#!/usr/bin/env python
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework
#
# usage: train.py [-h] -i DATA [-m EIGEN] [-M REIGEN] [-e ENSEMBLE] [-b]
#                 [-R RANDOM] [-n NEIGHBORS] [-f FACES] [-c] [-s SEED]
#                 [-t SPLIT] [-2] [-p] [-l] [-r RECONSTRUCT] [-cm] [-q] [-pr]
#                 [-alt]

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import sys
import random
import os
import json
import scipy.io
from random import randint
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import DistanceMetric
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
import argparse
import numpy as np
from numpy import genfromtxt
from numpy import linalg as LA
from timeit import default_timer as timer
from scipy.spatial.distance import cdist

#prob query, gal train
def re_ranking(probFea,galFea,k1,k2,lambda_value, MemorySave = False, Minibatch = 2000):

    query_num = probFea.shape[0]
    all_num = query_num + galFea.shape[0]    
    feat = np.append(probFea,galFea,axis = 0)
    feat = feat.astype(np.float16)
    print('computing original distance')
    if MemorySave:
        original_dist = np.zeros(shape = [all_num,all_num],dtype = np.float16)
        i = 0
        while True:
            it = i + Minibatch
            if it < np.shape(feat)[0]:
                original_dist[i:it,] = np.power(cdist(feat[i:it,],feat),2).astype(np.float16)
            else:
                original_dist[i:,:] = np.power(cdist(feat[i:,],feat),2).astype(np.float16)
                break
            i = it
    else:
        original_dist = cdist(feat,feat).astype(np.float16)  
        original_dist = np.power(original_dist,2).astype(np.float16)
    del feat    
    gallery_num = original_dist.shape[0]
    original_dist = np.transpose(original_dist/np.max(original_dist,axis = 0))
    V = np.zeros_like(original_dist).astype(np.float16)
    initial_rank = np.argsort(original_dist).astype(np.int32)

    
    print('starting re_ranking')
    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] = weight/np.sum(weight)
    original_dist = original_dist[:query_num,]    
    if k2 != 1:
        V_qe = np.zeros_like(V,dtype=np.float16)
        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.float16)

    for i in range(query_num):
        temp_min = np.zeros(shape=[1,gallery_num],dtype=np.float16)
        indNonZero = np.where(V[i,:] != 0)[0]
        indImages = []
        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
    del V
    del jaccard_dist
    final_dist = final_dist[:query_num,query_num:]
        
    return final_dist

def draw_results(args, target_test, target_pred):
    acc_sc = accuracy_score(target_test, target_pred)
    cm = confusion_matrix(target_test, target_pred)
    print('Accuracy: ', acc_sc)
    if (args.conf_mat):
        plt.matshow(cm, cmap='Blues')
        plt.colorbar()
        plt.ylabel('Actual')
        plt.xlabel('Predicted')
        plt.show()
    return

def test_model(train_data, test_data, target_train, target_test, args):
    classifier = KNeighborsClassifier(n_neighbors=args.neighbors, metric='euclidean')
#    else:
#        S = LA.inv(np.cov(train_data, rowvar=False))
#        print(S.shape)
#        classifier = KNeighborsClassifier(n_neighbors=args.neighbors, metric='mahalanobis', metric_params={'VI':S})
    classifier.fit(train_data, target_train)
    target_pred = classifier.predict(test_data)
    dist, nn_idx = classifier.kneighbors(test_data)
    #USE NN_IDX TO RECOVER NEIGHBORS
    return target_pred

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("-R", "--random", help="Number of eigen value to randomise", type=int)
    parser.add_argument("-n", "--neighbors", help="How many neighbors to use", type=int, default = 1)
    parser.add_argument("-c", "--principal", help="Show principal components", action='store_true')
    parser.add_argument("-s", "--seed", help="Seed to use", type=int, default=0)
    parser.add_argument("-t", "--split", help="Fractoin of data to use for testing", type=float, default=0.3)
    parser.add_argument("-2", "--grapheigen", help="Swow 2D graph of targets versus principal components",
            action='store_true')
    parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true')
    parser.add_argument("-q", "--pca_r", help="Use Reduced PCA", action='store_true')
    parser.add_argument("-pr", "--prob", help="Certainty on each guess", action='store_true')
    parser.add_argument("-km", "--kmean", help="Perform Kmeans", action='store_true', default=0)
    parser.add_argument("-ma", "--mala", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
    parser.add_argument("-e", "--eucl", help="Standard euclidean", action='store_true', default=0)
    parser.add_argument("-ka", "--reranka", help="Parameter 1 for Rerank", type=int, default = 20)
    parser.add_argument("-kb", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6)
    args = parser.parse_args()

    ###PART2 INPUT DATA
    mat = scipy.io.loadmat('data/cuhk03_new_protocol_config_labeled.mat')
    camId = mat['camId']
    filelist = mat['filelist']
    gallery_idx = mat['gallery_idx']
    labels = mat['labels']
    query_idx = mat['query_idx']
    train_idx = mat['train_idx']
    with open("data/feature_data.json", "r") as read_file:
        feature_vectors = np.array(json.load(read_file))
    
    query_cam_1 = 0
    for i in range(query_idx.size):
        if camId[query_idx[i]] == 1:
            query_cam_1 = query_cam_1 + 1
    query_cam_2 = query_idx.size - query_cam_1
    
    train_cam_1 = 0
    for i in range(gallery_idx.size):
        if camId[gallery_idx[i]] == 1:
            train_cam_1 = train_cam_1 + 1
    train_cam_2 = gallery_idx.size - train_cam_1
    
    train_data_1 = np.zeros(((train_cam_1),(feature_vectors.shape[1])))
    train_label_1 = np.zeros(train_cam_1)
    test_data_1 = np.zeros(((query_cam_1),(feature_vectors.shape[1])))
    test_label_1 = np.zeros(query_cam_1)
    
    train_data_2 = np.zeros(((train_cam_2),(feature_vectors.shape[1])))
    train_label_2 = np.zeros(train_cam_2)
    test_data_2 = np.zeros(((query_cam_2),(feature_vectors.shape[1])))
    test_label_2 = np.zeros(query_cam_2)
    
    i_1 = 0
    i_2 = 0
    for i in range(gallery_idx.size):
        if camId[gallery_idx[i]] == 1:
            train_data_1[i_1] = feature_vectors[gallery_idx[i]]
            i_1 = i_1 + 1
        else:
            train_data_2[i_2] = feature_vectors[gallery_idx[i]]
            i_2 = i_2 + 1
    i_1 = 0
    i_2 = 0           
    for i in range(query_idx.size):
        if camId[query_idx[i]] == 1:
            test_data_1[i_1] = feature_vectors[query_idx[i]]
            i_1 = i_1 + 1
        else:
            test_data_2[i_2] = feature_vectors[query_idx[i]]
            i_2 = i_2 + 1
    i_1 = 0
    i_2 = 0              
    for i in range(gallery_idx.size):
        if camId[gallery_idx[i]] == 1:
            train_label_1[i_1] = labels[gallery_idx[i]]
            i_1 = i_1 + 1
        else:
            train_label_2[i_2] = labels[gallery_idx[i]]
            i_2 = i_2 + 1
    i_1 = 0
    i_2 = 0            
    for i in range(query_idx.size):
        if camId[query_idx[i]] == 1:
            test_label_1[i_1] = labels[query_idx[i]]
            i_1 = i_1 + 1
        else:
            test_label_2[i_2] = labels[query_idx[i]]
            i_2 = i_2 + 1
    
    if (args.mala):
        final_dist = re_ranking(test_data_1, train_data_2, args.reranka, args.rerankb, 0.3)
        target_pred = np.zeros(final_dist.shape[0])
        for i in range(test_label_1.size):
            target_pred[i] = train_label_2[np.argmin(final_dist[i])]
        draw_results(args, test_label_1, target_pred)
    
        final_dist2 = re_ranking(test_data_2, train_data_1, args.reranka, args.rerankb, 0.3)
        target_pred2 = np.zeros(final_dist2.shape[0])
        for i in range(test_label_2.size):
            target_pred2[i] = train_label_1[np.argmin(final_dist2[i])]
        draw_results(args, test_label_2, target_pred2)
        
    elif(args.kmean):
        km_labels_1 = np.arange(1,np.max(labels)+1)
        km_labels_2 = np.arange(1,np.max(labels)+1)
        km_train_data_1 = np.zeros(((km_labels_1.size),(feature_vectors.shape[1])))
        km_train_data_2 = np.zeros(((km_labels_2.size),(feature_vectors.shape[1])))
        km_train_data_1 = KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train_data_1)
        km_train_data_2 = KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train_data_2)

        km_idx_1 = km_train_data_1.labels_ 
        for i in range(np.max(labels)):
            class_vote = np.zeros(np.max(labels))
            for q in range(km_idx_1.size):
                if km_idx_1[q]==i:
                    class_vote[int(train_label_1[q])-1] = class_vote[int(train_label_1[q])-1] + 1
            km_labels_1[i] = np.argmax(class_vote) + 1
            
        target_pred = test_model(km_train_data_1.cluster_centers_, test_data_2, km_labels_1, test_label_2, args)    
        draw_results(args, test_label_2, target_pred)
        
        km_idx_2 = km_train_data_2.labels_ 
        for i in range(np.max(labels)):
            class_vote = np.zeros(np.max(labels))
            for q in range(km_idx_2.size):
                if km_idx_2[q]==i:
                    class_vote[int(train_label_2[q])-1] = class_vote[int(train_label_2[q])-1] + 1
            km_labels_2[i] = np.argmax(class_vote) + 1
            
        target_pred = test_model(km_train_data_2.cluster_centers_, test_data_1, km_labels_2, test_label_1, args)    
        draw_results(args, test_label_1, target_pred)
    
    elif(args.eucl):    
        target_pred = test_model(train_data_2, test_data_1, train_label_2, test_label_1, args)    
        draw_results(args, test_label_1, target_pred)
        target_pred = test_model(train_data_1, test_data_2, train_label_1, test_label_2, args)    
        draw_results(args, test_label_2, target_pred)
        
    
    print('N-Query from cam 1:', test_data_1.shape)
    print('N-Query from cam 2:', test_data_2.shape)
    print('Complete')

if __name__ == "__main__":
    main()