feat generate slogan | to product image | slogan 接口部署

app/service/generate_image/service_generate_product_image.py

@@ -0,0 +1,181 @@
+#!/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
+from io import BytesIO
+
+import cv2
+import minio
+import redis
+import tritonclient.grpc as grpcclient
+import numpy as np
+from minio import Minio
+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.adjust_contrast import adjust_contrast
+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, upload_stain_png_sd
+
+logger = logging.getLogger()
+
+
+class GenerateProductImage:
+    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)
+        self.tasks_id = request_data.tasks_id
+        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
+        self.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
+        self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_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.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)
+            generate_data, _ = self.read_tasks_status()
+            return generate_data
+        except Exception as e:
+            self.gen_product_data['status'] = "FAILURE"
+            self.gen_product_data['message'] = str(e)
+            self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+            raise Exception(str(e))
+        finally:
+            dict_gen_product_data, str_gen_product_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=GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES, body=str_gen_product_data)
+            logger.info(f" [x] Sent {json.dumps(dict_gen_product_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="",
+    )
+    server = GenerateImage(rd)
+    print(server.get_result())

app/service/generate_image/service_generate_single_logo.py

@@ -0,0 +1,72 @@
+#!/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 redis
+from minio import Minio
+from app.core.config import *
+from app.schemas.generate_image import GenerateSingleLogoImageModel
+
+logger = logging.getLogger()
+
+
+class GenerateSingleLogoImage:
+    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)
+        self.tasks_id = request_data.tasks_id
+        self.user_id = self.tasks_id[self.tasks_id.rfind('-') + 1:]
+        self.gen_single_logo_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
+        self.redis_client.set(self.tasks_id, json.dumps(self.gen_single_logo_data))
+        self.redis_client.expire(self.tasks_id, 600)
+
+    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:
+            generate_data, _ = self.read_tasks_status()
+            return generate_data
+        except Exception as e:
+            self.gen_single_logo_data['status'] = "FAILURE"
+            self.gen_single_logo_data['message'] = str(e)
+            self.redis_client.set(self.tasks_id, json.dumps(self.gen_single_logo_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=GEN_SINGLE_LOGO_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 = GenerateSingleLogoImageModel(
+        tasks_id="123-8",
+        prompt='skeleton sitting by the side of a river looking soulful, concert poster, 4k, artistic',
+        image_url="",
+    )
+    server = GenerateSingleLogoImage(rd)
+    print(server.get_result())