AiDA_Python/app/service/generate_image/service_generate_relight_image.py

#!/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 settings, GRI_MODEL_URL, GRI_MODEL_NAME_SINGLE, GRI_MODEL_NAME_OVERALL, GRI_RABBITMQ_QUEUES
from app.schemas.generate_image import GenerateRelightImageModel
from app.service.generate_image.utils.mq import publish_status
from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
from app.service.utils.new_oss_client import oss_get_image

logger = logging.getLogger()


class GenerateRelightImage:
    def __init__(self, request_data):
        self.grpc_client = grpcclient.InferenceServerClient(url=GRI_MODEL_URL)
        self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.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 = pre_processing_image(self.image_url)
        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, priority=1)
            else:
                ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback, priority=1)

            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 not settings.DEBUG:
                publish_status(str_gen_product_data, GRI_RABBITMQ_QUEUES)


def pre_processing_image(image_url):
    image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
    # 目标图片的尺寸
    target_width = 512
    target_height = 768

    # 原始图片的尺寸
    original_width, original_height = image.size

    # 计算宽度和高度的缩放比例
    width_ratio = target_width / original_width
    height_ratio = target_height / original_height

    # 选择较小的缩放比例，确保图片能完整放入目标图片中
    scale_ratio = min(width_ratio, height_ratio)

    # 计算调整后的尺寸
    new_width = int(original_width * scale_ratio)
    new_height = int(original_height * scale_ratio)

    # 调整图片大小
    resized_image = image.resize((new_width, new_height))

    # 创建一个 512x768 的透明图片
    result_image = Image.new("RGBA", (target_width, target_height), (255, 255, 255, 0))

    # 计算需要粘贴的位置，使图片居中
    x_offset = (target_width - new_width) // 2
    y_offset = (target_height - new_height) // 2

    # 将调整大小后的图片粘贴到透明图片上
    if resized_image.mode == "RGBA":
        result_image.paste(resized_image, (x_offset, y_offset), mask=resized_image.split()[3])
    else:
        result_image.paste(resized_image, (x_offset, y_offset))

    image = np.array(result_image)

    # image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
    return image


def infer_cancel(tasks_id):
    redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.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="overall"
    )
    server = GenerateRelightImage(rd)
    print(server.get_result())