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import os
import glob
import time
import pandas as pd
from sqlalchemy import create_engine, Table, Column, String, Integer, DateTime, Float
from sqlalchemy.orm import sessionmaker
from sqlalchemy.ext.declarative import declarative_base
from app.utils.file_utils import load_recipe, load_csv_safe
from app.settings import app_cfg as cfg
connection_url = "mysql+mysqlconnector://{}:{}@{}/{}?charset=utf8mb4".format(
os.getenv("DB_USER"),
os.getenv("DB_PASS"),
os.getenv("DB_HOST"),
os.getenv("DB_NAME")
)
datasets = {}
loaded = False
Session = None
def list_datasets():
return [dataset.describe() for dataset in datasets.values()]
def get_dataset(name):
return datasets[name] if name in datasets else None
def get_table(name, table_name):
dataset = get_dataset(name)
return dataset.get_table(table_name) if dataset else None
def load_sql_datasets(replace=False, base_model=None):
global datasets, loaded, Session
if loaded:
return datasets
engine = create_engine(connection_url, encoding="utf-8", pool_recycle=3600)
# db.set_character_set('utf8')
# dbc = db.cursor()
# dbc.execute('SET NAMES utf8;')
# dbc.execute('SET CHARACTER SET utf8;')
# dbc.execute('SET character_set_connection=utf8;')
Session = sessionmaker(bind=engine)
for path in glob.iglob(os.path.join(cfg.DIR_FAISS_METADATA, "*")):
dataset = load_sql_dataset(path, replace, engine, base_model)
datasets[dataset.name] = dataset
loaded = True
return datasets
def load_sql_dataset(path, replace=False, engine=None, base_model=None):
name = os.path.basename(path)
dataset = SqlDataset(name, base_model=base_model)
for fn in glob.iglob(os.path.join(path, "*.csv")):
key = os.path.basename(fn).replace(".csv", "")
table = dataset.get_table(key)
if table is None:
continue
if replace:
print('loading dataset {}'.format(fn))
df = pd.read_csv(fn)
# fix columns that are named "index", a sql reserved word
df.reindex_axis(sorted(df.columns), axis=1)
# df.columns = sorted(table.__table__.columns).keys()
df.to_sql(name=table.__tablename__, con=engine, if_exists='replace', index=False)
return dataset
class SqlDataset:
"""
Bridge between the facial information CSVs connected to the datasets, and MySQL
- each dataset should have files that can be loaded into these database models
- names will be fixed to work in SQL (index -> id)
- we can then have more generic models for fetching this info after doing a FAISS query
"""
def __init__(self, name, engine=None, base_model=None):
self.name = name
self.tables = {}
if base_model is None:
self.engine = create_engine(connection_url, encoding="utf-8", pool_recycle=3600)
base_model = declarative_base(engine)
self.base_model = base_model
def describe(self):
"""
List the available SQL tables for a given dataset.
"""
return {
'name': self.name,
'tables': list(self.tables.keys()),
}
def get_identity(self, id):
"""
Get an identity given an ID.
"""
table = self.get_table('identity')
# id += 1
identity = table.query.filter(table.record_id <= id).order_by(table.record_id.desc()).first().toJSON()
return {
'uuid': self.select('uuids', id),
'identity': identity,
'roi': self.select('roi', id),
'pose': self.select('pose', id),
}
def search_name(self, q):
"""
Find an identity by name.
"""
table = self.get_table('identity')
identity_list = table.query.filter(table.fullname.like(q)).order_by(table.fullname.desc()).limit(10)
return identity_list
def get_file_records_for_identities(self, identity_list):
"""
Given a list of identities, map these to file records.
"""
identities = []
file_record_table = self.get_table('file_record')
for row in identity_list:
file_record = file_record_table.query.filter(file_record_table.id == row.record_id).first()
identities.append({
'file_record': file_record.toJSON(),
'identity': row.toJSON(),
})
return identities
def select(self, table, id):
"""
Perform a generic select.
"""
table = self.get_table(table)
if not table:
return None
session = Session()
# for obj in session.query(table).filter_by(id=id):
# print(table)
obj = session.query(table).filter(table.id == id).first()
session.close()
return obj.toJSON()
def get_table(self, type):
"""
Get one of these memoized, dynamically generated tables.
"""
if type in self.tables:
return self.tables[type]
elif type == 'file_record':
self.tables[type] = self.file_record_table()
elif type == 'identity':
self.tables[type] = self.identity_table()
elif type == 'face_roi':
self.tables[type] = self.face_roi_table()
elif type == 'face_pose':
self.tables[type] = self.face_pose_table()
else:
return None
return self.tables[type]
# ==> file_record.csv <==
# index,ext,fn,identity_key,sha256,subdir,uuid,identity_index
def file_record_table(self):
class FileRecord(self.base_model):
__tablename__ = self.name + "_file_record"
id = Column(Integer, primary_key=True)
ext = Column(String(3, convert_unicode=True), nullable=False)
fn = Column(String(36, convert_unicode=True), nullable=False)
identity_key = Column(String(36, convert_unicode=True), nullable=False)
sha256 = Column(String(36, convert_unicode=True), nullable=False)
subdir = Column(String(36, convert_unicode=True), nullable=False)
uuid = Column(String(36, convert_unicode=True), nullable=False)
identity_index = Column(Integer)
def toJSON(self):
return {
'id': self.id,
'uuid': self.uuid,
'identity_index': self.identity_index,
}
return FileRecord
# ==> identity.csv <==
# index,description,gender,images,fullname
# 0,A. J. Cook,Canadian actress,f,1,0
def identity_table(self):
class Identity(self.base_model):
__tablename__ = self.name + "_identity"
id = Column(Integer, primary_key=True)
description = Column(String(36, convert_unicode=True), nullable=False)
gender = Column(String(1, convert_unicode=True), nullable=False)
images = Column(Integer, nullable=False)
fullname = Column(String(36, convert_unicode=True), nullable=False)
def toJSON(self):
return {
'id': self.id,
'fullname': self.fullname,
'images': self.images,
'gender': self.gender,
'description': self.description,
}
return Identity
# ==> face_roi.csv <==
# index,h,image_height,image_index,image_width,w,x,y
# 0,0.33000000000000007,250,0,250,0.32999999999999996,0.33666666666666667,0.35
def face_roi_table(self):
class FaceROI(self.base_model):
__tablename__ = self.name + "_roi"
id = Column(Integer, primary_key=True)
h = Column(Float, nullable=False)
image_height = Column(Integer, nullable=False)
record_index = Column(Integer, nullable=False)
image_width = Column(Integer, nullable=False)
w = Column(Float, nullable=False)
x = Column(Float, nullable=False)
y = Column(Float, nullable=False)
def toJSON(self):
return {
'id': self.id,
'record_index': self.record_index,
'image_height': self.image_height,
'image_width': self.image_width,
'w': self.w,
'h': self.h,
'x': self.x,
'y': self.y,
}
return FaceROI
# ==> face_pose.csv <==
# index,record_index,pitch,roll,yaw
# 0,0,11.16264458441435,10.415885631337728,22.99719032415318
def face_pose_table(self):
class FacePose(self.base_model):
__tablename__ = self.name + "_pose"
id = Column(Integer, primary_key=True)
record_id = Column(Integer, nullable=False)
pitch = Column(Float, nullable=False)
roll = Column(Float, nullable=False)
yaw = Column(Float, nullable=False)
def toJSON(self):
return {
'id': self.id,
'record_id': self.record_id,
'pitch': self.pitch,
'roll': self.roll,
'yaw': self.yaw,
}
return FacePose
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