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eval.py
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eval.py
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from __future__ import print_function, unicode_literals
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pip
import argparse
import json
def install(package):
if hasattr(pip, 'main'):
pip.main(['install', package])
else:
pip._internal.main(['install', package])
try:
import open3d as o3d
except:
install('open3d-python')
import open3d as o3d
try:
from scipy.linalg import orthogonal_procrustes
except:
install('scipy')
from scipy.linalg import orthogonal_procrustes
try:
from utils.fh_utils import *
from utils.eval_util import EvalUtil
except:
from fh_utils import *
from eval_util import EvalUtil
def verts2pcd(verts, color=None):
pcd = o3d.PointCloud()
pcd.points = o3d.Vector3dVector(verts)
if color is not None:
if color == 'r':
pcd.paint_uniform_color([1, 0.0, 0])
if color == 'g':
pcd.paint_uniform_color([0, 1.0, 0])
if color == 'b':
pcd.paint_uniform_color([0, 0, 1.0])
return pcd
def calculate_fscore(gt, pr, th=0.01):
gt = verts2pcd(gt)
pr = verts2pcd(pr)
d1 = o3d.compute_point_cloud_to_point_cloud_distance(gt, pr) # closest dist for each gt point
d2 = o3d.compute_point_cloud_to_point_cloud_distance(pr, gt) # closest dist for each pred point
if len(d1) and len(d2):
recall = float(sum(d < th for d in d2)) / float(len(d2)) # how many of our predicted points lie close to a gt point?
precision = float(sum(d < th for d in d1)) / float(len(d1)) # how many of gt points are matched?
if recall+precision > 0:
fscore = 2 * recall * precision / (recall + precision)
else:
fscore = 0
else:
fscore = 0
precision = 0
recall = 0
return fscore, precision, recall
def align_w_scale(mtx1, mtx2, return_trafo=False):
""" Align the predicted entity in some optimality sense with the ground truth. """
# center
t1 = mtx1.mean(0)
t2 = mtx2.mean(0)
mtx1_t = mtx1 - t1
mtx2_t = mtx2 - t2
# scale
s1 = np.linalg.norm(mtx1_t) + 1e-8
mtx1_t /= s1
s2 = np.linalg.norm(mtx2_t) + 1e-8
mtx2_t /= s2
# orth alignment
R, s = orthogonal_procrustes(mtx1_t, mtx2_t)
# apply trafos to the second matrix
mtx2_t = np.dot(mtx2_t, R.T) * s
mtx2_t = mtx2_t * s1 + t1
if return_trafo:
return R, s, s1, t1 - t2
else:
return mtx2_t
def align_by_trafo(mtx, trafo):
t2 = mtx.mean(0)
mtx_t = mtx - t2
R, s, s1, t1 = trafo
return np.dot(mtx_t, R.T) * s * s1 + t1 + t2
class curve:
def __init__(self, x_data, y_data, x_label, y_label, text):
self.x_data = x_data
self.y_data = y_data
self.x_label = x_label
self.y_label = y_label
self.text = text
def createHTML(outputDir, curve_list):
curve_data_list = list()
for item in curve_list:
fig1 = plt.figure()
ax = fig1.add_subplot(111)
ax.plot(item.x_data, item.y_data)
ax.set_xlabel(item.x_label)
ax.set_ylabel(item.y_label)
img_path = os.path.join(outputDir, "img_path_path.png")
plt.savefig(img_path, bbox_inches=0, dpi=300)
# write image and create html embedding
data_uri1 = open(img_path, 'rb').read().encode('base64').replace('\n', '')
img_tag1 = 'src="data:image/png;base64,{0}"'.format(data_uri1)
curve_data_list.append((item.text, img_tag1))
os.remove(img_path)
htmlString = '''<!DOCTYPE html>
<html>
<body>
<h1>Detailed results:</h1>'''
for i, (text, img_embed) in enumerate(curve_data_list):
htmlString += '''
<h2>%s</h2>
<p>
<img border="0" %s alt="FROC" width="576pt" height="432pt">
</p>
<p>Raw curve data:</p>
<p>x_axis: <small>%s</small></p>
<p>y_axis: <small>%s</small></p>
''' % (text, img_embed, curve_list[i].x_data, curve_list[i].y_data)
htmlString += '''
</body>
</html>'''
htmlfile = open(os.path.join(outputDir, "scores.html"), "w")
htmlfile.write(htmlString)
htmlfile.close()
def _search_pred_file(pred_path, pred_file_name):
""" Tries to select the prediction file. Useful, in case people deviate from the canonical prediction file name. """
pred_file = os.path.join(pred_path, pred_file_name)
if os.path.exists(pred_file):
# if the given prediction file exists we are happy
return pred_file
print('Predition file "%s" was NOT found' % pred_file_name)
# search for a file to use
print('Trying to locate the prediction file automatically ...')
files = [os.path.join(pred_path, x) for x in os.listdir(pred_path) if x.endswith('.json')]
if len(files) == 1:
pred_file_name = files[0]
print('Found file "%s"' % pred_file_name)
return pred_file_name
else:
print('Found %d candidate files for evaluation' % len(files))
raise Exception('Giving up, because its not clear which file to evaluate.')
def main(gt_path, pred_path, output_dir, pred_file_name=None, set_name=None):
if pred_file_name is None:
pred_file_name = 'pred.json'
if set_name is None:
set_name = 'evaluation'
# load eval annotations
xyz_list, verts_list = json_load(os.path.join(gt_path, '%s_xyz.json' % set_name)), json_load(os.path.join(gt_path, '%s_verts.json' % set_name))
# load predicted values
pred_file = _search_pred_file(pred_path, pred_file_name)
print('Loading predictions from %s' % pred_file)
with open(pred_file, 'r') as fi:
pred = json.load(fi)
assert len(pred) == 2, 'Expected format mismatch.'
assert len(pred[0]) == len(xyz_list), 'Expected format mismatch.'
assert len(pred[1]) == len(xyz_list), 'Expected format mismatch.'
# init eval utils
eval_xyz, eval_xyz_aligned = EvalUtil(), EvalUtil()
eval_mesh_err, eval_mesh_err_aligned = EvalUtil(num_kp=778), EvalUtil(num_kp=778)
f_score, f_score_aligned = list(), list()
f_threshs = [0.005, 0.015]
shape_is_mano = None
try:
from tqdm import tqdm
rng = tqdm(range(db_size(set_name)))
except:
rng = range(db_size(set_name))
# iterate over the dataset once
for idx in rng:
if idx >= db_size(set_name):
break
xyz, verts = xyz_list[idx], verts_list[idx]
xyz, verts = [np.array(x) for x in [xyz, verts]]
xyz_pred, verts_pred = pred[0][idx], pred[1][idx]
xyz_pred, verts_pred = [np.array(x) for x in [xyz_pred, verts_pred]]
# Not aligned errors
eval_xyz.feed(
xyz,
np.ones_like(xyz[:, 0]),
xyz_pred
)
if shape_is_mano is None:
if verts_pred.shape[0] == verts.shape[0]:
shape_is_mano = True
else:
shape_is_mano = False
if shape_is_mano:
eval_mesh_err.feed(
verts,
np.ones_like(verts[:, 0]),
verts_pred
)
# align predictions
xyz_pred_aligned = align_w_scale(xyz, xyz_pred)
if shape_is_mano:
verts_pred_aligned = align_w_scale(verts, verts_pred)
else:
# use trafo estimated from keypoints
trafo = align_w_scale(xyz, xyz_pred, return_trafo=True)
verts_pred_aligned = align_by_trafo(verts_pred, trafo)
# Aligned errors
eval_xyz_aligned.feed(
xyz,
np.ones_like(xyz[:, 0]),
xyz_pred_aligned
)
if shape_is_mano:
eval_mesh_err_aligned.feed(
verts,
np.ones_like(verts[:, 0]),
verts_pred_aligned
)
# F-scores
l, la = list(), list()
for t in f_threshs:
# for each threshold calculate the f score and the f score of the aligned vertices
f, _, _ = calculate_fscore(verts, verts_pred, t)
l.append(f)
f, _, _ = calculate_fscore(verts, verts_pred_aligned, t)
la.append(f)
f_score.append(l)
f_score_aligned.append(la)
# Calculate results
xyz_mean3d, _, xyz_auc3d, pck_xyz, thresh_xyz = eval_xyz.get_measures(0.0, 0.05, 100)
print('Evaluation 3D KP results:')
print('auc=%.3f, mean_kp3d_avg=%.2f cm' % (xyz_auc3d, xyz_mean3d * 100.0))
xyz_al_mean3d, _, xyz_al_auc3d, pck_xyz_al, thresh_xyz_al = eval_xyz_aligned.get_measures(0.0, 0.05, 100)
print('Evaluation 3D KP ALIGNED results:')
print('auc=%.3f, mean_kp3d_avg=%.2f cm\n' % (xyz_al_auc3d, xyz_al_mean3d * 100.0))
if shape_is_mano:
mesh_mean3d, _, mesh_auc3d, pck_mesh, thresh_mesh = eval_mesh_err.get_measures(0.0, 0.05, 100)
print('Evaluation 3D MESH results:')
print('auc=%.3f, mean_kp3d_avg=%.2f cm' % (mesh_auc3d, mesh_mean3d * 100.0))
mesh_al_mean3d, _, mesh_al_auc3d, pck_mesh_al, thresh_mesh_al = eval_mesh_err_aligned.get_measures(0.0, 0.05, 100)
print('Evaluation 3D MESH ALIGNED results:')
print('auc=%.3f, mean_kp3d_avg=%.2f cm\n' % (mesh_al_auc3d, mesh_al_mean3d * 100.0))
else:
mesh_mean3d, mesh_auc3d, mesh_al_mean3d, mesh_al_auc3d = -1.0, -1.0, -1.0, -1.0
pck_mesh, thresh_mesh = np.array([-1.0, -1.0]), np.array([0.0, 1.0])
pck_mesh_al, thresh_mesh_al = np.array([-1.0, -1.0]), np.array([0.0, 1.0])
print('F-scores')
f_out = list()
f_score, f_score_aligned = np.array(f_score).T, np.array(f_score_aligned).T
for f, fa, t in zip(f_score, f_score_aligned, f_threshs):
print('F@%.1fmm = %.3f' % (t*1000, f.mean()), '\tF_aligned@%.1fmm = %.3f' % (t*1000, fa.mean()))
f_out.append('f_score_%d: %f' % (round(t*1000), f.mean()))
f_out.append('f_al_score_%d: %f' % (round(t*1000), fa.mean()))
# Dump results
score_path = os.path.join(output_dir, 'scores.txt')
with open(score_path, 'w') as fo:
xyz_mean3d *= 100
xyz_al_mean3d *= 100
fo.write('xyz_mean3d: %f\n' % xyz_mean3d)
fo.write('xyz_auc3d: %f\n' % xyz_auc3d)
fo.write('xyz_al_mean3d: %f\n' % xyz_al_mean3d)
fo.write('xyz_al_auc3d: %f\n' % xyz_al_auc3d)
mesh_mean3d *= 100
mesh_al_mean3d *= 100
fo.write('mesh_mean3d: %f\n' % mesh_mean3d)
fo.write('mesh_auc3d: %f\n' % mesh_auc3d)
fo.write('mesh_al_mean3d: %f\n' % mesh_al_mean3d)
fo.write('mesh_al_auc3d: %f\n' % mesh_al_auc3d)
for t in f_out:
fo.write('%s\n' % t)
print('Scores written to: %s' % score_path)
# scale to cm
thresh_xyz *= 100.0
thresh_xyz_al *= 100.0
thresh_mesh *= 100.0
thresh_mesh_al *= 100.0
createHTML(
output_dir,
[
curve(thresh_xyz, pck_xyz, 'Distance in cm', 'Percentage of correct keypoints', 'PCK curve for keypoint error'),
curve(thresh_xyz_al, pck_xyz_al, 'Distance in cm', 'Percentage of correct keypoints', 'PCK curve for aligned keypoint error'),
curve(thresh_mesh, pck_mesh, 'Distance in cm', 'Percentage of correct vertices', 'PCV curve for mesh error'),
curve(thresh_mesh_al, pck_mesh_al, 'Distance in cm', 'Percentage of correct vertices', 'PCV curve for aligned mesh error')
]
)
pck_curve_data = {
'xyz': [thresh_xyz.tolist(), pck_xyz.tolist()],
'xyz_al': [thresh_xyz_al.tolist(), pck_xyz_al.tolist()],
'mesh': [thresh_mesh.tolist(), pck_mesh.tolist()],
'mesh_al': [thresh_mesh_al.tolist(), pck_mesh_al.tolist()],
}
with open('pck_data.json', 'w') as fo:
json.dump(pck_curve_data, fo)
print('Evaluation complete.')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Show some samples from the dataset.')
parser.add_argument('input_dir', type=str,
help='Path to where prediction the submited result and the ground truth is.')
parser.add_argument('output_dir', type=str,
help='Path to where the eval result should be.')
parser.add_argument('--pred_file_name', type=str, default='pred.json',
help='Name of the eval file.')
args = parser.parse_args()
# call eval
main(
os.path.join(args.input_dir, 'ref'),
os.path.join(args.input_dir, 'res'),
args.output_dir,
args.pred_file_name,
set_name='evaluation'
)