sess,tf.train.latest_checkpoint('D:/python/workspace/flower/model1/'))graph = tf.get_default_graph()x = graph.get_tensor_by_name("x:0")feed_dict = {x:data}logits = graph.get_tensor_by_name("logits_eval:0")classification_result = sess.run(logits,feed_dict)#打印出预测矩阵print(classification_result)#打印出预测矩阵每一行最大值的索引print(tf.argmax(classification_result,1).eval())#根据索引通过字典对应花的分类output = []output = tf.argmax(classification_result,1).eval()for i in range(len(output)):print("第",i+1,"朵花预测:"+flower_dict[output[i]])
结果如下：
可以看出我们的模型是成功的，准确识别出了对应文件夹的图片数据。
第三步，准备Web界面
把准备好的web前端页面引入到templates文件夹中，然后使用flask 搭建web服务器。
然后写个接口，用来上传图片以及调用之前训练好的模型对花朵数据进行识别。
def getType(path):w = 100h = 100c = 3img = io.imread(path)data = []data.append(transform.resize(img,(w,h,3)))with tf.Session() as sess:saver = tf.train.import_meta_graph('D:/python/workspace/flower/model1/model.ckpt.meta')saver.restore(sess, tf.train.latest_checkpoint('D:/python/workspace/flower/model1/'))graph = tf.get_default_graph()x = graph.get_tensor_by_name("x:0")feed_dict = {x: data}logits = graph.get_tensor_by_name("logits_eval:0")classification_result = sess.run(logits, feed_dict)# 打印出预测矩阵每一行最大值的索引print(classification_result)output = tf.argmax(classification_result, 1).eval()if (output[0] == 1):return "不是花"tf.reset_default_graph()with tf.Session() as sess1:saver1 = tf.train.import_meta_graph('D:/python/workspace/flower/model/model.ckpt.meta')saver1.restore(sess1, tf.train.latest_checkpoint('D:/python/workspace/flower/model/'))graph = tf.get_default_graph()x = graph.get_tensor_by_name("x:0")feed_dict = {x: data}logits = graph.get_tensor_by_name("logits_eval:0")classification_result = sess1.run(logits, feed_dict)# 打印出预测矩阵每一行最大值的索引output = tf.argmax(classification_result, 1).eval()return flower_dict[output[0]]@app.route('/upload',methods=['POST'])
def upload():file = request.files.get('file')type = getType(file)res = file.filename + "，类型是：" + typereturn json.dumps(res, ensure_ascii=False)
上传一张图片测试一下，看反馈结果。
成功识别出了对应的图片信息，代表系统已经开发完成。
本系统以上传至本人Github ，如果可以帮助大家欢迎大家star，follow
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