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# -*- coding: utf-8 -*-
# @Organization : Alibaba XR-Lab
# @Author : Lingteng Qiu
# @Email : [email protected]
# @Time : 2025-03-03 10:28:47
# @Function : easy to use SSIM and LPIPS metric
import os
import pdb
import shutil
from collections import defaultdict
import numpy as np
import torch
from PIL import Image
from prettytable import PrettyTable
from torch.utils.data import Dataset
from torchmetrics.image import StructuralSimilarityIndexMeasure
from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
from torchvision import transforms
from tqdm import tqdm
def write_json(path, x):
"""write a json file.
Args:
path (str): path to write json file.
x (dict): dict to write.
"""
import json
with open(path, "w") as f:
json.dump(x, f, indent=2)
def img_center_padding(img_np, pad_ratio=0.2):
ori_w, ori_h = img_np.shape[:2]
w = round((1 + pad_ratio) * ori_w)
h = round((1 + pad_ratio) * ori_h)
img_pad_np = (np.ones((w, h, 3), dtype=img_np.dtype) * 255).astype(np.uint8)
offset_h, offset_w = (w - img_np.shape[0]) // 2, (h - img_np.shape[1]) // 2
img_pad_np[
offset_h : offset_h + img_np.shape[0] :, offset_w : offset_w + img_np.shape[1]
] = img_np
return img_pad_np, offset_w, offset_h
def scan_files_in_dir(directory, postfix=None, progress_bar=None) -> list:
file_list = []
progress_bar = (
tqdm(total=0, desc=f"Scanning", ncols=100)
if progress_bar is None
else progress_bar
)
for entry in os.scandir(directory):
if entry.is_file():
if postfix is None or os.path.splitext(entry.path)[1] in postfix:
file_list.append(entry)
progress_bar.total += 1
progress_bar.update(1)
elif entry.is_dir():
file_list += scan_files_in_dir(
entry.path, postfix=postfix, progress_bar=progress_bar
)
return file_list
class EvalDataset(Dataset):
def __init__(self, gt_folder, pred_folder, height=1024):
self.gt_folder = gt_folder
self.pred_folder = pred_folder
self.height = height
self.data = self.prepare_data()
self.to_tensor = transforms.ToTensor()
def extract_id_from_filename(self, filename):
# find first number in filename
start_i = None
for i, c in enumerate(filename):
if c.isdigit():
start_i = i
break
if start_i is None:
assert False, f"Cannot find number in filename {filename}"
return filename[start_i : start_i + 8]
def prepare_data(self):
gt_files = scan_files_in_dir(self.gt_folder, postfix={".jpg", ".png"})
gt_dict = {self.extract_id_from_filename(file.name): file for file in gt_files}
pred_files = scan_files_in_dir(self.pred_folder, postfix={".jpg", ".png"})
pred_files = list(filter(lambda x: "visualization" not in x.name, pred_files))
tuples = []
for pred_file in pred_files:
pred_id = self.extract_id_from_filename(pred_file.name)
if pred_id not in gt_dict:
print(f"Cannot find gt file for {pred_file}")
else:
tuples.append((gt_dict[pred_id].path, pred_file.path))
return tuples
def resize(self, img):
w, h = img.size
new_w = int(w * self.height / h)
return img.resize((new_w, self.height), Image.LANCZOS)
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
gt_path, pred_path = self.data[idx]
gt, pred = self.resize(Image.open(gt_path)), self.resize(Image.open(pred_path))
if gt.height != self.height:
gt = self.resize(gt)
if pred.height != self.height:
pred = self.resize(pred)
gt = self.to_tensor(gt)
pred = self.to_tensor(pred)
return gt, pred
def copy_resize_gt(gt_folder, height):
new_folder = os.path.join(
os.path.dirname(gt_folder[:-1] if gt_folder[-1] == "/" else gt_folder),
f"resize_{height}",
)
if not os.path.exists(new_folder):
os.makedirs(new_folder, exist_ok=True)
for file in tqdm(os.listdir(gt_folder)):
img = Image.open(os.path.join(gt_folder, file))
img = np.asarray(img)
img, _, _ = img_center_padding(img)
img = Image.fromarray(img)
w, h = img.size
img.save(os.path.join(new_folder, file))
return new_folder
@torch.no_grad()
def ssim(dataloader):
ssim_score = 0
ssim = StructuralSimilarityIndexMeasure(data_range=1.0).to("cuda")
for gt, pred in tqdm(dataloader, desc="Calculating SSIM"):
batch_size = gt.size(0)
gt, pred = gt.to("cuda"), pred.to("cuda")
ssim_score += ssim(pred, gt) * batch_size
return ssim_score / len(dataloader.dataset)
@torch.no_grad()
def lpips(dataloader):
lpips_score = LearnedPerceptualImagePatchSimilarity(net_type="squeeze").to("cuda")
score = 0
for gt, pred in tqdm(dataloader, desc="Calculating LPIPS"):
batch_size = gt.size(0)
pred = pred.to("cuda")
gt = gt.to("cuda")
# LPIPS needs the images to be in the [-1, 1] range.
gt = (gt * 2) - 1
pred = (pred * 2) - 1
score += lpips_score(gt, pred) * batch_size
return score / len(dataloader.dataset)
def eval(pred_folder, gt_folder):
# Check gt_folder has images with target height, resize if not
pred_sample = os.listdir(pred_folder)[0]
gt_sample = os.listdir(gt_folder)[0]
img = Image.open(os.path.join(pred_folder, pred_sample))
gt_img = Image.open(os.path.join(gt_folder, gt_sample))
copy_folder = None
if img.height != gt_img.height:
title = "--" * 30 + "Resizing GT Images to height {img.height}" + "--" * 30
print(title)
gt_folder = copy_resize_gt(gt_folder, img.height)
print("-" * len(title))
copy_folder = gt_folder
# Form dataset
dataset = EvalDataset(gt_folder, pred_folder, img.height)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=16,
num_workers=0,
shuffle=False,
drop_last=False,
)
# Calculate Metrics
header = []
row = []
header += ["SSIM", "LPIPS"]
ssim_ = ssim(dataloader).item()
lpips_ = lpips(dataloader).item()
row += [ssim_, lpips_]
# Print Results
print("GT Folder : ", gt_folder)
print("Pred Folder: ", pred_folder)
table = PrettyTable()
table.field_names = header
table.add_row(row)
if copy_folder is not None:
shutil.rmtree(copy_folder)
return ssim_, lpips_
def get_parse():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-f1", "--folder1", type=str, required=True)
parser.add_argument("-f2", "--folder2", type=str, required=True)
parser.add_argument("--pre", type=str, default="")
parser.add_argument("--pad", action="store_true", help="if the gt pad?")
parser.add_argument("--debug", action="store_true")
args = parser.parse_args()
return args
if __name__ == "__main__":
opt = get_parse()
input_folder = opt.folder1
target_folder = opt.folder2
valid_txt = os.path.join(input_folder, "front_view.txt")
target_folder = target_folder[:-1] if target_folder[-1] == "/" else target_folder
target_key = target_folder.split("/")[-2:]
save_folder = os.path.join(f"./exps/metrics{opt.pre}", "psnr_results", *target_key)
os.makedirs(save_folder, exist_ok=True)
with open(valid_txt) as f:
items = f.read().splitlines()
items = [x.split(" ")[0] for x in items]
results_dict = defaultdict(dict)
lpips_list = []
ssim_list = []
for item in items:
target_item_folder = os.path.join(input_folder, item)
input_item_folder = os.path.join(target_folder, item, "rgb")
if os.path.exists(input_item_folder) and os.path.exists(target_item_folder):
ssim_, lpips_ = eval(input_item_folder, target_item_folder)
if ssim_ == -1:
continue
lpips_list.append(lpips_)
ssim_list.append(ssim_)
results_dict[item]["lpips"] = lpips_
results_dict[item]["ssim"] = ssim_
if opt.debug:
break
print(results_dict)
results_dict["all_mean"]["lpips"] = np.mean(lpips_list)
results_dict["all_mean"]["ssim"] = np.mean(ssim_list)
write_json(os.path.join(save_folder, "lpips_ssim.json"), results_dict)
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