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 ########################################
