Resolve conflict lambda

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()
-