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sample_query
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@mabaorui
mabaorui committed May 13, 2021
1 parent 992fb22 commit c8e012d
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53 changes: 19 additions & 34 deletions NeuralPull.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,6 +19,7 @@
import trimesh
from im2mesh.utils import libmcubes
from im2mesh.utils.libkdtree import KDTree
import re

parser = argparse.ArgumentParser()
parser.add_argument('--train',action='store_true', default=False)
Expand All @@ -42,11 +43,11 @@
#INPUT_DIR = '/home/mabaorui/AtlasNetOwn/data/sphere/'
OUTPUT_DIR = a.out_dir
if(a.dataset=="shapenet"):
GT_DIR = '/data/mabaorui/project4/data/ShapeNet/' + a.class_idx + '/'
GT_DIR = './data/ShapeNet_GT/' + a.class_idx + '/'
if(a.dataset=="famous"):
GT_DIR = '/data1/mabaorui/AtlasNetOwn/data/famous/famous_noisefree/03_meshes/'
GT_DIR = './data/famous_noisefree/03_meshes/'
if(a.dataset=="ABC"):
GT_DIR = '/data1/mabaorui/AtlasNetOwn/data/abc_noisefree/03_meshes/'
GT_DIR = './data/abc_noisefree/03_meshes/'

TRAIN = a.train
bd = 0.55
Expand Down Expand Up @@ -190,27 +191,26 @@ def av_dist_None(array1, array2):
files_path = []

if(a.dataset == "shapenet"):
print('iii')
f = open('/data1/mabaorui/AtlasNetOwn/data/val.txt','r')
f = open('./data/shapenet_val.txt','r')
for index,line in enumerate(f):
if(line.strip().split('/')[0]==a.class_idx):
#print(line)
files.append(line.strip().split('/')[1])
f.close()

if(a.dataset == "famous"):
f = open('/data1/mabaorui/AtlasNetOwn/data/famous/famous_noisefree/testset.txt','r')
f = open('./data/famous_testset.txt','r')
for index,line in enumerate(f):
#print(line)
files.append(line.strip('\n'))
f.close()

if(a.dataset == "ABC"):
f = open('/data1/mabaorui/AtlasNetOwn/data/abc_noisefree/testset.txt','r')
for index,line in enumerate(f):
#print(line)
files.append(line.strip('\n'))
f.close()
if(a.dataset == "ABC" or a.dataset == "other"):
fileAll = os.listdir(INPUT_DIR)
for file in fileAll:
if(re.findall(r'.*.npz', file, flags=0)):
print(file.strip().split('.')[0])
files.append(file.strip().split('.')[0])

for file in files:
files_path.append(INPUT_DIR + file + '.npz')
Expand Down Expand Up @@ -355,7 +355,7 @@ def av_dist_None(array1, array2):
cd = 0
nc = 0
cd2 = 0
#or epoch in range(4,5):
#for epoch in range(20):
for epoch in range(test_num):
print('test:',epoch)
# if(os.path.exists(OUTPUT_DIR + file_test[epoch] + '.npz')):
Expand Down Expand Up @@ -394,8 +394,7 @@ def av_dist_None(array1, array2):
vox_min = np.min(vox.reshape((-1)))
print('max_min:',vox_max,vox_min)

#threshs = [0.0,0.00025,0.0005,0.00075,0.01,0.0125,0.015,0.0175,0.02]
threshs = [0.01]
threshs = [0.005]
for thresh in threshs:
print(np.sum(vox>thresh),np.sum(vox<thresh))

Expand Down Expand Up @@ -428,25 +427,11 @@ def av_dist_None(array1, array2):
mesh.export(OUTPUT_DIR + '/occn_' + files[epoch] + '_'+ str(thresh) + '.off')


# feature_bs = []
# for j in range(vertices.shape[0]):
# t = np.zeros(ONE_HOT)
# t[epoch] = 1
# feature_bs.append(t)
# feature_bs = np.asarray(feature_bs)
# grad_norm_c = sess.run([grad_norm],feed_dict={input_points_3d:vertices.reshape(1,-1,3),feature:feature_bs.reshape(1,-1,ONE_HOT)})
# #print('grad_norm',grad_norm_c)
# grad_norm_c = np.asarray(grad_norm_c)
# #print(grad_norm_c.shape)
# grad_norm_c = grad_norm_c.reshape(-1,3)


# mesh = trimesh.Trimesh(vertices, triangles,
# vertex_normals=grad_norm_c,
# process=False)
mesh = trimesh.Trimesh(vertices, triangles,
vertex_normals=None,
process=False)
if(a.dataset == 'other'):
continue
if(a.dataset=="shapenet"):
ps, idx = mesh.sample(1000000, return_index=True)
else:
Expand All @@ -457,16 +442,16 @@ def av_dist_None(array1, array2):
if(a.dataset=="shapenet"):
data = np.load(GT_DIR + files[epoch] + '/pointcloud.npz')
#data = np.load(file_test[epoch])
pointcloud = data['points'].reshape(1,-1,3)
normal = data['normals'].reshape(1,-1,3)
pointcloud = data['points']
normal = data['normals']
else:
mesh_gt = trimesh.load(GT_DIR + files[epoch] + '.ply')
pointcloud, idx_gt = mesh_gt.sample(10000, return_index=True)
pointcloud = pointcloud.astype(np.float32)
normal = mesh_gt.face_normals[idx_gt]

nc_t,cd_t,cd2_t = eval_pointcloud(ps,pointcloud.astype(np.float32),normals_pred.astype(np.float32),normal.astype(np.float32))
#np.savez(OUTPUT_DIR + files[epoch]+ '_'+ str(thresh),pp = ps, np = normals_pred, p = pointcloud[0,:,:], n = normal[0,:,:], nc = nc_t, cd = cd_t, cd2 = cd2_t)
np.savez(OUTPUT_DIR + files[epoch]+ '_'+ str(thresh),pp = ps, np = normals_pred, p = pointcloud, n = normal, nc = nc_t, cd = cd_t, cd2 = cd2_t)
nc = nc + nc_t
cd = cd + cd_t
cd2 = cd2 + cd2_t
Expand Down
100 changes: 100 additions & 0 deletions data/abc_noisefree_testset.txt
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22 changes: 22 additions & 0 deletions data/famous_noisefree_testset.txt
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@@ -0,0 +1,22 @@
3DBenchy
Armadillo
Cup34
Liberty
LibertyBase
Utah_teapot_(solid)
angel
bunny
column
dragon
eg07_dragon_original
flower
galera
hand
happy
horse
netsuke
serapis
tortuga
xyzrgb_dragon_clean
xyzrgb_statuette
yoda
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