import cv2
import numpy as np
import pickle as pickle

import LFUtilities
import ImageRecognitionSettings as settings
#from BEBLIDRescorerDB import BEBLIDRescorerDB
#from BEBLIDRescorerFAISS import BEBLIDRescorerFAISS
from BEBLIDRescorer import BEBLIDRescorer
import SearcherParameters

from FAISSSearchEngine2 import FAISSSearchEngine
import FeatureExtractor as fe
import BEBLIDExtractorQ as lfQ
import BEBLIDExtractorD as lfD
import logging


class Searcher:

    def __init__(self):
        # self.dataset = h5py.File(settings.dataset_file, 'r')['rmac'][...]

        # np.save('/media/Data/data/beni_culturali/deploy/dataset', self.dataset)
        self.search_engine = FAISSSearchEngine()
        self.rescorer = BEBLIDRescorer()

    def get_indexed_ids(self):
        return self.search_engine.get_indexed_ids()

    def get_id(self, idx):
        return self.search_engine.get_id(idx)

    def exists(self, doc_id):
        return self.search_engine.exists(doc_id)

    def add(self, img_file, doc_id):
        desc = fe.extract(img_file)
        self.search_engine.add(desc, doc_id)

        kp, des = lfD.extract(img_file)
        self.rescorer.add(doc_id, kp, des)

        #orb = lf.extract(img_file)

        self.save(True)
        logging.info('added ' + doc_id)

    def remove(self, doc_id):
        self.search_engine.remove(doc_id)
        self.rescorer.remove(doc_id)
        self.save(True)
        logging.info('removed ' + doc_id)

    def search_by_id(self, query_id, k=10, search_threshold=0.25, search_deep_level=1):
        kq = k
        if search_deep_level > 0:
            kq = SearcherParameters.SEARCH_DEEP_K[search_deep_level]
        res = self.search_engine.search_by_id(query_id, kq)
        if search_deep_level > 0:
            res_lf = self.rescorer.rescore_by_id(query_id, res)
            res = res_lf if res_lf else res[:k]
        res = [result for result in res if result[1] >= search_threshold]
        return res

    def search_by_img(self, query_img, k=10, search_threshold=0.25, search_deep_level=1):
        kq = k
        if search_deep_level:
            kq = SearcherParameters.SEARCH_DEEP_K[search_deep_level]
        query_desc = fe.extract(query_img)
        res = self.search_engine.search_by_img(query_desc, kq)
        if search_deep_level > 0:
            query_lf = lfQ.extract(query_img)
            res_lf = self.rescorer.rescore_by_img(query_lf, res)
            #res = res_lf if res_lf else res[:k]
            res = res_lf if res_lf else res[:k]
        res = [result for result in res if result[1] >= search_threshold]
        return res

    def save(self, is_backup=False):
        self.search_engine.save(is_backup)
        self.rescorer.save(is_backup)