def predict(features: numpy.ndarray) -> float:
x, idx = svmutil.gen_svm_nodearray(
features, isKernel=(model.param.kernel_type == svmutil.PRECOMPUTED)
)
nr_classifier = 1
prob_estimates = (svmutil.c_double * nr_classifier)()
return svmutil.libsvm.svm_predict_probability(model, x, prob_estimates)
def calculate_image_quality_score(brisque_features):
model = svmutil.svm_load_model('utils/brisque/brisque_svm.txt')
scaled_brisque_features = scale_features(brisque_features)
x, idx = svmutil.gen_svm_nodearray(
scaled_brisque_features,
isKernel=(model.param.kernel_type == svmutil.PRECOMPUTED))
nr_classifier = 1
prob_estimates = (svmutil.c_double * nr_classifier)()
return svmutil.libsvm.svm_predict_probability(model, x, prob_estimates)
def _calculate_score(self, scaled_feature):
"""Calculate score from scaled brisque feature.
:param scaled_feature: Scaled brisque feature.
:type scaled_feature: np.ndarray
"""
x, idx = gen_svm_nodearray(
scaled_feature.tolist(),
isKernel=(self._model.param.kernel_type == 'PRECOMPUTED'))
nr_classifier = 1
prob_estimates = (c_double * nr_classifier)()
return svmutil.libsvm.svm_predict_probability(self._model, x,
prob_estimates)
def calculate_image_quality_score(brisque_features):
'''
Using a pre-trained SVR model we calculate the quality assessment scores.
'''
model = svmutil.svm_load_model(os.path.join("deeppixel", "iqa", "brisque_svm.txt"))
scaled_brisque_features = scaled(brisque_features)
x, idx = svmutil.gen_svm_nodearray(
scaled_brisque_features,
isKernel=(model.param.kernel_type == svmutil.PRECOMPUTED))
nr_classifier = 1
prob_estimates = (svmutil.c_double * nr_classifier)()
return svmutil.libsvm.svm_predict_probability(model, x, prob_estimates)
def predict(data, path):
if not os.path.exists(path):
print 'Model path does not exist, do nothing.'
return
print "Loading features."
integerizer = tools.integerization.CIntegerization.read(os.path.join(path, 'int'))
model = svmutil.svm_load_model(os.path.join(path, "scr"))
print "Decoding."
for sent in posreader.readpossent(data):
print sent
for start in range(len(sent)):
for end in range(start, len(sent)):
label, feature = extractfeat.extractOneFeat(sent, (start, end, None), integerizer)
assert label == -1
nodes, x = svmutil.gen_svm_nodearray(feature)
label = svm.libsvm.svm_predict(model, nodes)
probabilities = (ctypes.c_double*2)()
label = svm.libsvm.svm_predict_probability(model, nodes, probabilities)
print start, end, probabilities[0], probabilities[1]
def predict(data, path):
if not os.path.exists(path):
print 'Model path does not exist, do nothing.'
return
print "Loading features."
integerizer = tools.integerization.CIntegerization.read(os.path.join(path, 'int'))
model = svmutil.svm_load_model(os.path.join(path, "scr"))
print "Working."
for sent in io.getsent(data):
labels = []
for index in range(len(sent)):
f = feature.extractFeatures(sent, index, integerizer)
nodes, x = svmutil.gen_svm_nodearray(f)
probabilities = (ctypes.c_double*2)()
label = svm.libsvm.svm_predict_probability(model, nodes, probabilities)
x = 1
if probabilities[0] > cutoff:
x = 0
labels.append(str(x))
print ' '.join(labels)
def predict(data, path):
if not os.path.exists(path):
print 'Model path does not exist, do nothing.'
return
print "Loading features."
integerizer = tools.integerization.CIntegerization.read(
os.path.join(path, 'int'))
model = svmutil.svm_load_model(os.path.join(path, "scr"))
print "Working."
for sent in io.getsent(data):
labels = []
for index in range(len(sent)):
f = feature.extractFeatures(sent, index, integerizer)
nodes, x = svmutil.gen_svm_nodearray(f)
probabilities = (ctypes.c_double * 2)()
label = svm.libsvm.svm_predict_probability(model, nodes,
probabilities)
x = 1
if probabilities[0] > cutoff:
x = 0
labels.append(str(x))
print ' '.join(labels)
