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milvus2_create_all_vectors.py
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milvus2_create_all_vectors.py
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import pymssql
#from tensorflow.keras.preprocessing import image
from PIL import Image
import requests
import numpy as np
from scipy import spatial
from sklearn.preprocessing import minmax_scale
import json
import time
import logging
from pymilvus import (
connections,
utility,
FieldSchema, CollectionSchema, DataType,
Collection,
)
# Gets or creates a logger
logger = logging.getLogger(__name__)
# set log level
logger.setLevel(logging.DEBUG)
# define file handler and set formatter
file_handler = logging.FileHandler('log_KAM_create_vector.log')
formatter = logging.Formatter('%(asctime)s : %(levelname)s : %(name)s : %(message)s')
file_handler.setFormatter(formatter)
# add file handler to logger
logger.addHandler(file_handler)
conn = None
GAP = 10 # seconds to sleep between the loop steps
# handle milvus collection
print("start connecting to Milvus")
connections.connect("default", host="localhost", port="19530")
has = utility.has_collection("artifact")
print(f"Does collection artifact exist in Milvus: {has}")
fields = [
FieldSchema(name="pk", dtype=DataType.INT64, is_primary=True, auto_id=False),
FieldSchema(name="artifact_type", dtype=DataType.INT64),
FieldSchema(name="embeddings", dtype=DataType.FLOAT_VECTOR, dim=2048)
]
# description as "artifact" below is mandatory to connect
schema = CollectionSchema(fields, "artifact")
print("Collection artifact")
artifact = Collection("artifact", schema, consistency_level="Strong")
# Model REST API - tf serving - predict service URL
# tf_serving_url = 'http://localhost:8501/v1/models/similarityModel:predict'
tf_serving_url = 'http://localhost:8501/v1/models/resnet50:predict'
headers = {"content-type": "application/json"}
# mount path to access the file Server
# fs = "/mnt/muesfs/mues-images/image/ak/" # prod
fs_kam = "/mnt/muesfs/mues/mueskam-images/dev/image/ak/" # dev
fs_mues = "/mnt/muesfs/mues/mues-images/dev/image/ak/"
# MILVUS REST API URL
#milvus_url = 'http://localhost:19121/collections/artifact/vectors'
#milvus_url = 'http://localhost:19121/collections/kam/vectors'
def prepare_image(img, target_size=(224,224)):
img = img.resize(target_size)
if img.mode != 'RGB':
img = img.convert('RGB')
img = np.array(img)
img = np.expand_dims(img, axis=0)
# img = preprocess_input(img)
return img
def connect_to_db():
global conn
try:
conn = pymssql.connect(server='10.1.37.177', port='1033', user='muest', password='Mues*test.1', database='mues_test')
logger.info('DB connected successfully')
except Exception as e:
logger.critical(e)
def create_MUES_vectors():
cursor = conn.cursor()
cursor.execute("select DISTINCT TOP 100 F.ESER_ID, F.FOTOGRAF_PATH from ESER_FOTOGRAF F "
"LEFT JOIN ESER E ON F.ESER_ID=E.ID "
"WHERE permanentId is not NULL AND E.AKTIF=1 AND E.SILINMIS=0 AND F.ANA_FOTOGRAF=1 AND F.FEATURE_VECTOR_STATE is NULL ORDER BY F.ESER_ID")
records = cursor.fetchall()
ids = []
vectors = []
artifact_types = []
ok_list = []
err_list = []
for row in records:
try:
logger.info("id:" + str(row[0]) + " : " + str(row[1]))
print(("id: " + str(row[0]) + " : " + str(row[1])))
img = Image.open(fs_mues + row[1])
# img = image.load_img(fs + row[1])
img_data = prepare_image(img)
# prepare for tf serving service
# give the photo and get the vector from the model
data = json.dumps({"signature_name": "serving_default", "instances": img_data.tolist()})
response = requests.post(tf_serving_url, data=data, headers=headers)
dict_resp = json.loads(response.text)
feature_np = np.array(dict_resp["predictions"])
# min-max scale the data between 0 and 1
scaled_vec = minmax_scale(feature_np.flatten())
result_vec = np.round(scaled_vec, 2)
# print(result_vec)
# for milvus request
ids.append(row[0])
artifact_types.append(1)
# print(artifact_types)
vectors.append(result_vec.tolist())
ok_list.append(str(row[0]))
except (FileNotFoundError, IOError):
logger.error("File not found: " + fs_mues + row[1])
err_list.append(str(row[0])) # marking for FileNotFound
except ValueError as e:
logger.error("Decoding JSON has failed")
logger.error(e)
except (requests.HTTPError, requests.RequestException) as e:
logger.error("HTTP/Request error occurred")
logger.error(e)
try:
# save the n vector to the Milvus DB
if(len(vectors) > 0):
entities = [ids, artifact_types, vectors]
# print(entities)
insert_result = artifact.insert(entities)
# print(f"Number of entities in Milvus: {artifact.num_entities}") # check the num_entites
except Exception as e:
logger.error("MILVUS post request error")
logger.error(e)
try:
# commit for top N selected records
if(len(ok_list)>0):
cursor.execute("UPDATE ESER_FOTOGRAF set FEATURE_VECTOR_STATE='1' where ANA_FOTOGRAF=1 AND ESER_ID in {}".format(str(tuple(ok_list)).replace(',)', ')')))
if(len(err_list)>0):
cursor.execute("UPDATE ESER_FOTOGRAF set FEATURE_VECTOR_STATE='-1' where ANA_FOTOGRAF=1 AND ESER_ID in {}".format(str(tuple(err_list)).replace(',)', ')')))
conn.commit()
except Exception as e:
logger.error(e)
logger.info("Trying to reconnect to the DB...")
conn.close()
connect_to_db()
return len(records)
def create_KAM_vectors():
cursor = conn.cursor()
cursor.execute("select DISTINCT TOP 100 K.uid, F.FOTOGRAF_PATH, F.artifactId from KAM_ARTIFACT_VIEW K "
"LEFT JOIN Kam_ArtifactPhotograph F ON K.artifactId = F.artifactId "
"WHERE K.artifactType!='INVENTORY_ARTIFACT' AND K.aktif=1 AND K.silinmis=0 AND F.ANA_FOTOGRAF=1 AND F.FOTOGRAF_PATH is not null AND F.FEATURE_VECTOR_STATE is NULL ORDER BY K.uid")
records = cursor.fetchall()
ids = []
vectors = []
artifact_types = []
ok_list = []
err_list = []
for row in records:
try:
logger.info("uid:" + str(row[0]) + " : " + str(row[1]))
print(("uid: " + str(row[0]) + " : " + str(row[1])))
img = Image.open(fs_kam + row[1])
img_data = prepare_image(img)
# prepare for tf serving service
# give the photo and get the vector from the model
data = json.dumps({"signature_name": "serving_default", "instances": img_data.tolist()})
response = requests.post(tf_serving_url, data=data, headers=headers)
dict_resp = json.loads(response.text)
feature_np = np.array(dict_resp["predictions"])
# min-max scale the data between 0 and 1
scaled_vec = minmax_scale(feature_np.flatten())
result_vec = np.round(scaled_vec, 2)
# print(result_vec)
# for milvus request
ids.append(row[0])
# KAM artifact_type = 2, MUES artifact_type = 1
artifact_types.append(2)
# print(artifact_types)
vectors.append(result_vec.tolist())
ok_list.append(str(row[2]))
except (FileNotFoundError, IOError):
logger.error("KAM - File not found: " + fs_kam + row[1])
err_list.append(str(row[0])) # marking for FileNotFound
except ValueError as e:
logger.error("KAM - Decoding JSON has failed")
logger.error(e)
except (requests.HTTPError, requests.RequestException) as e:
logger.error("KAM - HTTP/Request error occurred")
logger.error(e)
try:
# save the n vector to the Milvus DB
if(len(vectors)):
entities = [ids, artifact_types, vectors]
insert_result = artifact.insert(entities)
except Exception as e:
logger.error("KAM - MILVUS post request error (KAM)")
logger.error(e)
try:
# commit for top N selected records
if(len(ok_list)>0):
cursor.execute("UPDATE Kam_ArtifactPhotograph set FEATURE_VECTOR_STATE='1' where ANA_FOTOGRAF=1 AND artifactId in {}".format(str(tuple(ok_list)).replace(',)', ')')))
if(len(err_list)>0):
cursor.execute("UPDATE Kam_ArtifactPhotograph set FEATURE_VECTOR_STATE='-1' where ANA_FOTOGRAF=1 AND artifactId in {}".format(str(tuple(err_list)).replace(',)', ')')))
conn.commit()
except Exception as e:
logger.error(e)
logger.info("KAM - Trying to reconnect to the DB...")
conn.close()
connect_to_db()
return len(records)
def create_all():
while True:
records_len = create_MUES_vectors()
print(str(records_len) + " MUES vectors created successfully")
logger.info(str(records_len) + " MUES vectors created successfully")
time.sleep(GAP)
records_len = create_KAM_vectors()
print(str(records_len) + " KAM vectors created successfully")
logger.info(str(records_len) + " KAM vectors created successfully")
time.sleep(GAP)
if __name__ == "__main__":
connect_to_db()
create_all()