def train(self, image_lst, loc_lst, save):
"""
The training phase
"""
# get the training data
data, l, _ = self.get_data(image_lst, loc_lst)
labels = []
print "Get data done"
training_data = []
features = []
for idx, im in enumerate(data):
f = self._extraction.compute(im, self._extraction.detect(im))[1]
if f is not None:
features.append(f)
labels.append(l[idx])
learning_data = np.vstack(features)
# sparse code
self.alpha = 10
sparsecode = sparse.DictLearning(learning_data, 300, self.alpha, save)
self.dictionary = sparsecode.dictionary
print "Build the dictionary done"
# pooling
for feature in features:
coeffs = sparse.encode(feature, self.dictionary, self.alpha)
vector = pooling.max_pooling(coeffs)
training_data.append(vector)
print "Max pooling function done"
training_data = np.vstack(training_data)
labels = np.float32(labels)
print "The number of positive samples:", len(labels[labels==1])
print "The number of false samples:", len(labels[labels==0])
# svm
print training_data.shape
self._classifier.auto_train(training_data, labels)
print "train done"
return self._classifier
def test(self, image, loc_lst, viz=False):
""" test an image """
demo = self.imread(image)
locations = loc_lst
data, l, segments = self.get_data([image], [loc_lst])
num_samples = len(data)
labels = []
features = []
for idx, im in enumerate(data):
f = self._extraction.compute(im, self._extraction.detect(im))[1]
if f is not None:
features.append(f)
labels.append(l[idx])
testing_data = []
for feature in features:
coeffs = sparse.encode(feature, self.dictionary, self.alpha)
vector = pooling.max_pooling(coeffs)
testing_data.append(vector)
testing_data = np.vstack(testing_data)
testing_data = np.float32(testing_data)
labels = np.float32(labels)
result = self._classifier.predict(testing_data)
for predicted, expected, s in zip(result, labels, segments):
if predicted == expected:
s.detected = True
correct = len(filter(lambda x: x.detected, segments))
if viz:
com.visualize_segments(demo, segments, locations)
com.debug_im(demo)
return correct, num_samples
