"""
* This file is part of PYSLAM 
*
* Copyright (C) 2016-present Luigi Freda <luigi dot freda at gmail dot com> 
*
* PYSLAM is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* PYSLAM is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with PYSLAM. If not, see <http://www.gnu.org/licenses/>.
"""


import config
config.cfg.set_lib('l2net_keras') 

import os
import sys
import time

import cv2
import numpy as np


from L2_Net import L2Net 

from utils_sys import Printer 

from utils_features import extract_patches_tensor, extract_patches_array, extract_patches_array_cpp


kVerbose = True   


# interface for pySLAM
class L2NetKerasFeature2D: 
    def __init__(self, do_tf_logging=False):  
        print('Using L2NetKerasFeature2D')   
        
        #  One of "L2Net-HP", "L2Net-HP+", "L2Net-LIB", "L2Net-LIB+", "L2Net-ND", "L2Net-ND+", "L2Net-YOS", "L2Net-YOS+",
        self.net_name = 'L2Net-HP+'
                
        # mag_factor is how many times the original keypoint scale
        # is enlarged to generate a patch from a keypoint
        self.mag_factor = 3
        
        # inference batch size        
        self.batch_size = 512 
        self.process_all = True # process all the patches at once   
        
        print('==> Loading pre-trained network.')
        self.l2net = L2Net(self.net_name, do_tf_logging=do_tf_logging)        
        print('==> Successfully loaded pre-trained network.')         
        
        
    def compute(self, frame, kps, mask=None):  #mask is a fake input  
        #print('kps: ', kps)
        if len(kps)>0:
            if False: 
                # use python code 
                patches = extract_patches_array(frame, kps, patch_size=32, mag_factor=self.mag_factor)
            else:
                # use faster cpp code 
                patches = extract_patches_array_cpp(frame, kps, patch_size=32, mag_factor=self.mag_factor)    
            patches = np.asarray(patches)             
            patches = np.expand_dims(patches, -1)
            self.des = self.l2net.calc_descriptors(patches)                    
        else:
            self.des = []
        if kVerbose:
            print('descriptor: L2NET, #features: ', len(kps), ', frame res: ', frame.shape[0:2])                           
        return kps, self.des
           
                   
