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 image_to_features(self, img):
c_feat = utils.color_feat(img)
#Iron
thres_iron = np.linspace(0.3, 1.0, 14, endpoint=False)
iron_feat = np.array([self.get_threshold_features(img, self.iron_temp, thres)
for thres in thres_iron])
#Grass
thres_grass = np.linspace(0.8, 1.0, 14, endpoint=False)
grass_feat = np.array([self.get_threshold_features(img, self.grass_temp, thres)
for thres in thres_grass])
#Total features
features = np.concatenate([iron_feat, grass_feat, c_feat]).tolist()
return features
def image_to_features(self, img):
if self.roof_model is None:
return np.zeros((80,))
c_feat = utils.color_feat(img)
#Iron
thres_iron = np.linspace(0.3, 1.0, 14, endpoint=False)
iron_feat = np.array([self.get_threshold_features(img, self.iron_temp, thres, self.roof_model)
for thres in thres_iron])
iron_feat = np.concatenate(np.transpose(iron_feat))
#Grass
thres_grass = np.linspace(0.8, 1.0, 14, endpoint=False)
grass_feat = np.array([self.get_threshold_features(img, self.grass_temp, thres, self.roof_model)
for thres in thres_grass])
grass_feat = np.concatenate(np.transpose(grass_feat))
#Total features
features = np.concatenate([iron_feat, grass_feat, c_feat]).tolist()
return features
def image_to_features(self, img):
features = utils.color_feat(img)
return self.clf.predict_proba(features)
