ND = 0 BD = 0 COMPUTE = 0 neuron_distance_csv = data_DIR + '/neuron_distances_with_silver_gt.csv' ###### resample #?and sort #rp.resample_and_sort(original_dir,resampled_dir,sorted_dir,GEN_QSUB=0,overwrite_sorted=0) if COMPUTE > 0: ###### sliver data table rc_SILVER_CSV = data_DIR + '/recon_table.csv' rp.recon_table_gen(original_dir, lookup_image_id_table_file, rc_SILVER_CSV) ##### merge to get the common set between silver gt and silver rc merged_csv_file = data_DIR + '/recon_shared_with_silver_gt.csv' rp.merge_gold_silver(SILVER_CSV, rc_SILVER_CSV, merged_csv_file) ##### report which gold dataset did not have any recons? df_merge = pd.read_csv(merged_csv_file) df_silver_gt = pd.read_csv(SILVER_CSV) m = pd.unique(df_merge.image_file_name) g = pd.unique(df_silver_gt.image_file_name) print "\n\nSilver gt dataset contains " + str(g.size) + " image dataset" print "There are " + str( df_merge.shape[0]) + " reconstructions are generated from " + str( pd.unique(df_merge.algorithm).size) + " algorithms." for i in g: if i not in m: print "No reconstructions for image: " + i ########################### distance calculation ########################################
COMPUTE = 0 neuron_distance_csv = data_DIR +'/neuron_distances_with_silver_gt.csv' ###### resample #?and sort #rp.resample_and_sort(original_dir,resampled_dir,sorted_dir,GEN_QSUB=0,overwrite_sorted=0) if COMPUTE>0: ###### sliver data table rc_SILVER_CSV = data_DIR+'/recon_table.csv' rp.recon_table_gen(original_dir,lookup_image_id_table_file,rc_SILVER_CSV) ##### merge to get the common set between silver gt and silver rc merged_csv_file = data_DIR+'/recon_shared_with_silver_gt.csv' rp.merge_gold_silver(SILVER_CSV,rc_SILVER_CSV,merged_csv_file) ##### report which gold dataset did not have any recons? df_merge = pd.read_csv(merged_csv_file) df_silver_gt = pd.read_csv(SILVER_CSV) m = pd.unique(df_merge.image_file_name) g = pd.unique(df_silver_gt.image_file_name) print "\n\nSilver gt dataset contains " +str(g.size) +" image dataset" print "There are " + str(df_merge.shape[0])+" reconstructions are generated from " + str(pd.unique(df_merge.algorithm).size) +" algorithms." for i in g: if i not in m: print "No reconstructions for image: " + i ########################### distance calculation ########################################
COMPUTE = 1 MEDIAN = 0 ################################################################################ if COMPUTE: ###### resample #?and sort # rp.resample_and_sort(original_dir,resampled_dir,sorted_dir,GEN_QSUB=0,overwrite_sorted=0) ###### sliver data table SILVER_CSV = data_DIR + "/recon_table.csv" rp.recon_table_gen(original_dir, lookup_image_id_table_file, SILVER_CSV) ##### merge to get the common set between gold and silver merged_csv_file = data_DIR + "/recon_shared_with_gold_set.csv" rp.merge_gold_silver(GOLD_CSV, SILVER_CSV, merged_csv_file) ##### report which gold dataset did not have any recons? df_merge = pd.read_csv(merged_csv_file) df_gold = pd.read_csv(GOLD_CSV) m = pd.unique(df_merge.image_file_name) g = pd.unique(df_gold.image_file_name) print "\n\nGold dataset contains " + str(g.size) + " image dataset" print "There are " + str(df_merge.shape[0]) + " reconstructions are generated from " + str( pd.unique(df_merge.algorithm).size ) + " algorithms." for i in g: if i not in m: print "No reconstructions for image: " + i ########################### distance calculation ########################################
BD = 0 COMPUTE = 1 MEDIAN = 0 ################################################################################ if COMPUTE: ###### resample #?and sort #rp.resample_and_sort(original_dir,resampled_dir,sorted_dir,GEN_QSUB=0,overwrite_sorted=0) ###### sliver data table SILVER_CSV = data_DIR + '/recon_table.csv' rp.recon_table_gen(original_dir, lookup_image_id_table_file, SILVER_CSV) ##### merge to get the common set between gold and silver merged_csv_file = data_DIR + '/recon_shared_with_gold_set.csv' rp.merge_gold_silver(GOLD_CSV, SILVER_CSV, merged_csv_file) ##### report which gold dataset did not have any recons? df_merge = pd.read_csv(merged_csv_file) df_gold = pd.read_csv(GOLD_CSV) m = pd.unique(df_merge.image_file_name) g = pd.unique(df_gold.image_file_name) print "\n\nGold dataset contains " + str(g.size) + " image dataset" print "There are " + str( df_merge.shape[0]) + " reconstructions are generated from " + str( pd.unique(df_merge.algorithm).size) + " algorithms." for i in g: if i not in m: print "No reconstructions for image: " + i ########################### distance calculation ########################################