def __init__(self):
# data structure
self.data = []
self.features = []
self.width = 27
self.height = 27
self.featureExtractor = AutoEncoderFeatureExtractor(
self.width, self.height)
self.processor = PreProcessor()
self.driver = ProbDriver()
def __init__(self):
# data structure
self.data = []
self.features = []
self.width = 27
self.height = 27
self.featureExtractor = AutoEncoderFeatureExtractor(self.width, self.height)
self.processor = PreProcessor()
self.driver = ProbDriver()
def get_data_from_susig_file(fileName):
preProcessor = PreProcessor()
print "Getting data from %s" % fileName
X = []
Y = []
P = []
with open(fileName) as fp:
lines = fp.readlines()
for line in lines[2:]:
items = line.split()
x = float(items[0])
y = float(items[1])
p = float(items[3])
X.append(x)
Y.append(y)
P.append(p)
X, Y = preProcessor.size_normalization(X, Y, 400, 200)
return X, Y, P
def get_data_from_susig_file(fileName):
preProcessor = PreProcessor()
print "Getting data from %s" % fileName
X = []
Y = []
P = []
with open(fileName) as fp:
lines = fp.readlines()
for line in lines[2:]:
items = line.split()
x = float(items[0])
y = float(items[1])
p = float(items[3])
X.append(x)
Y.append(y)
P.append(p)
X, Y = preProcessor.size_normalization(X,Y,400,200)
return X, Y, P
def inference(bmodel_path, input_path, loops, tpu_id, compare_path):
""" Load a bmodel and do inference.
Args:
bmodel_path: Path to bmodel
input_path: Path to input file
loops: Number of loops to run
tpu_id: ID of TPU to use
compare_path: Path to correct result file
Returns:
True for success and False for failure
"""
# init Engine to load bmodel and allocate input and output tensors
engine = sail.Engine(bmodel_path, tpu_id, sail.SYSIO)
# init preprocessor and postprocessor
preprocessor = PreProcessor([127.5, 127.5, 127.5], 0.0078125)
postprocessor = PostProcessor([0.5, 0.3, 0.7])
reference = postprocessor.get_reference(compare_path)
status = True
# pipeline of inference
for i in range(loops):
# read image
image = cv2.imread(input_path)
image = cv2.transpose(image)
# run PNet, the first stage of MTCNN
boxes = run_pnet(engine, preprocessor, postprocessor, image)
if boxes is not None and len(boxes) > 0:
# run RNet, the second stage of MTCNN
boxes = run_rnet(engine, preprocessor, postprocessor, boxes, image)
if boxes is not None and len(boxes) > 0:
# run ONet, the third stage of MTCNN
boxes = run_onet(engine, preprocessor, postprocessor, boxes,
image)
# print detected result
if postprocessor.compare(reference, boxes, i):
print_result(boxes, tpu_id)
else:
status = False
break
return status
class AutoEncoderDriver(AutoEncoderFeatureDriver):
def __init__(self):
# data structure
self.data = []
self.features = []
self.width = 27
self.height = 27
self.featureExtractor = AutoEncoderFeatureExtractor(self.width, self.height)
self.processor = PreProcessor()
self.driver = ProbDriver()
def load_data(self):
dataDir = "../data/Task2"
os.chdir(dataDir)
curDir = os.getcwd()
self.data = []
for uid in range(1, settings.USER_COUNT + 1):
uidData = []
for sid in range(1, 41):
fileName = "U%dS%d.TXT" % (uid, sid)
X, Y, T, P = self.get_data_from_file(fileName)
uidData.append((X, Y))
self.data.append(uidData)
os.chdir("../..")
def size_normalization(self):
data = []
for uid in range(40):
uidData = []
for sid in range(40):
X, Y = self.processor.size_normalization(
self.data[uid][sid][0], self.data[uid][sid][1], self.width, self.height
)
uidData.append((X, Y))
data.append(uidData)
self.data = data
def imagize(self):
data = []
for uid in range(40):
uidData = []
for sid in range(40):
image = self.featureExtractor.imagize(self.data[uid][sid][0], self.data[uid][sid][1])
uidData.append(image)
data.append(uidData)
self.data = data
def train(self, layer_sizes=[500, 300, 100, 50], epoch=1000):
if not self.data:
self.imagize()
train_set_x = numpy.asarray(self.data)
(uCnt, sCnt, pCnt) = train_set_x.shape
train_set_x = train_set_x.reshape((uCnt * sCnt, pCnt))
n_ins = (self.width + 1) * (self.height + 1)
# train data
# self.featureExtractor.train(train_set_x, n_ins, layer_sizes, epoch)
self.featureExtractor.train_with_mnist(
pretraining_epochs=15, training_epochs=epoch, hidden_layers_sizes=layer_sizes
)
def generate_features(self):
"""
generate feature from image to features using stacked autoencoder
"""
# train data first
self.train()
print "generating features..."
self.features = []
for uid in range(40):
uidFeatures = []
for sid in range(40):
feature = self.featureExtractor.generate_features(self.data[uid][sid])
uidFeatures.append(feature)
# print ">>>uid: %d, sid: %d ends" % (uid, sid)
self.features.append(uidFeatures)
def dump_feature(self):
print "... dumpint features"
dataDir = "./data"
os.chdir(dataDir)
autoFeatureDir = "auto_features"
if not os.path.exists(autoFeatureDir):
os.mkdir(autoFeatureDir)
os.chdir(autoFeatureDir)
for uid in range(40):
for sid in range(40):
fileName = "u%ds%d.txt" % (uid, sid)
numpy.savetxt(fileName, self.features[uid][sid], fmt="%10.5f")
os.chdir("../..")
def train_test_set(self, uid, cnt):
uidFeatures = self.features[uid]
train_set_x = []
pos_set_x = []
neg_set_x_ori = []
neg_set_x_oth = []
for sid in range(cnt):
train_set_x.append(uidFeatures[sid].tolist())
for sid in range(cnt, 20):
pos_set_x.append(uidFeatures[sid].tolist())
for sid in range(20, 40):
neg_set_x_ori.append(uidFeatures[sid].tolist())
for i in range(40):
if i == uid:
continue
for sid in range(40):
neg_set_x_oth.append(self.features[i][sid].tolist())
return train_set_x, pos_set_x, neg_set_x_ori, neg_set_x_oth
def score_of_uid(self, uid, cnt):
train_set_x, pos_set_x, neg_set_x_ori, neg_set_x_oth = self.train_test_set(uid, cnt)
driver = ProbDriver()
# print ">>> training..."
driver.ps_temp(train_set_x)
# print ">>> train set"
trainPS = []
for X in train_set_x:
ps = driver.PS(X)
# print ps
trainPS.append(ps)
threshold = min(trainPS)
# print ">>> train set min is ", threshold
def _score_of_set(set_x, pos=True):
size = len(set_x)
setPS = []
for X in set_x:
ps = driver.PS(X)
setPS.append(ps)
if pos:
correctSize = len([ps for ps in setPS if ps >= threshold])
else:
correctSize = len([ps for ps in setPS if ps <= threshold])
return correctSize / float(size)
# testing process
# print ">>> postive test set"
scoreOfPos = _score_of_set(pos_set_x, pos=True)
# print ">>> total postive set %d, greater than threshold %f" % (len(pos_set_x), scoreOfPos)
# print ">>> negtive test set"
scoreOfNegOri = _score_of_set(neg_set_x_ori, pos=False)
# print ">>> original negtive set %d, less than threshold %f" % (len(neg_set_x_ori), scoreOfNegOri)
# print ">>> other negtive test set"
scoreOfNegOth = _score_of_set(neg_set_x_oth, pos=False)
# print ">>> total negtive set %d, less than threhold %f" % (len(neg_set_x_oth), scoreOfNegOth)
return scoreOfPos, scoreOfNegOri, scoreOfNegOth
def score(self):
self.load_feature()
scoreOfPos = []
scoreOfNegOri = []
scoreOfNegOth = []
for cnt in [3, 5, 7, 10, 15]:
for uid in range(40):
pos, negOri, negOth = self.score_of_uid(uid, cnt)
scoreOfPos.append(pos)
scoreOfNegOri.append(negOri)
scoreOfNegOth.append(negOth)
print numpy.mean(scoreOfPos), numpy.mean(scoreOfNegOri), numpy.mean(scoreOfNegOth)
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
class AutoEncoderDriver(AutoEncoderFeatureDriver):
def __init__(self):
# data structure
self.data = []
self.features = []
self.width = 27
self.height = 27
self.featureExtractor = AutoEncoderFeatureExtractor(
self.width, self.height)
self.processor = PreProcessor()
self.driver = ProbDriver()
def load_data(self):
dataDir = "../data/Task2"
os.chdir(dataDir)
curDir = os.getcwd()
self.data = []
for uid in range(1, settings.USER_COUNT + 1):
uidData = []
for sid in range(1, 41):
fileName = "U%dS%d.TXT" % (uid, sid)
X, Y, T, P = self.get_data_from_file(fileName)
uidData.append((X, Y))
self.data.append(uidData)
os.chdir("../..")
def size_normalization(self):
data = []
for uid in range(40):
uidData = []
for sid in range(40):
X, Y = self.processor.size_normalization(
self.data[uid][sid][0], self.data[uid][sid][1], self.width,
self.height)
uidData.append((X, Y))
data.append(uidData)
self.data = data
def imagize(self):
data = []
for uid in range(40):
uidData = []
for sid in range(40):
image = self.featureExtractor.imagize(self.data[uid][sid][0],
self.data[uid][sid][1])
uidData.append(image)
data.append(uidData)
self.data = data
def train(self, layer_sizes=[500, 300, 100, 50], epoch=1000):
if not self.data:
self.imagize()
train_set_x = numpy.asarray(self.data)
(uCnt, sCnt, pCnt) = train_set_x.shape
train_set_x = train_set_x.reshape((uCnt * sCnt, pCnt))
n_ins = (self.width + 1) * (self.height + 1)
# train data
# self.featureExtractor.train(train_set_x, n_ins, layer_sizes, epoch)
self.featureExtractor.train_with_mnist(pretraining_epochs=15,
training_epochs=epoch,
hidden_layers_sizes=layer_sizes)
def generate_features(self):
"""
generate feature from image to features using stacked autoencoder
"""
# train data first
self.train()
print "generating features..."
self.features = []
for uid in range(40):
uidFeatures = []
for sid in range(40):
feature = self.featureExtractor.generate_features(
self.data[uid][sid])
uidFeatures.append(feature)
# print ">>>uid: %d, sid: %d ends" % (uid, sid)
self.features.append(uidFeatures)
def dump_feature(self):
print "... dumpint features"
dataDir = "./data"
os.chdir(dataDir)
autoFeatureDir = "auto_features"
if not os.path.exists(autoFeatureDir):
os.mkdir(autoFeatureDir)
os.chdir(autoFeatureDir)
for uid in range(40):
for sid in range(40):
fileName = "u%ds%d.txt" % (uid, sid)
numpy.savetxt(fileName, self.features[uid][sid], fmt="%10.5f")
os.chdir("../..")
def train_test_set(self, uid, cnt):
uidFeatures = self.features[uid]
train_set_x = []
pos_set_x = []
neg_set_x_ori = []
neg_set_x_oth = []
for sid in range(cnt):
train_set_x.append(uidFeatures[sid].tolist())
for sid in range(cnt, 20):
pos_set_x.append(uidFeatures[sid].tolist())
for sid in range(20, 40):
neg_set_x_ori.append(uidFeatures[sid].tolist())
for i in range(40):
if i == uid:
continue
for sid in range(40):
neg_set_x_oth.append(self.features[i][sid].tolist())
return train_set_x, pos_set_x, neg_set_x_ori, neg_set_x_oth
def score_of_uid(self, uid, cnt):
train_set_x, pos_set_x, neg_set_x_ori, neg_set_x_oth = self.train_test_set(
uid, cnt)
driver = ProbDriver()
# print ">>> training..."
driver.ps_temp(train_set_x)
# print ">>> train set"
trainPS = []
for X in train_set_x:
ps = driver.PS(X)
# print ps
trainPS.append(ps)
threshold = min(trainPS)
# print ">>> train set min is ", threshold
def _score_of_set(set_x, pos=True):
size = len(set_x)
setPS = []
for X in set_x:
ps = driver.PS(X)
setPS.append(ps)
if pos:
correctSize = len([ps for ps in setPS if ps >= threshold])
else:
correctSize = len([ps for ps in setPS if ps <= threshold])
return correctSize / float(size)
# testing process
# print ">>> postive test set"
scoreOfPos = _score_of_set(pos_set_x, pos=True)
# print ">>> total postive set %d, greater than threshold %f" % (len(pos_set_x), scoreOfPos)
# print ">>> negtive test set"
scoreOfNegOri = _score_of_set(neg_set_x_ori, pos=False)
# print ">>> original negtive set %d, less than threshold %f" % (len(neg_set_x_ori), scoreOfNegOri)
# print ">>> other negtive test set"
scoreOfNegOth = _score_of_set(neg_set_x_oth, pos=False)
# print ">>> total negtive set %d, less than threhold %f" % (len(neg_set_x_oth), scoreOfNegOth)
return scoreOfPos, scoreOfNegOri, scoreOfNegOth
def score(self):
self.load_feature()
scoreOfPos = []
scoreOfNegOri = []
scoreOfNegOth = []
for cnt in [3, 5, 7, 10, 15]:
for uid in range(40):
pos, negOri, negOth = self.score_of_uid(uid, cnt)
scoreOfPos.append(pos)
scoreOfNegOri.append(negOri)
scoreOfNegOth.append(negOth)
print numpy.mean(scoreOfPos), numpy.mean(
scoreOfNegOri), numpy.mean(scoreOfNegOth)
def test_pre_processor():
file = get_file("tests/test_sample/82251504.png")
new_file = PreProcessor(100).run(file)
assert type(new_file) == io.BytesIO
def test_ocr_processor():
file = get_file("tests/test_sample/82251504.png")
fixed_text = ocr_precess(file, PreProcessor(100), OCRTesseractProcessor(), PostProcessor())
assert type(fixed_text) == str
