feat generate 迁移
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@@ -58,11 +58,13 @@ RABBITMQ_PARAMS = {
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# SR service config
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SR_MODEL_NAME = "super_resolution"
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SR_TRITON_URL = "10.1.1.240:10031"
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SR_MINIO_BUCKET = "aida-users"
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SR_RABBITMQ_QUEUES = os.getenv("SR_RABBITMQ_QUEUES", "SuperResolution-local")
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# GenerateImage service config
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GI_MODEL_NAME = '_stable_diffusion'
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GI_MODEL_URL = '10.1.1.240:7001'
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GI_MINIO_BUCKET = "aida-users"
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GI_RABBITMQ_QUEUES = os.getenv("GI_RABBITMQ_QUEUES", f"GenerateImage-{RABBITMQ_ENV}")
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# SEG service config
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@@ -130,8 +130,7 @@ class GenerateImage:
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if self.category == "sketch":
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image = remove_background(np.asarray(image))
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image_url = upload_png_sd(image, user_id=self.user_id, category=f"{self.category}",
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object_name=f"{generate_uuid()}_{i}.png", )
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image_url = upload_png_sd(image, user_id=self.user_id, category=f"{self.category}", object_name=f"{generate_uuid()}_{i}.png", )
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url_list.append(image_url)
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generate_data = json.dumps({'status': 'SUCCESS', 'message': 'success', 'data': f'{url_list}'})
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self.channel.basic_publish(exchange='', routing_key=GI_RABBITMQ_QUEUES, body=generate_data)
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@@ -26,7 +26,7 @@ def upload_png_sd(image, user_id, category, object_name):
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image.save(image_data, format='PNG')
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image_data.seek(0)
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image_bytes = image_data.read()
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image_url = f"aida-users/{minio_client.put_object(f'aida-users', f'{user_id}/{category}/{object_name}', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
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image_url = f"aida-users/{minio_client.put_object(f'{GI_MINIO_BUCKET}', f'{user_id}/{category}/{object_name}', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
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return image_url
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except Exception as e:
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@@ -1,10 +1,7 @@
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import io
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import logging
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import time
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from io import BytesIO
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import minio.error
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import pika
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import redis
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import json
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import cv2
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@@ -12,10 +9,9 @@ import numpy as np
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import torch
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import tritonclient.grpc as grpcclient
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from minio import Minio
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from app.core.config import MINIO_IP, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE, MINIO_PORT, REDIS_HOST, REDIS_PORT, REDIS_DB, SR_MODEL_NAME, RABBITMQ_PARAMS, SR_RABBITMQ_QUEUES, SR_TRITON_URL
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from app.core.config import *
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from app.schemas.super_resolution import SuperResolutionModel
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from app.service.utils.decorator import RunTime
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from app.service.utils.generate_uuid import generate_uuid
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logger = logging.getLogger()
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@@ -25,6 +21,7 @@ class SuperResolution:
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self.triton_client = grpcclient.InferenceServerClient(url=SR_TRITON_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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self.tasks_id = data.sr_tasks_id
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self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
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self.sr_image_url = data.sr_image_url
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self.sr_xn = data.sr_xn
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self.minio_client = Minio(
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@@ -108,11 +105,11 @@ class SuperResolution:
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# output_url = self.upload_img_sr(output, generate_uuid())
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# return output_url
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def upload_img_sr(self, image, object_name):
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def upload_img_sr(self, image):
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try:
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image_bytes = cv2.imencode('.jpg', image)[1].tobytes()
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image_url = f"test/{self.minio_client.put_object(f'test', f'{object_name}.jpg', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
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res = self.minio_client.put_object(f'{SR_MINIO_BUCKET}', f'{self.user_id}/sr/output/{self.tasks_id}.jpg', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png')
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image_url = f"aida-users/{res.object_name}"
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return image_url
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except Exception as e:
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logger.warning(f"upload_png_mask runtime exception : {e}")
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@@ -129,7 +126,7 @@ class SuperResolution:
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if output.ndim == 3:
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output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
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output = (output * 255.0).round().astype(np.uint8)
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output_url = self.upload_img_sr(output, generate_uuid())
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output_url = self.upload_img_sr(output)
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sr_data = json.dumps({'tasks_id': self.tasks_id, 'status': 'SUCCESS', 'message': 'success', 'data': f'{output_url}'})
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self.channel.basic_publish(exchange='', routing_key=SR_RABBITMQ_QUEUES, body=sr_data)
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logger.info(f" [x] Sent {sr_data}")
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@@ -1,31 +1,2 @@
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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 torch
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import tritonclient.http as httpclient
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import tritonclient.grpc as grpcclient
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from PIL import Image
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triton_client = grpcclient.InferenceServerClient(url=f"10.1.1.150:7001")
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sample = cv2.imread("1709713346.806274.png", cv2.IMREAD_COLOR).astype(np.float32) / 255.
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sample = np.transpose(sample if sample.shape[2] == 1 else sample[:, :, [2, 1, 0]], (2, 0, 1))
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sample = torch.from_numpy(sample).float().unsqueeze(0).numpy()
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inputs = [
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grpcclient.InferInput("input", sample.shape, datatype="FP32")
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]
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inputs[0].set_data_from_numpy(sample
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# , binary_data=True
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)
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start_time = time.time()
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results = triton_client.async_infer(model_name="super_resolution", inputs=inputs)
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print(time.time() - start_time)
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sr_output = torch.from_numpy(results.as_numpy(f"output"))
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output = sr_output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
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if output.ndim == 3:
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output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
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output = (output * 255.0).round().astype(np.uint8)
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cv2.imshow("", output)
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cv2.waitKey(0)
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a = "123-86"
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print(a[a.rfind('-') + 1:])
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