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# -*- coding: utf-8 -*-
# @Organization : Alibaba XR-Lab
# @Author : Lingteng Qiu
# @Email : [email protected]
# @Time : 2025-03-03 10:29:00
# @Function : easy to use FaceSimilarity metric
import os
import pdb
import shutil
import sys
sys.path.append("./")
from collections import defaultdict
import numpy as np
import torch
import torch.nn.functional as F
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
from openlrm.models.arcface_utils import ResNetArcFace
from openlrm.utils.face_detector import FaceDetector
device = "cuda"
model_path = "./pretrained_models/gagatracker/vgghead/vgg_heads_l.trcd"
face_detector = FaceDetector(model_path=model_path, device=device)
id_face_net = ResNetArcFace()
id_face_net.cuda()
id_face_net.eval()
def get_image_paths_current_dir(folder_path):
image_extensions = {
".jpg",
".jpeg",
".png",
".gif",
".bmp",
".tiff",
".webp",
".jfif",
}
return sorted(
[
os.path.join(folder_path, f)
for f in os.listdir(folder_path)
if os.path.splitext(f)[1].lower() in image_extensions
]
)
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 crop_face_image(image_path):
rgb = np.array(Image.open(image_path))
rgb = torch.from_numpy(rgb).permute(2, 0, 1)
bbox = face_detector(rgb)
head_rgb = rgb[:, int(bbox[1]) : int(bbox[3]), int(bbox[0]) : int(bbox[2])]
head_rgb = head_rgb.permute(1, 2, 0)
head_rgb = head_rgb.cpu().numpy()
return head_rgb
def gray_resize_for_identity(out, size=128):
out_gray = (
0.2989 * out[:, 0, :, :] + 0.5870 * out[:, 1, :, :] + 0.1140 * out[:, 2, :, :]
)
out_gray = out_gray.unsqueeze(1)
out_gray = F.interpolate(
out_gray, (size, size), mode="bilinear", align_corners=False
)
return out_gray
@torch.no_grad()
def eval(input_folder, target_folder, front_view_idx, device="cuda"):
src_img = os.path.join(target_folder, f"{front_view_idx:05d}.png")
if not os.path.exists(src_img):
return -1
head_img = crop_face_image(src_img)
input_imgs = get_image_paths_current_dir(input_folder)
if "visualization" in input_imgs[-1]:
input_imgs = input_imgs[:-1]
to_tensor = transforms.ToTensor()
head_img = to_tensor(head_img).unsqueeze(0).to(device)
src_head_tensor = gray_resize_for_identity(head_img)
src_head_feature = id_face_net(src_head_tensor).detach()
face_id_loss_list = []
for input_img in input_imgs:
try:
input_img = crop_face_image(input_img)
input_head_tensor = gray_resize_for_identity(
to_tensor(input_img).unsqueeze(0).to(device)
)
input_head_feature = id_face_net(input_head_tensor).detach()
face_id_loss = F.l1_loss(input_head_feature, src_head_feature)
face_id_loss_list.append(face_id_loss.item())
except:
continue
if len(face_id_loss_list) > 0:
return min(face_id_loss_list) # return max similarity view.
else:
return -1
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("--pad", action="store_true")
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("./exps/metrics", "psnr_results", *target_key)
os.makedirs(save_folder, exist_ok=True)
with open(valid_txt) as f:
items = f.read().splitlines()
items = [x.split(" ") for x in items]
results_dict = defaultdict(dict)
face_similarity_list = []
for item_ in tqdm(items):
try:
item, front_view_idx = item_
front_view_idx = int(front_view_idx)
except:
print(item_)
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):
fs_ = eval(input_item_folder, target_item_folder, front_view_idx)
if fs_ == -1:
continue
face_similarity_list.append(fs_)
results_dict[item]["face_similarity"] = fs_
if opt.debug:
break
print(results_dict)
results_dict["all_mean"]["face_similarity"] = np.mean(face_similarity_list)
write_json(os.path.join(save_folder, "face_similarity.json"), results_dict)
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