mean_descriptor.py

def __create_feature_vector_mean(self, file_name, piquete_id, score, height):
        rgb_mean = [0] * 3
        image_path = self.__image_files_root_folder+ str(piquete_id)+'/'+file_name
        image = cv2.imread(image_path)
        print(image_path)
        means = cv2.mean(image)
        
        if means is not None:
            #raw = image.flatten()
            print(str(means[:3])+'\n')
            
            rgb_mean[0] = means[0]
            rgb_mean[1] = means[1]
            rgb_mean[2] = means[2]
            #rgb_mean[3] = height * 1000

            #print(preprocessing.scale(rgb_mean))

            #means[3] = height
            #scaled_means = preprocessing.scale(means[:3])
            if self.__check_for_bad_images(file_name, piquete_id, score):
                self.__bullgreen_dataset.feature_vector.append(preprocessing.scale(rgb_mean))
                #self.__bullgreen_dataset.feature_vector.append(preprocessing.scale(means))
                return True
            else:
                return False
        else:
            return False