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summary.py
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summary.py
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import matplotlib.pyplot as plt
import numpy as np
def run(logdir):
num_groups = 1
num_inner = 1
rank1 = []
rank5 = []
ha = 0
hb = 0
ma = 0
mb = 0
hc = 0
mc = 0
ranksets = []
ta = 0
fa = 0
tr = 0
fr = 0
accScores = []
rejScores = []
for i in range(num_groups):
for j in range(num_inner):
rfile = open(logdir + '/result.txt')
r1 = 0
r5 = 0
count = 0
curve = [0.0 for k in range(0,5)]
for line in rfile:
if line == '':
continue
rank = int(line.split(',')[1])
score = float(line.split(',')[2])
if rank >= 0:
rankA = max(rank-1,0)
for k in range(rankA,5):
curve[k] += 1
count += 1
if rank == 1 or rank == 0:
r1 += 1
if rank <= 5 and rank >= 0:
r5 += 1
'''if rank > 0:
ta += 1
accScores.append(score)
if rank == 0:
tr += 1
rejScores.append(score)
if rank == -1:
fr += 1
accScores.append(score)
if rank == -2:
fa += 1
rejScores.append(score)'''
for k in range(len(curve)):
curve[k] /= count
# print(r1)
# print(count)
ranksets.append(curve)
rank1.append(r1 / float(count))
rank5.append(r5 / float(count))
print(rank1)
print(rank5)
return rank1, rank5
#meanr1 = sum(rank1)/len(rank1)
#meanr5 = sum(rank5)/len(rank5)
#
#print('Rank 1')
#print(' mean: {0}'.format(meanr1))
#print(' median: {0}'.format(np.median(np.array(rank1))))
#print(' min: {0}'.format(min(rank1)))
#print(' max: {0}'.format(max(rank1)))
#print(' stdev: {0}'.format(np.std(np.array(rank1))))
#print
#if (fa+tr != 0):
# print(' TA: {0}'.format(ta/float(ta+fr)))
# print(' FA: {0}'.format(fa/float(fa+tr)))
# print('{0},{1}\n'.format(ta/float(ta+fr),fa/float(fa+tr)))
# plt.hist(np.array(rejScores),bins=5,normed=True,hold=True,color='r',alpha=0.5,label='No Match in Gallery')
# plt.hist(np.array(accScores),bins=25,normed=True,hold=True,color='g',alpha=0.5,label='Match in Gallery')
# plt.plot((-.83,-.83),(0,40),hold=True)
# plt.xlabel('Match Score')
# plt.ylabel('% of accept/reject images')
# plt.legend()
# plt.show()
#
#print('Rank 5')
#print(' mean: {0}'.format(meanr5))
#print(' median: {0}'.format(np.median(np.array(rank5))))
#print(' min: {0}'.format(min(rank5)))
#print(' max: {0}'.format(max(rank5)))
#
#cmcs = np.array(ranksets) * 100
#minCMC = cmcs.min(0)
#maxCMC = cmcs.max(0)
#meanCMC = cmcs.mean(0)
#
#plt.plot(np.array(range(1,6)),minCMC,'r-',hold=True,label='min')
#plt.plot(np.array(range(1,6)),meanCMC,'b-',hold=True,label='mean')
#plt.plot(np.array(range(1,6)),maxCMC,'g-',hold=True,label='max')
#plt.axis([1,5,80,102])
#plt.yticks([i for i in range(80,101,2)])
#plt.xticks([1,2,3,4,5])
#plt.grid()
#plt.legend(loc='lower-right')
#plt.title('Closed-Set')
#plt.xlabel('Rank')
#plt.ylabel('Cumulative Accuracy (%)')
#plt.show()