diff options
| -rwxr-xr-x | part2.py | 102 |
1 files changed, 68 insertions, 34 deletions
@@ -32,18 +32,21 @@ 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("-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("-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("-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() @@ -52,7 +55,6 @@ 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) @@ -63,7 +65,7 @@ def draw_results(test_label, pred_label): plt.ylabel('Actual') plt.xlabel('Predicted') plt.show() - return + return acc_sc def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam, probe_cam, showfiles_train, showfiles_test, args): @@ -81,38 +83,47 @@ def test_model(gallery_data, probe_data, gallery_label, probe_label, gallery_cam 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) + 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]]): + 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): + 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 - 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[probe_idx] = probe_label[probe_idx] - else: - target_pred[probe_idx] = nneighbors[probe_idx][0] + + 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)] @@ -150,6 +161,10 @@ def main(): 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)) @@ -211,13 +226,32 @@ def main(): 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) + + 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() |