#!/usr/bin/env python
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework
#
# usage: part2.py [-h] [-t] [-cm] [-km] [-ma] [-e] [-r] [-ka RERANKA]
#                [-kb RERANKB] [-v]

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 NearestNeighbors
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
sys.path.append('lib')
from rerank import re_ranking
from kmean import create_kmean_clusters
import logging
from logging import debug

parser = argparse.ArgumentParser()

parser.add_argument("-t", "--train", help="Use train data instead of query and gallery", action='store_true')
parser.add_argument("-c", "--conf_mat", help="Show visual confusion matrix", action='store_true')
parser.add_argument("-k", "--kmean_alt", help="Perform clustering with generalized labels(not actual kmean)", action='store_true')
parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true')
parser.add_argument("-e", "--euclidean", help="Use standard euclidean distance", action='store_true')
parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
parser.add_argument("-a", "--reranka", help="Parameter k1 for rerank", type=int, default = 9)
parser.add_argument("-b", "--rerankb", help="Parameter k2 for rerank", type=int, default = 3)
parser.add_argument("-l", "--rerankl", help="Parameter lambda fo rerank", type=float, default = 0.3)
parser.add_argument("-n", "--neighbors", help="Use customized ranklist size NEIGHBORS", type=int, default = 1)
parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true')
parser.add_argument("-s", "--showrank", help="Save ranklist pics id in a txt file for first SHOWRANK queries", type=int, default = 0)
parser.add_argument("-1", "--normalise", help="Normalise features", action='store_true')
parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to MULTRANK", type=int, default=1)
parser.add_argument("-C", "--comparison", help="Compare baseline and improved metric", action='store_true')
parser.add_argument("--data", help="Folder containing data", default='data')
parser.add_argument("-K", "--kmean", help="Perform Kmean clustering, KMEAN number of clusters", type=int, default=0)
parser.add_argument("-A", "--mAP", help="Display Mean Average Precision", action='store_true')
parser.add_argument("-P", "--PCA", help="Perform pca with PCA eigenvectors", type=int, default=0)

args = parser.parse_args()

if args.verbose:
    logging.basicConfig(level=logging.DEBUG)

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

def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args):

    debug("probe shape: %s", probe_data.shape)
    debug("gallery shape: %s", gallery_data.shape)

    if args.rerank:
        distances = re_ranking(probe_data, gallery_data, 
                               args.reranka, args.rerankb, args.rerankl, 
                               MemorySave = False, Minibatch = 2000)
    else:
        if args.mahalanobis:
            # metric = 'jaccard' is also valid
            cov_inv = np.linalg.inv(np.cov(gallery_data.T))
            distances = np.zeros((probe_data.shape[0], gallery_data.shape[0]))
            for i in range(int(probe_data.shape[0]/10)):
                print("Comupting from", i*10, "to", (i+1)*10-1)
                distances[i*10:(i+1)*10-1] = cdist(probe_data[i*10:(i+1)*10-1], gallery_data, 'mahalanobis', VI=cov_inv)
        else:
            distances = cdist(probe_data, gallery_data, 'euclidean')

    ranklist = np.argsort(distances, axis=1)

    test_table = np.arange(1, args.multrank+1)
    target_pred = np.zeros((args.multrank, ranklist.shape[0]))
    nsize = args.neighbors
    if (args.multrank != 1):
        nsize = test_table[args.multrank-1]
    nneighbors = np.zeros((ranklist.shape[0],nsize))
    nnshowrank = (np.zeros((ranklist.shape[0],nsize))).astype(object)

    for i in range(args.multrank):
        if args.multrank!= 1:
            args.neighbors = test_table[i]
        for probe_idx in range(probe_data.shape[0]):
            row = ranklist[probe_idx]
            n = 0
            q = 0
            while (q < args.neighbors):
                while (probe_cam[probe_idx] == gallery_cam[row[n]] and
                  probe_label[probe_idx] == gallery_label[row[n]]):
                    n += 1
                nneighbors[probe_idx][q] = gallery_label[row[n]]
                nnshowrank[probe_idx][q] = showfiles_train[row[n]] #
                q += 1
                n += 1

            if (args.neighbors) and (probe_label[probe_idx] in nneighbors[probe_idx]):
                target_pred[i][probe_idx] = probe_label[probe_idx]
            else:
                target_pred[i][probe_idx] = nneighbors[probe_idx][0]


        if (args.showrank):
            with open("ranklist.txt", "w") as text_file:
                text_file.write(np.array2string(nnshowrank[:args.showrank]))
            with open("query.txt", "w") as text_file:
                text_file.write(np.array2string(showfiles_test[:args.showrank]))
        
        if args.mAP:
            precision = np.zeros((probe_label.shape[0], args.neighbors))
            recall = np.zeros((probe_label.shape[0], args.neighbors))
            mAP = np.zeros(probe_label.shape[0])
            max_level_precision = np.zeros((probe_label.shape[0],11))
            
            for i in range(probe_label.shape[0]):
                truth_count=0
                false_count=0
                for j in range(args.neighbors):
                    if probe_label[i] == nneighbors[i][j]:
                        truth_count+=1
                        precision[i][j] = truth_count/(j+1)
                    else:
                        false_count+=1
                        precision[i][j]= 1 - false_count/(j+1)
                if truth_count!=0:
                    recall_step = 1/truth_count
                    for j in range(args.neighbors):
                        if probe_label[i] == nneighbors[i][j]:
                            recall[i][j:] += recall_step
                else:
                    recall[i][:] = 1
            for i in range(probe_label.shape[0]):
                for j in range(11):
                    max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))])
                    #print(mAP[i])
            for i in range(probe_label.shape[0]):
                mAP[i] = sum(max_level_precision[i])/11
                #mAP[i] = sum(precision[i])/args.neighbors
            print('mAP:',np.mean(mAP))
            return np.mean(mAP)
        
    return target_pred

def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
    
    if args.train:
        cam = camId[train_idx]
        cam = cam.reshape((cam.shape[0],1))
        labs = labels[train_idx].reshape((labels[train_idx].shape[0],1))
        tt = np.hstack((train_idx, cam)) 
        train, test, train_label, test_label = train_test_split(tt, labs, test_size=0.3, random_state=0)
        #to make it smaller we do a double split
        del labs
        del cam
        train_data = feature_vectors[train[:,0]]
        test_data = feature_vectors[test[:,0]]
        train_cam = train[:,1]
        test_cam = test[:,1]
        showfiles_train = filelist[train[:,0]]
        showfiles_test = filelist[train[:,0]]
        del train
        del test 
        del tt
    else:
        query_idx = query_idx.reshape(query_idx.shape[0])
        gallery_idx = gallery_idx.reshape(gallery_idx.shape[0])
        camId = camId.reshape(camId.shape[0])

        showfiles_train = filelist[gallery_idx]
        showfiles_test = filelist[query_idx]
        train_data = feature_vectors[gallery_idx]
        test_data = feature_vectors[query_idx]
        train_label = labels[gallery_idx]
        test_label = labels[query_idx]
        train_cam = camId[gallery_idx]
        test_cam = camId[query_idx]

    train_idx = train_idx.reshape(train_idx.shape[0])
    train_model = feature_vectors[train_idx]
    
    if(args.PCA):
        pca=PCA(n_components=args.PCA) #Data variance @100 is 94%
        train_model=pca.fit_transform(train_model)
        train_data=pca.transform(train_data)
        test_data=pca.transform(test_data)

    accuracy = np.zeros((2, args.multrank))
    test_table = np.arange(1, args.multrank+1)

    if (args.normalise):
        debug("Normalising data")
        train_data = np.divide(train_data,LA.norm(train_data,axis=0))
        test_data = np.divide(test_data, LA.norm(test_data,axis=0))
    if(args.kmean_alt):
        debug("Using Kmeans")
        train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels,gallery_idx,camId)
        
    if args.kmean:
        kmeans = KMeans(n_clusters=args.kmean, random_state=0).fit(train_data)
        neigh = NearestNeighbors(n_neighbors=1)
        neigh.fit(kmeans.cluster_centers_)
        neighbors = neigh.kneighbors(test_data, return_distance=False)
        target_pred = np.zeros(test_data.shape[0])

        for i in range(test_data.shape[0]):
            td = test_data[i].reshape(1,test_data.shape[1])
            tc = np.array([test_cam[i]])
            tl = np.array([test_label[i]])
            target_pred[i] = (test_model(train_data[np.where(kmeans.labels_==neighbors[i])], td, train_label[np.where(kmeans.labels_==neighbors[i])], tl, train_cam[np.where(kmeans.labels_==neighbors[i])], tc, showfiles_train[np.where(kmeans.labels_==neighbors[i])], showfiles_test[i], args))
         
        accuracy[0] = draw_results(test_label, target_pred)
    else:
        for q in range(args.comparison+1):
            if args.mAP:
                return test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
            
            target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args)
            for i in range(args.multrank):
                return draw_results(test_label, target_pred[i])
            args.rerank = True
            args.neighbors = 1

    if(args.multrank != 1):
        plt.plot(test_table[:(args.multrank)], 100*accuracy[0])
        if(args.comparison):
            plt.plot(test_table[:(args.multrank)], 100*accuracy[1])
            plt.legend(['Baseline NN', 'NN+Reranking'], loc='upper left')
        plt.xlabel('Top k')
        plt.ylabel('Identification Accuracy (%)')
        plt.grid(True)
        plt.show()

def kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
    axis = 0
    search = 0
    steps = 0
    vertical = True
    neg = False
    outofaxis = False
    start = np.array([1,1])
    if args.mAP:
        args.neighbors = 10
    args.PCA = 50
    args.train = True
    args.rerank = True
    args.reranka = 1
    args.rerankb = 1
    opt = np.array([1,1])
    checktab = np.zeros((100,100))
    checktab[1][1]=1
    max_acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
    print('origin')
    print('vertical')
    while steps<3:
        steps+=1
        while axis<4:
            axis+=1
            p = start[0]
            q = start[1]
            while search <5:
                search+=1
                if vertical:
                    if neg:
                        p = start[0] - 2*search
                        if p < 1:
                            p = 1
                            search = 5
                            outofaxis = True
                    else:
                        p = search*2 + start[0]
                    args.reranka = p
                    if not outofaxis:
                        if checktab[p][q] == 0:
                            checktab[p][q] = 1
                            print('p:',p,' q:',q)
                            acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
                    if acc > max_acc:
                        print('new p:',p, ' for accuracy:', acc)
                        max_acc=acc
                        opt[0] = p
                        start[0] = p
                        axis=0
                        steps=0
                        search=6
                else:
                    if neg:
                        q = start[1] - 2*search
                        if q < 1:
                            q = 1
                            search = 5
                            outofaxis = True
                    else:
                        q = search*2 + start[1]
                    args.rerankb = q
                    if not outofaxis:
                        if checktab[p][q] == 0:
                            checktab[p][q]=1
                            print('p:',p,' q:',q)
                            acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
                    if acc > max_acc:
                        print('new q:',q, ' for accuracy:', acc)
                        max_acc=acc
                        opt[1] = q
                        start[1] = q
                        axis=0
                        steps=0
                        search=6
            if search==5:
                outofaxis = False
                vertical = not vertical
                print('vertical:',vertical)
            search=0
            if axis==2 or axis == 4:
                neg = not neg
        axis=0
        start[0]+=2
        start[1]+=2
        p=start[0]
        q=start[1]
        args.reranka = start[0]
        args.rerankb = start[1]
        print('p:',p,' q:',q)
        acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
        if acc > max_acc:
            print('new p:',p,'new q:',q, ' for accuracy:', acc)
            max_acc=acc
            opt[0] = start[0]
            opt[1] = start[1]
            steps=0
            vertical=True
    print('Maximum Accuracy:',max_acc,' found at p:',opt[0],'|q:',opt[1])
    return max_acc, opt

def main():
    mat = scipy.io.loadmat(os.path.join(args.data,'cuhk03_new_protocol_config_labeled.mat'))
    camId = mat['camId']
    filelist = mat['filelist']
    labels = mat['labels']
    gallery_idx = mat['gallery_idx'] - 1
    query_idx = mat['query_idx'] - 1
    train_idx = mat['train_idx'] - 1
    with open(os.path.join(args.data,'feature_data.json'), 'r') as read_file:
        feature_vectors = np.array(json.load(read_file))
    l=0
    max_acc = np.zeros(11)
    opt = np.zeros((11,2))
    while l < 11:
        args.rerankl = l/10
        print('testing for lambda:',args.rerankl)
        max_acc[l], opt[l] = kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
        l +=1
    print('Max accuracy:',np.max(max_acc),' at p:',opt[np.argmax(max_acc)][0], '| q:',opt[np.argmax(max_acc)][1],'| lambda:',np.argmax(max_acc)/10)
    
if __name__ == "__main__":
    main()