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Profile.py
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Profile.py
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import os
import shutil
import typing
import numpy as np
import PySide2
from PySide2.QtCore import QObject, Signal, Slot
class Profile(QObject):
recognized_signal = Signal(list)
enrolled_signal = Signal(int)
deleted_signal = Signal()
def __init__(self,
parent: typing.Optional[PySide2.QtCore.QObject] = ...,
file_dir: str = ...,
embedding_size: int = ...) -> None:
super().__init__(parent)
self.embedding_size = embedding_size
self.file_dir = file_dir
self.delete_dir = f"{self.file_dir}_Delete"
self.load()
def load(self):
self.user_embeddings = {}
self.user_profile = {}
self.user_norm = {}
if not os.path.exists(self.file_dir):
os.mkdir(self.file_dir)
for filaname in os.listdir(self.file_dir):
with open(os.path.join(self.file_dir, filaname), 'rb') as f:
embeddings = []
try:
embeddings.append(
np.load(f, allow_pickle=False, fix_imports=False))
except:
continue
while embeddings[-1].size == self.embedding_size:
try:
embeddings.append(
np.load(f, allow_pickle=False, fix_imports=False))
except:
username = filaname.split('.')[0]
self.user_embeddings[username] = embeddings
self.user_profile[username] = np.mean(np.array(
embeddings, copy=False),
axis=0)
self.user_norm[username] = np.linalg.norm(
self.user_profile[username], ord=2)
break
@Slot()
def recognize(self, embedding: np.ndarray):
"""
embedding: np.ndarray with shape [embedding_size, ]
"""
user_score = {}
x_norm = np.linalg.norm(embedding, ord=2)
for username in self.user_profile.keys():
user_score[username] = np.dot(embedding,
self.user_profile[username]) / (
x_norm * self.user_norm[username])
user_score[username] = float(user_score[username])
user_score_sorted = sorted(user_score.items(),
key=lambda item: item[1],
reverse=True)
self.recognized_signal.emit(user_score_sorted)
return user_score_sorted
@Slot()
def enroll(self, embeddings: np.ndarray, username: str):
"""
embeddings: np.ndarray with shape [bs, embedding_size]
username: user's name
return: enrolled_count
"""
with open(os.path.join(self.file_dir, f"{username}.npy"),
mode="ab") as f:
for embedding in embeddings:
self.user_embeddings.setdefault(username, []).append(embedding)
np.save(f, embedding, allow_pickle=False, fix_imports=False)
self.user_profile[username] = np.mean(np.array(
self.user_embeddings[username], copy=False),
axis=0)
self.user_norm[username] = np.linalg.norm(self.user_profile[username],
ord=2)
enrolled_count = len(self.user_embeddings[username])
self.enrolled_signal.emit(enrolled_count)
return enrolled_count
@Slot()
def delete(self, username: str):
"""
username: user's name
"""
del self.user_embeddings[username]
del self.user_profile[username]
del self.user_norm[username]
if not os.path.exists(self.delete_dir):
os.mkdir(self.delete_dir)
shutil.move(os.path.join(self.file_dir, f"{username}.npy"),
os.path.join(self.delete_dir, f"{username}.npy"))
self.deleted_signal.emit()
if "__main__" == __name__:
profile = Profile(None)
print("Done")