#!/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 import cv2 import numpy as np import redis import tritonclient.grpc as grpcclient from PIL import Image from tritonclient.utils import np_to_triton_dtype from app.core.config import * from app.schemas.generate_image import GenerateRelightImageModel from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image from app.service.utils.oss_client import oss_get_image logger = logging.getLogger() class GenerateRelightImage: def __init__(self, request_data): if DEBUG is False: 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=GRI_MODEL_URL) self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True) self.category = "relight_image" self.batch_size = 1 self.prompt = request_data.prompt self.seed = "1" self.product_type = request_data.product_type self.negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality' self.direction = request_data.direction self.image_url = request_data.image_url self.image = oss_get_image(bucket=self.image_url.split('/')[0], object_name=self.image_url[self.image_url.find('/') + 1:], data_type="cv2") self.tasks_id = request_data.tasks_id self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:] self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''} self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data)) self.redis_client.expire(self.tasks_id, 600) def callback(self, result, error): if error: self.gen_product_data['status'] = "FAILURE" if 'mask_list' in str(error): self.gen_product_data['status'] = "NO_FACE" self.gen_product_data['message'] = str(error) self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data)) else: # pil图像转成numpy数组 if self.product_type == 'single': image = result.as_numpy("generated_relight_image") else: image = result.as_numpy("generated_inpaint_image") image_result = Image.fromarray(np.squeeze(image.astype(np.uint8))) image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png") self.gen_product_data['status'] = "SUCCESS" self.gen_product_data['message'] = "success" self.gen_product_data['image_url'] = str(image_url) self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data)) def read_tasks_status(self): status_data = self.redis_client.get(self.tasks_id) return json.loads(status_data), status_data def get_result(self): try: prompts = [self.prompt] * self.batch_size image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB) image = cv2.resize(image, (512, 768)) images = [image.astype(np.uint8)] * self.batch_size seeds = [self.seed] * self.batch_size nagetive_prompts = [self.negative_prompt] * self.batch_size directions = [self.direction] * self.batch_size if self.product_type == 'single': text_obj = np.array(prompts, dtype="object").reshape((-1, 1)) image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3)) na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((-1, 1)) seed_obj = np.array(seeds, dtype="object").reshape((-1, 1)) direction_obj = np.array(directions, dtype="object").reshape((-1, 1)) else: text_obj = np.array(prompts, dtype="object").reshape((1)) image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3)) na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1)) seed_obj = np.array(seeds, dtype="object").reshape((1)) direction_obj = np.array(directions, dtype="object").reshape((1)) input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype)) input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8") input_natext = grpcclient.InferInput("negative_prompt", na_text_obj.shape, np_to_triton_dtype(na_text_obj.dtype)) input_seed = grpcclient.InferInput("seed", seed_obj.shape, np_to_triton_dtype(seed_obj.dtype)) input_direction = grpcclient.InferInput("direction", direction_obj.shape, np_to_triton_dtype(direction_obj.dtype)) input_text.set_data_from_numpy(text_obj) input_image.set_data_from_numpy(image_obj) input_natext.set_data_from_numpy(na_text_obj) input_seed.set_data_from_numpy(seed_obj) input_direction.set_data_from_numpy(direction_obj) inputs = [input_text, input_natext, input_image, input_seed, input_direction] if self.product_type == 'single': ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_SINGLE, inputs=inputs, callback=self.callback) else: ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback) time_out = 600 while time_out > 0: gen_product_data, _ = self.read_tasks_status() if gen_product_data['status'] in ["REVOKED", "FAILURE", "NO_FACE"]: ctx.cancel() break elif gen_product_data['status'] == "SUCCESS": break time_out -= 1 time.sleep(0.1) gen_product_data, _ = self.read_tasks_status() return gen_product_data except Exception as e: self.gen_product_data['status'] = "FAILURE" self.gen_product_data['message'] = str(e) self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data)) raise Exception(str(e)) finally: dict_gen_product_data, str_gen_product_data = self.read_tasks_status() if DEBUG is False: self.channel.basic_publish(exchange='', routing_key=GRI_RABBITMQ_QUEUES, body=str_gen_product_data) logger.info(f" [x] Sent to： {GRI_RABBITMQ_QUEUES} data：@@@@ {json.dumps(dict_gen_product_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'} gen_product_data = json.dumps(data) redis_client.set(tasks_id, gen_product_data) return data if __name__ == '__main__': rd = GenerateRelightImageModel( tasks_id="123-89", # prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere", prompt="Colorful black", image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png', direction="Right Light", product_type="single" ) server = GenerateRelightImage(rd) print(server.get_result())