def show_descriptors_match_distances(orgres2_distance, fnum=1, db_name='', **kwargs): disttype_list = orgres2_distance.itervalues().next().keys() orgtype_list = orgres2_distance.keys() (nRow, nCol) = len(orgtype_list), len(disttype_list) nColors = nRow * nCol color_list = df2.distinct_colors(nColors) df2.figure(fnum=fnum, docla=True, doclf=True) pnum_ = lambda px: (nRow, nCol, px + 1) plot_type = helpers.get_arg('--plot-type', default='plot') # Remember min and max val for each distance type (l1, emd...) distkey2_min = {distkey: np.uint64(-1) for distkey in disttype_list} distkey2_max = {distkey: 0 for distkey in disttype_list} def _distplot(dists, color, label, distkey, plot_type=plot_type): data = sorted(dists) ax = df2.gca() min_ = distkey2_min[distkey] max_ = distkey2_max[distkey] if plot_type == 'plot': df2.plot(data, color=color, label=label) #xticks = np.linspace(np.min(data), np.max(data), 3) #yticks = np.linspace(0, len(data), 5) #ax.set_xticks(xticks) #ax.set_yticks(yticks) ax.set_ylim(min_, max_) ax.set_xlim(0, len(dists)) ax.set_ylabel('distance') ax.set_xlabel('matches indexes (sorted by distance)') df2.legend(loc='lower right') if plot_type == 'pdf': df2.plot_pdf(data, color=color, label=label) ax.set_ylabel('pr') ax.set_xlabel('distance') ax.set_xlim(min_, max_) df2.legend(loc='upper right') df2.dark_background(ax) df2.small_xticks(ax) df2.small_yticks(ax) px = 0 for orgkey in orgtype_list: for distkey in disttype_list: dists = orgres2_distance[orgkey][distkey] if len(dists) == 0: continue min_ = dists.min() max_ = dists.max() distkey2_min[distkey] = min(distkey2_min[distkey], min_) distkey2_max[distkey] = max(distkey2_max[distkey], max_) for orgkey in orgtype_list: for distkey in disttype_list: print(((orgkey, distkey))) dists = orgres2_distance[orgkey][distkey] df2.figure(fnum=fnum, pnum=pnum_(px)) color = color_list[px] title = distkey + ' ' + orgkey label = 'P(%s | %s)' % (distkey, orgkey) _distplot(dists, color, label, distkey, **kwargs) #ax = df2.gca() #ax.set_title(title) px += 1 subtitle = 'the matching distances between sift descriptors' title = '(sift) matching distances' if db_name != '': title = db_name + ' ' + title df2.set_figtitle(title, subtitle) df2.adjust_subplots_safe()
def precompute_akmeans(data, num_clusters, max_iters=100, flann_params=None, cache_dir=None, force_recomp=False, same_data=True, uid=''): 'precompute aproximate kmeans' if flann_params is None: flann_params = {} print('[algos] pre_akmeans()') if same_data: data_uid = helpers.hashstr_arr(data, 'dID') uid += data_uid clusters_fname = 'akmeans_clusters' datax2cl_fname = 'akmeans_datax2cl' try: if not force_recomp: clusters = io.smart_load(cache_dir, clusters_fname, uid, '.npy', can_fail=False) datax2_clusterx = io.smart_load(cache_dir, datax2cl_fname, uid, '.npy', can_fail=False) else: raise Exception('forcing') # Hack to refine akmeans with a few more iterations if '--refine' in sys.argv or '--refine-exit' in sys.argv: max_iters_override = helpers.get_arg('--refine', type_=int) print('Overriding max_iters=%r' % max_iters_override) if not max_iters_override is None: max_iters = max_iters_override datax2_clusterx_old = datax2_clusterx print('[algos] refining:') print('[algos] ' + '_'.join([clusters_fname, uid]) + '.npy') print('[algos] ' + '_'.join([datax2cl_fname, uid]) + '.npy') (datax2_clusterx, clusters) = __akmeans_iterate(data, clusters, datax2_clusterx_old, max_iters, flann_params, 0, 10) io.smart_save(clusters, cache_dir, clusters_fname, uid, '.npy') io.smart_save(datax2_clusterx, cache_dir, datax2cl_fname, uid, '.npy') if '--refine-exit' in sys.argv: print('exiting after refine') sys.exit(1) print('[algos] pre_akmeans(): ... loaded akmeans.') except Exception as ex: print('[algos] pre_akmeans(): ... could not load akmeans.') errstr = helpers.indent(repr(ex), '[algos] ') print('[algos] pre_akmeans(): ... caught ex:\n %s ' % errstr) print('[algos] pre_akmeans(): printing debug_smart_load') print('---- <DEBUG SMART LOAD>---') io.debug_smart_load(cache_dir, clusters_fname) io.debug_smart_load(cache_dir, datax2cl_fname) print('----</DEBUG SMART LOAD>---') #print('[algos] Press Ctrl+C to stop k-means early (and save)') #signal.signal(signal.SIGINT, force_quit_akmeans) # set ctrl+c behavior print('[algos] computing:') print('[algos] ' + '_'.join([clusters_fname, uid]) + '.npy') print('[algos] ' + '_'.join([datax2cl_fname, uid]) + '.npy') print('[algos] pre_akmeans(): calling akmeans') (datax2_clusterx, clusters) = akmeans(data, num_clusters, max_iters, flann_params) print('[algos] pre_akmeans(): finished running akmeans') io.smart_save(clusters, cache_dir, clusters_fname, uid, '.npy') io.smart_save(datax2_clusterx, cache_dir, datax2cl_fname, uid, '.npy') #print('[algos] Removing Ctrl+C signal handler') #signal.signal(signal.SIGINT, signal.SIG_DFL) # reset ctrl+c behavior print('[algos] pre_akmeans(): return') return (datax2_clusterx, clusters)
#import re import warnings # Scientific import numpy as np # Hotspotter import draw_func2 as df2 import extract_patch from hscom import fileio as io from hscom import helpers #from interaction import interact_keypoints, interact_chipres, interact_chip # NOQA FNUMS = dict(image=1, chip=2, res=3, inspect=4, special=5, name=6) IN_IMAGE_OVERRIDE = helpers.get_arg('--in-image-override', type_=bool, default=None) SHOW_QUERY_OVERRIDE = helpers.get_arg('--show-query-override', type_=bool, default=None) NO_LABEL_OVERRIDE = helpers.get_arg('--no-label-override', type_=bool, default=None) @profile def draw(): df2.adjust_subplots_safe() df2.draw()
#import re import warnings # Scientific import numpy as np # Hotspotter import draw_func2 as df2 import extract_patch from hscom import fileio as io from hscom import helpers #from interaction import interact_keypoints, interact_chipres, interact_chip # NOQA FNUMS = dict(image=1, chip=2, res=3, inspect=4, special=5, name=6) IN_IMAGE_OVERRIDE = helpers.get_arg('--in-image-override', type_=bool, default=None) SHOW_QUERY_OVERRIDE = helpers.get_arg('--show-query-override', type_=bool, default=None) NO_LABEL_OVERRIDE = helpers.get_arg('--no-label-override', type_=bool, default=None) @profile def draw(): df2.adjust_subplots_safe() df2.draw() def register_FNUMS(FNUMS_): global FNUMS FNUMS = FNUMS_
from hscom import helpers from hscom import helpers as util from hsviz import viz import multiprocessing import numpy as np # NOQA if __name__ == '__main__': multiprocessing.freeze_support() # Debugging vars chip_cfg = None #l')=103.7900s cx_list = None kwargs = {} # --- LOAD TABLES --- # args = argparse2.parse_arguments(defaultdb='NAUTS') hs = api.HotSpotter(args) hs.load_tables() hs.update_samples() # --- LOAD CHIPS --- # force_compute = helpers.get_flag('--force', default=False) cc2.load_chips(hs, force_compute=force_compute) cx = helpers.get_arg('--cx', type_=int) if not cx is None: #tau = np.pi * 2 #hs.change_theta(cx, tau / 8) viz.show_chip(hs, cx, draw_kpts=False, fnum=1) viz.show_image(hs, hs.cx2_gx(cx), fnum=2) else: print('usage: feature_compute.py --cx [cx]') exec(viz.df2.present())
# valid_cxs = np.where(hs.tables.cx2_cid > 0)[0] if not np.iterable(cx_list): valid_cxs = [cx_list] cx_list = np.array(cx_list) # HACK hs.load_chips(cx_list=cx_list) hs.load_features(cx_list=cx_list) #%% # ============================================================================= # Detail plot function # ============================================================================= (print, print_, print_on, print_off, rrr, profile, printDBG) = \ __common__.init(__name__, '[viz]', DEBUG=False) NO_LABEL_OVERRIDE = helpers.get_arg('--no-label-override', type_=bool, default=None) # COLORS ORANGE = np.array((255, 127, 0, 255)) / 255.0 RED = np.array((255, 0, 0, 255)) / 255.0 GREEN = np.array(( 0, 255, 0, 255)) / 255.0 BLUE = np.array(( 0, 0, 255, 255)) / 255.0 YELLOW = np.array((255, 255, 0, 255)) / 255.0 BLACK = np.array(( 0, 0, 0, 255)) / 255.0 WHITE = np.array((255, 255, 255, 255)) / 255.0 GRAY = np.array((127, 127, 127, 255)) / 255.0 DEEP_PINK = np.array((255, 20, 147, 255)) / 255.0 PINK = np.array((255, 100, 100, 255)) / 255.0 FALSE_RED = np.array((255, 51, 0, 255)) / 255.0 TRUE_GREEN = np.array(( 0, 255, 0, 255)) / 255.0
#!/usr/env python from __future__ import division, print_function from hotspotter import HotSpotterAPI as api from hotspotter import feature_compute2 as fc2 from hscom import helpers from hscom import helpers as util from hsviz import viz from hscom import argparse2 import multiprocessing if __name__ == '__main__': multiprocessing.freeze_support() print('[fc2] __main__ = feature_compute2.py') # Read Args cx = helpers.get_arg('--cx', type_=int) delete_features = helpers.get_flag('--delete-features', default=False) nRandKpts = helpers.get_arg('--nRandKpts', type_=int) # Debugging vars feat_cfg = None cx_list = None kwargs = {} # --- LOAD TABLES --- # args = argparse2.parse_arguments(db='NAUTS') hs = api.HotSpotter(args) hs.load_tables() # --- LOAD CHIPS --- # hs.update_samples() hs.load_chips() # Delete features if needed if delete_features: fc2.clear_feature_cache(hs)
def test_configurations(hs, qcx_list, test_cfg_name_list, fnum=1): if __QUIET__: mc3.print_off() from hotspotter import HotSpotterAPI as api api.print_off() # Test Each configuration if not __QUIET__: print( textwrap.dedent(""" [harn]================ [harn] experiment_harness.test_configurations()""").strip()) hs.update_samples() # Grab list of algorithm configurations to test cfg_list = get_cfg_list(hs, test_cfg_name_list) if not __QUIET__: print('[harn] Testing %d different parameters' % len(cfg_list)) print('[harn] %d different chips' % len(qcx_list)) # Preallocate test result aggregation structures sel_cols = params.args.sel_cols # FIXME sel_rows = params.args.sel_rows # FIXME sel_cols = [] if sel_cols is None else sel_cols sel_rows = [] if sel_rows is None else sel_rows nCfg = len(cfg_list) nQuery = len(qcx_list) #rc2_res = np.empty((nQuery, nCfg), dtype=list) # row/col -> result mat_list = [] qreq = ds.QueryRequest() # TODO Add to argparse2 nocache_testres = util.get_flag('--nocache-testres', False) test_results_verbosity = 2 - (2 * __QUIET__) test_cfg_verbosity = 2 dbname = hs.get_db_name() testnameid = dbname + ' ' + str(test_cfg_name_list) msg = textwrap.dedent(''' --------------------- [harn] TEST_CFG %d/%d: ''' + testnameid + ''' ---------------------''') mark_progress = util.simple_progres_func(test_cfg_verbosity, msg, '+') nomemory = params.args.nomemory # Run each test configuration # Query Config / Col Loop dcxs = hs.get_indexed_sample() for cfgx, query_cfg in enumerate(cfg_list): if not __QUIET__: mark_progress(cfgx + 1, nCfg) # Set data to the current config qreq = mc3.prep_query_request(qreq=qreq, qcxs=qcx_list, dcxs=dcxs, query_cfg=query_cfg) # Run the test / read cache with util.Indenter2('[%s cfg %d/%d]' % (dbname, cfgx + 1, nCfg)): qx2_bestranks = get_test_results2(hs, qcx_list, qreq, cfgx, nCfg, nocache_testres, test_results_verbosity) if not nomemory: mat_list.append(qx2_bestranks) # Store the results if not __QUIET__: print('[harn] Finished testing parameters') if nomemory: print('ran tests in memory savings mode. exiting') return #-------------------- # Print Best Results rank_mat = np.hstack( mat_list) # concatenate each query rank across configs # Label the rank matrix: _colxs = np.arange(nCfg) lbld_mat = util.debug_vstack([_colxs, rank_mat]) _rowxs = np.arange(nQuery + 1).reshape(nQuery + 1, 1) - 1 lbld_mat = np.hstack([_rowxs, lbld_mat]) #------------ # Build row labels qx2_lbl = [] for qx in xrange(nQuery): qcx = qcx_list[qx] label = 'qx=%d) q%s ' % (qx, hs.cidstr(qcx, notes=True)) qx2_lbl.append(label) qx2_lbl = np.array(qx2_lbl) #------------ # Build col labels cfgx2_lbl = [] for cfgx in xrange(nCfg): test_uid = cfg_list[cfgx].get_uid() test_uid = cfg_list[cfgx].get_uid() cfg_label = 'cfgx=(%3d) %s' % (cfgx, test_uid) cfgx2_lbl.append(cfg_label) cfgx2_lbl = np.array(cfgx2_lbl) #------------ indent = util.indent @ArgGaurdFalse def print_rowlbl(): print('=====================') print('[harn] Row/Query Labels: %s' % testnameid) print('=====================') print('[harn] queries:\n%s' % '\n'.join(qx2_lbl)) print('--- /Row/Query Labels ---') print_rowlbl() #------------ @ArgGaurdFalse def print_collbl(): print('') print('=====================') print('[harn] Col/Config Labels: %s' % testnameid) print('=====================') print('[harn] configs:\n%s' % '\n'.join(cfgx2_lbl)) print('--- /Col/Config Labels ---') print_collbl() #------------ # Build Colscore qx2_min_rank = [] qx2_argmin_rank = [] new_hard_qx_list = [] new_qcid_list = [] new_hardtup_list = [] for qx in xrange(nQuery): ranks = rank_mat[qx] min_rank = ranks.min() bestCFG_X = np.where(ranks == min_rank)[0] qx2_min_rank.append(min_rank) qx2_argmin_rank.append(bestCFG_X) # Mark examples as hard if ranks.max() > 0: new_hard_qx_list += [qx] for qx in new_hard_qx_list: # New list is in cid format instead of cx format # because you should be copying and pasting it notes = ' ranks = ' + str(rank_mat[qx]) qcx = qcx_list[qx] qcid = hs.tables.cx2_cid[qcx] new_hardtup_list += [(qcid, notes)] new_qcid_list += [qcid] @ArgGaurdFalse def print_rowscore(): print('') print('=======================') print('[harn] Scores per Query: %s' % testnameid) print('=======================') for qx in xrange(nQuery): bestCFG_X = qx2_argmin_rank[qx] min_rank = qx2_min_rank[qx] minimizing_cfg_str = indent('\n'.join(cfgx2_lbl[bestCFG_X]), ' ') #minimizing_cfg_str = str(bestCFG_X) print('-------') print(qx2_lbl[qx]) print(' best_rank = %d ' % min_rank) if len(cfgx2_lbl) != 1: print(' minimizing_cfg_x\'s = %s ' % minimizing_cfg_str) print_rowscore() #------------ @ArgGaurdFalse def print_hardcase(): print('===') print('--- hard new_hardtup_list (w.r.t these configs): %s' % testnameid) print('\n'.join(map(repr, new_hardtup_list))) print('There are %d hard cases ' % len(new_hardtup_list)) print(sorted([x[0] for x in new_hardtup_list])) print('--- /Print Hardcase ---') print_hardcase() @ArgGaurdFalse def echo_hardcase(): print('====') print('--- hardcase commandline: %s' % testnameid) hardcids_str = ' '.join(map(str, [' ', '--qcid'] + new_qcid_list)) print(hardcids_str) print('--- /Echo Hardcase ---') echo_hardcase() #------------ # Build Colscore X_list = [1, 5] # Build a dictionary mapping X (as in #ranks < X) to a list of cfg scores nLessX_dict = {int(X): np.zeros(nCfg) for X in iter(X_list)} for cfgx in xrange(nCfg): ranks = rank_mat[:, cfgx] for X in iter(X_list): #nLessX_ = sum(np.bitwise_and(ranks < X, ranks >= 0)) nLessX_ = sum(np.logical_and(ranks < X, ranks >= 0)) nLessX_dict[int(X)][cfgx] = nLessX_ @ArgGaurdFalse def print_colscore(): print('') print('==================') print('[harn] Scores per Config: %s' % testnameid) print('==================') for cfgx in xrange(nCfg): print('[score] %s' % (cfgx2_lbl[cfgx])) for X in iter(X_list): nLessX_ = nLessX_dict[int(X)][cfgx] print(' ' + rankscore_str(X, nLessX_, nQuery)) print('--- /Scores per Config ---') print_colscore() #------------ @ArgGaurdFalse def print_latexsum(): print('') print('==========================') print('[harn] LaTeX: %s' % testnameid) print('==========================') # Create configuration latex table criteria_lbls = ['#ranks < %d' % X for X in X_list] db_name = hs.get_db_name(True) cfg_score_title = db_name + ' rank scores' cfgscores = np.array([nLessX_dict[int(X)] for X in X_list]).T replace_rowlbl = [(' *cfgx *', ' ')] tabular_kwargs = dict(title=cfg_score_title, out_of=nQuery, bold_best=True, replace_rowlbl=replace_rowlbl, flip=True) tabular_str = latex_formater.make_score_tabular( cfgx2_lbl, criteria_lbls, cfgscores, **tabular_kwargs) #latex_formater.render(tabular_str) print(tabular_str) print('--- /LaTeX ---') print_latexsum() #------------ best_rankscore_summary = [] to_intersect_list = [] # print each configs scores less than X=thresh for X, cfgx2_nLessX in nLessX_dict.iteritems(): max_LessX = cfgx2_nLessX.max() bestCFG_X = np.where(cfgx2_nLessX == max_LessX)[0] best_rankscore = '[cfg*] %d cfg(s) scored ' % len(bestCFG_X) best_rankscore += rankscore_str(X, max_LessX, nQuery) best_rankscore_summary += [best_rankscore] to_intersect_list += [cfgx2_lbl[bestCFG_X]] intersected = to_intersect_list[0] if len(to_intersect_list) > 0 else [] for ix in xrange(1, len(to_intersect_list)): intersected = np.intersect1d(intersected, to_intersect_list[ix]) @ArgGaurdFalse def print_bestcfg(): print('') print('==========================') print('[harn] Best Configurations: %s' % testnameid) print('==========================') # print each configs scores less than X=thresh for X, cfgx2_nLessX in nLessX_dict.iteritems(): max_LessX = cfgx2_nLessX.max() bestCFG_X = np.where(cfgx2_nLessX == max_LessX)[0] best_rankscore = '[cfg*] %d cfg(s) scored ' % len(bestCFG_X) best_rankscore += rankscore_str(X, max_LessX, nQuery) uid_list = cfgx2_lbl[bestCFG_X] #best_rankcfg = ''.join(map(wrap_uid, uid_list)) best_rankcfg = format_uid_list(uid_list) #indent('\n'.join(uid_list), ' ') print(best_rankscore) print(best_rankcfg) print('[cfg*] %d cfg(s) are the best of %d total cfgs' % (len(intersected), nCfg)) print(format_uid_list(intersected)) print('--- /Best Configurations ---') print_bestcfg() #------------ @ArgGaurdFalse def print_rankmat(): print('') print('-------------') print('RankMat: %s' % testnameid) print(' nRows=%r, nCols=%r' % lbld_mat.shape) print(' labled rank matrix: rows=queries, cols=cfgs:') #np.set_printoptions(threshold=5000, linewidth=5000, precision=5) with util.NpPrintOpts(threshold=5000, linewidth=5000, precision=5): print(lbld_mat) print('[harn]-------------') print_rankmat() #------------ sumstrs = [] sumstrs.append('') sumstrs.append('||===========================') sumstrs.append('|| [cfg*] SUMMARY: %s' % testnameid) sumstrs.append('||---------------------------') sumstrs.append(util.joins('\n|| ', best_rankscore_summary)) sumstrs.append('||===========================') print('\n' + '\n'.join(sumstrs) + '\n') #print('--- /SUMMARY ---') # Draw results if not __QUIET__: print('remember to inspect with --sel-rows (-r) and --sel-cols (-c) ') if len(sel_rows) > 0 and len(sel_cols) == 0: sel_cols = range(len(cfg_list)) if len(sel_cols) > 0 and len(sel_rows) == 0: sel_rows = range(len(qcx_list)) if params.args.view_all: sel_rows = range(len(qcx_list)) sel_cols = range(len(cfg_list)) sel_cols = list(sel_cols) sel_rows = list(sel_rows) total = len(sel_cols) * len(sel_rows) rciter = itertools.product(sel_rows, sel_cols) prev_cfg = None skip_to = util.get_arg('--skip-to', default=None) dev_mode = util.get_arg('--devmode', default=False) skip_list = [] if dev_mode: hs.prefs.display_cfg.N = 3 df2.FONTS.axtitle = df2.FONTS.smaller df2.FONTS.xlabel = df2.FONTS.smaller df2.FONTS.figtitle = df2.FONTS.smaller df2.SAFE_POS['top'] = .8 df2.SAFE_POS['bottom'] = .01 for count, (r, c) in enumerate(rciter): if skip_to is not None: if count < skip_to: continue if count in skip_list: continue # Get row and column index qcx = qcx_list[r] query_cfg = cfg_list[c] print('\n\n___________________________________') print(' --- VIEW %d / %d --- ' % (count + 1, total)) print('--------------------------------------') print('viewing (r, c) = (%r, %r)' % (r, c)) # Load / Execute the query qreq = mc3.prep_query_request(qreq=qreq, qcxs=[qcx], dcxs=dcxs, query_cfg=query_cfg) qcx2_res = mc3.process_query_request(hs, qreq, safe=True) res = qcx2_res[qcx] # Print Query UID print(res.uid) # Draw Result #res.show_top(hs, fnum=fnum) if prev_cfg != query_cfg: # This is way too aggro. Needs to be a bit lazier hs.refresh_features() prev_cfg = query_cfg fnum = count title_uid = res.uid title_uid = title_uid.replace('_FEAT', '\n_FEAT') res.show_analysis(hs, fnum=fnum, aug='\n' + title_uid, annote=1, show_name=False, show_gname=False, time_appart=False) df2.adjust_subplots_safe() if params.args.save_figures: from hsviz import allres_viz allres_viz.dump(hs, 'analysis', quality=True, overwrite=False) if not __QUIET__: print('[harn] EXIT EXPERIMENT HARNESS')
def precompute_akmeans(data, num_clusters, max_iters=100, flann_params=None, cache_dir=None, force_recomp=False, same_data=True, uid=''): 'precompute aproximate kmeans' if flann_params is None: flann_params = {} print('[algos] pre_akmeans()') if same_data: data_uid = helpers.hashstr_arr(data, 'dID') uid += data_uid clusters_fname = 'akmeans_clusters' datax2cl_fname = 'akmeans_datax2cl' try: if not force_recomp: clusters = io.smart_load(cache_dir, clusters_fname, uid, '.npy', can_fail=False) datax2_clusterx = io.smart_load(cache_dir, datax2cl_fname, uid, '.npy', can_fail=False) else: raise Exception('forcing') # Hack to refine akmeans with a few more iterations if '--refine' in sys.argv or '--refine-exit' in sys.argv: max_iters_override = helpers.get_arg('--refine', type_=int) print('Overriding max_iters=%r' % max_iters_override) if not max_iters_override is None: max_iters = max_iters_override datax2_clusterx_old = datax2_clusterx print('[algos] refining:') print('[algos] ' + '_'.join([clusters_fname, uid]) + '.npy') print('[algos] ' + '_'.join([datax2cl_fname, uid]) + '.npy') (datax2_clusterx, clusters) = __akmeans_iterate( data, clusters, datax2_clusterx_old, max_iters, flann_params, 0, 10) io.smart_save(clusters, cache_dir, clusters_fname, uid, '.npy') io.smart_save(datax2_clusterx, cache_dir, datax2cl_fname, uid, '.npy') if '--refine-exit' in sys.argv: print('exiting after refine') sys.exit(1) print('[algos] pre_akmeans(): ... loaded akmeans.') except Exception as ex: print('[algos] pre_akmeans(): ... could not load akmeans.') errstr = helpers.indent(repr(ex), '[algos] ') print('[algos] pre_akmeans(): ... caught ex:\n %s ' % errstr) print('[algos] pre_akmeans(): printing debug_smart_load') print('---- <DEBUG SMART LOAD>---') io.debug_smart_load(cache_dir, clusters_fname) io.debug_smart_load(cache_dir, datax2cl_fname) print('----</DEBUG SMART LOAD>---') #print('[algos] Press Ctrl+C to stop k-means early (and save)') #signal.signal(signal.SIGINT, force_quit_akmeans) # set ctrl+c behavior print('[algos] computing:') print('[algos] ' + '_'.join([clusters_fname, uid]) + '.npy') print('[algos] ' + '_'.join([datax2cl_fname, uid]) + '.npy') print('[algos] pre_akmeans(): calling akmeans') (datax2_clusterx, clusters) = akmeans(data, num_clusters, max_iters, flann_params) print('[algos] pre_akmeans(): finished running akmeans') io.smart_save(clusters, cache_dir, clusters_fname, uid, '.npy') io.smart_save(datax2_clusterx, cache_dir, datax2cl_fname, uid, '.npy') #print('[algos] Removing Ctrl+C signal handler') #signal.signal(signal.SIGINT, signal.SIG_DFL) # reset ctrl+c behavior print('[algos] pre_akmeans(): return') return (datax2_clusterx, clusters)