ax0 = plt.subplot(L, 2, 1 + 2 * (i - offset))
else:
ax = plt.subplot(L, 2, 1 + 2 * (i - offset), sharex=ax0)
#print i
#print dct
#print '#neg', len(dct['neg_llhs'])
#print '#pos', len(dct['pos_llhs'])
#print 'unique negs', len(np.unique(dct['neg_llhs']))
#print '------------'
#import pdb; pdb.set_trace()
if adjusted:
neg_R = dct['neg_llhs'].copy().reshape((-1, 1))
pos_R = dct['pos_llhs'].copy().reshape((-1, 1))
nonparametric_rescore(neg_R, dct['start'], dct['step'], dct['points'])
nonparametric_rescore(pos_R, dct['start'], dct['step'], dct['points'])
#neg_R = (neg_R - dct['neg_llhs'].mean()) / dct['neg_llhs'].std()
#pos_R = (pos_R - dct['neg_llhs'].mean()) / dct['neg_llhs'].std()
#neg_R = (neg_R - dct['pos_llhs'].mean()) / dct['pos_llhs'].std()
#pos_R = (pos_R - dct['pos_llhs'].mean()) / dct['pos_llhs'].std()
plt.hist(neg_R, alpha=0.5, label='neg', bins=bins, normed=True)
plt.hist(pos_R, alpha=0.5, label='pos', bins=bins, normed=True)
else:
plt.hist(dct['neg_llhs'],
alpha=0.5,
label='neg',
bins=bins,
normed=True)
plt.hist(dct['pos_llhs'],
#R = float((X * weights).sum())
#Rst = (R - detector.fixed_train_mean[k]) / detector.fixed_train_std[k]
if detector.indices is not None:
R = multifeature_correlate2d_with_indices(X,
weights.astype(np.float64),
detector.indices[k][m])[0, 0]
else:
R = multifeature_correlate2d(X, weights.astype(np.float64))[0, 0]
from gv.fast import nonparametric_rescore
Rsts = np.zeros(detector.num_bkg_mixtures)
for m in xrange(detector.num_bkg_mixtures):
Rarray = R * np.ones((1, 1))
info = detector.standardization_info[k][m]
nonparametric_rescore(Rarray, info['start'], info['step'],
info['points'])
#Rst = R
Rsts[m] = Rarray[0, 0]
Rst = Rsts[det['bkgcomp']]
# Replace bounding boxes with this single one
#fileobj.boxes[:] = [bbobj]
fn = 'det-{0}.png {1}'.format(i, bbobj.img_id)
#print '{batsu} {fn}: {standardized:.2f} {raw:.2f} ({mixcomp}, {bkgcomp})'.format(fn=fn, standardized=Rst, raw=det['confidence'], batsu=['X', '.'][det['correct']], mixcomp=det['mixcomp'], bkgcomp=det['bkgcomp'])
print '{batsu} {fn}: {standardized:.2f} {raw:.2f} ({mixcomp}, {bkgcomp}) dist={dist}'.format(
fn=fn,
standardized=Rst,
raw=det['confidence'],
batsu=['X', '.'][det['correct']],
ax0 = plt.subplot(L, 2, 1 + 2*(i-offset))
else:
ax = plt.subplot(L, 2, 1 + 2*(i-offset), sharex=ax0)
#print i
#print dct
#print '#neg', len(dct['neg_llhs'])
#print '#pos', len(dct['pos_llhs'])
#print 'unique negs', len(np.unique(dct['neg_llhs']))
#print '------------'
#import pdb; pdb.set_trace()
if adjusted:
neg_R = dct['neg_llhs'].copy().reshape((-1, 1))
pos_R = dct['pos_llhs'].copy().reshape((-1, 1))
nonparametric_rescore(neg_R, dct['start'], dct['step'], dct['points'])
nonparametric_rescore(pos_R, dct['start'], dct['step'], dct['points'])
#neg_R = (neg_R - dct['neg_llhs'].mean()) / dct['neg_llhs'].std()
#pos_R = (pos_R - dct['neg_llhs'].mean()) / dct['neg_llhs'].std()
#neg_R = (neg_R - dct['pos_llhs'].mean()) / dct['pos_llhs'].std()
#pos_R = (pos_R - dct['pos_llhs'].mean()) / dct['pos_llhs'].std()
plt.hist(neg_R, alpha=0.5, label='neg', bins=bins, normed=True)
plt.hist(pos_R, alpha=0.5, label='pos', bins=bins, normed=True)
else:
plt.hist(dct['neg_llhs'], alpha=0.5, label='neg', bins=bins, normed=True)
plt.hist(dct['pos_llhs'], alpha=0.5, label='pos', bins=bins, normed=True)
neg_N = len(dct['neg_llhs'])
pos_N = len(dct['pos_llhs'])
from gv.fast import multifeature_correlate2d_with_indices, multifeature_correlate2d
#R = float((X * weights).sum())
#Rst = (R - detector.fixed_train_mean[k]) / detector.fixed_train_std[k]
if detector.indices is not None:
R = multifeature_correlate2d_with_indices(X, weights.astype(np.float64), detector.indices[k][m])[0,0]
else:
R = multifeature_correlate2d(X, weights.astype(np.float64))[0,0]
from gv.fast import nonparametric_rescore
Rsts = np.zeros(detector.num_bkg_mixtures)
for m in xrange(detector.num_bkg_mixtures):
Rarray = R * np.ones((1, 1))
info = detector.standardization_info[k][m]
nonparametric_rescore(Rarray, info['start'], info['step'], info['points'])
#Rst = R
Rsts[m] = Rarray[0,0]
Rst = Rsts[det['bkgcomp']]
# Replace bounding boxes with this single one
#fileobj.boxes[:] = [bbobj]
fn = 'det-{0}.png {1}'.format(i, bbobj.img_id)
#print '{batsu} {fn}: {standardized:.2f} {raw:.2f} ({mixcomp}, {bkgcomp})'.format(fn=fn, standardized=Rst, raw=det['confidence'], batsu=['X', '.'][det['correct']], mixcomp=det['mixcomp'], bkgcomp=det['bkgcomp'])
print '{batsu} {fn}: {standardized:.2f} {raw:.2f} ({mixcomp}, {bkgcomp}) dist={dist}'.format(
fn=fn, standardized=Rst, raw=det['confidence'], batsu=['X', '.'][det['correct']], mixcomp=det['mixcomp'], bkgcomp=det['bkgcomp'], dist=dists,
)
#import IPython
#IPython.embed()
