def query2(querydir, querysurf, dbdir, mainOutputDir, nClosestCells, copytopmatch, params, copy_top_n_percell=0):
lat, lon = info.getQuerySURFCoord(querysurf)
closest_cells = util.getclosestcells(lat, lon, dbdir)
assert dict(closest_cells)['37.8732916331,-122.268346029'] < 236 or dict(closest_cells)['37.8714489427,-122.272389514'] < 236 or dict(closest_cells)['37.8696062215,-122.273737308'] < 236
built = [
'37.8732916331,-122.268346029',
'37.8714489427,-122.272389514',
'37.8696062215,-122.273737308',
]
closest_cells = filter(lambda c: c[0] in built, closest_cells)
outputFilePaths = []
cells_in_range = [(cell, dist) for cell, dist in closest_cells[0:nClosestCells] if dist < cellradius + ambiguity+matchdistance]
latquery, lonquery = info.getQuerySURFCoord(querysurf)
if verbosity > 0:
print "checking query: {0} \t against {1} \t cells".format(querysurf, len(cells_in_range))
for cell, dist in cells_in_range:
latcell, loncell = cell.split(',')
latcell = float(latcell)
loncell = float(loncell)
actualdist = info.distance(latquery, lonquery, latcell, loncell)
if verbosity > 1:
print "querying cell: {0}, distance: {1} with:{2}".format(cell, actualdist, querysurf)
outputFilePath = os.path.join(mainOutputDir, querysurf + ',' + cell + ',' + str(actualdist) + ".res")
outputFilePaths.append(outputFilePath)
# start query
query.run_parallel(dbdir, [c for c,d in cells_in_range], querydir, querysurf, outputFilePaths, params)
# end query
for cell, dist in cells_in_range:
latcell, loncell = cell.split(',')
latcell = float(latcell)
loncell = float(loncell)
actualdist = info.distance(latquery, lonquery, latcell, loncell)
outputFilePath = os.path.join(mainOutputDir, querysurf + ',' + cell + ',' + str(actualdist) + ".res")
if copy_top_n_percell > 0:
outputDir = os.path.join(mainOutputDir, querysurf + ',' + cell + ',' + str(actualdist))
copy_topn_results(os.path.join(dbdir, cell), outputDir, outputFilePath, 4)
# combined = combine_until_dup(outputFilePaths, 1000)
combined = combine_topn_votes(outputFilePaths, float('inf'))
# combined = filter_in_range(combined, querysurf)
# write_scores(querysurf, combined, "/media/data/combined")
[g, y, r, b, o] = check_topn_img(querysurf, combined, topnresults)
if copytopmatch:
match = g or y or r or b or o
copy_top_match(querydir, querysurf.split('surf.npy')[0], combined, match)
return [g, y, r, b, o]
def get_num_imgs_in_range(range, celldir, querydir='/home/zhangz/.gvfs/data on 128.32.43.40/query3/'):
queries = util.getSURFFileNames(querydir)
total_in_range=0
for querysurf in queries:
lat, lon = info.getQuerySURFCoord(querysurf)
cell, dist = util.getclosestcell(lat, lon, celldir)
numinrange = util.getNumJPGInRange(lat, lon, os.path.join(celldir,cell), range)
total_in_range+=numinrange
print "average # of imgs within {0} meters of query: {1}".format(range, float(total_in_range)/len(queries))
def filter_in_range(ranked_matches, querysurf):
qlat, qlon = info.getQuerySURFCoord(querysurf)
weighted_matches = []
for matchedimg, score in ranked_matches:
clat = float(matchedimg.split(",")[0])
clon = float(matchedimg.split(",")[1][0:-5])
distance = info.distance(qlat, qlon, clat, clon)
if distance < ambiguity+matchdistance:
weighted_matches.append((matchedimg, 2 * score))
weighted_matches.sort(key=lambda x: x[1], reverse=True)
return weighted_matches
def filter_in_range(ranked_matches, querysurf):
qlat, qlon = info.getQuerySURFCoord(querysurf)
weighted_matches = []
for matchedimg, score in ranked_matches:
clat = float(matchedimg.split(",")[0])
clon = float(matchedimg.split(",")[1][0:-5])
distance = info.distance(qlat, qlon, clat, clon)
if distance < ambiguity + matchdistance:
weighted_matches.append((matchedimg, 2 * score))
weighted_matches.sort(key=lambda x: x[1], reverse=True)
return weighted_matches
def get_num_imgs_in_range(
range,
celldir,
querydir='/home/zhangz/.gvfs/data on 128.32.43.40/query3/'):
queries = util.getSURFFileNames(querydir)
total_in_range = 0
for querysurf in queries:
lat, lon = info.getQuerySURFCoord(querysurf)
cell, dist = util.getclosestcell(lat, lon, celldir)
numinrange = util.getNumJPGInRange(lat, lon,
os.path.join(celldir, cell), range)
total_in_range += numinrange
print "average # of imgs within {0} meters of query: {1}".format(
range,
float(total_in_range) / len(queries))
def query2(querydir,
querysurf,
dbdir,
mainOutputDir,
nClosestCells,
copytopmatch,
params,
copy_top_n_percell=0):
lat, lon = info.getQuerySURFCoord(querysurf)
closest_cells = util.getclosestcells(lat, lon, dbdir)
assert dict(closest_cells)['37.8732916331,-122.268346029'] < 236 or dict(
closest_cells)['37.8714489427,-122.272389514'] < 236 or dict(
closest_cells)['37.8696062215,-122.273737308'] < 236
built = [
'37.8732916331,-122.268346029',
'37.8714489427,-122.272389514',
'37.8696062215,-122.273737308',
]
closest_cells = filter(lambda c: c[0] in built, closest_cells)
outputFilePaths = []
cells_in_range = [(cell, dist)
for cell, dist in closest_cells[0:nClosestCells]
if dist < cellradius + ambiguity + matchdistance]
latquery, lonquery = info.getQuerySURFCoord(querysurf)
if verbosity > 0:
print "checking query: {0} \t against {1} \t cells".format(
querysurf, len(cells_in_range))
for cell, dist in cells_in_range:
latcell, loncell = cell.split(',')
latcell = float(latcell)
loncell = float(loncell)
actualdist = info.distance(latquery, lonquery, latcell, loncell)
if verbosity > 1:
print "querying cell: {0}, distance: {1} with:{2}".format(
cell, actualdist, querysurf)
outputFilePath = os.path.join(
mainOutputDir,
querysurf + ',' + cell + ',' + str(actualdist) + ".res")
outputFilePaths.append(outputFilePath)
# start query
query.run_parallel(dbdir, [c for c, d in cells_in_range], querydir,
querysurf, outputFilePaths, params)
# end query
for cell, dist in cells_in_range:
latcell, loncell = cell.split(',')
latcell = float(latcell)
loncell = float(loncell)
actualdist = info.distance(latquery, lonquery, latcell, loncell)
outputFilePath = os.path.join(
mainOutputDir,
querysurf + ',' + cell + ',' + str(actualdist) + ".res")
if copy_top_n_percell > 0:
outputDir = os.path.join(
mainOutputDir, querysurf + ',' + cell + ',' + str(actualdist))
copy_topn_results(os.path.join(dbdir, cell), outputDir,
outputFilePath, 4)
# combined = combine_until_dup(outputFilePaths, 1000)
combined = combine_topn_votes(outputFilePaths, float('inf'))
# combined = filter_in_range(combined, querysurf)
# write_scores(querysurf, combined, "/media/data/combined")
[g, y, r, b, o] = check_topn_img(querysurf, combined, topnresults)
if copytopmatch:
match = g or y or r or b or o
copy_top_match(querydir,
querysurf.split('surf.npy')[0], combined, match)
return [g, y, r, b, o]
