def read_model(model_filename):
model = detector_model_pb2.DetectorModel()
f = open(model_filename, "rb")
model.ParseFromString(f.read())
f.close()
if not model.IsInitialized():
print("Input file seems not to be a DetectorModel, " \
"trying as MultiScalesDetectorModel")
model = detector_model_pb2.MultiScalesDetectorModel()
f = open(model_filename, "rb")
model.ParseFromString(f.read())
f.close()
if not model.IsInitialized():
print("Input file seems not to be "\
"a DetectorModel nor a MultiScalesDetectorModel")
raise Exception("Unknown input file format")
print(model.detector_name)
if model.training_dataset_name:
print("trained on dataset:", model.training_dataset_name)
if type(model) is detector_model_pb2.DetectorModel \
and model.soft_cascade_model:
#print("Model shrinking factor ==",
# model.soft_cascade_model.shrinking_factor)
print("Model channels description ==",
model.soft_cascade_model.channels_description)
return model
def read_model(model_filename):
model = detector_model_pb2.DetectorModel()
f = open(model_filename, "rb")
model.ParseFromString(f.read())
f.close()
return model
def read_model_old(model_filepath):
model = detector_model_pb2.DetectorModel()
f = open(model_filepath, "rb")
model.ParseFromString(f.read())
f.close()
assert model.IsInitialized()
return model
def read_model(model_filename):
model = detector_model_pb2.DetectorModel()
f = open(model_filename, "rb")
model.ParseFromString(f.read())
f.close()
if not model.IsInitialized():
print("Input file seems not to be a DetectorModel, " \
"trying as MultiScalesDetectorModel")
model = detector_model_pb2.MultiScalesDetectorModel()
f = open(model_filename, "rb")
model.ParseFromString(f.read())
f.close()
if not model.IsInitialized():
print("Input file seems not to be "\
"a DetectorModel nor a MultiScalesDetectorModel")
raise Exception("Unknown input file format")
return model