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#!/usr/bin/env python
# Author: Vasil Zlatanov, Nunzio Pucci
# EE4 Pattern Recognition coursework
#
# usage: opt.py [-h] [-t] [-c] [-k] [-m] [-e] [-r] [-a RERANKA]
# [-b RERANKB] [-l RERANKL] [-n NEIGHBORS] [-v]
# [-s SHOWRANK] [-1] [-2] [-M MULTRANK] [-C] [DATA]
# [-K KMEAN] [-A] [-P PCA]
import matplotlib.pyplot as plt
import sys
import os
import json
import scipy.io
from sklearn.neighbors import NearestNeighbors
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
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
from evaluate import test_model
from evaluate import draw_results
parser = argparse.ArgumentParser()
parser.add_argument("-t", "--train", help="Use train data instead of query and gallery", action='store_true')
parser.add_argument("-c", "--conf_mat", help="Show visual confusion matrix", action='store_true')
parser.add_argument("-k", "--kmean_alt", help="Perform clustering with generalized labels(not actual kmean)", action='store_true')
parser.add_argument("-m", "--mahalanobis", help="Perform Mahalanobis Distance metric", action='store_true')
parser.add_argument("-e", "--euclidean", help="Use standard euclidean distance", action='store_true')
parser.add_argument("-r", "--rerank", help="Use k-reciprocal rernaking", action='store_true')
parser.add_argument("-a", "--reranka", help="Parameter k1 for rerank", type=int, default = 9)
parser.add_argument("-b", "--rerankb", help="Parameter k2 for rerank", type=int, default = 3)
parser.add_argument("-l", "--rerankl", help="Parameter lambda for rerank", type=float, default = 0.3)
parser.add_argument("-n", "--neighbors", help="Use customized ranklist size 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 pics id in a txt file for first SHOWRANK queries", type=int, default = 0)
parser.add_argument("-1", "--normalise", help="Normalise features", action='store_true')
parser.add_argument("-2", "--standardise", help="Standardise features", action='store_true')
parser.add_argument("-M", "--multrank", help="Run for different ranklist sizes equal to MULTRANK", type=int, default=1)
parser.add_argument("-C", "--comparison", help="Compare baseline and improved metric", action='store_true')
parser.add_argument("--data", help="Folder containing data", default='data')
parser.add_argument("-K", "--kmean", help="Perform Kmean clustering, KMEAN number of clusters", type=int, default=0)
parser.add_argument("-A", "--mAP", help="Display Mean Average Precision", action='store_true')
parser.add_argument("-P", "--PCA", help="Perform pca with PCA eigenvectors", type=int, default=50)
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.DEBUG)
def eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
if args.train:
cam = camId[train_idx]
cam = cam.reshape((cam.shape[0],1))
labs = labels[train_idx].reshape((labels[train_idx].shape[0],1))
tt = np.hstack((train_idx, cam))
train, test, train_label, test_label = train_test_split(tt, labs, test_size=0.3, random_state=0)
del labs
del cam
train_data = feature_vectors[train[:,0]]
test_data = feature_vectors[test[:,0]]
train_cam = train[:,1]
test_cam = test[:,1]
showfiles_train = filelist[train[:,0]]
showfiles_test = filelist[train[:,0]]
del train
del test
del tt
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]
train_idx = train_idx.reshape(train_idx.shape[0])
train_model = feature_vectors[train_idx]
if(args.PCA):
pca=PCA(n_components=args.PCA) #Data variance @100 is 94%
train_model=pca.fit_transform(train_model)
train_data=pca.transform(train_data)
test_data=pca.transform(test_data)
if args.mAP:
target_pred, mAP = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
return mAP
else:
target_pred = test_model(train_data, test_data, train_label, test_label, train_cam, test_cam, showfiles_train, showfiles_test, train_model, args)
target_pred = target_pred.reshape(target_pred.shape[1])
return draw_results(test_label, target_pred)
def kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args):
axis = 0
search = 0
steps = 0
vertical = True
neg = False
outofaxis = False
start = np.array([1,1])
if args.mAP:
args.neighbors = 10
args.train = True
args.rerank = True
args.reranka = 1
args.rerankb = 1
opt = np.array([1,1])
checktab = np.zeros((100,100))
checktab[1][1]=1
max_acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
print('origin')
print('vertical')
while steps<3:
steps+=1
while axis<4:
axis+=1
p = start[0]
q = start[1]
while search <5:
search+=1
if vertical:
if neg:
p = start[0] - 2*search
if p < 1:
p = 1
search = 5
outofaxis = True
else:
p = search*2 + start[0]
args.reranka = p
if not outofaxis:
if checktab[p][q] == 0:
checktab[p][q] = 1
print('p:',p,' q:',q)
acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
if acc > max_acc:
print('new p:',p, ' for accuracy:', acc)
max_acc=acc
opt[0] = p
start[0] = p
axis=0
steps=0
search=6
else:
if neg:
q = start[1] - 2*search
if q < 1:
q = 1
search = 5
outofaxis = True
else:
q = search*2 + start[1]
args.rerankb = q
if not outofaxis:
if checktab[p][q] == 0:
checktab[p][q]=1
print('p:',p,' q:',q)
acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
if acc > max_acc:
print('new q:',q, ' for accuracy:', acc)
max_acc=acc
opt[1] = q
start[1] = q
axis=0
steps=0
search=6
if search==5:
outofaxis = False
vertical = not vertical
print('vertical:',vertical)
search=0
if axis==2 or axis == 4:
neg = not neg
axis=0
start[0]+=2
start[1]+=2
p=start[0]
q=start[1]
args.reranka = start[0]
args.rerankb = start[1]
print('p:',p,' q:',q)
acc = eval(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
if acc > max_acc:
print('new p:',p,'new q:',q, ' for accuracy:', acc)
max_acc=acc
opt[0] = start[0]
opt[1] = start[1]
steps=0
vertical=True
print('Maximum Accuracy:',max_acc,' found at p:',opt[0],'|q:',opt[1])
return max_acc, opt
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))
l=0
max_acc = np.zeros(11)
opt = np.zeros((11,2))
while l < 11:
args.rerankl = l/10
print('testing for lambda:',args.rerankl)
max_acc[l], opt[l] = kopt(camId, filelist, labels, gallery_idx, train_idx, feature_vectors, args)
l +=1
print('Max accuracy:',np.max(max_acc),' at p:',opt[np.argmax(max_acc)][0], '| q:',opt[np.argmax(max_acc)][1],'| lambda:',np.argmax(max_acc)/10)
if __name__ == "__main__":
main()
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