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-rwxr-xr-xpart2.py195
1 files changed, 0 insertions, 195 deletions
diff --git a/part2.py b/part2.py
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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
-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", "--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()
-
-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 , 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():
- 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):
- 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):
- 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()
-