def display_info(ibs, invindex, annots_df):
################
from ibeis.dev import dbinfo
print(ibs.get_infostr())
dbinfo.get_dbinfo(ibs, verbose=True)
################
print('Inverted Index Stats: vectors per word')
print(utool.stats_str(map(len, invindex.wx2_idxs.values())))
################
#qfx2_vec = annots_df['vecs'][1]
centroids = invindex.words
num_pca_dims = 3 # 3
whiten = False
kwd = dict(
num_pca_dims=num_pca_dims,
whiten=whiten,
)
#clustertool.rrr()
def makeplot_(fnum, prefix, data, labels='centroids', centroids=centroids):
return clustertool.plot_centroids(data,
centroids,
labels=labels,
fnum=fnum,
prefix=prefix + '\n',
**kwd)
makeplot_(1, 'centroid vecs', centroids)
def plot_seperability(hs, qcx_list, fnum=1):
print('[dev] plot_seperability(fnum=%r)' % fnum)
qcx2_res = get_qcx2_res(hs, qcx_list)
qcx2_separability = get_seperatbility(hs, qcx2_res)
sep_score_list = qcx2_separability.values()
df2.figure(fnum=fnum, doclf=True, docla=True)
print('[dev] seperability stats: ' + utool.stats_str(sep_score_list))
sorted_sepscores = sorted(sep_score_list)
df2.plot(sorted_sepscores, color=df2.DEEP_PINK, label='seperation score',
yscale=YSCALE)
df2.set_xlabel('true chipmatch index (%d)' % len(sep_score_list))
df2.set_logyscale_from_data(sorted_sepscores)
df2.dark_background()
rowid = qcx2_res.itervalues().next().rowid
df2.set_figtitle('seperability\n' + rowid)
df2.legend()
fnum += 1
return fnum
def display_info(ibs, invindex, annots_df):
################
from ibeis.dev import dbinfo
print(ibs.get_infostr())
dbinfo.get_dbinfo(ibs, verbose=True)
################
print('Inverted Index Stats: vectors per word')
print(utool.stats_str(map(len, invindex.wx2_idxs.values())))
################
#qfx2_vec = annots_df['vecs'][1]
centroids = invindex.words
num_pca_dims = 3 # 3
whiten = False
kwd = dict(num_pca_dims=num_pca_dims,
whiten=whiten,)
#clustertool.rrr()
def makeplot_(fnum, prefix, data, labels='centroids', centroids=centroids):
return clustertool.plot_centroids(data, centroids, labels=labels,
fnum=fnum, prefix=prefix + '\n', **kwd)
makeplot_(1, 'centroid vecs', centroids)
def plot_scores2(hs, qcx_list, fnum=1):
print('[dev] plot_scores(fnum=%r)' % fnum)
qcx2_res = get_qcx2_res(hs, qcx_list)
all_score_list = []
gtscore_ys = []
gtscore_xs = []
gtscore_ranks = []
EXCLUDE_ZEROS = False
N = 1
# Append all scores to a giant list
for res in qcx2_res.itervalues():
cx2_score = res.cx2_score
# Get gt scores first
#gt_cxs = hs.get_other_indexed_cxs(res.qcx)
gt_cxs = np.array(res.topN_cxs(hs, N=N, only_gt=True))
gt_ys = cx2_score[gt_cxs]
if EXCLUDE_ZEROS:
nonzero_cxs = np.where(cx2_score != 0)[0]
gt_cxs = gt_cxs[gt_ys != 0]
gt_ranks = res.get_gt_ranks(gt_cxs)
gt_cxs = np.array(utool.list_index(nonzero_cxs, gt_cxs))
gt_ys = gt_ys[gt_ys != 0]
score_list = cx2_score[nonzero_cxs].tolist()
else:
score_list = cx2_score.tolist()
gt_ranks = res.get_gt_ranks(gt_cxs)
gtscore_ys.extend(gt_ys)
gtscore_xs.extend(gt_cxs + len(all_score_list))
gtscore_ranks.extend(gt_ranks)
# Append all scores
all_score_list.extend(score_list)
all_score_list = np.array(all_score_list)
gtscore_ranks = np.array(gtscore_ranks)
gtscore_ys = np.array(gtscore_ys)
# Sort all chipmatch scores
allx_sorted = all_score_list.argsort() # mapping from sortedallx to allx
allscores_sorted = all_score_list[allx_sorted]
# Change the groundtruth positions to correspond to sorted cmatch scores
# Find position of gtscore_xs in allx_sorted
gtscore_sortxs = utool.list_index(allx_sorted, gtscore_xs)
gtscore_sortxs = np.array(gtscore_sortxs)
# Draw and info
rank_bounds = [
(0, 1),
(1, 5),
(5, None)
]
rank_colors = [
df2.TRUE_GREEN,
df2.UNKNOWN_PURP,
df2.FALSE_RED
]
print('[dev] matching chipscore stats: ' + utool.stats_str(all_score_list))
df2.figure(fnum=fnum, doclf=True, docla=True)
# Finds the knee
df2.plot(allscores_sorted, color=df2.ORANGE, label='all scores')
# get positions which are within rank bounds
for count, ((low, high), rankX_color) in reversed(list(enumerate(zip(rank_bounds, rank_colors)))):
rankX_flag_low = gtscore_ranks >= low
if high is not None:
rankX_flag_high = gtscore_ranks < high
rankX_flag = np.logical_and(rankX_flag_low, rankX_flag_high)
else:
rankX_flag = rankX_flag_low
rankX_allgtx = np.where(rankX_flag)[0]
rankX_gtxs = gtscore_sortxs[rankX_allgtx]
rankX_gtys = gtscore_ys[rankX_allgtx]
rankX_label = '%d <= gt rank' % low
if high is not None:
rankX_label += ' < %d' % high
if len(rankX_gtxs) > 0:
df2.plot(rankX_gtxs, rankX_gtys, 'o', color=rankX_color, label=rankX_label)
rowid = qcx2_res.itervalues().next().rowid
df2.set_logyscale_from_data(allscores_sorted)
df2.set_xlabel('chipmatch index')
df2.dark_background()
df2.set_figtitle('matching scores\n' + rowid)
df2.legend(loc='upper left')
#xmin = 0
#xmax = utool.order_of_magnitude_ceil(len(allscores_sorted))
#print('len_ = %r' % (len(allscores_sorted),))
#print('xmin = %r' % (xmin,))
#print('xmax = %r' % (xmax,))
#df2.gca().set_xlim(xmin, xmax)
df2.update()
#utool.embed()
# Second Plot
#data = sorted(zip(gtscore_sortxs, gtscore_ys, gtscore_ranks))
#gtxs = [x for (x, y, z) in data]
#gtys = [y for (x, y, z) in data]
#gtrs = [z for (x, y, z) in data]
#nongtxs = np.setdiff1d(np.arange(gtxs[0], gtxs[-1]), gtxs)
#min_ = min(gtxs)
#max_ = len(allscores_sorted)
#len_ = max_ - min_
#normsum = 0
#ratsum = 0
#gtxs = np.array(gtxs)
#nongtxs = np.array(nongtxs)
#for ix in xrange(min_, max_):
#nongtxs_ = nongtxs[nongtxs >= ix]
#gtxs_ = gtxs[gtxs >= ix]
#numer = allscores_sorted[gtxs_].sum()
#denom = allscores_sorted[nongtxs_].sum()
#ratio = (1 + numer) / (denom + 1)
#total_support = (len(gtxs_) + len(nongtxs_))
#normrat = ratio / total_support
#ratsum += ratio
#normsum += normrat
#print(total_support)
#print(ratsum / len_)
#print(normsum / len_)
#print(ratsum / allscores_sorted[min_:max_].sum())
#print(normsum / allscores_sorted[min_:max_].sum())
#index_gap = np.diff(gtxs)
#score_gap = np.diff(gtys)
#badness = (index_gap - 1) * score_gap
#np.arange(len(gtxs))
fnum += 1
return fnum
def plot_scores(hs, qcx_list, fnum=1):
print('[dev] plot_scores(fnum=%r)' % fnum)
topN_gt = 1
topN_ranks = 3
qcx2_res = get_qcx2_res(hs, qcx_list)
data_scores = [] # matching scores
data_qpairs = [] # query info (qcx, cx)
data_gtranks = [] # query info (gtrank)
# Append all scores to a giant list
for res in qcx2_res.itervalues():
# Get gt scores first
qcx = res.qcx
top_cxs = np.array(res.topN_cxs(hs, N=topN_ranks, only_gt=False))
gt_cxs = np.array(res.topN_cxs(hs, N=topN_gt, only_gt=True))
top_scores = res.cx2_score[top_cxs]
np.intersect1d(top_cxs, gt_cxs)
# list of ranks if score is ground truth otherwise -1
isgt_ranks = [tx if cx in set(gt_cxs) else -1 for (tx, cx) in enumerate(top_cxs)]
qcx_pairs = [(hs.cx2_cid(qcx), hs.cx2_cid(cx)) for cx in top_cxs]
# Append all scores
data_scores.extend(top_scores.tolist())
data_qpairs.extend(qcx_pairs)
data_gtranks.extend(isgt_ranks)
data_scores = np.array(data_scores)
data_qpairs = np.array(data_qpairs)
data_gtranks = np.array(data_gtranks)
data_sortx = data_scores.argsort()
sorted_scores = data_scores[data_sortx]
# Draw and info
rank_colorbounds = [
((-1, 0), df2.GRAY),
((0, 1), df2.TRUE_GREEN),
((1, 5), df2.UNKNOWN_PURP),
((5, None), df2.FALSE_RED),
]
print('[dev] matching chipscore stats: ' + utool.stats_str(data_scores))
df2.figure(fnum=fnum, doclf=True, docla=True)
# Finds the knee
df2.plot(sorted_scores, color=df2.ORANGE, label='all scores')
# Plot results with ranks within (low, high) bounds
colorbounds_iter = reversed(list(enumerate(rank_colorbounds)))
for count, ((low, high), rankX_color) in colorbounds_iter:
datarank_flag = utool.inbounds(data_gtranks, low, high)
rankX_xs = np.where(datarank_flag[data_sortx])[0]
rankX_ys = sorted_scores[rankX_xs]
if high is None:
rankX_label = '%d <= gt rank' % low
else:
rankX_label = '%d <= gt rank < %d' % (low, high)
if len(rankX_ys) > 0:
df2.plot(rankX_xs, rankX_ys, 'o', color=rankX_color, label=rankX_label, alpha=.5)
rowid = qcx2_res.itervalues().next().rowid
df2.set_logyscale_from_data(data_scores)
df2.set_xlabel('chipmatch index')
df2.dark_background()
df2.set_figtitle('matching scores\n' + rowid)
df2.legend(loc='upper left')
df2.iup()
fnum += 1
score_table = np.vstack((data_scores, data_gtranks, data_qpairs.T)).T
score_table = score_table[data_sortx[::-1]]
column_labels = ['score', 'gtrank', 'qcid', 'cid']
header = 'score_table\nuid=%r' % rowid
column_type = [float, int, int, int]
csv_txt = utool.util_csv.numpy_to_csv(score_table, column_labels, header, column_type)
print(csv_txt)
#utool.embed()
return fnum