#!/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', default=0)
parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true', default=0)
parser.add_argument("-e", "--euclidean", help="Use standard euclidean distance", action='store_true', default=0)
parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
parser.add_argument("-p", "--reranka", help="Parameter k1 for Rerank -p '$k1val' -ARGUMENT REQUIRED, default=9-", type=int, default = 9)
parser.add_argument("-q", "--rerankb", help="Parameter k2 for rerank -q '$k2val' -ARGUMENT REQUIRED, default=3-", type=int, default = 3)
parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances(lambda) -l '$lambdaval' -ARGUMENT REQUIRED, default=0.3-", type=float, default = 0.3)
parser.add_argument("-n", "--neighbors", help="Use customized ranklist size -n 'size' -ARGUMENT REQUIRED, default=1-", type=int, default = 1)
parser.add_argument("-v", "--verbose", help="Use verbose output", action='store_true')
parser.add_argument("-s", "--showrank", help="Save ranklist pics id in a txt file. Number of ranklists saved specified as -s '$number' -ARGUMENT REQUIRED, default=0-", type=int, default = 0)
parser.add_argument("-1", "--normalise", help="Normalise features", action='store_true', default=0)
parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to M -ARGUMENT REQUIRED, default=1-", type=int, default=1)
parser.add_argument("-C", "--comparison", help="Set to 2 to obtain a comparison of baseline and improved metric -ARGUMENT REQUIRED, default=1-", type=int, default=1)
parser.add_argument("--data", help="You can either put the data in a folder called 'data', or specify the location with --data 'path' -ARGUMENT REQUIRED, default='data'-", default='data')
parser.add_argument("-K", "--kmean", help="Perform Kmean clustering of size specified through -K '$size' -ARGUMENT REQUIRED, default=0-", type=int, default=0)
parser.add_argument("-P", "--mAP", help="Display Mean Average Precision for ranklist of size -n '$size'", action='store_true')
parser.add_argument("-2", "--PCA", help="Use PCA with -2 '$n_components' -ARGUMENT REQUIRED, default=0-", 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, train_model, 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(train_model.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 target_pred

def main():
    logging.debug("Verbose mode is on")
    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))
    
    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)
        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], train_model, args))
         
        accuracy[0] = draw_results(test_label, target_pred)
    else:
        for q in range(args.comparison):
            target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
            for i in range(args.multrank):
                accuracy[q][i] = 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!=1):
            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()


if __name__ == "__main__":
    main()