def testTrimTrack(self):
"""
Test correct trimming on real (albeit contrived) data
(assume a single track - remove leading/trailing silences)
"""
voicesubsamples = CP.load(open('dummyVoice.p'))
voicetrack = Segmentor.segmentVoice(voicesubsamples)[0]
self.assert_(min(voicetrack) == 45 and max(voicetrack) == 104)
def doTapeFlip(self):
""" For 2-sided tape, save the first side and resume... """
self.status.config(text='Flip tape, then play...')
# Save side one
self.recsound.stop()
(sampfreq,subrate) = (44100,20000)
samps = Segmentor.getMonoAmpSamples(self.recsound,
subrate)
trackAssign = Segmentor.segmentVoice(samps)
(tstart,tend) = (min(trackAssign[0])*subrate,
max(trackAssign[0])*subrate)
self.sideone = tkSnack.Sound()
self.sideone.copy(self.recsound,start=tstart,end=tend)
# Restart recording
self.recsound = tkSnack.Sound()
self.recsound.configure(channels="Stereo")
self.recsound.configure(frequency=44100)
self.recsound.record()
def testSplitTracks(self):
"""
Test correct track-splitting on real (albeit contrived) data
"""
tracksubsamples = CP.load(open('dummyTrack.p'))
assign = Segmentor.segmentTracks(tracksubsamples)
truth = CP.load(open('trackCorrect.p'))
self.assert_(all([a == t for (a,t) in
zip(assign,truth)]))
def testToyData(self):
""" Test track splitting on synthetic data """
# Generate synthetic data
testruns = [('gap',20),
('track',50), # Track 1
('gap',10),
('track',30),('gap',3),('track',20), # Track 2
('gap',15),
('track',70), # Track 3
('gap',10),('track',1),('gap',10)]
(data, labels) = (ToyData.generateData(testruns),
ToyData.generateLabels(testruns))
# Segment tracks
assign = Segmentor.segmentTracks(data)
# There should be 3 tracks found
# (fake gap and fake track should be ignored)
self.assert_(len(assign) == 3)
def VegetationClassification(Img):
'''
This function is used to classify the green vegetation from GSV image,
This is based on object based and otsu automatically thresholding method
The season of GSV images were also considered in this function
Img: the numpy array image, eg. Img = np.array(Image.open(StringIO(response.content)))
return the percentage of the green vegetation pixels in the GSV image
By Xiaojiang Li
'''
import numpy as np
# import pymeanshift as pms
#
# # use the meanshift segmentation algorithm to segment the original GSV image
# (segmented_image, labels_image, number_regions) = pms.segment(Img,spatial_radius=6,
# range_radius=7, min_density=40)
import Segmentor
segmented_image = Segmentor.segment(Img)
I = segmented_image/255.0
red = I[:,:,0]
green = I[:,:,1]
blue = I[:,:,2]
# calculate the difference between green band with other two bands
green_red_Diff = green - red
green_blue_Diff = green - blue
ExG = green_red_Diff + green_blue_Diff
diffImg = green_red_Diff*green_blue_Diff
redThreImgU = red < 0.6
greenThreImgU = green < 0.9
blueThreImgU = blue < 0.6
shadowRedU = red < 0.3
shadowGreenU = green < 0.3
shadowBlueU = blue < 0.3
del red, blue, green, I
greenImg1 = redThreImgU * blueThreImgU*greenThreImgU
greenImgShadow1 = shadowRedU*shadowGreenU*shadowBlueU
del redThreImgU, greenThreImgU, blueThreImgU
del shadowRedU, shadowGreenU, shadowBlueU
greenImg3 = diffImg > 0.0
greenImg4 = green_red_Diff > 0
threshold = graythresh(ExG, 0.1)
if threshold > 0.1:
threshold = 0.1
elif threshold < 0.05:
threshold = 0.05
greenImg2 = ExG > threshold
greenImgShadow2 = ExG > 0.05
greenImg = greenImg1*greenImg2 + greenImgShadow2*greenImgShadow1
del ExG,green_blue_Diff,green_red_Diff
del greenImgShadow1,greenImgShadow2
# image_show(greenImg)
# plt.show(block=False)
# plt.pause(1)
# plt.close()
# calculate the percentage of the green vegetation
greenPxlNum = len(np.where(greenImg != 0)[0])
greenPercent = greenPxlNum/(400.0*400)*100
del greenImg1,greenImg2
del greenImg3,greenImg4
return greenPercent
def main():
'''
Read in CIFAR10 data (limited to 2 classes), initialize your model, and train and
test your model for a number of epochs. We recommend that you train for
10 epochs and at most 25 epochs. For CS2470 students, you must train within 10 epochs.
You should receive a final accuracy on the testing examples for cat and dog of >=70%.
:return: None
'''
# Use the titan
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
# Segmentor or Classifier (0 or 1)
choose = 1
# Initialize the model
if choose:
model = Classifier.Model()
else:
model = Segmentor.Model()
# For saving/loading models
# Get the date and time in a string
now = datetime.now()
if choose:
mod_str = '_classifier'
else:
mod_str = '_segmentor'
dt_string = now.strftime("%d.%m.%Y_%H.%M") + mod_str
checkpoint_dir = dt_string
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(checkpoint,
checkpoint_dir,
max_to_keep=80)
print(manager.checkpoints)
print(manager.latest_checkpoint)
# Restore latest checkpoint
# checkpoint.restore('./checkpoints_data/ckpt-672')
checkpoint.restore(manager.latest_checkpoint)
# Train it
if choose:
train_class(model, '/media/user1/My4TBHD1/Lung/processed_data',
import_excel('./Lung-PET.xlsx', 'Lung-PENN'), manager, 0,
checkpoint_dir)
else:
train_seg(model, '/media/user1/My4TBHD1/Lung/processed_data', manager,
0, checkpoint_dir)
for i in range(10):
train_seg(model, '/media/user1/My4TBHD1/Lung/processed_data',
manager, 0, checkpoint_dir)
manager.save()
# Graph loss
fig = plt.figure()
ax = plt.axes()
loss_graph = np.load(checkpoint_dir + '/loss.npy')
print(len(loss_graph))
x = list(range(len(loss_graph)))
ax.plot(x, loss_graph)
plt.show()
# Test it
if choose:
print('hi')
else:
patients = sorted(os.listdir('./processed_data/'))
for patient in patients:
print('Current patient: ', patient)
test_model(model, './processed_data/' + patient)
import datetime
import json
import pprint
import copy
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition import NMF, LatentDirichletAllocation
import random
import gensim
import jieba
import jieba.posseg as pseg
from re import compile as _Re
from gensim.models.word2vec import Word2Vec
from gensim.summarization import keywords
from Segmentor import *
segmenter = Segmentor()
tagger = POSTagger()
import re
from elasticsearch import Elasticsearch
es = Elasticsearch([{'host': '192.168.2.10', 'port': 9200}])
def content_segmentor(article):
article = ""
sentences = Tokenizer.ToSents(article)
for sent in sentences:
# 在斷詞
words = segmenter.segment(sent)
if words != []:
article += ' '.join(words)
def vegetation_classification(img):
"""
This function is used to classify the green vegetation from GSV image,
This is based on object based and otsu automatically thresholding method
The season of GSV images were also considered in this function
img: the numpy array image, eg. img = numpy.array(Image.open(StringIO(response.content)))
return the percentage of the green vegetation pixels in the GSV image
By Xiaojiang Li
"""
# import pymeanshift as pms
#
# # use the meanshift segmentation algorithm to segment the original GSV image
# (segmented_image, labels_image, number_regions) = pms.segment(img,spatial_radius=6,
# range_radius=7, min_density=40)
import Segmentor
segmented_image = Segmentor.segment(img)
image_norm = segmented_image / 255.0
red = image_norm[:, :, 0]
green = image_norm[:, :, 1]
blue = image_norm[:, :, 2]
# calculate the difference between green band with other two bands
green_red_diff = green - red
green_blue_diff = green - blue
exg = green_red_diff + green_blue_diff
diff_img = green_red_diff * green_blue_diff
red_thre_img_u = red < 0.6
green_thre_img_u = green < 0.9
blue_thre_img_u = blue < 0.6
shadow_red_u = red < 0.3
shadow_green_u = green < 0.3
shadow_blue_u = blue < 0.3
del red, blue, green, image_norm
gree_img1 = red_thre_img_u * blue_thre_img_u * green_thre_img_u
green_img_shadow1 = shadow_red_u * shadow_green_u * shadow_blue_u
del red_thre_img_u, green_thre_img_u, blue_thre_img_u
del shadow_red_u, shadow_green_u, shadow_blue_u
gree_img3 = diff_img > 0.0
gree_img4 = green_red_diff > 0
threshold = graythresh(exg, 0.1)
if threshold > 0.1:
threshold = 0.1
elif threshold < 0.05:
threshold = 0.05
gree_img2 = exg > threshold
green_img_shadow2 = exg > 0.05
green_img = gree_img1 * gree_img2 + green_img_shadow2 * green_img_shadow1
del exg, green_blue_diff, green_red_diff
del green_img_shadow1, green_img_shadow2
# image_show(greenImg)
# plt.show(block=False)
# plt.pause(1)
# plt.close()
# calculate the percentage of the green vegetation
green_pxl_num = len(numpy.where(green_img != 0)[0])
green_percent = green_pxl_num / (400.0 * 400) * 100
del gree_img1, gree_img2
del gree_img3, gree_img4
return green_percent
VALIDATION_SIZE = 1000 TRAIN_SIZE = 5172 X = data['training_data'] Y = data['training_labels'].T.ravel() randomIndex = np.random.choice(TRAIN_SIZE, TRAIN_SIZE, replace=False) # Split Data xTrain = X[randomIndex[:-VALIDATION_SIZE]] yTrain = Y[randomIndex[:-VALIDATION_SIZE]] xValidate = X[randomIndex[-VALIDATION_SIZE:]] yValidate = Y[randomIndex[-VALIDATION_SIZE:]] xTest = data['test_data'] segmentor = Segmentor() print "============= Decision Tree ==========" tree = DTree(Impurity.impurity, segmentor, depth=20) tree.train(xTrain, yTrain) labels = tree.predict(xValidate) counts = np.bincount(tree.predict(xTrain) == yTrain) error = 1.0 - (counts[True] / float(counts[True] + counts[False])) print "Training Error: %f" % (error) counts = np.bincount(labels == yValidate) error = 1.0 - (counts[True] / float(counts[True] + counts[False])) print "Validation Error: %f" % (error) #import pdb; pdb.set_trace()
def recordSound(self):
""" Start/stop recording from the mic input """
if(self.state == 0):
#
# Start recording
#
self.recsound = tkSnack.Sound()
self.recsound.configure(channels="Stereo")
self.recsound.configure(frequency=44100)
self.recsound.record()
self.status.config(text='Recording...')
self.record_sound.config(text="STOP",bg="red")
self.state = 1
elif(self.state == 1):
#
# Stop recording, write tracks out to disk for burning
#
self.recsound.stop()
# Wave sampling freq (Hz)
sampfreq = 44100
# Convert to mono (take max amplitude over channels) and subsample
subrate = 20000
samps = Segmentor.getMonoAmpSamples(self.recsound,
subrate)
# Process subsampled audio
self.status.config(text='Processing audio...')
if(self.tracksplit.get() == 0):
# VOICE: just find single track
trackAssign = Segmentor.segmentVoice(samps)
elif(self.tracksplit.get() == 2):
# MUSIC: segment recording into individual tracks
trackAssign = Segmentor.segmentTracks(samps)
# Write these tracks out to disk
if(self.sideone != None):
# We have a previous side-one track, write it out
fn = os.path.join(datadir,'track%d.wav' % 0)
self.sideone.write(fn)
# Then write out the current track
(tstart,tend) = (min(trackAssign[0])*subrate,
max(trackAssign[0])*subrate)
newTrack = tkSnack.Sound()
newTrack.copy(self.recsound,start=tstart,end=tend)
fn = os.path.join(datadir,'track%d.wav' % 1)
newTrack.write(fn)
else:
# No side-one, just write these tracks out to disk
for (tracknum,ta) in enumerate(trackAssign):
(tstart,tend) = (min(ta)*subrate,max(ta)*subrate)
newTrack = tkSnack.Sound()
newTrack.copy(self.recsound,start=tstart,end=tend)
fn = os.path.join(datadir,'track%d.wav' % tracknum)
newTrack.write(fn)
self.status.config(text='Ready to burn!')
self.recsound = None
self.record_sound.config(text="BURN",bg='red')
self.state = 2
elif(self.state == 2):
#
# Burn tracks from disk to a blank CD
#
tracks = [fn for fn in os.listdir(datadir)
if re.match('track\d+\.wav',fn)]
def sortfn(a,b):
aval = int(re.match('track(\d+)\.wav',a).group(1))
bval = int(re.match('track(\d+)\.wav',b).group(1))
return aval-bval
tracks.sort(sortfn)
# Construct cdrecord command for CD burning
cmd = 'cdrecord fs=4096k -v -useinfo speed=1 '
cmd += '-dao -eject -pad -audio '
cmd += '-dev=%s ' % (self.dev)
for track in tracks:
cmd += '\"%s\" ' % os.path.join(datadir,track)
self.status.config(text='Burning CD...')
os.system(cmd)
self.status.config(text='Done - CD completed')
# Now that we're done, cleanup by deleting temp track files
for track in tracks:
os.remove(os.path.join(datadir,track))
# Reset state
self.state = 0
