def classify(pred, x, keep_prob, jpg):
applyStore = TrainStore(ws, 'apply')
labels = applyStore.getLabels()
imgArray = jb.imgResize2Array(jpg, imgWidth)
classification = pred.eval(feed_dict={x: [imgArray], keep_prob: 1.0})
label = labels[classification[0]]
mvTo = jpg.replace('/apply/', '/apply/' + label + '/')
print(mvTo, label)
os.rename(jpg, mvTo)
return label, mvTo
def prod_apply(username, password, channel):
modelMeta = ws.modelMeta(model)
print(modelMeta)
with tf.Session() as sess:
saver = tf.train.import_meta_graph(modelMeta, clear_devices=True)
saver.restore(sess, modelStore)
x = tf.get_collection('x')[0]
y_conv = tf.get_collection('y_conv')[0]
keep_prob = tf.get_collection('keep_prob')[0]
pred = tf.argmax(y_conv, 1)
applyStore = TrainStore(ws, 'apply').getRootPath()
urlSnapshot = UrlSnapshot(applyStore, username, password, channel)
while (True):
jpgFile = urlSnapshot.pull()
if jpgFile is not None:
# print detail
imgArray = jb.imgResize2Array(jpgFile, imgWidth)
print(y_conv.eval(feed_dict={x: [imgArray], keep_prob: 1.0}))
label, moveTo = classify(pred, x, keep_prob, jpgFile)
if sendmail:
conf['email']['body'] = label
jbMail = JbMail(conf)
jbMail.attachImages([moveTo])
print('sending email ...')
# jbMail.send()
jbMail.send_ssl()
sleep(15)
break
def robot(username, password):
learnStore = TrainStore(ws, 'learn').getRootPath()
urlSnapshot = UrlSnapshot(learnStore, username, password, channel=2)
for i in range(360):
jpgFilename = urlSnapshot.pull()
print(jpgFilename)
sleep(10)
def learn():
x = tf.placeholder(tf.float32, [None, imgWidth * imgHeight])
# Define loss and optimizer
y_ = tf.placeholder(tf.float32, [None, labelSize])
y_conv, keep_prob, train_step, accuracy = mnstModel(
x, y_, imgWidth, imgHeight, labelSize)
# for save model
saver = tf.train.Saver()
tf.add_to_collection('x', x)
tf.add_to_collection('keep_prob', keep_prob)
tf.add_to_collection('y_conv', y_conv)
# Import data
learnStore = TrainStore(ws, 'learn')
trainData = jb.ImgLabelData(learnStore)
applyStore = TrainStore(ws, 'apply')
testData = jb.ImgLabelData(applyStore)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(trainIteration):
batch = trainData.nextBatch(batchSize, imgWidth)
if i % 50 == 0:
train_accuracy = accuracy.eval(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 1.0
})
print('step %d, training accuracy %g' % (i, train_accuracy))
train_step.run(feed_dict={
x: batch[0],
y_: batch[1],
keep_prob: 0.5
})
saver.save(sess, modelStore)
print("model saved in", modelStore)
testBatch = testData.nextBatch(2)
print('test accuracy %g' % accuracy.eval(feed_dict={
x: testBatch[0],
y_: testBatch[1],
keep_prob: 1.0
}))
def daily_apply():
modelMeta = ws.modelMeta(model)
print(modelMeta)
with tf.Session() as sess:
saver = tf.train.import_meta_graph(modelMeta, clear_devices=True)
saver.restore(sess, modelStore)
x = tf.get_collection('x')[0]
y_conv = tf.get_collection('y_conv')[0]
keep_prob = tf.get_collection('keep_prob')[0]
pred = tf.argmax(y_conv, 1)
applyStore = TrainStore(ws, 'apply')
labels = applyStore.getLabels()
count = {}
for label in labels:
count[label] = 0
archives = getDailyList()
print(archives)
for archive in archives:
if ".tar.gz" not in archive:
continue
file = getDailyArchive(archive)
dailyApplyFolder = extractArchive(file)
for label in labels:
wd = dailyApplyFolder + '/' + label
if not os.path.exists(wd):
os.makedirs(wd)
print(dailyApplyFolder)
jpgs = glob.glob(dailyApplyFolder + '/*.jpg')
for jpgFile in jpgs:
imgArray = jb.imgResize2Array(jpgFile, imgWidth)
print(y_conv.eval(feed_dict={x: [imgArray], keep_prob: 1.0}))
label, moveTo = dailyClassify(pred, x, keep_prob, jpgFile,
dailyApplyFolder)
count[label] = count[label] + 1
# if sendmail and label == 'visitor':
# conf['email']['body'] = label
# jbMail = JbMail(conf)
# jbMail.attachImages([moveTo])
# print('sending email ...')
# # jbMail.send()
# jbMail.send_ssl()
delDailyArchive(archive)
print(count)
break
def take_snapshot(username, password):
url = 'http://114.35.223.91/cgi-bin/net_jpeg.cgi?ch=2' # + ch # + '&1514199511126'
request = Request(url)
credentials = ('%s:%s' % (username, password))
encoded_credentials = base64.b64encode(credentials.encode('ascii'))
request.add_header('Authorization',
'Basic %s' % encoded_credentials.decode("ascii"))
learnStore = TrainStore(ws, 'learn').getRootPath()
for i in range(72):
response = urlopen(request)
data = response.read()
timestamp = datetime.datetime.now()
jpgFilename = learnStore + '/at_' + timestamp.strftime(
"%Y%m%d_%H%M%S_%f") + '.jpg'
jpgFile = open(jpgFilename, "wb")
jpgFile.write(data)
jpgFile.close()
print(jpgFilename)
sleep(3600)