#!/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 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 test data instead of query", action='store_true') parser.add_argument("-c", "--conf_mat", help="Show visual confusion matrix", action='store_true') parser.add_argument("-k", "--kmean", help="Perform Kmeans", 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="Standard euclidean", 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 1 for Rerank", type=int, default = 20) parser.add_argument("-q", "--rerankb", help="Parameter 2 for rerank", type=int, default = 6) parser.add_argument("-l", "--rerankl", help="Coefficient to combine distances", type=int, default = 0.3) parser.add_argument("-n", "--neighbors", help="Number of 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 pic id in a txt file", type=int, default = 0) parser.add_argument("-2", "--graphspace", help="Graph space", action='store_true', default=0) parser.add_argument("-1", "--normalise", help="Normalized features", action='store_true', default=0) parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to M", type=int, default=1) parser.add_argument("-C", "--comparison", help="Set to 2 to obtain a comparison of baseline and Improved metric", type=int, default=1) parser.add_argument("--data", help="Data folder with features data", default='data') 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 distances = cdist(probe_data, gallery_data, 'jaccard') 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.graphspace: # Colors for distinct individuals cols = ['#{:06x}'.format(randint(0, 0xffffff)) for i in range(1467)] gallery_label_tmp = np.subtract(gallery_label, 1) pltCol = [cols[int(k)] for k in gallery_label_tmp] fig = plt.figure() ax = fig.add_subplot(111, projection='3d') ax.scatter(gallery_data[:, 0], gallery_data[:, 1], gallery_data[:, 2], marker='o', color=pltCol) plt.show() 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: query_idx = train_idx.reshape(train_idx.shape[0]) gallery_idx = train_idx.reshape(train_idx.shape[0]) 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] 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): debug("Using Kmeans") train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels, gallery_idx, camId) 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, 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 kNN', 'Improved metric'], loc='upper left') plt.xlabel('k rank') plt.ylabel('Recognition Accuracy (%)') plt.grid(True) plt.show() if __name__ == "__main__": main()