203 lines
8.9 KiB
Python
203 lines
8.9 KiB
Python
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#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：service_att_recognition.py
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@Author ：周成融
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@Date ：2023/7/26 12:01:05
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@detail ：
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"""
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import io
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import json
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import logging
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import time
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import cv2
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import redis
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import tritonclient.grpc as grpcclient
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import numpy as np
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from PIL import Image, ImageOps
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from minio import Minio
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from tritonclient.utils import np_to_triton_dtype
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from app.core.config import *
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from app.schemas.generate_image import GenerateRelightImageModel
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from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
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logger = logging.getLogger()
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class GenerateRelightImage:
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def __init__(self, request_data):
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if DEBUG is False:
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self.connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
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self.channel = self.connection.channel()
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self.minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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self.grpc_client = grpcclient.InferenceServerClient(url=GRI_MODEL_URL)
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self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
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self.category = "relight_image"
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self.batch_size = 1
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self.prompt = request_data.prompt
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self.seed = "12345"
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# TODO aida design 结果图背景改为白色
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# self.image, self.image_size = self.get_image(request_data.image_url)
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self.image = request_data.image_url
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# TODO image 填充并resize成512*768
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self.tasks_id = request_data.tasks_id
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self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
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self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
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self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
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self.redis_client.expire(self.tasks_id, 600)
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def get_image(self, image_url):
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response = self.minio_client.get_object(image_url.split('/')[0], image_url[image_url.find('/') + 1:])
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image_bytes = io.BytesIO(response.read())
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# 转换为PIL图像对象
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image = Image.open(image_bytes)
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target_height = 768
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target_width = 512
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aspect_ratio = image.width / image.height
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new_width = int(target_height * aspect_ratio)
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resized_image = image.resize((new_width, target_height))
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left = (target_width - resized_image.width) // 2
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top = (target_height - resized_image.height) // 2
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right = target_width - resized_image.width - left
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bottom = target_height - resized_image.height - top
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image = ImageOps.expand(resized_image, (left, top, right, bottom), fill="white")
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image_size = image.size
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if image.mode in ('RGBA', 'LA') or (image.mode == 'P' and 'transparency' in image.info):
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# 创建白色背景
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background = Image.new("RGB", image.size, (255, 255, 255))
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# 将图片粘贴到白色背景上
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background.paste(image, mask=image.split()[3])
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image = np.array(background)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# image_file = BytesIO(response.data)
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# image_array = np.asarray(bytearray(image_file.read()), dtype=np.uint8)
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# image_cv2 = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
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# image = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
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# image = cv2.resize(image_rbg, (1024, 1024))
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return image, image_size
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def callback(self, result, error):
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if error:
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self.gen_product_data['status'] = "FAILURE"
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self.gen_product_data['message'] = str(error)
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# self.gen_product_data['data'] = str(error)
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self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
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else:
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# pil图像转成numpy数组
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image = result.as_numpy("generated_inpaint_image")
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image_result = Image.fromarray(np.squeeze(image.astype(np.uint8))).resize(self.image_size)
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image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", object_name=f"{self.tasks_id}.png")
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# logger.info(f"upload image SUCCESS ： {image_url}")
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self.gen_product_data['status'] = "SUCCESS"
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self.gen_product_data['message'] = "success"
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self.gen_product_data['image_url'] = str(image_url)
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self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
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def read_tasks_status(self):
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status_data = self.redis_client.get(self.tasks_id)
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return json.loads(status_data), status_data
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def infer(self, inputs):
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return self.grpc_client.async_infer(
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model_name=GRI_MODEL_NAME,
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inputs=inputs,
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callback=self.callback
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)
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def get_result(self):
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try:
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direction = "Right Light"
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negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
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self.prompt = 'beautiful woman, detailed face, sunshine, outdoor, warm atmosphere'
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prompts = [self.prompt] * self.batch_size
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text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
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input_text = grpcclient.InferInput(
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"prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype)
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)
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input_text.set_data_from_numpy(text_obj)
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negative_prompts = [negative_prompt] * self.batch_size
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text_obj_neg = np.array(negative_prompts, dtype="object").reshape((-1, 1))
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input_text_neg = grpcclient.InferInput(
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"negative_prompt", text_obj_neg.shape, np_to_triton_dtype(text_obj_neg.dtype)
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)
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input_text_neg.set_data_from_numpy(text_obj_neg)
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seed = np.array(self.seed, dtype="object").reshape((-1, 1))
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input_seed = grpcclient.InferInput(
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"seed", seed.shape, np_to_triton_dtype(seed.dtype)
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)
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input_seed.set_data_from_numpy(seed)
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input_images = [self.image] * self.batch_size
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text_obj_images = np.array(input_images, dtype="object").reshape((-1, 1))
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input_input_images = grpcclient.InferInput(
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"input_image", text_obj_images.shape, np_to_triton_dtype(text_obj_images.dtype)
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)
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input_input_images.set_data_from_numpy(text_obj_images)
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directions = [direction] * self.batch_size
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text_obj_directions = np.array(directions, dtype="object").reshape((-1, 1))
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input_directions = grpcclient.InferInput(
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"direction", text_obj_directions.shape, np_to_triton_dtype(text_obj_directions.dtype)
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)
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input_directions.set_data_from_numpy(text_obj_directions)
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output_img = grpcclient.InferRequestedOutput("generated_image")
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request_start = time.time()
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inputs = [input_text, input_text_neg, input_input_images, input_seed, input_directions]
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ctx = self.infer(inputs)
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time_out = 600
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while time_out > 0:
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gen_product_data, _ = self.read_tasks_status()
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# logger.info(gen_product_data)
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if gen_product_data['status'] in ["REVOKED", "FAILURE"]:
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ctx.cancel()
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break
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elif gen_product_data['status'] == "SUCCESS":
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break
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time_out -= 1
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time.sleep(0.1)
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# logger.info(time_out, gen_product_data)
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gen_product_data, _ = self.read_tasks_status()
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return gen_product_data
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except Exception as e:
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self.gen_product_data['status'] = "FAILURE"
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self.gen_product_data['message'] = str(e)
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self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
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raise Exception(str(e))
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finally:
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dict_gen_product_data, str_gen_product_data = self.read_tasks_status()
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if DEBUG is False:
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self.channel.basic_publish(exchange='', routing_key=GPI_RABBITMQ_QUEUES, body=str_gen_product_data)
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# self.channel.basic_publish(exchange='', routing_key=GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES, body=str_gen_product_data)
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logger.info(f" [x] Sent to： {GPI_RABBITMQ_QUEUES} data：@@@@ {json.dumps(dict_gen_product_data, indent=4)}")
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def infer_cancel(tasks_id):
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redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
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data = {'tasks_id': tasks_id, 'status': 'REVOKED', 'message': "revoked", 'data': 'revoked'}
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gen_product_data = json.dumps(data)
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redis_client.set(tasks_id, gen_product_data)
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return data
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if __name__ == '__main__':
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rd = GenerateRelightImageModel(
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tasks_id="123-89",
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prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
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image_url="/workspace/i3.png",
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)
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server = GenerateRelightImage(rd)
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print(server.get_result())
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