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comparison_on_analytically_computed_orthographic_normal_maps.py
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comparison_on_analytically_computed_orthographic_normal_maps.py
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import time
import os
import numpy as np
import cv2
import argparse
import sys
sys.path.append("methods")
sys.path.append("data")
from methods.orthographic_discrete_poisson import OrthographicPoisson
from methods.orthographic_five_point_plane_fitting import OrthographicFivePoint
from methods.orthographic_four_point_plane_fitting import OrthographicFourPoint
from methods.orthographic_DGP import OrthographicDiscreteGeometryProcessing
from methods.orthographic_discrete_functional import OrthographicDiscreteFunctional
from data.data_sphere import sphere_orth_generator
from data.data_vase import vase_generator
from data.data_anisotropic_gaussian import anisotropic_gaussian_generator
parser = argparse.ArgumentParser()
parser.add_argument('-n', "--noise", type=float, default=0, help='the standard deviation of Gaussian noise added to the input normal map (default 0)')
parser.add_argument('-o', "--outlier", type=float, default=0, help='the percentage of outliers (range from 0 to 1) added to the input normal map (default 0)')
par = parser.parse_args()
str_time = str(time.strftime('%Y_%m_%d_%H_%M_%S', time.localtime(time.time())))
objs = [
sphere_orth_generator(128),
vase_generator(128),
anisotropic_gaussian_generator(150)
]
for obj in objs:
result_dir = os.path.join("results", str_time, obj.fname)
if not os.path.exists(result_dir):
os.makedirs(result_dir)
if par.noise and par.outlier:
obj.add_outlier_on_noise_map(par.outlier, par.noise)
obj.input_normal_map = obj.n_outlier_noise.copy()
elif par.noise:
obj.add_noise(par.noise)
obj.input_normal_map = obj.n_noise.copy()
elif par.outlier:
obj.add_outlier(par.outlier)
obj.input_normal_map = obj.n_outlier.copy()
else:
obj.input_normal_map = obj.n.copy()
obj.n = obj.input_normal_map.copy()
from utils import camera_to_object
n_vis = (camera_to_object(obj.n) + 1)/2
n_vis[~obj.mask] = 1
cv2.imwrite(os.path.join(result_dir, "input_normal_map.png"),
cv2.cvtColor((n_vis * 255).astype(np.uint8), cv2.COLOR_BGR2RGB))
results = [
OrthographicFivePoint(obj),
OrthographicFourPoint(obj),
OrthographicPoisson(obj),
OrthographicDiscreteFunctional(obj),
OrthographicDiscreteGeometryProcessing(obj)
]
absolute_difference_maps = []
rmse_list = []
mae_list = []
std_list = []
method_name_list = []
solver_runtime_list = []
total_runtime_list = []
for z_est in results:
method_name_list.append(z_est.method_name)
solver_runtime_list.append(z_est.solver_runtime)
total_runtime_list.append(z_est.total_runtime)
offset = np.nanmean(obj.depth_gt - z_est.depth_map)
offset_depth = z_est.depth_map + offset
absolute_difference_map = np.abs(offset_depth - obj.depth_gt)
rmse = np.sqrt(np.nanmean(absolute_difference_map ** 2))
absolute_difference_maps.append(absolute_difference_map)
rmse_list.append(rmse)
mae_list.append(np.nanmean(absolute_difference_map))
std_list.append(np.nanstd(absolute_difference_map))
z_est.surface.points[:, 2] += offset
z_est.surface.save(os.path.join(result_dir, "mesh_{}.ply".format(z_est.method_name)), binary=True)
obj.surf.save(os.path.join(result_dir, "mesh_gt.ply"), binary=True)
from utils import apply_jet_on_multiple_error_maps
absolute_difference_maps_JET = apply_jet_on_multiple_error_maps(absolute_difference_maps, sigma_multiplier=2)
for idx, abs_diff_map_jet in enumerate(absolute_difference_maps_JET):
cv2.imwrite(filename=os.path.join(result_dir, "absolute_difference_map_{}.png".format(results[idx].method_name)),
img=abs_diff_map_jet)
import csv
with open(os.path.join(result_dir, 'evaluation_metric_summary.csv'), 'w') as file:
writer = csv.writer(file)
writer.writerow(["metric"] + method_name_list)
writer.writerow(["RMSE"] + rmse_list)
writer.writerow(["MAbsE"] + mae_list)
writer.writerow(["std"] + std_list)
writer.writerow(["solver_time (sec)"] + solver_runtime_list)
writer.writerow(["total_runtime (sec)"] + total_runtime_list)
from glob import glob
crop_img_list = glob(os.path.join(result_dir, "absolute_difference_map*.png"))
crop_img_list += glob(os.path.join(result_dir, "input_normal_map.png"))
from utils import crop_a_set_of_images
try:
crop_a_set_of_images(*crop_img_list)
except:
pass