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202 lines
8.8 KiB
Python
202 lines
8.8 KiB
Python
#!/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 json
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import logging
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import time
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import cv2
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import numpy as np
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import redis
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import tritonclient.grpc as grpcclient
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from PIL import Image
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from tritonclient.utils import np_to_triton_dtype
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from app.core.config import settings, GRI_MODEL_URL, GRI_MODEL_NAME_SINGLE, GRI_MODEL_NAME_OVERALL, GRI_RABBITMQ_QUEUES
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from app.schemas.generate_image import GenerateRelightImageModel
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from app.service.generate_image.utils.mq import publish_status
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from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
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from app.service.utils.new_oss_client import oss_get_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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self.grpc_client = grpcclient.InferenceServerClient(url=GRI_MODEL_URL)
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self.redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.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 = "1"
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self.product_type = request_data.product_type
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self.negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
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self.direction = request_data.direction
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self.image_url = request_data.image_url
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self.image = pre_processing_image(self.image_url)
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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 callback(self, result, error):
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if error:
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self.gen_product_data['status'] = "FAILURE"
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if 'mask_list' in str(error):
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self.gen_product_data['status'] = "NO_FACE"
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self.gen_product_data['message'] = 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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if self.product_type == 'single':
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image = result.as_numpy("generated_relight_image")
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else:
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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)))
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image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
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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 get_result(self):
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try:
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prompts = [self.prompt] * self.batch_size
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image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
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image = cv2.resize(image, (512, 768))
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images = [image.astype(np.uint8)] * self.batch_size
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seeds = [self.seed] * self.batch_size
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nagetive_prompts = [self.negative_prompt] * self.batch_size
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directions = [self.direction] * self.batch_size
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if self.product_type == 'single':
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text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
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image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
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na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((-1, 1))
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seed_obj = np.array(seeds, dtype="object").reshape((-1, 1))
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direction_obj = np.array(directions, dtype="object").reshape((-1, 1))
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else:
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text_obj = np.array(prompts, dtype="object").reshape((1))
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image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
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na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1))
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seed_obj = np.array(seeds, dtype="object").reshape((1))
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direction_obj = np.array(directions, dtype="object").reshape((1))
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input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
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input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
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input_natext = grpcclient.InferInput("negative_prompt", na_text_obj.shape, np_to_triton_dtype(na_text_obj.dtype))
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input_seed = grpcclient.InferInput("seed", seed_obj.shape, np_to_triton_dtype(seed_obj.dtype))
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input_direction = grpcclient.InferInput("direction", direction_obj.shape, np_to_triton_dtype(direction_obj.dtype))
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input_text.set_data_from_numpy(text_obj)
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input_image.set_data_from_numpy(image_obj)
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input_natext.set_data_from_numpy(na_text_obj)
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input_seed.set_data_from_numpy(seed_obj)
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input_direction.set_data_from_numpy(direction_obj)
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inputs = [input_text, input_natext, input_image, input_seed, input_direction]
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if self.product_type == 'single':
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ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_SINGLE, inputs=inputs, callback=self.callback, priority=1)
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else:
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ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback, priority=1)
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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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if gen_product_data['status'] in ["REVOKED", "FAILURE", "NO_FACE"]:
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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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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 not settings.DEBUG:
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publish_status(str_gen_product_data, GRI_RABBITMQ_QUEUES)
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def pre_processing_image(image_url):
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image = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="PIL")
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# 目标图片的尺寸
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target_width = 512
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target_height = 768
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# 原始图片的尺寸
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original_width, original_height = image.size
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# 计算宽度和高度的缩放比例
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width_ratio = target_width / original_width
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height_ratio = target_height / original_height
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# 选择较小的缩放比例，确保图片能完整放入目标图片中
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scale_ratio = min(width_ratio, height_ratio)
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# 计算调整后的尺寸
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new_width = int(original_width * scale_ratio)
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new_height = int(original_height * scale_ratio)
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# 调整图片大小
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resized_image = image.resize((new_width, new_height))
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# 创建一个 512x768 的透明图片
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result_image = Image.new("RGBA", (target_width, target_height), (255, 255, 255, 0))
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# 计算需要粘贴的位置，使图片居中
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x_offset = (target_width - new_width) // 2
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y_offset = (target_height - new_height) // 2
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# 将调整大小后的图片粘贴到透明图片上
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if resized_image.mode == "RGBA":
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result_image.paste(resized_image, (x_offset, y_offset), mask=resized_image.split()[3])
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else:
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result_image.paste(resized_image, (x_offset, y_offset))
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image = np.array(result_image)
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# image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
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return image
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def infer_cancel(tasks_id):
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redis_client = redis.StrictRedis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.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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prompt="Colorful black",
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image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png',
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direction="Right Light",
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product_type="overall"
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)
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server = GenerateRelightImage(rd)
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print(server.get_result())
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