176 lines
7.4 KiB
Python
176 lines
7.4 KiB
Python
#!/usr/bin/env python
|
||
# -*- coding: UTF-8 -*-
|
||
"""
|
||
@Project ：trinity_client
|
||
@File ：service_att_recognition.py
|
||
@Author ：周成融
|
||
@Date ：2023/7/26 12:01:05
|
||
@detail ：
|
||
"""
|
||
import json
|
||
import logging
|
||
import time
|
||
from io import BytesIO
|
||
|
||
import cv2
|
||
import minio
|
||
import redis
|
||
import tritonclient.grpc as grpcclient
|
||
import numpy as np
|
||
from minio import Minio
|
||
from tritonclient.utils import np_to_triton_dtype
|
||
|
||
from app.core.config import *
|
||
from app.schemas.generate_image import GenerateImageModel
|
||
from app.service.generate_image.utils.image_processing import remove_background, stain_detection
|
||
from app.service.generate_image.utils.upload_sd_image import upload_png_sd
|
||
|
||
logger = logging.getLogger()
|
||
|
||
|
||
class GenerateImage:
|
||
def __init__(self, request_data):
|
||
if DEBUG is False:
|
||
self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
|
||
self.channel = self.connection.channel()
|
||
# self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
|
||
# self.channel = self.connection.channel()
|
||
self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
|
||
self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL)
|
||
self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
|
||
if request_data.mode == "img2img":
|
||
self.image = self.get_image(request_data.image_url)
|
||
self.prompt = request_data.prompt
|
||
else:
|
||
self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
|
||
self.prompt = request_data.prompt
|
||
|
||
self.tasks_id = request_data.tasks_id
|
||
self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
|
||
self.mode = request_data.mode
|
||
self.batch_size = 1
|
||
self.category = request_data.category
|
||
self.index = 0
|
||
self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'data': ''}
|
||
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
|
||
self.redis_client.expire(self.tasks_id, 600)
|
||
|
||
def get_image(self, image_url):
|
||
# Get data of an object.
|
||
# Read data from response.
|
||
try:
|
||
response = self.minio_client.get_object(image_url.split('/')[0], image_url[image_url.find('/') + 1:])
|
||
image_file = BytesIO(response.data)
|
||
image_array = np.asarray(bytearray(image_file.read()), dtype=np.uint8)
|
||
image_cv2 = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
|
||
image = cv2.resize(image_cv2, (1024, 1024))
|
||
except minio.error.S3Error:
|
||
image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
|
||
return image
|
||
|
||
def callback(self, result, error):
|
||
if error:
|
||
self.generate_data['status'] = "FAILURE"
|
||
self.generate_data['message'] = str(error)
|
||
self.generate_data['data'] = str(error)
|
||
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
|
||
else:
|
||
image_result = result.as_numpy("generated_image")[0]
|
||
is_smudge = True
|
||
if self.category == "sketch":
|
||
# 去背景
|
||
remove_bg_image = remove_background(np.asarray(image_result))
|
||
# 污点检测
|
||
is_smudge, not_smudge_image = stain_detection(remove_bg_image)
|
||
image_result = not_smudge_image
|
||
if is_smudge: # 无污点
|
||
image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
|
||
# logger.info(f"upload image SUCCESS ： {image_url}")
|
||
self.generate_data['status'] = "SUCCESS"
|
||
self.generate_data['message'] = "success"
|
||
self.generate_data['data'] = str(image_url)
|
||
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
|
||
else: # 有污点
|
||
self.generate_data['status'] = "SUCCESS"
|
||
self.generate_data['message'] = "success"
|
||
self.generate_data['data'] = str(GI_SYS_IMAGE_URL)
|
||
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
|
||
# logger.info(f"stain_detection result : {self.generate_data}")
|
||
|
||
def read_tasks_status(self):
|
||
status_data = self.redis_client.get(self.tasks_id)
|
||
return json.loads(status_data), status_data
|
||
|
||
def infer(self, inputs):
|
||
return self.grpc_client.async_infer(
|
||
model_name=GI_MODEL_NAME,
|
||
inputs=inputs,
|
||
callback=self.callback
|
||
)
|
||
|
||
def get_result(self):
|
||
try:
|
||
prompts = [self.prompt] * self.batch_size
|
||
modes = [self.mode] * self.batch_size
|
||
images = [self.image.astype(np.float16)] * self.batch_size
|
||
|
||
text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
|
||
mode_obj = np.array(modes, dtype="object").reshape((-1, 1))
|
||
image_obj = np.array(images, dtype=np.float16).reshape((-1, 1024, 1024, 3))
|
||
|
||
input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
|
||
input_image = grpcclient.InferInput("input_image", image_obj.shape, "FP16")
|
||
input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(text_obj.dtype))
|
||
|
||
input_text.set_data_from_numpy(text_obj)
|
||
input_image.set_data_from_numpy(image_obj)
|
||
input_mode.set_data_from_numpy(mode_obj)
|
||
|
||
inputs = [input_text, input_image, input_mode]
|
||
ctx = self.infer(inputs)
|
||
time_out = 600
|
||
generate_data = None
|
||
while time_out > 0:
|
||
generate_data, _ = self.read_tasks_status()
|
||
# logger.info(generate_data)
|
||
if generate_data['status'] in ["REVOKED", "FAILURE"]:
|
||
ctx.cancel()
|
||
break
|
||
elif generate_data['status'] == "SUCCESS":
|
||
break
|
||
time_out -= 1
|
||
time.sleep(0.1)
|
||
# logger.info(time_out, generate_data)
|
||
return generate_data
|
||
except Exception as e:
|
||
self.generate_data['status'] = "FAILURE"
|
||
self.generate_data['message'] = "failure"
|
||
self.generate_data['data'] = str(e)
|
||
self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
|
||
raise Exception(str(e))
|
||
finally:
|
||
dict_generate_data, str_generate_data = self.read_tasks_status()
|
||
if DEBUG is False:
|
||
self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=str_generate_data)
|
||
logger.info(f" [x] Sent {json.dumps(dict_generate_data, indent=4)}")
|
||
|
||
|
||
def infer_cancel(tasks_id):
|
||
redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
|
||
data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
|
||
generate_data = json.dumps(data)
|
||
redis_client.set(tasks_id, generate_data)
|
||
return data
|
||
|
||
|
||
if __name__ == '__main__':
|
||
rd = GenerateImageModel(
|
||
tasks_id="123-89",
|
||
prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic',
|
||
image_url="",
|
||
mode='txt2img',
|
||
category="test"
|
||
)
|
||
server = GenerateImage(rd)
|
||
print(server.get_result())
|