這個例子實現了可以完成通過拍照發送到指定的微信賬號，自動回覆花的類型。當然需要預先安裝Python運行環境以及TensorFlow相關工具。具體通過搜索引擎查一下，很多博客文章可以參考。我的安裝環境如下：
C:\Users\Administrator.WIN7-1609091712>python
Python 3.6.1 (v3.6.1:69c0db5, Mar 21 2017, 18:41:36) [MSC v.1900 64 bit (AMD64)]
on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.__version__
'1.4.0'
>>> tf.__path__
['D:\\Program Files\\Python\\Python36\\lib\\site-packages\\tensorflow']
>>>實現步驟：
1.下載花卉樣本數據集
首先下載flowers樣本數據
鏈接：https://pan.baidu.com/s/1Sh3f09K27RV64q0yxC-GHw 密碼：zx7x
保存到D:\study\tensorflow\flower_photos
2.通過訓練程序生成識別模型並保存
新建python文件flower.py，內容如下：
from skimage import io,transform
import glob
import os
import tensorflow as tf
import numpy as np
import time
#數據集地址
path='D:/study/tensorflow/flower_photos/'
#模型保存地址
model_path='D:/study/tensorflow/flower/model.ckpt'
#將所有的圖片resize成100*100
w=100
h=100
c=3
#讀取圖片
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):
for im in glob.glob(folder+'/*.jpg'):
print('reading the images:%s'%(im))
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)
data,label=read_img(path)
#打亂順序
num_example=data.shape[0]
arr=np.arange(num_example)
np.random.shuffle(arr)
data=data[arr]
label=label[arr]
#將所有數據分爲訓練集和驗證集
ratio=0.8
s=np.int(num_example*ratio)
x_train=data[:s]
y_train=label[:s]
x_val=data[s:]
y_val=label[s:]
#-----------------構建網絡----------------------
#佔位符
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*128
reshaped = 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_biases
return logit
#---------------------------網絡結束---------------------------
regularizer = tf.contrib.layers.l2_regularizer(0.0001)
logits = inference(x,False,regularizer)
#(小處理)將logits乘以1賦值給logits_eval，定義name，方便在後續調用模型時通過tensor名字調用輸出tensor
b = 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))
#定義一個函數，按批次取數據
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]
n_epoch=10
batch_size=64
saver=tf.train.Saver()
sess=tf.Session()
sess.run(tf.global_variables_initializer())
for epoch in range(n_epoch):
start_time = time.time()
#training
train_loss, train_acc, n_batch = 0, 0, 0
for 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 += 1
print(" train loss: %f" % (np.sum(train_loss)/ n_batch))
print(" train acc: %f" % (np.sum(train_acc)/ n_batch))
#validation
val_loss, val_acc, n_batch = 0, 0, 0
for 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 += 1
print(" validation loss: %f" % (np.sum(val_loss)/ n_batch))
print(" validation acc: %f" % (np.sum(val_acc)/ n_batch))
saver.save(sess,model_path)
sess.close()運行python flower.py
D:\study\tensorflow>python flower.py
reading the images:D:/study/tensorflow/flower_photos/daisy\100080576_f52e8ee070_
n.jpg
D:\Program Files\Python\Python36\lib\site-packages\skimage\transform\_warps.py:8
4: UserWarning: The default mode, 'constant', will be changed to 'reflect' in sk
image 0.15.
warn("The default mode, 'constant', will be changed to 'reflect' in "
reading the images:D:/study/tensorflow/flower_photos/daisy\10140303196_b88d3d6ce
c.jpg
reading the images:D:/study/tensorflow/flower_photos/daisy\10172379554_b296050f8
2_n.jpg運行完成後，在flower目錄下生成訓練後的模型。包含四個文件。
D:\study\tensorflow\flower>tree /f
文件夾 PATH 列表
卷序列號爲 FEAC-6D64
D:.
checkpoint
model.ckpt.data-00000-of-00001
model.ckpt.index
model.ckpt.meta3.安裝Itchat工具包和scikit-image工具包
pip install itchat
pip install scikit-image
安裝出現問的話，可以下載藍燈代理。
itchat是一個開源的微信個人號接口，使用python調用微信,簡化操作。
scikit-image (a.k.a. skimage) 是一個圖像處理和計算機視覺的算法集合
具體查閱ItChat介紹以及scikit-image官網
4.訓練模型調用代碼文件imagerec.py
import sys, getopt
from skimage import io, transform
import tensorflow as tf
import numpy as np
flower_dict = {
0: '菊花',
1: '蒲公英',
2: '玫瑰',
3: '向日葵',
4: '鬱金香'
}
def main(argv):
inputfile = ""
try:
opts, args = getopt.getopt(argv, "hi:o:", ["infile=", "outfile="])
except getopt.GetoptError:
print('Error: test_arg.py -i <inputfile> -o <outputfile>')
print(' or: test_arg.py --infile=<inputfile> --outfile=<outputfile>')
sys.exit(2)
for opt, arg in opts:
if opt == "-h":
print('test_arg.py -i <inputfile> -o <outputfile>')
print('or: test_arg.py --infile=<inputfile> --outfile=<outputfile>')
sys.exit()
elif opt in ("-i", "--infile"):
inputfile = arg
print(inputfile)
print(recgnize(inputfile))
def read_one_image(path):
img = io.imread(path)
img = transform.resize(img, (100, 100))
return np.asarray(img)
def recgnize(filename):
with tf.Session() as sess:
data = []
data.append(read_one_image(filename))
saver = tf.train.import_meta_graph('D:/study/tensorflow/flower/model.ckpt.meta')
saver.restore(sess, tf.train.latest_checkpoint('D:/study/tensorflow/flower/'))
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)# 打印出預測矩陣
#print(classification_result)# 打印出預測矩陣每一行最大值的索引
#print(tf.argmax(classification_result, 1).eval())# 根據索引通過字典對應花的分類
output = []
output = tf.argmax(classification_result, 1).eval()
for i in range(len(output)):
#print("第", i + 1, "朵花預測:" + flower_dict[output[i]])
return flower_dict[output[i]]
if __name__ == "__main__":
main(sys.argv[1: ])5.微信集成wx.py
import itchat, time
from itchat.content import *
from imagerec import recgnize
@itchat.msg_register([TEXT, MAP, CARD, NOTE, SHARING])
def text_reply(msg):
msg.user.send('%s: %s' % ('回覆', msg["Text"]))
@itchat.msg_register([PICTURE, RECORDING, ATTACHMENT, VIDEO])
def download_files(msg):
msg.download(msg.fileName)
type1 = recgnize(msg.fileName)
print(type1)
typeSymbol = {
PICTURE: 'img',
VIDEO: 'vid', }.get(msg.type, 'fil')
msg.user.send(type1)
#return '@%s@%s' % (typeSymbol, msg.fileName)
@itchat.msg_register(FRIENDS)
def add_friend(msg):
msg.user.verify()
msg.user.send('Nice to meet you!')
itchat.auto_login()
itchat.run(True)運行python wx.py
微信掃描生成的QR.PNG文件，登錄確認，這樣你的微信可以進行花卉識別了。
找個好友發個玫瑰花的圖片給你，效果如下：
