isunchy
commented
3 years ago In the paper, the chamfer distance between surface is used to evaluate the network's effectiveness, but i didn't find this part in code, could you release the evaluation code?
We random sample 5000 points from scanning the shape mesh, and sample 5000 points on the cube surface according to the cube size, then compute the L2 chamfer distance between them.
import math
import os
import random
import struct
import miniball
import numpy as np
from open3d import *
import quaternion
sample_points = 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], dtype=np.float32) # [3, n]
n_sample_points = sample_points.shape[1]
sample_points = np.transpose(sample_points) # [n, 3]
surface_index = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
], dtype=np.int32)
def generate_surface_points(resolution=16):
'''
generate points on cube surface, and rescale to [-1, 1]
n_points = resolution**3 - (resolution-2)**3
'''
r = resolution
n_points = r**3 - (r-2)**3
sample_points = np.ndarray([n_points, 3], dtype=np.float32)
surface_index = np.ndarray([n_points], dtype=np.int32)
index = 0
for i in range(r):
for j in range(r):
for k in range(r):
if (i == 0 or i == r-1) or (j == 0 or j == r-1) or (k == 0 or k == r-1):
sample_points[index] = np.array([i, j, k], dtype=np.float32)
if i == 0 or i == r-1:
surface_index[index] = 0
elif j == 0 or j == r-1:
surface_index[index] = 1
else:
surface_index[index] = 2
index += 1
print(index, n_points)
assert(index == n_points)
sample_points -= (r-1) / 2
sample_points /= (r-1) / 2 # rescale to [-1, 1]
return sample_points, surface_index
def load_points_file(filename, offset=None, normalize=False):
'''
load point cloud from .points file
'''
assert(filename.endswith('.points'))
with open(filename, "rb") as f:
n = struct.unpack("i", f.read(4))[0]
points_raw = f.read(4*n*3)
points = np.asarray(struct.unpack("f"*n*3, points_raw)).reshape(n, 3)
if offset is not None:
points -= offset
if normalize:
mb = miniball.Miniball(points)
# print('Center', mb.center())
# print('Radius', math.sqrt(mb.squared_radius()))
points -= mb.center()
points /= math.sqrt(mb.squared_radius())*2
return points
def get_points_weights(cube_param, cube_count):
'''
get points weights w.r.t. surface area
'''
weights = np.ndarray([cube_count, n_sample_points], dtype=np.float32)
for i in range(cube_count):
x, y, z = cube_param['z'][i]
weights[i][surface_index == 0] = y * z
weights[i][surface_index == 1] = x * z
weights[i][surface_index == 2] = x * y
return np.reshape(weights, [-1])
def load_obj_file(filename, offset=None):
'''
load cube parameters from .obj file, sample points on cube surface
'''
cube_count = 0
cube_param = {'z':[], 'q':[], 't':[]}
with open(filename) as f:
for line in f:
if line[0] == '#':
param = [float(line.split()[1:][p]) for p in range(10)]
cube_param['z'].append(param[0:3])
cube_param['q'].append(param[3:7])
cube_param['t'].append(param[7:])
cube_count += 1
cube_param['z'] = np.array(cube_param['z'])
cube_param['q'] = np.array(cube_param['q'])
cube_param['t'] = np.array(cube_param['t'])
points = np.ndarray([cube_count*n_sample_points, 3], dtype=np.float32)
weights = get_points_weights(cube_param, cube_count)
for i in range(cube_count):
z, q, t = [cube_param[v][i] for v in ['z', 'q', 't']]
p = sample_points * z
rot = np.quaternion(q[0], q[1], q[2], q[3])
rot = quaternion.as_rotation_matrix(rot)
p = np.transpose(np.matmul(rot, np.transpose(p)))
p += t
points[i*n_sample_points:(i+1)*n_sample_points] = p
if offset is not None:
points -= offset
return points, weights
def pick_points(points, n_max=5000):
'''
random pick n_max points from input point cloud
'''
random.seed( 10 )
n = points.shape[0]
assert(points.shape[0] >= n_max)
points = points[np.random.randint(0, n, n_max), :]
return points
def pick_points_with_weights(points, weights, n_max=5000):
'''
random pick n_max points from input point cloud according to their weights
'''
# assert(points.shape[0] >= n_max)
if points.shape[0] < n_max:
print("Warning: points number is less than n_max, maybe the input shape is unfrequent.")
index = np.arange(points.shape[0])
index = random.choices(index, weights=weights, k=n_max)
points = points[index, :]
return points
def inter_points_distance(src_points, des_points, draw_points=False):
'''
compute the chamfer distance from src_points to des_points
'''
des_pcd = PointCloud()
des_pcd.points = Vector3dVector(des_points)
des_pcd.paint_uniform_color([0.3, 0.3, 0.7])
des_points_tree = KDTreeFlann(des_pcd)
src_pcd = PointCloud()
src_pcd.points = Vector3dVector(src_points)
src_pcd.paint_uniform_color([0.7, 0.7, 0.7])
distance = 0
for i in range(src_points.shape[0]):
point = src_pcd.points[i]
[k, idx, _] = des_points_tree.search_knn_vector_3d(point, 1)
d = np.linalg.norm(point - des_pcd.points[idx[0]])
distance += d
if draw_points: draw_geometries([des_pcd, src_pcd])
return distance / src_points.shape[0]
def compute_chamfer_distance_kernel(points_1, points_2, draw_points=False):
'''
chamfer distance is the mean distance of A2B and B2A
'''
# print(np.min(points_1, axis=0), np.max(points_1, axis=0))
# print(np.min(points_2, axis=0), np.max(points_2, axis=0))
distance_1 = inter_points_distance(points_1, points_2, draw_points=draw_points)
distance_2 = inter_points_distance(points_2, points_1)
# print(distance_1, distance_2)
return (distance_1 + distance_2)/2
def compute_chamfer_distance(points_file_1, points_file_2,
points_file_1_offset=None, points_file_2_offset=None, verbose=False):
'''
compute chamfer distance of the two input points file
'''
assert(os.path.isfile(points_file_1) and points_file_1.endswith('.points'))
assert(os.path.isfile(points_file_2) and points_file_2.endswith('.points'))
points_1 = load_points_file(points_file_1, offset=points_file_1_offset if points_file_1_offset is not None else None)
points_2 = load_points_file(points_file_2, offset=points_file_2_offset if points_file_2_offset is not None else None)
points_1 = pick_points(points_1)
points_2 = pick_points(points_2)
if verbose:
print(points_1.shape)
print(points_2.shape)
cd = compute_chamfer_distance_kernel(points_1, points_2)
return cd
def compute_chamfer_distance_pointsfile_objfile(points_file, obj_file,
points_file_offset=None, obj_file_offset=None, verbose=False, draw_points=False):
'''
compute chamfer distance between two file:
.points: contain point cloud coordinates
.obj: contain cube params [z, q, t]
'''
assert(os.path.isfile(points_file) and points_file.endswith('.points'))
assert(os.path.isfile(obj_file) and obj_file.endswith('.obj'))
points_1 = load_points_file(points_file,
offset=points_file_offset if points_file_offset is not None else None,
normalize=False)
points_2, weights = load_obj_file(obj_file,
offset=obj_file_offset if obj_file_offset is not None else None)
points_1 = pick_points(points_1)
# points_2 = pick_points(points_2)
if points_2.size == 0:
return 0.05
points_2 = pick_points_with_weights(points_2, weights)
if verbose:
print(points_1.shape)
print(points_2.shape)
cd = compute_chamfer_distance_kernel(points_1, points_2, draw_points=draw_points)
return cd
def run_batch(cube_folder, phase):
shape_count = {'train': 2832, 'test': 808}
n_shape = shape_count[phase]
level_error = []
for cube_level in ['cube_1', 'cube_2', 'cube_3', 'predict_correction_assembly_cube']:
mean_diatance = 0
for i in range(n_shape):
cube_filename = os.path.join(cube_folder, phase, '{}_{:04d}.obj'.format(cube_level, i))
if i < 2: print('cube_file:', cube_filename)
assert(os.path.isfile(cube_filename))
points_folder = 'points/{}'.format(phase)
points_filename = os.path.join(points_folder, '{:06d}.points'.format(i))
if i < 2: print('points_file:', points_filename)
assert(os.path.isfile(points_filename))
distance = compute_chamfer_distance_pointsfile_objfile(points_filename,
cube_filename, points_file_offset=None, obj_file_offset=None, verbose=False, draw_points=False)
print('{} {:.04f}'.format(i, distance))
mean_diatance += distance
sys.stdout.flush()
mean_diatance /= n_shape
level_error.append(mean_diatance*1000)
print('\n{}, mean_diatance*1000: {:.04f}'.format(cube_level, mean_diatance*1000), end='\n\n\n\n')
print(level_error)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--cube_folder',
type=str,
help='cube_folder',
required=True)
parser.add_argument('--phase',
type=str,
help='train or test',
required=True)
args = parser.parse_args()
run_batch(args.cube_folder, args.phase)
pass
if __name__ == '__main__':
main()
SilenKZYoung
In the paper, the chamfer distance between surface is used to evaluate the network's effectiveness, but i didn't find this part in code, could you release the evaluation code?