diff options
Diffstat (limited to 'opt.py')
| -rwxr-xr-x | opt.py | 163 |
1 files changed, 11 insertions, 152 deletions
@@ -8,21 +8,14 @@ # [-K KMEAN] [-A] [-P PCA] 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 NearestNeighbors -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 @@ -30,11 +23,14 @@ 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 +from evaluate import test_model +from evaluate import draw_results parser = argparse.ArgumentParser() @@ -63,108 +59,6 @@ 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: - cov_inv = np.linalg.inv(np.cov(gallery_data.T)) - distances = np.zeros((probe_data.shape[0], gallery_data.shape[0])) - for i in range(int(probe_data.shape[0]/10)): - print("Comupting from", i*10, "to", (i+1)*10-1) - distances[i*10:(i+1)*10-1] = cdist(probe_data[i*10:(i+1)*10-1], gallery_data, 'mahalanobis', VI=cov_inv) - 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.mAP: - precision = np.zeros((probe_label.shape[0], args.neighbors)) - recall = np.zeros((probe_label.shape[0], args.neighbors)) - mAP = np.zeros(probe_label.shape[0]) - max_level_precision = np.zeros((probe_label.shape[0],11)) - - for i in range(probe_label.shape[0]): - truth_count=0 - false_count=0 - for j in range(args.neighbors): - if probe_label[i] == nneighbors[i][j]: - truth_count+=1 - precision[i][j] = truth_count/(j+1) - else: - false_count+=1 - precision[i][j]= 1 - false_count/(j+1) - if truth_count!=0: - recall_step = 1/truth_count - for j in range(args.neighbors): - if probe_label[i] == nneighbors[i][j]: - recall[i][j:] += recall_step - else: - recall[i][:] = 1 - for i in range(probe_label.shape[0]): - for j in range(11): - max_level_precision[i][j] = np.max(precision[i][np.where(recall[i]>=(j/10))]) - for i in range(probe_label.shape[0]): - mAP[i] = sum(max_level_precision[i])/11 - print('mAP:',np.mean(mAP)) - return np.mean(mAP) - - return target_pred - def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args): if args.train: @@ -188,7 +82,6 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args) 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] @@ -197,7 +90,6 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args) test_label = labels[query_idx] train_cam = camId[gallery_idx] test_cam = camId[query_idx] - train_idx = train_idx.reshape(train_idx.shape[0]) train_model = feature_vectors[train_idx] @@ -210,48 +102,15 @@ def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args) 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_alt): - debug("Using Kmeans") - train_data, train_label, train_cam = create_kmean_clusters(feature_vectors, labels,gallery_idx,camId) + for q in range(args.comparison+1): + if args.mAP: + return test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args) - if args.kmean: - kmeans = KMeans(n_clusters=args.kmean, random_state=0).fit(train_data) - neigh = NearestNeighbors(n_neighbors=1) - neigh.fit(kmeans.cluster_centers_) - neighbors = neigh.kneighbors(test_data, return_distance=False) - target_pred = np.zeros(test_data.shape[0]) - - for i in range(test_data.shape[0]): - td = test_data[i].reshape(1,test_data.shape[1]) - tc = np.array([test_cam[i]]) - tl = np.array([test_label[i]]) - target_pred[i] = (test_model(train_data[np.where(kmeans.labels_==neighbors[i])], td, train_label[np.where(kmeans.labels_==neighbors[i])], tl, train_cam[np.where(kmeans.labels_==neighbors[i])], tc, showfiles_train[np.where(kmeans.labels_==neighbors[i])], showfiles_test[i], args)) - - accuracy[0] = draw_results(test_label, target_pred) - else: - for q in range(args.comparison+1): - if args.mAP: - return test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, args) - - 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): - return 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): - plt.plot(test_table[:(args.multrank)], 100*accuracy[1]) - plt.legend(['Baseline NN', 'NN+Reranking'], loc='upper left') - plt.xlabel('Top k') - plt.ylabel('Identification Accuracy (%)') - plt.grid(True) - plt.show() + target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args) + for i in range(args.multrank): + return draw_results(test_label, target_pred[i]) + args.rerank = True + args.neighbors = 1 def kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args): axis = 0 |