path: root/evaluate.py

#!/usr/bin/env python
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework
#
# usage: evaluate.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-a RERANKA]
#               [-b RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v]
#               [-s SHOWRANK] [-1] [-M MULTRANK] [-C] [DATA]
#               [-K KMEAN] [-A] [-P PCA]

import matplotlib.pyplot as plt
import sys
import os
import json
import scipy.io
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
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 for 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, 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:
            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)):
                debug("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))
            AP = 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))])
            for i in range(probe_label.shape[0]):
                AP[i] = sum(max_level_precision[i])/11
            mAP = np.mean(AP) 
            print('mAP:',mAP)

    if args.mAP:
        return target_pred, mAP
    else:
        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]])
            if args.mAP:
                target_pred[i], mAP = (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))
            else: 
                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+1):
            if args.mAP:
                target_pred, mAP = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
            else:
                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):
            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()