def get_mean_insSize_by_sem(dataset_path, train_areas):
from helper_data_s3dis import Data_Configs as Data_Configs
configs = Data_Configs()
mean_insSize_by_sem = {}
for sem in configs.sem_ids:
mean_insSize_by_sem[sem] = []
for a in train_areas:
print('get mean insSize, check train area:', a)
files = sorted(glob.glob(dataset_path + a + '*.h5'))
for file_path in files:
fin = h5py.File(file_path, 'r')
semIns_labels = fin['semIns_labels'][:].reshape([-1, 2])
ins_labels = semIns_labels[:, 1]
sem_labels = semIns_labels[:, 0]
ins_idx = np.unique(ins_labels)
for ins_id in ins_idx:
tmp = (ins_labels == ins_id)
sem = scipy.stats.mode(sem_labels[tmp])[0][0]
mean_insSize_by_sem[sem].append(
np.sum(np.asarray(tmp, dtype=np.float32)))
for sem in mean_insSize_by_sem:
mean_insSize_by_sem[sem] = np.mean(mean_insSize_by_sem[sem])
return mean_insSize_by_sem
def load_net_data(dataset_path, train_areas, test_areas, model_path):
#######
from main_3D_BoNet import BoNet
from helper_data_s3dis import Data_Configs as Data_Configs
configs = Data_Configs()
net = BoNet(configs=configs)
####### 1. networks
net.X_pc = tf.placeholder(shape=[None, None, net.points_cc],
dtype=tf.float32,
name='X_pc')
net.is_train = tf.placeholder(dtype=tf.bool, name='is_train')
with tf.variable_scope('backbone'):
#net.point_features, net.global_features, net.y_psem_pred = net.backbone_pointnet(net.X_pc, net.is_train)
net.point_features, net.global_features, net.y_psem_pred = net.backbone_pointnet2(
net.X_pc, net.is_train)
with tf.variable_scope('bbox'):
net.y_bbvert_pred_raw, net.y_bbscore_pred_raw = net.bbox_net(
net.global_features)
with tf.variable_scope('pmask'):
net.y_pmask_pred_raw = net.pmask_net(net.point_features,
net.global_features,
net.y_bbvert_pred_raw,
net.y_bbscore_pred_raw)
####### 2. restore trained model
if not os.path.isfile(model_path + '.data-00000-of-00001'):
print('please download the released model!')
return
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.visible_device_list = '0'
net.sess = tf.Session(config=config)
tf.train.Saver().restore(net.sess, model_path)
print('Model restored sucessful!')
####### 3. load data
from helper_data_s3dis import Data_S3DIS as Data
data = Data(dataset_path, train_areas, test_areas)
return net, data
if ep % 5 == 0 and i == total_train_batch_num - 1:
from main_eval import Evaluation
result_path = './log/test_res/' + str(ep).zfill(3) + '_' + test_areas[0] + '/'
Evaluation.ttest(net, data, result_path, test_batch_size=20)
Evaluation.evaluation(dataset_path, train_areas, result_path)
print('full eval finished!')
############
if __name__ == '__main__':
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = '0' ## specify the GPU to use
from main_3D_BoNet import BoNet
from helper_data_s3dis import Data_Configs as Data_Configs
configs = Data_Configs()
net = BoNet(configs=configs)
net.creat_folders(name='log', re_train=False)
net.build_graph()
####
from helper_data_s3dis import Data_S3DIS as Data
train_areas = ['Area_1', 'Area_2', 'Area_3', 'Area_4', 'Area_6']
test_areas = ['Area_5']
dataset_path = './data_s3dis/'
data = Data(dataset_path, train_areas, test_areas, train_batch_size=4)
train(net, data)
def evaluation(dataset_path, train_areas, result_path):
from helper_data_s3dis import Data_Configs as Data_Configs
configs = Data_Configs()
mean_insSize_by_sem = Eval_Tools.get_mean_insSize_by_sem(
dataset_path, train_areas)
TP_FP_Total = {}
for sem_id in configs.sem_ids:
TP_FP_Total[sem_id] = {}
TP_FP_Total[sem_id]['TP'] = 0
TP_FP_Total[sem_id]['FP'] = 0
TP_FP_Total[sem_id]['Total'] = 0
res_scenes = sorted(os.listdir(result_path + 'res_by_scene/'))
# add SCN
USE_SCN = True
SCN_semantic_tranform_array = np.array(
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])
idx = 0
for scene_name in res_scenes:
print('eval scene', scene_name)
scene_result = scipy.io.loadmat(
result_path + 'res_by_scene/' + scene_name,
verify_compressed_data_integrity=False)
pc_all = []
ins_gt_all = []
sem_pred_all = []
sem_gt_all = []
gap = 5e-3
volume_num = int(1. / gap) + 2
volume = -1 * np.ones([volume_num, volume_num, volume_num]).astype(
np.int32)
volume_sem = -1 * np.ones([volume_num, volume_num, volume_num
]).astype(np.int32)
SCN_precition_this_scene = []
if USE_SCN:
f = open(os.path.join(BASE_DIR, './data_s3dis/SCN_prediction/area5_2', \
scene_name[7:-7] + '.txt'), 'r')
for line in f:
SCN_precition_this_scene.append(line)
SCN_precition_this_scene = np.array(SCN_precition_this_scene)
def look_up_SCN(x):
return SCN_precition_this_scene[x]
def SCN_to_ScanNet(x):
return SCN_semantic_tranform_array[int(x)]
for i in range(len(scene_result)):
block = 'block_' + str(i).zfill(4)
if block not in scene_result: continue
pc = scene_result[block][0]['pc'][0]
ins_gt = scene_result[block][0]['ins_gt'][0][0]
sem_gt = scene_result[block][0]['sem_gt'][0][0]
pmask_pred_raw = scene_result[block][0]['pmask_pred_raw'][0]
sem_pred_raw = scene_result[block][0]['sem_pred_raw'][0]
pc_indices_raw = scene_result[block][0]['pc_indices_raw'][
0].squeeze(1)
SCN_pred_raw = list(map(look_up_SCN, pc_indices_raw))
sem_pred = list(map(SCN_to_ScanNet, SCN_pred_raw))
sem_pred = np.array(sem_pred)
pmask_pred = pmask_pred_raw
ins_pred = np.argmax(pmask_pred, axis=-2)
ins_sem_dic = Eval_Tools.get_sem_for_ins(ins_by_pts=ins_pred,
sem_by_pts=sem_pred)
Eval_Tools.BlockMerging(volume, volume_sem, pc[:, 6:9],
ins_pred, ins_sem_dic, gap)
pc_all.append(pc)
ins_gt_all.append(ins_gt)
sem_pred_all.append(sem_pred)
sem_gt_all.append(sem_gt)
##
pc_all = np.concatenate(pc_all, axis=0)
ins_gt_all = np.concatenate(ins_gt_all, axis=0)
sem_pred_all = np.concatenate(sem_pred_all, axis=0)
sem_gt_all = np.concatenate(sem_gt_all, axis=0)
pc_xyz_int = (pc_all[:, 6:9] / gap).astype(np.int32)
ins_pred_all = volume[tuple(pc_xyz_int.T)]
### if you need to visulize, please uncomment the follow lines
from helper_data_plot import Plot as Plot
Plot.draw_pc(
np.concatenate([pc_all[:, 9:12], pc_all[:, 3:6]], axis=1), idx)
idx += 1
Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12],
idx=idx,
pc_semins=ins_gt_all)
idx += 1
Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12],
idx=idx,
pc_semins=ins_pred_all)
idx += 1
# Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12], idx = idx ,pc_semins=sem_gt_all )
# idx+=1
# Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12], idx = idx ,pc_semins=sem_pred_all)
# idx+=1
# if idx > 100 : break
##
###################
# pred ins
ins_pred_by_sem = {}
for sem in configs.sem_ids:
ins_pred_by_sem[sem] = []
ins_idx, cnts = np.unique(ins_pred_all, return_counts=True)
for ins_id, cn in zip(ins_idx, cnts):
if ins_id <= -1: continue
tmp = (ins_pred_all == ins_id)
sem = scipy.stats.mode(sem_pred_all[tmp])[0][0]
if cn <= 0.3 * mean_insSize_by_sem[sem]:
continue # remove small instances
ins_pred_by_sem[sem].append(tmp)
# gt ins
ins_gt_by_sem = {}
for sem in configs.sem_ids:
ins_gt_by_sem[sem] = []
ins_idx = np.unique(ins_gt_all)
for ins_id in ins_idx:
if ins_id <= -1: continue
tmp = (ins_gt_all == ins_id)
sem = scipy.stats.mode(sem_gt_all[tmp])[0][0]
if len(np.unique(sem_gt_all[ins_gt_all == ins_id])) != 1:
print('sem ins label error')
exit()
ins_gt_by_sem[sem].append(tmp)
# to associate
for sem_id, sem_name in zip(configs.sem_ids, configs.sem_names):
ins_pred_tp = ins_pred_by_sem[sem_id]
ins_gt_tp = ins_gt_by_sem[sem_id]
flag_pred = np.zeros(len(ins_pred_tp), dtype=np.int8)
for i_p, ins_p in enumerate(ins_pred_tp):
iou_max = -1
for i_g, ins_g in enumerate(ins_gt_tp):
u = ins_g | ins_p
i = ins_g & ins_p
iou_tp = float(np.sum(i)) / (np.sum(u) + 1e-8)
if iou_tp > iou_max:
iou_max = iou_tp
if iou_max >= 0.5:
flag_pred[i_p] = 1
###
TP_FP_Total[sem_id]['TP'] += np.sum(flag_pred)
TP_FP_Total[sem_id]['FP'] += len(flag_pred) - np.sum(flag_pred)
TP_FP_Total[sem_id]['Total'] += len(ins_gt_tp)
###############
pre_all = []
rec_all = []
for sem_id, sem_name in zip(configs.sem_ids, configs.sem_names):
TP = TP_FP_Total[sem_id]['TP']
FP = TP_FP_Total[sem_id]['FP']
Total = TP_FP_Total[sem_id]['Total']
pre = float(TP) / (TP + FP + 1e-8)
rec = float(TP) / (Total + 1e-8)
if Total > 0:
pre_all.append(pre)
rec_all.append(rec)
out_file = result_path + 'PreRec_' + str(sem_id).zfill(
2) + '_' + sem_name + '_' + str(round(pre, 4)) + '_' + str(
round(rec, 4))
np.savez_compressed(out_file + '.npz', tp={0, 0})
out_file = result_path + 'PreRec_mean_' + str(
round(np.mean(pre_all), 4)) + '_' + str(round(np.mean(rec_all), 4))
np.savez_compressed(out_file + '.npz', tp={0, 0})
return 0
def evaluation(dataset_path, train_areas, result_path):
from helper_data_s3dis import Data_Configs as Data_Configs
configs = Data_Configs()
mean_insSize_by_sem = Eval_Tools.get_mean_insSize_by_sem(
dataset_path, train_areas)
TP_FP_Total = {}
for sem_id in configs.sem_ids:
TP_FP_Total[sem_id] = {}
TP_FP_Total[sem_id]['TP'] = 0
TP_FP_Total[sem_id]['FP'] = 0
TP_FP_Total[sem_id]['Total'] = 0
res_scenes = sorted(os.listdir(result_path + 'res_by_scene/'))
for scene_name in res_scenes:
print('eval scene', scene_name)
scene_result = scipy.io.loadmat(
result_path + 'res_by_scene/' + scene_name,
verify_compressed_data_integrity=False)
pc_all = []
ins_gt_all = []
sem_pred_all = []
sem_gt_all = []
gap = 5e-3
volume_num = int(1. / gap) + 2
volume = -1 * np.ones([volume_num, volume_num, volume_num]).astype(
np.int32)
volume_sem = -1 * np.ones([volume_num, volume_num, volume_num
]).astype(np.int32)
for i in range(len(scene_result)):
block = 'block_' + str(i).zfill(4)
if block not in scene_result: continue
pc = scene_result[block][0]['pc'][0]
ins_gt = scene_result[block][0]['ins_gt'][0][0]
sem_gt = scene_result[block][0]['sem_gt'][0][0]
bbscore_pred_raw = scene_result[block][0]['bbscore_pred_raw'][
0][0]
pmask_pred_raw = scene_result[block][0]['pmask_pred_raw'][0]
sem_pred_raw = scene_result[block][0]['sem_pred_raw'][0]
sem_pred = np.argmax(sem_pred_raw, axis=-1)
pmask_pred = pmask_pred_raw * np.tile(
bbscore_pred_raw[:, None], [1, pmask_pred_raw.shape[-1]])
ins_pred = np.argmax(pmask_pred, axis=-2)
ins_sem_dic = Eval_Tools.get_sem_for_ins(ins_by_pts=ins_pred,
sem_by_pts=sem_pred)
Eval_Tools.BlockMerging(volume, volume_sem, pc[:, 6:9],
ins_pred, ins_sem_dic, gap)
pc_all.append(pc)
ins_gt_all.append(ins_gt)
sem_pred_all.append(sem_pred)
sem_gt_all.append(sem_gt)
##
pc_all = np.concatenate(pc_all, axis=0)
ins_gt_all = np.concatenate(ins_gt_all, axis=0)
sem_pred_all = np.concatenate(sem_pred_all, axis=0)
sem_gt_all = np.concatenate(sem_gt_all, axis=0)
pc_xyz_int = (pc_all[:, 6:9] / gap).astype(np.int32)
ins_pred_all = volume[tuple(pc_xyz_int.T)]
#### if you need to visulize, please uncomment the follow lines
#from helper_data_plot import Plot as Plot
#Plot.draw_pc(np.concatenate([pc_all[:,9:12], pc_all[:,3:6]], axis=1))
#Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12], pc_semins=ins_gt_all)
#Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12], pc_semins=ins_pred_all)
#Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12], pc_semins=sem_gt_all)
#Plot.draw_pc_semins(pc_xyz=pc_all[:, 9:12], pc_semins=sem_pred_all)
####
###################
# pred ins
ins_pred_by_sem = {}
for sem in configs.sem_ids:
ins_pred_by_sem[sem] = []
ins_idx, cnts = np.unique(ins_pred_all, return_counts=True)
for ins_id, cn in zip(ins_idx, cnts):
if ins_id <= -1: continue
tmp = (ins_pred_all == ins_id)
sem = scipy.stats.mode(sem_pred_all[tmp])[0][0]
if cn <= 0.3 * mean_insSize_by_sem[sem]:
continue # remove small instances
ins_pred_by_sem[sem].append(tmp)
# gt ins
ins_gt_by_sem = {}
for sem in configs.sem_ids:
ins_gt_by_sem[sem] = []
ins_idx = np.unique(ins_gt_all)
for ins_id in ins_idx:
if ins_id <= -1: continue
tmp = (ins_gt_all == ins_id)
sem = scipy.stats.mode(sem_gt_all[tmp])[0][0]
if len(np.unique(sem_gt_all[ins_gt_all == ins_id])) != 1:
print('sem ins label error')
exit()
ins_gt_by_sem[sem].append(tmp)
# to associate
for sem_id, sem_name in zip(configs.sem_ids, configs.sem_names):
ins_pred_tp = ins_pred_by_sem[sem_id]
ins_gt_tp = ins_gt_by_sem[sem_id]
flag_pred = np.zeros(len(ins_pred_tp), dtype=np.int8)
for i_p, ins_p in enumerate(ins_pred_tp):
iou_max = -1
for i_g, ins_g in enumerate(ins_gt_tp):
u = ins_g | ins_p
i = ins_g & ins_p
iou_tp = float(np.sum(i)) / (np.sum(u) + 1e-8)
if iou_tp > iou_max:
iou_max = iou_tp
if iou_max >= 0.5:
flag_pred[i_p] = 1
###
TP_FP_Total[sem_id]['TP'] += np.sum(flag_pred)
TP_FP_Total[sem_id]['FP'] += len(flag_pred) - np.sum(flag_pred)
TP_FP_Total[sem_id]['Total'] += len(ins_gt_tp)
###############
pre_all = []
rec_all = []
for sem_id, sem_name in zip(configs.sem_ids, configs.sem_names):
TP = TP_FP_Total[sem_id]['TP']
FP = TP_FP_Total[sem_id]['FP']
Total = TP_FP_Total[sem_id]['Total']
pre = float(TP) / (TP + FP + 1e-8)
rec = float(TP) / (Total + 1e-8)
if Total > 0:
pre_all.append(pre)
rec_all.append(rec)
out_file = result_path + 'PreRec_' + str(sem_id).zfill(
2) + '_' + sem_name + '_' + str(round(pre, 4)) + '_' + str(
round(rec, 4))
np.savez_compressed(out_file + '.npz', tp={0, 0})
out_file = result_path + 'PreRec_mean_' + str(
round(np.mean(pre_all), 4)) + '_' + str(round(np.mean(rec_all), 4))
np.savez_compressed(out_file + '.npz', tp={0, 0})
return 0