def row_to_features(self, img, row):
labels = list()
features = list()
for roof in row.roofs:
labels.append(type_dict[roof['type']])
roof_patch = utils.crop_from_center(img, roof['x'], roof['y'])
features.append(utils.color_feat(roof_patch))
return labels, features
def get_threshold_features(self, img, template, thres, roof_classifier):
moments = self.detect_objects(img, template, thres)
num_obj = moments.shape[0]
if num_obj == 0:
return num_obj, 0
else:
#Get classfication scores
patches = [utils.crop_from_center(img, x, y) for x, y in zip(moments['x'], moments['y'])]
#Probabilities of being iron
scores = [roof_classifier.image_to_features(patch)[0][1] for patch in patches]
mean_sc = np.mean(scores)
return num_obj, mean_sc