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evaluate.py
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evaluate.py
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import os
import numpy as np
import pickle
import argparse
import sys
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append(BASE_DIR)
sys.path.append(os.path.join(BASE_DIR, 'utils'))
import visualization
import utils
def l2_norm(v):
norm_v = np.sqrt(np.sum(np.square(v), axis=1))
return norm_v
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
BASELINE_DIR = os.path.dirname(os.path.abspath(__file__))
# BASE_DIR = os.path.abspath(os.path.join(BASELINE_DIR, os.pardir))
parser = argparse.ArgumentParser()
parser.add_argument('--normal_results_path', type=str, default='./log/GraphFit/results_599/', help='Log dir [default: log]')
parser.add_argument('--data_path', type=str, default='./data/pcpnet/', help='Relative path to data directory')
parser.add_argument('--sparse_patches', type=int, default=True, help='Evaluate on a sparse subset or on the entire point cloud')
parser.add_argument('--dataset_list', type=str,
default=['testset_no_noise', 'testset_low_noise', 'testset_med_noise', 'testset_high_noise',
'testset_vardensity_striped', 'testset_vardensity_gradient'], nargs='+',
help='list of .txt files containing sets of point cloud names for evaluation')
FLAGS = parser.parse_args()
EXPORT = False # export some visualizations
print(BASE_DIR)
PC_PATH = os.path.join(BASE_DIR, FLAGS.data_path)
normal_results_path = FLAGS.normal_results_path
results_path = os.path.abspath(os.path.join(normal_results_path, os.pardir))
sparse_patches = FLAGS.sparse_patches
if not os.path.exists(normal_results_path):
ValueError('Incorrect normal results path...')
dataset_list = FLAGS.dataset_list
for dataset in dataset_list:
normal_gt_filenames = PC_PATH + dataset + '.txt'
normal_gt_path = PC_PATH
# get all shape names in the dataset
shape_names = []
with open(normal_gt_filenames) as f:
shape_names = f.readlines()
shape_names = [x.strip() for x in shape_names]
shape_names = list(filter(None, shape_names))
outdir = os.path.join(normal_results_path, 'summary/')
if not os.path.exists(outdir):
os.makedirs(outdir)
LOG_FOUT = open(os.path.join(outdir, dataset + '_evaluation_results.txt'), 'w')
if EXPORT:
file_path = os.path.join(normal_results_path, 'images')
if not os.path.exists(file_path):
os.makedirs(file_path)
def log_string(out_str):
LOG_FOUT.write(out_str+'\n')
LOG_FOUT.flush()
print(out_str)
experts_exist = False
rms = []
rms_o = []
all_ang = []
pgp10 = []
pgp5 = []
pgp_alpha = []
for i, shape in enumerate(shape_names):
print('Processing ' + shape + '...')
if EXPORT:
# Organize the output folders
idx_1 = shape.find('_noise_white_')
idx_2 = shape.find('_ddist_')
if idx_1 == -1 and idx_2 == -1:
base_shape_name = shape
elif idx_1 == -1:
base_shape_name = shape[:idx_2]
else:
base_shape_name = shape[:idx_1]
vis_output_path = os.path.join(file_path, base_shape_name)
if not os.path.exists(vis_output_path):
os.makedirs(vis_output_path)
gt_normals_vis_output_path = os.path.join(vis_output_path, 'normal_gt')
if not os.path.exists(gt_normals_vis_output_path):
os.makedirs(gt_normals_vis_output_path)
pred_normals_vis_output_path = os.path.join(vis_output_path, 'normal_pred')
if not os.path.exists(pred_normals_vis_output_path):
os.makedirs(pred_normals_vis_output_path)
phi_teta_vis_output_path = os.path.join(vis_output_path, 'phi_teta_domain')
if not os.path.exists(phi_teta_vis_output_path):
os.makedirs(phi_teta_vis_output_path)
# load the data
points = np.loadtxt(os.path.join(normal_gt_path, shape + '.xyz')).astype('float32')
normals_gt = np.loadtxt(os.path.join(normal_gt_path, shape + '.normals')).astype('float32')
normals_results = np.loadtxt(os.path.join(normal_results_path, shape + '.normals')).astype('float32')
points_idx = np.loadtxt(os.path.join(normal_gt_path, shape + '.pidx')).astype('int')
n_points = points.shape[0]
n_normals = normals_results.shape[0]
if n_points != n_normals:
sparse_normals = True
else:
sparse_normals = False
points = points[points_idx, :]
normals_gt = normals_gt[points_idx, :]
# curvs_gt = curvs_gt[points_idx, :]
if sparse_patches and not sparse_normals:
normals_results = normals_results[points_idx, :]
else:
normals_results = normals_results[:, :]
normal_gt_norm = l2_norm(normals_gt)
normal_results_norm = l2_norm(normals_results)
normals_results = np.divide(normals_results, np.tile(np.expand_dims(normal_results_norm, axis=1), [1, 3]))
normals_gt = np.divide(normals_gt, np.tile(np.expand_dims(normal_gt_norm, axis=1), [1, 3]))
# Not oriented rms
nn = np.sum(np.multiply(normals_gt, normals_results), axis=1)
nn[nn > 1] = 1
nn[nn < -1] = -1
ang = np.rad2deg(np.arccos(np.abs(nn))) # unoriented
# error metrics
rms.append(np.sqrt(np.mean(np.square(ang))))
pgp10_shape = sum([j < 10.0 for j in ang]) / float(len(ang)) # portion of good points
pgp5_shape = sum([j < 5.0 for j in ang]) / float(len(ang)) # portion of good points
pgp10.append(pgp10_shape)
pgp5.append(pgp5_shape)
pgp_alpha_shape = []
for alpha in range(30):
pgp_alpha_shape.append(sum([j < alpha for j in ang]) / float(len(ang)))
pgp_alpha.append(pgp_alpha_shape)
# Oriented rms
rms_o.append(np.sqrt(np.mean(np.square(np.rad2deg(np.arccos(nn))))))
diff = np.arccos(nn)
diff_inv = np.arccos(-nn)
unoriented_normals = normals_results
unoriented_normals[diff_inv < diff, :] = -normals_results[diff_inv < diff, :]
avg_rms = np.mean(rms)
avg_rms_o = np.mean(rms_o)
avg_pgp10 = np.mean(pgp10)
avg_pgp5 = np.mean(pgp5)
avg_pgp_alpha = np.mean(np.array(pgp_alpha), axis=0)
log_string('RMS per shape: ' + str(rms))
log_string('RMS not oriented (shape average): ' + str(avg_rms))
log_string('RMS oriented (shape average): ' + str(avg_rms_o))
log_string('PGP10 per shape: ' + str(pgp10))
log_string('PGP5 per shape: ' + str(pgp5))
log_string('PGP10 average: ' + str(avg_pgp10))
log_string('PGP5 average: ' + str(avg_pgp5))
log_string('PGP alpha average: ' + str(avg_pgp_alpha))
LOG_FOUT.close()