def classify_image(self, scores): """ Return score as a probability [0,1] for this class. Scores should be a vector of scores of the detections for this image. """ # TODO: rename to classify_scores(), does not use image at all! vector = self.create_vector_from_scores(scores) return svm_proba(vector, self.svm)[0][1]
def classify_image(self, scores):
"""
Return score as a probability [0,1] for this class.
Scores should be a vector of scores of the detections for this image.
"""
# TODO: rename to classify_scores(), does not use image at all!
vector = self.create_vector_from_scores(scores)
return svm_proba(vector, self.svm)[0][1]
def train(self, pos, neg, kernel, C):
y = [1] * pos.shape[0] + [-1] * neg.shape[0]
x = np.concatenate((pos, neg))
model = train_svm(x, y, kernel, C)
self.svm = model
print "model.score(C=%d): %f" % (C, model.score(x, y))
table_t = svm_proba(x, model)
y2 = np.array(y)
y2 = (y2 + 1) / 2 # switch to 0/1
ap, _, _ = Evaluation.compute_cls_pr(table_t[:, 1], y2)
print "ap on train set: %f" % ap
filename = config.get_classifier_filename(self, self.cls, self.train_dataset)
self.svm = model
self.save_svm(model, filename)
return model
def train(self, pos, neg, kernel, C):
y = [1] * pos.shape[0] + [-1] * neg.shape[0]
x = np.concatenate((pos, neg))
model = train_svm(x, y, kernel, C)
self.svm = model
print 'model.score(C=%d): %f' % (C, model.score(x, y))
table_t = svm_proba(x, model)
y2 = np.array(y)
y2 = (y2 + 1) / 2 # switch to 0/1
ap, _, _ = Evaluation.compute_cls_pr(table_t[:, 1], y2)
print 'ap on train set: %f' % ap
filename = config.get_classifier_filename(self, self.cls,
self.train_dataset)
self.svm = model
self.save_svm(model, filename)
return model
