#!/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 from rerank import re_ranking parser = argparse.ArgumentParser() parser.add_argument("-t", "--test", help="Use test data instead of query", action='store_true') parser.add_argument("-cm", "--conf_mat", help="Show visual confusion matrix", action='store_true') parser.add_argument("-km", "--kmean", help="Perform Kmeans", action='store_true', default=0) parser.add_argument("-ma", "--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("-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) 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", "--norm", help="Normalized features", action='store_true', default=0) args = parser.parse_args() def verbose(*text): if args.verbose: print(text) #prob query, gal train 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 def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args): verbose("probe shape:", probe_data.shape) verbose("gallery shape:", gallery_data.shape) if args.rerank: distances = re_ranking(probe_data, gallery_data, args.reranka ,args.rerankb , 0.3, MemorySave = False, Minibatch = 2000) else: if args.mahalanobis: # metric = 'jaccard' is also valid covmat = LA.inv(np.cov(gallery_data)) distances = np.zeros((probe_label.size, gallery_label.size)) for i in range(probe_label.size): print('Mahalanobis step ', i, '/', probe_label.size) distances [i] = cdist(probe_data[i].reshape((1,2048)), gallery_data, 'mahalanobis', VI = covmat) else: distances = cdist(probe_data, gallery_data, 'euclidean') ranklist = np.argsort(distances, axis=1) target_pred = np.zeros(ranklist.shape[0]) nneighbors = np.zeros((ranklist.shape[0],args.neighbors)) nnshowrank = (np.zeros((ranklist.shape[0],args.neighbors))).astype(object) 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): target_pred[probe_idx] = probe_label[probe_idx] else: target_pred[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(): mat = scipy.io.loadmat('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("data/feature_data.json", "r") as read_file: feature_vectors = np.array(json.load(read_file)) gallery_idx = gallery_idx.reshape(gallery_idx.shape[0]) if args.test: query_idx = train_idx.reshape(train_idx.shape[0]) else: query_idx = query_idx.reshape(query_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] if (args.norm): 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): gallery1 = [] gallery2 = [] gallery1lab = [] gallery2lab = [] for i in range(gallery_idx.size): if camId[gallery_idx[i]] == 1: gallery1.append(feature_vectors[gallery_idx[i]]) gallery1lab.append(labels[gallery_idx[i]]) else: gallery2.append(feature_vectors[gallery_idx[i]]) gallery2lab.append(labels[gallery_idx[i]]) train1 = np.array(gallery1) train2 = np.array(gallery2) tlabel1 = np.array(gallery1lab) tlabel2 = np.array(gallery2lab) km_train_data_1 = KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train1) km_train_data_2 = KMeans(n_clusters=int(np.max(labels)),random_state=0).fit(train2) ###REMAP LABELS km_labels_1 = np.zeros(int(np.max(labels))) # clusters size km_labels_2 = np.zeros(int(np.max(labels))) 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(tlabel1[q])-1] += 1 km_labels_1[i] = np.argmax(class_vote) + 1 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(tlabel2[q])-1] += 1 km_labels_2[i] = np.argmax(class_vote) + 1 #MERGE CLUSTERS cl = [] cllab = [] clcam = [] clustercam1 = np.ones(km_labels_1.size) clustercam2 = np.add(np.ones(km_labels_2.size), 1) for i in range(km_labels_1.size): cl.append(km_train_data_1.cluster_centers_[i]) cllab.append(km_labels_1[i]) clcam.append(clustercam1[i]) for i in range(km_labels_2.size): cl.append(km_train_data_2.cluster_centers_[i]) cllab.append(km_labels_2[i]) clcam.append(clustercam2[i]) cluster = np.array(cl) clusterlabel = np.array(cllab) clustercam = np.array(clcam) target_pred = test_model(cluster, test_data, clusterlabel, test_label, clustercam, test_cam, showfiles_train, showfiles_test, args) draw_results(test_label, target_pred) else: target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args) draw_results(test_label, target_pred) if __name__ == "__main__": main()