import cv2
import numpy as np

import LFUtilities
import ORBParameters
import WebAppSettings as settings


class ORBRescorer:

    def __init__(self):
        self.lf = LFUtilities.load(settings.DATASET_LF)
        self.ids = np.loadtxt(settings.DATASET_IDS, dtype=str).tolist()

        #self.orb = cv2.ORB_create()
        self.bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)

    def rescore_by_id(self, query_id, resultset):
        query_idx = self.ids.index(query_id)
        return self.rescore_by_img(self.lf[query_idx], resultset)

    def rescore_by_img(self, query, resultset):
        max_inliers = -1
        res = []

        for data_id, _ in resultset:
            data_idx = self.ids.index(data_id)
            try:
                data_el = self.lf[data_idx]
                matches = self.bf.match(query[1], data_el[1])
                good = [m for m in matches if m.distance <= ORBParameters.THRESHOLD]
                if len(good) > ORBParameters.MIN_GOOD_MATCHES:
                    src_pts = np.float32([query[0][m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
                    dst_pts = np.float32([data_el[0][m.trainIdx].pt for m in good]).reshape(-1, 1, 2)

                    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 1.0)
                    matches_mask = mask.ravel().tolist()
                    # print(len(good))
                    inliers = np.count_nonzero(matches_mask)
                    # print(inliers)
                    if (inliers >= ORBParameters.MIN_INLIERS and inliers > max_inliers):
                        max_inliers = inliers
                        res.append((data_id, inliers))
            except:
                print('rescore error evaluating ' + data_id)
                pass

        if res:
            res.sort(key=lambda result: result[1], reverse=True)
            return res

    def add(self, lf):
        self.lf.append(lf)

    def remove(self, idx):
        self.descs = np.delete(self.descs, idx, axis=0)

    def save(self, is_backup=False):
        lf_save_file = settings.DATASET_LF
        ids_file = settings.DATASET_IDS_LF
        if lf_save_file != "None":
            if is_backup:
                lf_save_file += '.bak'
                ids_file += '.bak'

            LFUtilities.save(lf_save_file, self.lf)
            np.savetxt(ids_file, self.ids, fmt='%s')