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53fe336 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | # SPDX-FileCopyrightText: Copyright © 2025 Idiap Research Institute <contact@idiap.ch>
# SPDX-FileContributor: Francois Poh <francois.poh22@imperial.ac.uk>
# SPDX-License-Identifier: GPL-3.0-or-later
# ArtFace contains the code for the paper: https://www.idiap.ch/paper/artface/
# It provides a facial recognition model for historical portraits, and scripts to reproduce the experiments in the paper.
from PIL import Image
import cv2
import numpy as np
import torch
class ImagePreprocessor:
def __init__(self):
pass
def __call__(self, image):
# --------------------
# Accept path OR PIL image
# --------------------
if isinstance(image, str):
image = Image.open(image).convert("RGB")
elif isinstance(image, Image.Image):
image = image.convert("RGB")
else:
raise TypeError(
f"Unsupported input type {type(image)}. "
"Expected file path or PIL.Image."
)
return self.process(image)
def process(self, image):
raise NotImplementedError("Subclasses should implement this method.")
class FaceAligner(ImagePreprocessor):
def __init__(self, detector="buffalo_l", crop_size=(112, 112), padding=0):
crop_size = tuple(map(int, crop_size))
super().__init__()
from lib.face_alignment import mtcnn
from insightface.app import FaceAnalysis
# --------------------
# Device selection
# --------------------
self.use_cuda = torch.cuda.is_available()
if self.use_cuda:
device = "cuda:0"
providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
ctx_id = 0
else:
device = "cpu"
providers = ["CPUExecutionProvider"]
ctx_id = -1
# --------------------
# MTCNN (landmark warping)
# --------------------
self.mtcnn = mtcnn.MTCNN(
device=device,
crop_size=tuple(int(s) for s in crop_size),
padding=float(padding),
)
# --------------------
# InsightFace detector
# --------------------
self.detector = FaceAnalysis(
name=detector,
root=".",
providers=providers,
)
self.detector.prepare(ctx_id=ctx_id)
print(
f"✅ FaceAligner initialized | "
f"CUDA: {self.use_cuda} | "
f"providers: {providers}"
)
def process(self, image):
from lib.face_alignment import mtcnn
image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
# Detect face
_, kpss = self.detector.det_model.detect(image, max_num=1, metric="default")
if kpss is None or len(kpss) == 0:
return None
# Convert landmarks
landmarks = np.array(
[np.concatenate([kpss[:, :, 0][0], kpss[:, :, 1][0]], axis=0)]
)
facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)]
# Warp & crop
warped_face = mtcnn.warp_and_crop_face(
image,
facial5points,
self.mtcnn.refrence,
crop_size=self.mtcnn.crop_size,
)
rgb_face = cv2.cvtColor(warped_face, cv2.COLOR_BGR2RGB)
return Image.fromarray(rgb_face)
preprocessors = {
"align": (FaceAligner, {"crop_size": (112, 112)}),
"align-224": (FaceAligner, {"crop_size": (224, 224)}),
"align-pad": (FaceAligner, {"crop_size": (224, 224), "padding": 0.5}),
}
def get_preprocessor(name, args={}):
model_args = {}
if isinstance(args, list):
for arg in args:
if "=" not in arg:
raise ValueError(
f"Invalid argument format for model arguments. Expected 'key=value' pairs, got '{arg}'."
)
key, value = arg.split("=", 1)
value = value.strip("'")
if "," in value:
value = [v.strip("'") for v in value.split(",")]
model_args[key] = value
if name in preprocessors:
return preprocessors[name][0](**{**preprocessors[name][1], **model_args})
else:
raise ValueError(
f"Unknown preprocessor: {name}\n Please choose from: {', '.join(preprocessors.keys())}"
)
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