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 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
def __init__(self):
self.svmProcessor = SVMProcessor()
self.probFeatureExtractor = ProbFeatureExtractor()
self.preProcessor = PreProcessor()
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
