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# encoding: utf-8

# based on:
# https://github.com/zhunzhong07/person-re-ranking

__all__ = ['re_ranking']

import numpy as np


def re_ranking(q_g_dist, q_q_dist, g_g_dist, k1: int = 20, k2: int = 6, lambda_value: float = 0.3):
    original_dist = np.concatenate(
        [np.concatenate([q_q_dist, q_g_dist], axis=1),
         np.concatenate([q_g_dist.T, g_g_dist], axis=1)],
        axis=0)
    original_dist = np.power(original_dist, 2).astype(np.float32)
    original_dist = np.transpose(1. * original_dist / np.max(original_dist, axis=0))
    V = np.zeros_like(original_dist).astype(np.float32)
    initial_rank = np.argsort(original_dist).astype(np.int32)

    query_num = q_g_dist.shape[0]
    gallery_num = q_g_dist.shape[0] + q_g_dist.shape[1]
    all_num = gallery_num

    for i in range(all_num):
        # k-reciprocal neighbors
        forward_k_neigh_index = initial_rank[i, :k1 + 1]
        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
        fi = np.where(backward_k_neigh_index == i)[0]
        k_reciprocal_index = forward_k_neigh_index[fi]
        k_reciprocal_expansion_index = k_reciprocal_index
        for j in range(len(k_reciprocal_index)):
            candidate = k_reciprocal_index[j]
            candidate_forward_k_neigh_index = initial_rank[candidate,
                                              :int(np.around(k1 / 2.)) + 1]
            candidate_backward_k_neigh_index = initial_rank[candidate_forward_k_neigh_index,
                                               :int(np.around(k1 / 2.)) + 1]
            fi_candidate = np.where(candidate_backward_k_neigh_index == candidate)[0]
            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[fi_candidate]
            if len(np.intersect1d(candidate_k_reciprocal_index, k_reciprocal_index)) > 2. / 3 * len(
                    candidate_k_reciprocal_index):
                k_reciprocal_expansion_index = np.append(k_reciprocal_expansion_index, candidate_k_reciprocal_index)

        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
        weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
        V[i, k_reciprocal_expansion_index] = 1. * weight / np.sum(weight)
    original_dist = original_dist[:query_num, ]
    if k2 != 1:
        V_qe = np.zeros_like(V, dtype=np.float32)
        for i in range(all_num):
            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
        V = V_qe
        del V_qe
    del initial_rank
    invIndex = []
    for i in range(gallery_num):
        invIndex.append(np.where(V[:, i] != 0)[0])

    jaccard_dist = np.zeros_like(original_dist, dtype=np.float32)

    for i in range(query_num):
        temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float32)
        indNonZero = np.where(V[i, :] != 0)[0]
        indImages = [invIndex[ind] for ind in indNonZero]
        for j in range(len(indNonZero)):
            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(V[i, indNonZero[j]],
                                                                               V[indImages[j], indNonZero[j]])
        jaccard_dist[i] = 1 - temp_min / (2. - temp_min)

    final_dist = jaccard_dist * (1 - lambda_value) + original_dist * lambda_value
    del original_dist, V, jaccard_dist
    final_dist = final_dist[:query_num, query_num:]
    return final_dist