FreeGave/metrics.py at main · vLAR-group/FreeGave · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
#
# 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 lpips
import json
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser
import cv2


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 half_res_image(image):
    img = image[0].cpu().numpy().transpose(1, 2, 0)
    img = cv2.resize(img, (image.shape[3] // 2, image.shape[2] // 2), interpolation=cv2.INTER_AREA)
    return torch.tensor(img).permute(2, 0, 1).unsqueeze(0).cuda()


def evaluate(model_paths, split: str = "test", half_res: bool = False):
    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) / split

            ext = "_halfres" if half_res else ""

            for method in os.listdir(test_dir):
                if not method.startswith("ours"):
                    continue
                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)

                if half_res:
                    renders = [half_res_image(image) for image in renders]
                    gts = [half_res_image(image) for image in gts]

                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_fn(renders[idx], gts[idx]).detach())

                def mae_psnr(psnrs):
                    mae = 10 ** (-psnrs / 10)
                    return -10 * torch.log10(mae.mean())

                print("  SSIM : {:>12.7f}".format(torch.tensor(ssims).mean(), ".5"))
                print("  PSNR : {:>12.7f}".format(torch.tensor(psnrs).mean(), ".5"))
                print("  MAEPSNR : {:>12.7f}".format(mae_psnr(torch.tensor(psnrs)), ".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(),
                                                     "MAEPSNR": mae_psnr(torch.tensor(psnrs)).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 + f"/results_{split}{ext}.json", 'w') as fp:
                json.dump(full_dict[scene_dir], fp, indent=True)
            with open(scene_dir + f"/per_view_{split}{ext}.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)
    lpips_fn = lpips.LPIPS(net='vgg').to(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('--split', '-s', type=str, default="test")
    parser.add_argument('--half_res', action="store_true")
    args = parser.parse_args()
    evaluate(args.model_paths, args.split, args.half_res)