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 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 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
