metrics.py

#
# 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 kornia.metrics import ssim as dssim
import lpips
import json
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser
import logging
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())                #1,3,800,800
        image_names.append(fname)                                                   #
    return renders, gts, image_names
def ssim(image_pred, image_gt, reduction='mean'):
    """
    image_pred and image_gt: (1, 3, H, W)
    """
    dssim_ = dssim(image_pred, image_gt, 3) # dissimilarity in [0, 1]
    return dssim_.mean().item()

def evaluate(model_paths,use_logs=False):
    lpips_alex = lpips.LPIPS(net='alex').to("cuda:0")
    full_dict = {}
    per_view_dict = {}
    full_dict_polytopeonly = {}
    per_view_dict_polytopeonly = {}
    print("")

    for scene_dir in model_paths:         #
        # try:
            print("Scene:", scene_dir)
            if use_logs:
                logging.info(f"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)
                if use_logs:
                    logging.info(f"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"):
                    _,C,H,W=renders[idx].shape
                    ssims.append(ssim(renders[idx], gts[idx]))
                    psnrs.append(psnr(renders[idx], gts[idx]))
                    lpipss.append(lpips_alex(renders[idx], gts[idx],normalize=True))#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("")
                if use_logs:
                    logging.info(f"SSIM:{torch.tensor(ssims).mean().item()}  PSNR:{torch.tensor(psnrs).mean().item()}  LPIPS:{torch.tensor(lpipss).mean().item()}")
                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)