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NeuralPull_SVG.py
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NeuralPull_SVG.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jul 23 16:44:22 2020
@author: Administrator
"""
import numpy as np
import tensorflow as tf
import os
import shutil
import random
import math
import scipy.io as sio
import time
from skimage import measure
import binvox_rw
import argparse
import trimesh
from im2mesh.utils import libmcubes
from im2mesh.utils.libkdtree import KDTree
parser = argparse.ArgumentParser()
parser.add_argument('--train',action='store_true', default=False)
parser.add_argument('--data_dir', type=str, required=True)
parser.add_argument('--out_dir', type=str, required=True)
parser.add_argument('--class_idx', type=str, default="026911156")
parser.add_argument('--save_idx', type=int, default=-1)
parser.add_argument('--CUDA', type=int, default=0)
parser.add_argument('--class_name', type=int, default=0)
a = parser.parse_args()
cuda_idx = str(a.CUDA)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]= cuda_idx
BS = 1
POINT_NUM = 5000
POINT_NUM_GT = 20000
train_list = '/data/mabaorui/AtlasNetOwn/data/shapenetcore_ids/' + a.class_idx + '_trainids.txt'
val_list = '/data/mabaorui/AtlasNetOwn/data/shapenetcore_ids/' + a.class_idx + '_valids.txt'
test_list = '/data/mabaorui/AtlasNetOwn/data/shapenetcore_ids/' + a.class_idx + '_testids.txt'
INPUT_DIR = a.data_dir
OUTPUT_DIR = a.out_dir
t = np.load('/data/mabaorui/AtlasNetOwn/data/features/' + a.class_name + '.npz')
features_train = np.concatenate((t['train'],t['val']),axis = 0)
TRAIN = a.train
bd = 0.55
if os.path.exists(OUTPUT_DIR):
shutil.rmtree(OUTPUT_DIR)
print ('test_res_dir: deleted and then created!')
os.makedirs(OUTPUT_DIR)
def distance_p2p(points_src, normals_src, points_tgt, normals_tgt):
''' Computes minimal distances of each point in points_src to points_tgt.
Args:
points_src (numpy array): source points
normals_src (numpy array): source normals
points_tgt (numpy array): target points
normals_tgt (numpy array): target normals
'''
kdtree = KDTree(points_tgt)
dist, idx = kdtree.query(points_src)
if normals_src is not None and normals_tgt is not None:
normals_src = \
normals_src / np.linalg.norm(normals_src, axis=-1, keepdims=True)
normals_tgt = \
normals_tgt / np.linalg.norm(normals_tgt, axis=-1, keepdims=True)
# normals_dot_product = (normals_tgt[idx] * normals_src).sum(axis=-1)
# # Handle normals that point into wrong direction gracefully
# # (mostly due to mehtod not caring about this in generation)
# normals_dot_product = np.abs(normals_dot_product)
normals_dot_product = np.abs(normals_tgt[idx] * normals_src)
normals_dot_product = normals_dot_product.sum(axis=-1)
else:
normals_dot_product = np.array(
[np.nan] * points_src.shape[0], dtype=np.float32)
return dist, normals_dot_product
def eval_pointcloud(pointcloud, pointcloud_tgt,
normals=None, normals_tgt=None):
''' Evaluates a point cloud.
Args:
pointcloud (numpy array): predicted point cloud
pointcloud_tgt (numpy array): target point cloud
normals (numpy array): predicted normals
normals_tgt (numpy array): target normals
'''
# Return maximum losses if pointcloud is empty
pointcloud = np.asarray(pointcloud)
pointcloud_tgt = np.asarray(pointcloud_tgt)
# Completeness: how far are the points of the target point cloud
# from thre predicted point cloud
completeness, completeness_normals = distance_p2p(
pointcloud_tgt, normals_tgt, pointcloud, normals
)
completeness2 = completeness**2
completeness = completeness.mean()
completeness2 = completeness2.mean()
completeness_normals = completeness_normals.mean()
# Accuracy: how far are th points of the predicted pointcloud
# from the target pointcloud
accuracy, accuracy_normals = distance_p2p(
pointcloud, normals, pointcloud_tgt, normals_tgt
)
accuracy2 = accuracy**2
accuracy = accuracy.mean()
accuracy2 = accuracy2.mean()
accuracy_normals = accuracy_normals.mean()
print(completeness,accuracy,completeness2,accuracy2)
# Chamfer distance
chamferL2 = 0.5 * (completeness2 + accuracy2)
print('chamferL2:',chamferL2)
normals_correctness = (
0.5 * completeness_normals + 0.5 * accuracy_normals
)
chamferL1 = 0.5 * (completeness + accuracy)
print('normals_correctness:',normals_correctness,'chamferL1:',chamferL1)
return normals_correctness, chamferL1, chamferL2
def safe_norm_np(x, epsilon=1e-12, axis=1):
return np.sqrt(np.sum(x*x, axis=axis) + epsilon)
def safe_norm(x, epsilon=1e-12, axis=None):
return tf.sqrt(tf.reduce_sum(x ** 2, axis=axis) + epsilon)
def boundingbox(x,y,z):
return min(x),max(x),min(y),max(y),min(z),max(z)
def chamfer_distance_tf_None(array1, array2):
array1 = tf.reshape(array1,[-1,3])
array2 = tf.reshape(array2,[-1,3])
av_dist1 = av_dist_None(array1, array2)
av_dist2 = av_dist_None(array2, array1)
return av_dist1+av_dist2
def distance_matrix_None(array1, array2, num_point, num_features = 3):
"""
arguments:
array1: the array, size: (num_point, num_feature)
array2: the samples, size: (num_point, num_feature)
returns:
distances: each entry is the distance from a sample to array1
, it's size: (num_point, num_point)
"""
expanded_array1 = tf.tile(array1, (num_point, 1))
expanded_array2 = tf.reshape(
tf.tile(tf.expand_dims(array2, 1),
(1, num_point, 1)),
(-1, num_features))
distances = tf.norm(expanded_array1-expanded_array2, axis=1)
distances = tf.reshape(distances, (num_point, num_point))
return distances
def av_dist_None(array1, array2):
"""
arguments:
array1, array2: both size: (num_points, num_feature)
returns:
distances: size: (1,)
"""
distances = distance_matrix_None(array1, array2,points_input_num[0,0])
distances = tf.reduce_min(distances, axis=1)
distances = tf.reduce_mean(distances)
return distances
def get_data_from_filename(filename):
load_data = np.load(filename)
point = np.asarray(load_data['sample_near']).reshape(-1,POINT_NUM,3)
sample = np.asarray(load_data['sample']).reshape(-1,POINT_NUM,3)
rt = random.randint(0,sample.shape[0]-1)
#rt = random.randint(0,int((sample.shape[0]-1)/5))
sample = sample[rt,:,:].reshape(BS, POINT_NUM, 3)
point = point[rt,:,:].reshape(BS, POINT_NUM, 3)
#print('input_points_bs:',filename)
#print(input_points_bs)
return point.astype(np.float32), sample.astype(np.float32)
files = []
files_path = []
if(TRAIN):
f = open(train_list,'r')
for index,line in enumerate(f):
files.append(line.strip().split('/')[1])
f.close()
f = open(val_list,'r')
for index,line in enumerate(f):
files.append(line.strip().split('/')[1])
f.close()
else:
f = open(test_list,'r')
for index,line in enumerate(f):
files.append(line.strip().split('/')[1])
f.close()
for file in files:
#print(INPUT_DIR + file)
files_path.append(INPUT_DIR + file + '.npz')
SHAPE_NUM = len(files_path)
print('SHAPE_NUM:',SHAPE_NUM)
filelist = tf.placeholder(tf.string, shape=[None])
ds = tf.data.Dataset.from_tensor_slices((filelist))
ds = ds.map(
lambda item: tuple(tf.py_func(get_data_from_filename, [item], (tf.float32, tf.float32))),num_parallel_calls = 4)
ds = ds.repeat() # Repeat the input indefinitely.
ds = ds.batch(1)
ds = ds.prefetch(buffer_size = 50)
iterator = ds.make_initializable_iterator()
next_element = iterator.get_next()
feature = tf.placeholder(tf.float32, shape=[BS,None,512])
if(TRAIN):
points_target = tf.reshape(next_element[0],[BS,-1,3])
input_points_3d = tf.reshape(next_element[1],[BS,-1,3])
else:
points_target = tf.placeholder(tf.float32, shape=[BS,None,3])
input_points_3d = tf.placeholder(tf.float32, shape=[BS, None,3])
points_target_num = tf.placeholder(tf.int32, shape=[1,1])
points_input_num = tf.placeholder(tf.int32, shape=[1,1])
feature_f = tf.nn.relu(tf.layers.dense(feature,128))
net = tf.nn.relu(tf.layers.dense(input_points_3d, 512))
net = tf.concat([net,feature_f],2)
print('net:',net)
with tf.variable_scope('decoder', reuse=tf.AUTO_REUSE):
for i in range(8):
with tf.variable_scope("resnetBlockFC_%d" % i ):
b_initializer=tf.constant_initializer(0.0)
w_initializer = tf.random_normal_initializer(mean=0.0,stddev=np.sqrt(2) / np.sqrt(512))
net = tf.layers.dense(tf.nn.relu(net),512,kernel_initializer=w_initializer,bias_initializer=b_initializer)
b_initializer=tf.constant_initializer(-0.5)
w_initializer = tf.random_normal_initializer(mean=2*np.sqrt(np.pi) / np.sqrt(512), stddev = 0.000001)
print('net:',net)
sdf = tf.layers.dense(tf.nn.relu(net),1,kernel_initializer=w_initializer,bias_initializer=b_initializer)
print('sdf',sdf)
grad = tf.gradients(ys=sdf, xs=input_points_3d)
print('grad',grad)
print(grad[0])
normal_p_lenght = tf.expand_dims(safe_norm(grad[0],axis = -1),-1)
print('normal_p_lenght',normal_p_lenght)
grad_norm = grad[0]/normal_p_lenght
print('grad_norm',grad_norm)
g_points = input_points_3d - sdf * grad_norm
#loss = tf.losses.huber_loss(point_target_near, g_points)
#loss = chamfer_distance_tf_None(point_target_near, g_points)
loss = chamfer_distance_tf_None(points_target, g_points)
t_vars = tf.trainable_variables()
optim = tf.train.AdamOptimizer(learning_rate=0.0001, beta1=0.9)
loss_grads_and_vars = optim.compute_gradients(loss, var_list=t_vars)
loss_optim = optim.apply_gradients(loss_grads_and_vars)
config = tf.ConfigProto(allow_soft_placement=False)
saver_restore = tf.train.Saver(var_list=t_vars)
saver = tf.train.Saver(max_to_keep=2000000)
with tf.Session(config=config) as sess:
if(TRAIN):
print('train start')
sess.run(tf.global_variables_initializer())
start_time = time.time()
POINT_NUM_GT_bs = np.array(POINT_NUM_GT).reshape(1,1)
points_input_num_bs = np.array(POINT_NUM).reshape(1,1)
for i in range(4000000):
epoch_index = np.random.choice(SHAPE_NUM, SHAPE_NUM, replace = False)
ini_data_path_epoch = []
for fi in range(SHAPE_NUM):
ini_data_path_epoch.append(files_path[epoch_index[fi]])
sess.run(iterator.initializer, feed_dict={filelist: ini_data_path_epoch})
loss_i = 0
for epoch in epoch_index:
img_index = random.randint(0,23)
net_c = features_train[epoch*24+img_index,:]
net_c = net_c.reshape((-1))
net_c = np.expand_dims(net_c,0).repeat(POINT_NUM,axis=0)
net_c = net_c.reshape((1,POINT_NUM, 512))
feature_bs_t = net_c
_, loss_c = sess.run([loss_optim,loss],feed_dict={feature:feature_bs_t,points_target_num:POINT_NUM_GT_bs,points_input_num:points_input_num_bs})
loss_i = loss_i + loss_c
loss_i = loss_i / SHAPE_NUM
with open(OUTPUT_DIR + '/loss.txt','a') as f:
f.write(str(loss_i)+';')
if(i%100 == 0):
print('save model')
#print(p_points_bs)
saver.save(sess, os.path.join(OUTPUT_DIR, "model"), global_step=i+1)
end_time = time.time()
print('run_time:',end_time-start_time)
else:
pointclouds = []
samples = []
normals_gt = []
files = []
f = open(test_list,'r')
for index,line in enumerate(f):
files.append(line.strip().split('/')[1])
f.close()
for file in files:
data = np.load(INPUT_DIR + file + '/pointcloud.npz')
pointcloud = data['points'].reshape(1,-1,3)
normal = data['normals'].reshape(1,-1,3)
pointcloud = pointcloud.reshape(1,-1,3)
pointclouds.append(pointcloud)
normals_gt.append(normal)
t = np.load('/data/mabaorui/AtlasNetOwn/data/features/' + a.class_name + '.npz')
feature_test = t['test']
print('test start')
checkpoint = tf.train.get_checkpoint_state(OUTPUT_DIR).all_model_checkpoint_paths
print(checkpoint[-1])
saver.restore(sess, checkpoint[-1])
s = np.arange(-bd,bd, (2*bd)/128)
print(s.shape[0])
vox_size = s.shape[0]
POINT_NUM_GT_bs = np.array(vox_size).reshape(1,1)
points_input_num_bs = np.array(POINT_NUM).reshape(1,1)
input_points_2d_bs = []
for i in s:
for j in s:
for k in s:
input_points_2d_bs.append(np.asarray([i,j,k]))
input_points_2d_bs = np.asarray(input_points_2d_bs)
print('input_points_2d_bs',input_points_2d_bs.shape)
input_points_2d_bs = input_points_2d_bs.reshape((vox_size,vox_size,vox_size,3))
POINT_NUM_GT_bs = np.array(vox_size*vox_size).reshape(1,1)
test_num = SHAPE_NUM
print('test_num:',test_num)
cd = 0
nc = 0
for epoch in range(0,test_num):
with open(INPUT_DIR + 'gt_' + files[epoch] + '.txt','w') as f:
for i in range(pointclouds.shape[1]):
x = pointclouds[epoch,i,0]
y = pointclouds[epoch,i,1]
z = pointclouds[epoch,i,2]
f.write(str(x)+';')
f.write(str(y)+';')
f.write(str(z)+';\n')
nc_best = 10000000
cd_best = 10000000
#threshs = [-0.01,0.0,0.01]
threshs = [0.01]
for thresh in threshs:
#print('thresh:',thresh)
for img_index in range(24):
vox = []
for i in range(vox_size):
input_points_2d_bs_t = input_points_2d_bs[i,:,:,:]
input_points_2d_bs_t = input_points_2d_bs_t.reshape(BS, vox_size*vox_size, 3)
net_c = feature_test[epoch*24+img_index,:]
net_c = net_c.reshape((-1))
#print(net_c.shape)
net_c = np.expand_dims(net_c,0).repeat(vox_size*vox_size,axis=0)
#print(net_c.shape)
net_c = net_c.reshape((1,vox_size*vox_size, 512))
#print(net_c)
feature_bs_t = net_c
sdf_c = sess.run([sdf],feed_dict={input_points_3d:input_points_2d_bs_t,feature:feature_bs_t,points_target_num:POINT_NUM_GT_bs,points_input_num:points_input_num_bs})
vox.append(sdf_c)
vox = np.asarray(vox)
#print('vox',vox.shape)
vox = vox.reshape((vox_size,vox_size,vox_size))
vox_max = np.max(vox.reshape((-1)))
vox_min = np.min(vox.reshape((-1)))
#print('thresh:',thresh)
print('max_min:',vox_max,vox_min)
#print(abs(vox_max),abs(vox_min))
if(abs(vox_max)>abs(vox_min)):
thresh = 0.01
else:
thresh = -0.01
print('thresh:',thresh)
#print(np.sum(vox>thresh),np.sum(vox<thresh))
vertices, triangles = libmcubes.marching_cubes(vox, thresh)
if(vertices.shape[0]<10 or triangles.shape<10):
print('no sur---------------------------------------------')
continue
if(np.sum(vox>thresh)>np.sum(vox<thresh)):
triangles_t = []
for it in range(triangles.shape[0]):
tt = np.array([triangles[it,2],triangles[it,1],triangles[it,0]])
triangles_t.append(tt)
triangles_t = np.asarray(triangles_t)
else:
triangles_t = triangles
triangles_t = np.asarray(triangles_t)
vertices -= 0.5
# Undo padding
vertices -= 1
# Normalize to bounding box
vertices /= np.array([vox_size-1, vox_size-1, vox_size-1])
vertices = 1.1 * (vertices - 0.5)
mesh = trimesh.Trimesh(vertices, triangles_t,
vertex_normals=None,
process=False)
#mesh.export(OUTPUT_DIR + '/occn_' + files[epoch] + '_'+ str(thresh) + '_{}.off'.format(img_index))
mesh.export(OUTPUT_DIR + '/occn_' + files[epoch] + '_'+ '0.01' + '_{}.off'.format(img_index))
mesh = trimesh.Trimesh(vertices, triangles,
vertex_normals=None,
process=False)
ps, idx = mesh.sample(100000, return_index=True)
ps = ps.astype(np.float32)
normals_pred = mesh.face_normals[idx]
nc_t,cd_t,cd2_t = eval_pointcloud(ps,pointclouds[epoch,:,:].astype(np.float32),normals_pred.astype(np.float32),normals_gt[epoch,:,:].astype(np.float32))
#np.savez(OUTPUT_DIR + files[epoch] + '_{}'.format(img_index),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) + '_{}'.format(img_index), nc = nc_t, cd = cd_t, cd2 = cd2_t)
np.savez(OUTPUT_DIR + files[epoch] + '_'+ '0.01' + '_{}'.format(img_index), nc = nc_t, cd = cd_t, cd2 = cd2_t)
if(cd_t<cd_best):
cd_best = cd_t
nc_best = nc_t
print('model:',epoch,'nc_best:',nc_best,'cd_best:',cd_best)
nc = nc + nc_best
cd = cd + cd_best
print('mean_nc:',nc/test_num,'mean_cd:',cd/test_num)