def viz_threshold_matchings(hsA, hsB, count2rr_AB, thresh_out_dir):
'returns database, cx, database cx'
import numpy as np
valid_cxsB = hsB.cm.get_valid_cxs()
num_matching = 0
MATCH_THRESHOLD = __method_2_matchthresh__.get(__METHOD__, 10)
results_name = get_results_name(hsA, hsB)
print(' * Visualizing threshold matchings ' + results_name +
' give it some time to plot...')
threshdb_out_dir = join_mkdir(thresh_out_dir, results_name)
# For each query run in hsA vs hsB
for count in xrange(len(count2rr_AB)):
rr = count2rr_AB[count]
qcx = rr.qcx
res = QueryResult(hsB, rr, hsA)
qname = res.qhs.cm.cx2_name(qcx)
# Set matching threshold
res.top_thresh = MATCH_THRESHOLD
res.num_top_min = 0
res.num_top_max = 5
res.num_extra_return = 0
# Check to see if any matched over the threshold
top_cxs = res.top_cx()
top_names = res.hs.cm.cx2_name(top_cxs)
top_scores = res.scores()[top_cxs]
if len(top_cxs) > 0:
# Visualize the result
# Stupid segfaults #if qcx == 113: #41: #import IPython #IPython.embed() # Get the scores
num_matching += 1
res.visualize()
# Create a filename showing dataset, score, cx, match names
matchname_set = \
set([name.replace('Lionfish','') for name in (top_names+[qname])])
matchnames = '-'.join(list(matchname_set))
scorestr = str(int(round(top_scores[0])))
fig_fname = '-'.join([
results_name, 'score' + scorestr, 'cx' + str(qcx),
'MATCHES' + matchnames
]) + '.jpg'
fig = myfigure(0)
fig_fpath = join(threshdb_out_dir, fig_fname)
sys.stdout.write('.')
safe_savefig(fig, fig_fpath)
print(' * Visualized %d above thresh: %f from expt: %s ' %
(num_matching, MATCH_THRESHOLD, results_name))
def viz_threshold_matchings(hsA, hsB, count2rr_AB, thresh_out_dir):
'returns database, cx, database cx'
import numpy as np
valid_cxsB = hsB.cm.get_valid_cxs()
num_matching = 0
MATCH_THRESHOLD = __method_2_matchthresh__.get(__METHOD__, 10)
results_name = get_results_name(hsA, hsB)
print(' * Visualizing threshold matchings '+results_name+' give it some time to plot...')
threshdb_out_dir = join_mkdir(thresh_out_dir, results_name)
# For each query run in hsA vs hsB
for count in xrange(len(count2rr_AB)):
rr = count2rr_AB[count]
qcx = rr.qcx
res = QueryResult(hsB, rr, hsA)
qname = res.qhs.cm.cx2_name(qcx)
# Set matching threshold
res.top_thresh = MATCH_THRESHOLD
res.num_top_min = 0
res.num_top_max = 5
res.num_extra_return = 0
# Check to see if any matched over the threshold
top_cxs = res.top_cx()
top_names = res.hs.cm.cx2_name(top_cxs)
top_scores = res.scores()[top_cxs]
if len(top_cxs) > 0:
# Visualize the result
# Stupid segfaults #if qcx == 113: #41: #import IPython #IPython.embed() # Get the scores
num_matching += 1
res.visualize()
# Create a filename showing dataset, score, cx, match names
matchname_set = \
set([name.replace('Lionfish','') for name in (top_names+[qname])])
matchnames = '-'.join(list(matchname_set))
scorestr = str(int(round(top_scores[0])))
fig_fname = '-'.join([results_name,
'score'+scorestr,
'cx'+str(qcx),
'MATCHES'+matchnames])+'.jpg'
fig = myfigure(0)
fig_fpath = join(threshdb_out_dir, fig_fname)
sys.stdout.write('.')
safe_savefig(fig, fig_fpath)
print(' * Visualized %d above thresh: %f from expt: %s ' % (num_matching,
MATCH_THRESHOLD,
results_name))
def write_all_chipscore_results(hsA, hsB, count2rr_AB, txt_output_fpath):
txt_header = '''#<COMMENTED_XML_HEADER>
# <filename>
# ''' + txt_output_fpath + '''
# </filename>
#
# <description>
# Sorted Query Results. The results are stored in the format:
# The first line gives the query image name, the next lines are
# a sorted list of result image names and scores. Queries are
# separated by two new lines.
# </description>
#
# <data_format>
# queryA
# resultA1 scoreA1
# resultA2 scoreA2
# resultA3 scoreA3
# ...
#
# queryB-gname
# resultB1 scoreB1
# resultB2 scoreB2
# resultB3 scoreB3
# ...
#
# ...
# </data_format>
#</COMMENTED_XML_HEADER>
'''
txt_body = ''
for count in range(len(count2rr_AB)):
rr = count2rr_AB[count]
res = QueryResult(hsB, rr, hsA)
txt_body += hsA.cm.cx2_gname(rr.qcx) + '\n'
top_cxs = res.cx_sort()
scores = res.scores()
output_tup_list = []
for cx in top_cxs:
output_tup_list.append((hsB.cm.cx2_gname(cx), scores[cx]))
output_tup_list.sort(key=lambda tup: tup[1])
output_tup_list = output_tup_list[::-1]
for tup in output_tup_list:
txt_body += tup[0] + ' ' + str(tup[1]) + '\n'
txt_body += '\n'
with open(txt_output_fpath, 'w') as file:
file.write(txt_body)
def write_all_chipscore_results(hsA, hsB, count2rr_AB, txt_output_fpath):
txt_header = '''#<COMMENTED_XML_HEADER>
# <filename>
# '''+txt_output_fpath+'''
# </filename>
#
# <description>
# Sorted Query Results. The results are stored in the format:
# The first line gives the query image name, the next lines are
# a sorted list of result image names and scores. Queries are
# separated by two new lines.
# </description>
#
# <data_format>
# queryA
# resultA1 scoreA1
# resultA2 scoreA2
# resultA3 scoreA3
# ...
#
# queryB-gname
# resultB1 scoreB1
# resultB2 scoreB2
# resultB3 scoreB3
# ...
#
# ...
# </data_format>
#</COMMENTED_XML_HEADER>
'''
txt_body = ''
for count in range(len(count2rr_AB)):
rr = count2rr_AB[count]
res = QueryResult(hsB, rr, hsA)
txt_body += hsA.cm.cx2_gname(rr.qcx)+'\n'
top_cxs = res.cx_sort()
scores = res.scores()
output_tup_list = []
for cx in top_cxs:
output_tup_list.append((hsB.cm.cx2_gname(cx), scores[cx]))
output_tup_list.sort(key=lambda tup: tup[1])
output_tup_list = output_tup_list[::-1]
for tup in output_tup_list:
txt_body += tup[0]+' '+str(tup[1])+'\n'
txt_body+='\n'
with open(txt_output_fpath,'w') as file:
file.write(txt_body)