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#!/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:", probe_data.shape)
debug("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
distances = cdist(probe_data, gallery_data, 'sqeuclidean')
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()
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