198 lines
9.0 KiB
Python
198 lines
9.0 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 minio
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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 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 GenerateImageModel
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from app.service.generate_image.utils.image_processing import remove_background, stain_detection, generate_category_recognition, autoLevels, luminance_adjust
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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_png_sd
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from app.service.utils.oss_client import oss_get_image
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logger = logging.getLogger()
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class GenerateImage:
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def __init__(self, request_data):
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self.version = request_data.version
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if request_data.version == "fast":
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self.grpc_client = grpcclient.InferenceServerClient(url=FAST_GI_MODEL_URL)
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else:
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self.grpc_client = grpcclient.InferenceServerClient(url=GI_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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if request_data.mode == "img2img":
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# cv2 读图片是BGR PIL读图片是RGB
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self.image = self.get_image(request_data.image_url)
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else:
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self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
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self.prompt = request_data.prompt
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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.mode = request_data.mode
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self.batch_size = 1
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self.category = request_data.category
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if self.category == "sketch":
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self.prompt = f"{self.category},{self.prompt}"
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self.index = 0
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self.gender = request_data.gender
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self.generate_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': '', 'category': ''}
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_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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# Get data of an object.
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# Read data from response.
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# read image use cv2
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try:
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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_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_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
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image_cv2 = oss_get_image(bucket=image_url.split('/')[0], object_name=image_url[image_url.find('/') + 1:], data_type="cv2")
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image_rbg = cv2.cvtColor(image_cv2, cv2.COLOR_BGR2RGB)
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image = cv2.resize(image_rbg, (1024, 1024))
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except minio.error.S3Error:
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image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
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return image
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def callback(self, result, error):
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if error:
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self.generate_data['status'] = "FAILURE"
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self.generate_data['message'] = str(error)
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# self.generate_data['data'] = str(error)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_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_image")
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image_result = cv2.cvtColor(np.squeeze(image.astype(np.uint8)), cv2.COLOR_RGB2BGR)
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is_smudge = True
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if self.category == "sketch":
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if self.version == "fast":
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# 色阶调整
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cutoff = 1
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levels_img = autoLevels(image_result, cutoff)
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# 亮度调整
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luminance = luminance_adjust(0.3, levels_img)
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# 去背景
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remove_bg_image = remove_background(luminance)
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# 人脸检测
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# if face_detect_pic(remove_bg_image, self.user_id, self.category, self.tasks_id) > 0:
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# is_smudge = False
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# else:
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# 污点/
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is_smudge, not_smudge_image = stain_detection(remove_bg_image, self.user_id, self.category, self.tasks_id)
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# 类型识别
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category, scores, not_smudge_image = generate_category_recognition(image=remove_bg_image, gender=self.gender)
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self.generate_data['category'] = str(category)
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image_result = not_smudge_image
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else:
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category, scores, not_smudge_image = generate_category_recognition(image=image_result, gender=self.gender)
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self.generate_data['category'] = str(category)
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image_result = not_smudge_image
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if is_smudge: # 无污点
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# image_result = adjust_contrast(image_result)
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image_url = upload_png_sd(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
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# logger.info(f"upload image SUCCESS ： {image_url}")
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self.generate_data['status'] = "SUCCESS"
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self.generate_data['message'] = "success"
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self.generate_data['image_url'] = str(image_url)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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else: # 有污点 保存图片到本地 测试用
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self.generate_data['status'] = "SUCCESS"
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self.generate_data['message'] = "success"
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self.generate_data['image_url'] = str(GI_SYS_IMAGE_URL)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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# logger.info(f"stain_detection result : {self.generate_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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modes = [self.mode] * self.batch_size
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images = [self.image.astype(np.float16)] * self.batch_size
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text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
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mode_obj = np.array(modes, dtype="object").reshape((-1, 1))
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image_obj = np.array(images, dtype=np.float16).reshape((-1, 1024, 1024, 3))
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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, np_to_triton_dtype(image_obj.dtype))
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input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(mode_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_mode.set_data_from_numpy(mode_obj)
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inputs = [input_text, input_image, input_mode]
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if self.version == "fast":
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ctx = self.grpc_client.async_infer(model_name=FAST_GI_MODEL_NAME, inputs=inputs, callback=self.callback, priority=1)
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else:
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ctx = self.grpc_client.async_infer(model_name=GI_MODEL_NAME, inputs=inputs, callback=self.callback, priority=1)
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time_out = 600
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generate_data = None
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while time_out > 0:
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generate_data, _ = self.read_tasks_status()
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if generate_data['status'] in ["REVOKED", "FAILURE"]:
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ctx.cancel()
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break
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elif generate_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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return generate_data
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except Exception as e:
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self.generate_data['status'] = "FAILURE"
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self.generate_data['message'] = str(e)
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self.redis_client.set(self.tasks_id, json.dumps(self.generate_data))
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raise Exception(str(e))
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finally:
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dict_generate_data, str_generate_data = self.read_tasks_status()
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if not DEBUG:
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publish_status(str_generate_data, GI_RABBITMQ_QUEUES)
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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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generate_data = json.dumps(data)
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redis_client.set(tasks_id, generate_data)
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return data
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if __name__ == '__main__':
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rd = GenerateImageModel(
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tasks_id="123-89",
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prompt='a single item of sketch of Wabi-sabi, skirt, tiered, 4k, white background',
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image_url="aida-collection-element/87/Printboard/842c09cf-7297-42d9-9e6e-9c17d4a13cb5.jpg",
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mode='txt2img',
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category="test",
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gender="male",
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version="high"
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)
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server = GenerateImage(rd)
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print(server.get_result())
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