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Diffstat (limited to 'part2.py')
| -rwxr-xr-x | part2.py | 256 |
1 files changed, 0 insertions, 256 deletions
diff --git a/part2.py b/part2.py deleted file mode 100755 index 08b27ac..0000000 --- a/part2.py +++ /dev/null @@ -1,256 +0,0 @@ -#!/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", "--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", "--norm", 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) - - -args = parser.parse_args() - -def verbose(*text): - if args.verbose: - print(text) - -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): - - 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, 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(): - 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)) - 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.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) - - for q in range(args.comparison): - target_pred = test_model(cluster, test_data, clusterlabel, test_label, clustercam, 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 - - 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, 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() - |