mirror of
https://github.com/graphdeco-inria/gaussian-splatting
synced 2024-11-21 15:57:45 +00:00
104 lines
4.0 KiB
Python
104 lines
4.0 KiB
Python
#
|
|
# Copyright (C) 2023, Inria
|
|
# GRAPHDECO research group, https://team.inria.fr/graphdeco
|
|
# All rights reserved.
|
|
#
|
|
# This software is free for non-commercial, research and evaluation use
|
|
# under the terms of the LICENSE.md file.
|
|
#
|
|
# For inquiries contact george.drettakis@inria.fr
|
|
#
|
|
|
|
from pathlib import Path
|
|
import os
|
|
from PIL import Image
|
|
import torch
|
|
import torchvision.transforms.functional as tf
|
|
from utils.loss_utils import ssim
|
|
from lpipsPyTorch import lpips
|
|
import json
|
|
from tqdm import tqdm
|
|
from utils.image_utils import psnr
|
|
from argparse import ArgumentParser
|
|
|
|
def readImages(renders_dir, gt_dir):
|
|
renders = []
|
|
gts = []
|
|
image_names = []
|
|
for fname in os.listdir(renders_dir):
|
|
render = Image.open(renders_dir / fname)
|
|
gt = Image.open(gt_dir / fname)
|
|
renders.append(tf.to_tensor(render).unsqueeze(0)[:, :3, :, :].cuda())
|
|
gts.append(tf.to_tensor(gt).unsqueeze(0)[:, :3, :, :].cuda())
|
|
image_names.append(fname)
|
|
return renders, gts, image_names
|
|
|
|
def evaluate(model_paths):
|
|
|
|
full_dict = {}
|
|
per_view_dict = {}
|
|
full_dict_polytopeonly = {}
|
|
per_view_dict_polytopeonly = {}
|
|
print("")
|
|
|
|
for scene_dir in model_paths:
|
|
try:
|
|
print("Scene:", scene_dir)
|
|
full_dict[scene_dir] = {}
|
|
per_view_dict[scene_dir] = {}
|
|
full_dict_polytopeonly[scene_dir] = {}
|
|
per_view_dict_polytopeonly[scene_dir] = {}
|
|
|
|
test_dir = Path(scene_dir) / "test"
|
|
|
|
for method in os.listdir(test_dir):
|
|
print("Method:", method)
|
|
|
|
full_dict[scene_dir][method] = {}
|
|
per_view_dict[scene_dir][method] = {}
|
|
full_dict_polytopeonly[scene_dir][method] = {}
|
|
per_view_dict_polytopeonly[scene_dir][method] = {}
|
|
|
|
method_dir = test_dir / method
|
|
gt_dir = method_dir/ "gt"
|
|
renders_dir = method_dir / "renders"
|
|
renders, gts, image_names = readImages(renders_dir, gt_dir)
|
|
|
|
ssims = []
|
|
psnrs = []
|
|
lpipss = []
|
|
|
|
for idx in tqdm(range(len(renders)), desc="Metric evaluation progress"):
|
|
ssims.append(ssim(renders[idx], gts[idx]))
|
|
psnrs.append(psnr(renders[idx], gts[idx]))
|
|
lpipss.append(lpips(renders[idx], gts[idx], net_type='vgg'))
|
|
|
|
print(" SSIM : {:>12.7f}".format(torch.tensor(ssims).mean(), ".5"))
|
|
print(" PSNR : {:>12.7f}".format(torch.tensor(psnrs).mean(), ".5"))
|
|
print(" LPIPS: {:>12.7f}".format(torch.tensor(lpipss).mean(), ".5"))
|
|
print("")
|
|
|
|
full_dict[scene_dir][method].update({"SSIM": torch.tensor(ssims).mean().item(),
|
|
"PSNR": torch.tensor(psnrs).mean().item(),
|
|
"LPIPS": torch.tensor(lpipss).mean().item()})
|
|
per_view_dict[scene_dir][method].update({"SSIM": {name: ssim for ssim, name in zip(torch.tensor(ssims).tolist(), image_names)},
|
|
"PSNR": {name: psnr for psnr, name in zip(torch.tensor(psnrs).tolist(), image_names)},
|
|
"LPIPS": {name: lp for lp, name in zip(torch.tensor(lpipss).tolist(), image_names)}})
|
|
|
|
with open(scene_dir + "/results.json", 'w') as fp:
|
|
json.dump(full_dict[scene_dir], fp, indent=True)
|
|
with open(scene_dir + "/per_view.json", 'w') as fp:
|
|
json.dump(per_view_dict[scene_dir], fp, indent=True)
|
|
except:
|
|
print("Unable to compute metrics for model", scene_dir)
|
|
|
|
if __name__ == "__main__":
|
|
device = torch.device("cuda:0")
|
|
torch.cuda.set_device(device)
|
|
|
|
# Set up command line argument parser
|
|
parser = ArgumentParser(description="Training script parameters")
|
|
parser.add_argument('--model_paths', '-m', required=True, nargs="+", type=str, default=[])
|
|
args = parser.parse_args()
|
|
evaluate(args.model_paths)
|