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add_persons.py
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add_persons.py
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import argparse
import os
import shutil
import cv2
import numpy as np
import torch
from torchvision import transforms
from face_detection.scrfd.detector import SCRFD
from face_detection.yolov5_face.detector import Yolov5Face
from face_recognition.arcface.model import iresnet_inference
from face_recognition.arcface.utils import read_features
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Initialize the face detector (Choose one of the detectors)
detector = Yolov5Face(model_file="face_detection/yolov5_face/weights/yolov5n-face.pt")
#detector = SCRFD(model_file="face_detection/scrfd/weights/scrfd_2.5g_bnkps.onnx")
recognizer = iresnet_inference(
model_name="r100", path="face_recognition/arcface/weights/arcface_r100.pth", device=device
)
@torch.no_grad()
def get_feature(face_image):
"""
Extract facial features from an image using the face recognition model.
Args:
face_image (numpy.ndarray): Input facial image.
Returns:
numpy.ndarray: Extracted facial features.
"""
face_preprocess = transforms.Compose(
[
transforms.ToTensor(),
transforms.Resize((112, 112)),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
]
)
face_image = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
face_image = face_preprocess(face_image).unsqueeze(0).to(device)
emb_img_face = recognizer(face_image)[0].cpu().numpy()
images_emb = emb_img_face / np.linalg.norm(emb_img_face)
return images_emb
def add_persons(backup_dir, add_persons_dir, faces_save_dir, features_path):
images_name = []
images_emb = []
for name_person in os.listdir(add_persons_dir):
person_image_path = os.path.join(add_persons_dir, name_person)
person_face_path = os.path.join(faces_save_dir, name_person)
os.makedirs(person_face_path, exist_ok=True)
for image_name in os.listdir(person_image_path):
if image_name.endswith(("png", "jpg", "jpeg")):
input_image = cv2.imread(os.path.join(person_image_path, image_name))
bboxes, landmarks = detector.detect(image=input_image)
for i in range(len(bboxes)):
number_files = len(os.listdir(person_face_path))
x1, y1, x2, y2, score = bboxes[i]
face_image = input_image[y1:y2, x1:x2]
path_save_face = os.path.join(person_face_path, f"{number_files}.jpg")
cv2.imwrite(path_save_face, face_image)
images_emb.append(get_feature(face_image=face_image))
images_name.append(name_person)
if images_emb == [] and images_name == []:
print("No new person found!")
return None
images_emb = np.array(images_emb)
images_name = np.array(images_name)
features = read_features(features_path)
if features is not None:
old_images_name, old_images_emb = features
images_name = np.hstack((old_images_name, images_name))
images_emb = np.vstack((old_images_emb, images_emb))
print("Update features!")
np.savez_compressed(features_path, images_name=images_name, images_emb=images_emb)
for sub_dir in os.listdir(add_persons_dir):
dir_to_move = os.path.join(add_persons_dir, sub_dir)
shutil.move(dir_to_move, backup_dir, copy_function=shutil.copytree)
print("Successfully added new person!")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--backup-dir",
type=str,
default="./datasets/backup",
help="Directory to save person data.",
)
parser.add_argument(
"--add-persons-dir",
type=str,
default="./datasets/new_persons",
help="Directory to add new persons.",
)
parser.add_argument(
"--faces-save-dir",
type=str,
default="./datasets/data/",
help="Directory to save faces.",
)
parser.add_argument(
"--features-path",
type=str,
default="./datasets/face_features/feature",
help="Path to save face features.",
)
opt = parser.parse_args()
add_persons(**vars(opt))