AiDA_Python/app/service/generate_image/service_generate_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 minio
import redis
import tritonclient.grpc as grpcclient
import numpy as np
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, generate_category_recognition, autoLevels, luminance_adjust, face_detect_pic
from app.service.generate_image.utils.upload_sd_image import upload_png_sd
from app.service.utils.oss_client import oss_get_image

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":
            # cv2 读图片是BGR PIL读图片是RGB
            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.gender = request_data.gender
        self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': '', 'category': ''}
        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.
        # read image use cv2
        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_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)

            image_cv2 = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url, data_type="cv2")
            image_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
            image = cv2.resize(image_rbg, (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:
            # pil图像转成numpy数组
            image = result.as_numpy("generated_image")
            image_result = cv2.cvtColor(np.squeeze(image.astype(np.uint8)), cv2.COLOR_RGB2BGR)
            is_smudge = True
            if self.category == "sketch":
                # 色阶调整
                cutoff = 1
                levels_img = autoLevels(image_result, cutoff)
                # 亮度调整
                luminance = luminance_adjust(0.3, levels_img)
                # 去背景
                remove_bg_image = remove_background(luminance)
                # 人脸检测
                # if face_detect_pic(remove_bg_image, self.user_id, self.category, self.tasks_id) > 0:
                #     is_smudge = False
                # else:
                # 污点/
                is_smudge, not_smudge_image = stain_detection(remove_bg_image, self.user_id, self.category, self.tasks_id)
                # 类型识别
                category, scores, not_smudge_image = generate_category_recognition(image=remove_bg_image, gender=self.gender)
                self.generate_data['category'] = str(category)
                image_result = not_smudge_image
            if is_smudge:  # 无污点
                # image_result = adjust_contrast(image_result)
                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['image_url'] = str(image_url)
                self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
            else:  # 有污点 保存图片到本地 测试用
                self.generate_data['status'] = "SUCCESS"
                self.generate_data['message'] = "success"
                self.generate_data['image_url'] = 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'] = 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)
            # 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",
        gender="male"
    )
    server = GenerateImage(rd)
    print(server.get_result())