AiDA_Python/app/service/generate_image/service_pose_transform.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：trinity_client
@File    ：service_pose_transform.py
@Author  ：周成融
@Date    ：2023/7/26 12:01:05
@detail  ：
"""
import json
import logging
import time
from io import BytesIO

import imageio
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.pose_transform import PoseTransformModel
from app.service.generate_image.utils.mq import publish_status
from app.service.generate_image.utils.pose_transform_upload import upload_gif, upload_video, upload_first_image
from app.service.utils.oss_client import oss_get_image

logger = logging.getLogger()


class PoseTransformService:
    def __init__(self, request_data):
        self.grpc_client = grpcclient.InferenceServerClient(url=PT_MODEL_URL)
        self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
        self.category = "pose_transform"
        self.image_url = request_data.image_url
        self.pose_num = request_data.pose_id
        self.image = pre_processing_image(request_data.image_url)
        self.tasks_id = request_data.tasks_id
        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
        self.pose_transform_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'gif_url': '',
                                    'video_url': '', 'image_url': ''}

        self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
        self.redis_client.expire(self.tasks_id, 600)

    def callback(self, result, error):
        if error:
            self.pose_transform_data['status'] = "FAILURE"
            self.pose_transform_data['message'] = str(error)
            self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
        else:
            result_data = np.squeeze(result.as_numpy("generated_image_list").astype(np.uint8))[:, :, :, ::-1]

            # 第一帧图像
            first_image = Image.fromarray(result_data[0])
            first_image_url = upload_first_image(first_image, user_id=self.user_id,
                                                 category=f"{self.category}_first_img",
                                                 file_name=f"{self.tasks_id}.png")

            # 上传GIF
            gif_buffer = BytesIO()
            imageio.mimsave(gif_buffer, result_data, format='GIF', fps=5)
            gif_buffer.seek(0)
            gif_url = upload_gif(gif_buffer=gif_buffer, user_id=self.user_id, category=f"{self.category}_gif",
                                 file_name=f"{self.tasks_id}.gif")

            # 上传video
            video_url = upload_video(frames=result_data, user_id=self.user_id, category=f"{self.category}_video",
                                     file_name=f"{self.tasks_id}.mp4")

            self.pose_transform_data['status'] = "SUCCESS"
            self.pose_transform_data['message'] = "success"
            self.pose_transform_data['gif_url'] = str(gif_url)
            self.pose_transform_data['video_url'] = str(video_url)
            self.pose_transform_data['image_url'] = str(first_image_url)

            self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_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:
            pose_num = [self.pose_num] * 1
            pose_num_obj = np.array(pose_num, dtype="object").reshape((-1, 1))
            input_pose_num = grpcclient.InferInput("pose_num", pose_num_obj.shape,
                                                   np_to_triton_dtype(pose_num_obj.dtype))
            input_pose_num.set_data_from_numpy(pose_num_obj)

            image_files = [self.image.astype(np.uint8)] * 1
            image_files_obj = np.array(image_files, dtype=np.uint8).reshape((-1, 768, 512, 3))
            input_image_files = grpcclient.InferInput("image_file", image_files_obj.shape, "UINT8")
            input_image_files.set_data_from_numpy(image_files_obj)

            ctx = self.grpc_client.async_infer(model_name="animatex_1", inputs=[input_pose_num, input_image_files],
                                               callback=self.callback, client_timeout=60000)
            time_out = 60000
            while time_out > 0:
                pose_transform_data, _ = self.read_tasks_status()
                if pose_transform_data['status'] in ["REVOKED", "FAILURE"]:
                    ctx.cancel()
                    break
                elif pose_transform_data['status'] == "SUCCESS":
                    break
                time_out -= 1
                time.sleep(1)
            pose_transform_data, _ = self.read_tasks_status()
            return pose_transform_data
        except Exception as e:
            self.pose_transform_data['status'] = "FAILURE"
            self.pose_transform_data['message'] = str(e)
            self.redis_client.set(self.tasks_id, json.dumps(self.pose_transform_data))
            raise Exception(str(e))
        finally:
            dict_pose_transform_data, str_pose_transform_data = self.read_tasks_status()
            if not DEBUG:
                publish_status(json.dumps(str_pose_transform_data), PS_RABBITMQ_QUEUES)
            logger.info(
                f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@  {json.dumps(dict_pose_transform_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'}
    pose_transform_data = json.dumps(data)
    redis_client.set(tasks_id, pose_transform_data)
    return data


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))
    result_image = result_image.convert("RGB")
    image = np.array(result_image)

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

    return image


if __name__ == '__main__':
    rd = PoseTransformModel(
        tasks_id="123-89",
        image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png',
        pose_id="1"
    )
    server = PoseTransformService(rd)
    print(server.get_result())