File size: 8,267 Bytes
355b5d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import trimesh
import trimesh.proximity
import trimesh.sample
import numpy as np
import math
import os
from PIL import Image
import argparse
def euler_to_rot_mat(r_x, r_y, r_z):
R_x = np.array([[1, 0, 0],
[0, math.cos(r_x), -math.sin(r_x)],
[0, math.sin(r_x), math.cos(r_x)]
])
R_y = np.array([[math.cos(r_y), 0, math.sin(r_y)],
[0, 1, 0],
[-math.sin(r_y), 0, math.cos(r_y)]
])
R_z = np.array([[math.cos(r_z), -math.sin(r_z), 0],
[math.sin(r_z), math.cos(r_z), 0],
[0, 0, 1]
])
R = np.dot(R_z, np.dot(R_y, R_x))
return R
class MeshEvaluator:
_normal_render = None
@staticmethod
def init_gl():
from .render.gl.normal_render import NormalRender
MeshEvaluator._normal_render = NormalRender(width=512, height=512)
def __init__(self):
pass
def set_mesh(self, src_path, tgt_path, scale_factor=1.0, offset=0):
self.src_mesh = trimesh.load(src_path)
self.tgt_mesh = trimesh.load(tgt_path)
self.scale_factor = scale_factor
self.offset = offset
def get_chamfer_dist(self, num_samples=10000):
# Chamfer
src_surf_pts, _ = trimesh.sample.sample_surface(self.src_mesh, num_samples)
tgt_surf_pts, _ = trimesh.sample.sample_surface(self.tgt_mesh, num_samples)
_, src_tgt_dist, _ = trimesh.proximity.closest_point(self.tgt_mesh, src_surf_pts)
_, tgt_src_dist, _ = trimesh.proximity.closest_point(self.src_mesh, tgt_surf_pts)
src_tgt_dist[np.isnan(src_tgt_dist)] = 0
tgt_src_dist[np.isnan(tgt_src_dist)] = 0
src_tgt_dist = src_tgt_dist.mean()
tgt_src_dist = tgt_src_dist.mean()
chamfer_dist = (src_tgt_dist + tgt_src_dist) / 2
return chamfer_dist
def get_surface_dist(self, num_samples=10000):
# P2S
src_surf_pts, _ = trimesh.sample.sample_surface(self.src_mesh, num_samples)
_, src_tgt_dist, _ = trimesh.proximity.closest_point(self.tgt_mesh, src_surf_pts)
src_tgt_dist[np.isnan(src_tgt_dist)] = 0
src_tgt_dist = src_tgt_dist.mean()
return src_tgt_dist
def _render_normal(self, mesh, deg):
view_mat = np.identity(4)
view_mat[:3, :3] *= 2 / 256
rz = deg / 180. * np.pi
model_mat = np.identity(4)
model_mat[:3, :3] = euler_to_rot_mat(0, rz, 0)
model_mat[1, 3] = self.offset
view_mat[2, 2] *= -1
self._normal_render.set_matrices(view_mat, model_mat)
self._normal_render.set_normal_mesh(self.scale_factor*mesh.vertices, mesh.faces, mesh.vertex_normals, mesh.faces)
self._normal_render.draw()
normal_img = self._normal_render.get_color()
return normal_img
def _get_reproj_normal_error(self, deg):
tgt_normal = self._render_normal(self.tgt_mesh, deg)
src_normal = self._render_normal(self.src_mesh, deg)
error = ((src_normal[:, :, :3] - tgt_normal[:, :, :3]) ** 2).mean() * 3
return error, src_normal, tgt_normal
def get_reproj_normal_error(self, frontal=True, back=True, left=True, right=True, save_demo_img=None):
# reproj error
# if save_demo_img is not None, save a visualization at the given path (etc, "./test.png")
if self._normal_render is None:
print("In order to use normal render, "
"you have to call init_gl() before initialing any evaluator objects.")
return -1
side_cnt = 0
total_error = 0
demo_list = []
if frontal:
side_cnt += 1
error, src_normal, tgt_normal = self._get_reproj_normal_error(0)
total_error += error
demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
if back:
side_cnt += 1
error, src_normal, tgt_normal = self._get_reproj_normal_error(180)
total_error += error
demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
if left:
side_cnt += 1
error, src_normal, tgt_normal = self._get_reproj_normal_error(90)
total_error += error
demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
if right:
side_cnt += 1
error, src_normal, tgt_normal = self._get_reproj_normal_error(270)
total_error += error
demo_list.append(np.concatenate([src_normal, tgt_normal], axis=0))
if save_demo_img is not None:
res_array = np.concatenate(demo_list, axis=1)
res_img = Image.fromarray((res_array * 255).astype(np.uint8))
res_img.save(save_demo_img)
return total_error / side_cnt
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-r', '--root', type=str, required=True)
parser.add_argument('-t', '--tar_path', type=str, required=True)
args = parser.parse_args()
evaluator = MeshEvaluator()
evaluator.init_gl()
def run(root, exp_name, tar_path):
src_path = os.path.join(root, exp_name, 'recon')
rp_path = os.path.join(tar_path, 'RP', 'GEO', 'OBJ')
bf_path = os.path.join(tar_path, 'BUFF', 'GEO', 'PLY')
buff_files = [f for f in os.listdir(bf_path) if '.ply' in f]
src_names = ['0_0_00.obj', '90_0_00.obj', '180_0_00.obj', '270_0_00.obj']
total_vals = []
items = []
for file in buff_files:
tar_name = os.path.join(bf_path, file)
name = tar_name.split('/')[-1][:-4]
for src in src_names:
src_name = os.path.join(src_path, 'result_%s_%s' % (name, src))
if not os.path.exists(src_name):
continue
evaluator.set_mesh(src_name, tar_name, 0.13, -40)
vals = []
vals.append(0.1 * evaluator.get_chamfer_dist())
vals.append(0.1 * evaluator.get_surface_dist())
vals.append(4.0 * evaluator.get_reproj_normal_error(save_demo_img=os.path.join(src_path, '%s_%s.png' % (name, src[:-4]))))
item = {
'name': '%s_%s' % (name, src),
'vals': vals
}
total_vals.append(vals)
items.append(item)
vals = np.array(total_vals).mean(0)
buf_val = vals
np.save(os.path.join(root, exp_name, 'buff-item.npy'), np.array(items))
np.save(os.path.join(root, exp_name, 'buff-vals.npy'), total_vals)
rp_files = [f for f in os.listdir(rp_path) if '.obj' in f]
total_vals = []
items = []
for file in rp_files:
tar_name = os.path.join(rp_path, file)
name = tar_name.split('/')[-1][:-9]
for src in src_names:
src_name = os.path.join(src_path, 'result_%s_%s' % (name, src))
if not os.path.exists(src_name):
continue
evaluator.set_mesh(src_name, tar_name, 1.3, -120)
vals = []
vals.append(evaluator.get_chamfer_dist())
vals.append(evaluator.get_surface_dist())
vals.append(4.0 * evaluator.get_reproj_normal_error(save_demo_img=os.path.join(src_path, '%s_%s.png' % (name, src[:-4]))))
item = {
'name': '%s_%s' % (name, src),
'vals': vals
}
total_vals.append(vals)
items.append(item)
np.save(os.path.join(root, exp_name, 'rp-item.npy'), np.array(items))
np.save(os.path.join(root, exp_name, 'rp-vals.npy'), total_vals)
vals = np.array(total_vals).mean(0)
print('BUFF - chamfer: %.4f p2s: %.4f nml: %.4f' % (buf_val[0], buf_val[1], buf_val[2]))
print('RP - chamfer: %.4f p2s: %.4f nml: %.4f' % (vals[0], vals[1], vals[2]))
exp_list = ['pifuhd_final']
root = args.root
tar_path = args.tar_path
for exp in exp_list:
run(root, exp, tar_path) |