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metrics.py
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metrics.py
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#
# 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 [email protected]
#
import gc
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, exclude_dir, which_half):
renders = []
gts = []
image_names = []
exclude_fnames = [fname.split('.')[0] for fname in os.listdir(exclude_dir)]
print(exclude_fnames)
print('Render Dir:', renders_dir)
render_fnames = os.listdir(renders_dir)
split_point = len(render_fnames) // 2
sel_range = None
if which_half == 0:
sel_range = range(0, split_point)
elif which_half == 1:
sel_range = range(split_point, len(render_fnames))
for i in sel_range:
fname = render_fnames[i]
if fname.split('.')[0] in exclude_fnames:
print('Skipping', fname)
continue
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, exclude_path):
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) / "renders"
exclude_dir = Path(exclude_path) / "images"
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"
ssims = []
psnrs = []
lpipss = []
image_namess = []
for i in range(2):
print("Half:", i)
renders, gts, image_names = readImages(renders_dir, gt_dir, exclude_dir, i)
image_namess.extend(image_names)
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("")
del renders
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("")
print(f'{len(ssims)} images written')
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_namess)},
"PSNR": {name: psnr for psnr, name in zip(torch.tensor(psnrs).tolist(), image_namess)},
"LPIPS": {name: lp for lp, name in zip(torch.tensor(lpipss).tolist(), image_namess)}})
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 Exception as e:
print(e)
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=[])
parser.add_argument('--exclude_path', '-e', type=str, default=None)
args = parser.parse_args()
evaluate(args.model_paths, args.exclude_path)