Merge remote-tracking branch 'origin/develop' into develop

app/core/config.py

@@ -129,8 +129,14 @@ GSL_MODEL_NAME = 'stable_diffusion_xl_transparent'
 GEN_SINGLE_LOGO_RABBITMQ_QUEUES = os.getenv("GEN_SINGLE_LOGO_RABBITMQ_QUEUES", f"GenSingleLogo{RABBITMQ_ENV}")
 
 # Generate Product service config
+# GPI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"ToProductImage{RABBITMQ_ENV}")
+# GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all'
+# GPI_MODEL_URL = '10.1.1.243:10051'
+
+# Generate Product service config 旧版product img 模型
 GPI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"ToProductImage{RABBITMQ_ENV}")
-GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all'
+GPI_MODEL_NAME_OVERALL = 'diffusion_ensemble_all'
+GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet'
 GPI_MODEL_URL = '10.1.1.243:10051'
 
 # Generate Single Logo service config

app/service/design_fast/pipeline/print_painting.py

@@ -17,6 +17,22 @@ class PrintPainting:
         element_print = result['print']['element']
         result['single_image'] = None
         result['print_image'] = None
+        # TODO 给result['pattern_image'] resize 到resize_scale的大小
+        # TODO 给result['mask'] resize 到resize_scale的大小
+
+        if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
+            pass
+        else:
+            height, width = result['pattern_image'].shape[:2]
+            new_width = int(width * result['resize_scale'][0])
+            new_height = int(height * result['resize_scale'][1])
+
+            result['pattern_image'] = cv2.resize(result['pattern_image'], (new_width, new_height))
+            result['final_image'] = cv2.resize(result['final_image'], (new_width, new_height))
+            result['mask'] = cv2.resize(result['mask'], (new_width, new_height))
+            result['gray'] = cv2.resize(result['gray'], (new_width, new_height))
+
+            print(1)
         if overall_print['print_path_list']:
             painting_dict = {'dim_image_h': result['pattern_image'].shape[0], 'dim_image_w': result['pattern_image'].shape[1]}
             result['print_image'] = result['pattern_image']
@@ -37,9 +53,12 @@ class PrintPainting:
             print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
             mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
             for i in range(len(single_print['print_path_list'])):
+                if not (result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0):
+                    single_print['location'][i] = (int(single_print['location'][i][0] * result['resize_scale'][0]), int(single_print['location'][i][1] * result['resize_scale'][1]))
+
                 image, image_mode = self.read_image(single_print['print_path_list'][i])
                 if image_mode == "RGBA":
-                    new_size = (int(image.width * single_print['print_scale_list'][i]), int(image.height * single_print['print_scale_list'][i]))
+                    new_size = (int(result['pattern_image'].shape[1] * single_print['print_scale_list'][i][0]), int(result['pattern_image'].shape[0] * single_print['print_scale_list'][i][1]))
 
                     mask = image.split()[3]
                     resized_source = image.resize(new_size)
@@ -62,9 +81,12 @@ class PrintPainting:
                     mask = np.expand_dims(mask, axis=2)
                     mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
                     mask = cv2.bitwise_not(mask)
+
+                    mask = cv2.resize(mask, (int(result['final_image'].shape[0] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[1] * single_print['print_scale_list'][i][1])))
+                    image = cv2.resize(image, (int(result['final_image'].shape[0] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[1] * single_print['print_scale_list'][i][1])))
                     # 旋转后的坐标需要重新算
-                    rotate_mask, _ = self.img_rotate(mask, single_print['print_angle_list'][i], single_print['print_scale_list'][i])
+                    rotate_mask, _ = self.img_rotate(mask, single_print['print_angle_list'][i])
-                    rotate_image, rotated_new_size = self.img_rotate(image, single_print['print_angle_list'][i], single_print['print_scale_list'][i])
+                    rotate_image, rotated_new_size = self.img_rotate(image, single_print['print_angle_list'][i])
                     # x, y = int(result['print']['location'][i][0] - rotated_new_size[0] - (rotate_mask.shape[0] - image.shape[0]) / 2), int(result['print']['location'][i][1] - rotated_new_size[1] - (rotate_mask.shape[1] - image.shape[1]) / 2)
                     x, y = int(single_print['location'][i][0] - rotated_new_size[0]), int(single_print['location'][i][1] - rotated_new_size[1])
 
@@ -141,9 +163,11 @@ class PrintPainting:
             print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
             mask_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
             for i in range(len(element_print['element_path_list'])):
+                if not (result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0):
+                    element_print['location'][i] = (int(element_print['location'][i][0] * result['resize_scale'][0]), int(element_print['location'][i][1] * result['resize_scale'][1]))
                 image, image_mode = self.read_image(element_print['element_path_list'][i])
                 if image_mode == "RGBA":
-                    new_size = (int(image.width * element_print['element_scale_list'][i]), int(image.height * element_print['element_scale_list'][i]))
+                    new_size = (int(result['final_image'].shape[1] * element_print['element_scale_list'][i][0]), int(result['final_image'].shape[0] * element_print['element_scale_list'][i][1]))
 
                     mask = image.split()[3]
                     resized_source = image.resize(new_size)
@@ -165,9 +189,11 @@ class PrintPainting:
                     mask = np.expand_dims(mask, axis=2)
                     mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
                     mask = cv2.bitwise_not(mask)
+                    mask = cv2.resize(mask, (int(result['final_image'].shape[0] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[1] * single_print['print_scale_list'][i][1])))
+                    image = cv2.resize(image, (int(result['final_image'].shape[0] * single_print['print_scale_list'][i][0]), int(result['final_image'].shape[1] * single_print['print_scale_list'][i][1])))
                     # 旋转后的坐标需要重新算
-                    rotate_mask, _ = self.img_rotate(mask, element_print['element_angle_list'][i], element_print['element_scale_list'][i])
+                    rotate_mask, _ = self.img_rotate(mask, element_print['element_angle_list'][i])
-                    rotate_image, rotated_new_size = self.img_rotate(image, element_print['element_angle_list'][i], element_print['element_scale_list'][i])
+                    rotate_image, rotated_new_size = self.img_rotate(image, element_print['element_angle_list'][i])
                     # x, y = int(result['print']['location'][i][0] - rotated_new_size[0] - (rotate_mask.shape[0] - image.shape[0]) / 2), int(result['print']['location'][i][1] - rotated_new_size[1] - (rotate_mask.shape[1] - image.shape[1]) / 2)
                     x, y = int(element_print['location'][i][0] - rotated_new_size[0]), int(element_print['location'][i][1] - rotated_new_size[1])
 
@@ -409,7 +435,7 @@ class PrintPainting:
         return high, low
 
     @staticmethod
-    def img_rotate(image, angel, scale):
+    def img_rotate(image, angel):
         """顺时针旋转图像任意角度
 
         Args:
@@ -424,7 +450,7 @@ class PrintPainting:
         center = (w // 2, h // 2)
         # if type(angel) is not int:
         #     angel = 0
-        M = cv2.getRotationMatrix2D(center, -angel, scale)
+        M = cv2.getRotationMatrix2D(center, -angel, 1)
         # 调整旋转后的图像长宽
         rotated_h = int((w * np.abs(M[0, 1]) + (h * np.abs(M[0, 0]))))
         rotated_w = int((h * np.abs(M[0, 1]) + (w * np.abs(M[0, 0]))))
@@ -433,7 +459,7 @@ class PrintPainting:
         # 旋转图像
         rotated_img = cv2.warpAffine(image, M, (rotated_w, rotated_h))
 
-        return rotated_img, ((rotated_img.shape[1] - image.shape[1] * scale) // 2, (rotated_img.shape[0] - image.shape[0] * scale) // 2)
+        return rotated_img, ((rotated_img.shape[1] - image.shape[1]) // 2, (rotated_img.shape[0] - image.shape[0]) // 2)
         # return rotated_img, (0, 0)
 
     @staticmethod

app/service/design_fast/pipeline/split.py

@@ -21,11 +21,21 @@ class Split(object):
     def __call__(self, result):
         try:
 
-            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms','accessories'):
+            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms', 'accessories'):
-                front_mask = result['front_mask']
+
-                back_mask = result['back_mask']
+                if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
+                    front_mask = result['front_mask']
+                    back_mask = result['back_mask']
+                else:
+                    height, width = result['front_mask'].shape[:2]
+                    new_width = int(width * result['resize_scale'][0])
+                    new_height = int(height * result['resize_scale'][1])
+
+                    front_mask = cv2.resize(result['front_mask'], (new_width, new_height))
+                    back_mask = cv2.resize(result['back_mask'], (new_width, new_height))
+
                 rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
-                new_size = (int(rgba_image.shape[1] * result["scale"] * result["resize_scale"][0]), int(rgba_image.shape[0] * result["scale"] * result["resize_scale"][1]))
+                new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"]))
                 rgba_image = cv2.resize(rgba_image, new_size)
                 result_front_image = np.zeros_like(rgba_image)
                 front_mask = cv2.resize(front_mask, new_size)

app/service/design_fast/utils/design_ensemble.py

@@ -25,6 +25,7 @@ from app.core.config import *
 
 def keypoint_preprocess(img_path):
     img = mmcv.imread(img_path)
+    img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
     img_scale = (256, 256)
     h, w = img.shape[:2]
     img = cv2.resize(img, img_scale)
@@ -62,7 +63,11 @@ def keypoint_postprocess(output, scale_factor):
     scale_matrix = np.diag(scale_factor)
     nan = np.isinf(scale_matrix)
     scale_matrix[nan] = 0
-    return np.ceil(np.dot(segment_result, scale_matrix) * 4)
+    # 应用缩放因子
+    scaled_result = np.ceil(np.dot(segment_result, scale_matrix) * 4)
+    # 补偿边框偏移
+    compensated_result = scaled_result - 25
+    return compensated_result
 
 
 """

app/service/generate_image/service_generate_product_image.py

@@ -1,4 +1,196 @@
-#!/usr/bin/env python
+# #!/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
+#
+# import cv2
+# 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.generate_image import GenerateProductImageModel
+# from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
+# from app.service.utils.oss_client import oss_get_image
+#
+# 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=GPI_MODEL_URL)
+#         self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
+#         self.category = "product_image"
+#         self.image_strength = request_data.image_strength
+#         self.batch_size = 1
+#         self.product_type = request_data.product_type
+#         self.prompt = request_data.prompt
+#         self.image, self.image_size, self.left, self.top = 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.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 callback(self, result, error):
+#         if error:
+#             self.gen_product_data['status'] = "FAILURE"
+#             self.gen_product_data['message'] = str(error)
+#             self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
+#         else:
+#             # pil图像转成numpy数组
+#             image = result.as_numpy("generated_inpaint_image")
+#             image_result = Image.fromarray(np.squeeze(image.astype(np.uint8))).resize(self.image_size)
+#             cropped_image = post_processing_image(image_result, self.left, self.top)
+#             image_url = upload_SDXL_image(cropped_image, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
+#             self.gen_product_data['status'] = "SUCCESS"
+#             self.gen_product_data['message'] = "success"
+#             self.gen_product_data['image_url'] = str(image_url)
+#             self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_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:
+#             prompts = [self.prompt] * self.batch_size
+#             self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+#             self.image = cv2.resize(self.image, (1024, 1024))
+#             images = [self.image.astype(np.uint8)] * self.batch_size
+#
+#             if self.product_type == "single":
+#                 text_obj = np.array(prompts, dtype="object").reshape(-1, 1)
+#                 image_obj = np.array(images, dtype=np.uint8).reshape((-1, 1024, 1024, 3))
+#                 image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape(-1, 1)
+#             else:
+#                 text_obj = np.array(prompts, dtype="object").reshape((1))
+#                 image_obj = np.array(images, dtype=np.uint8).reshape((1024, 1024, 3))
+#                 image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((1))
+#
+#             # 假设 prompts、images 和 self.image_strength 已经定义
+#
+#             input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
+#             input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
+#             input_image_strength = grpcclient.InferInput("image_strength", image_strength_obj.shape, np_to_triton_dtype(image_strength_obj.dtype))
+#
+#             input_text.set_data_from_numpy(text_obj)
+#             input_image.set_data_from_numpy(image_obj)
+#             input_image_strength.set_data_from_numpy(image_strength_obj)
+#
+#             inputs = [input_text, input_image, input_image_strength]
+#
+#             if self.product_type == "single":
+#                 ctx = self.grpc_client.async_infer(model_name="stable_diffusion_xl_cnet_inpaint", inputs=inputs, callback=self.callback)
+#             else:
+#                 ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
+#
+#             time_out = 600
+#             while time_out > 0:
+#                 gen_product_data, _ = self.read_tasks_status()
+#                 if gen_product_data['status'] in ["REVOKED", "FAILURE"]:
+#                     ctx.cancel()
+#                     break
+#                 elif gen_product_data['status'] == "SUCCESS":
+#                     break
+#                 time_out -= 1
+#                 time.sleep(0.1)
+#             gen_product_data, _ = self.read_tasks_status()
+#             return gen_product_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=GPI_RABBITMQ_QUEUES, body=str_gen_product_data)
+#             logger.info(f" [x] Sent to： {GPI_RABBITMQ_QUEUES} data：@@@@  {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'}
+#     gen_product_data = json.dumps(data)
+#     redis_client.set(tasks_id, gen_product_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")
+#     # resize 原图至1024*1024
+#     image = image.resize((int(1024 / image.height * image.width), 1024))
+#
+#     # 原始图片的尺寸
+#     width, height = image.size
+#
+#     new_height, new_width = 1024, 1024
+#     # 创建一个新的画布，大小为添加 padding 后的尺寸，并设置为白色背景
+#     pad_image = Image.new('RGBA', (new_width, new_height), (0, 0, 0, 0))
+#
+#     # 将原始图片粘贴到新的画布中心
+#     left = (new_width - width) // 2
+#     top = (new_height - height) // 2
+#     pad_image.paste(image, (left, top))
+#
+#     # 将画布 resize 成宽度 1024，长度 1024
+#     resized_image = pad_image.resize((1024, 1024))
+#     image_size = (1024, 1024)
+#
+#     if resized_image.mode in ('RGBA', 'LA') or (resized_image.mode == 'P' and 'transparency' in resized_image.info):
+#         # 创建白色背景
+#         background = Image.new("RGB", image_size, (255, 255, 255))
+#         # 将图片粘贴到白色背景上
+#         background.paste(resized_image, mask=resized_image.split()[3])
+#         image = np.array(background)
+#         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+#     return image, image_size, left, top
+#
+#
+# def post_processing_image(image, left, top):
+#     resized_image = image.resize((int(image.width * (768 / image.height)), 768))
+#     # 计算裁剪的坐标
+#     left = (resized_image.width - 512) // 2
+#     upper = 0
+#     right = left + 512
+#     lower = 768
+#
+#     # 进行裁剪
+#     cropped_image = resized_image.crop((left, upper, right, lower))
+#     return cropped_image
+#
+#
+# if __name__ == '__main__':
+#     rd = GenerateProductImageModel(
+#         tasks_id="123-89",
+#         # prompt="",
+#         image_strength=0.7,
+#         prompt="The best quality, masterpiece,outwear, 8K realistic, HUD",
+#         image_url="aida-results/result_53381ada-ac64-11ef-ae9d-0242ac150002.png",
+#         product_type="overall"
+#     )
+#     server = GenerateProductImage(rd)
+#     print(server.get_result())
+
+# 旧版product
+# !/usr/bin/env python
 # -*- coding: UTF-8 -*-
 """
 @Project ：trinity_client
@@ -41,7 +233,7 @@ class GenerateProductImage:
         self.batch_size = 1
         self.product_type = request_data.product_type
         self.prompt = request_data.prompt
-        self.image, self.image_size, self.left, self.top = pre_processing_image(request_data.image_url)
+        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.gen_product_data = {'tasks_id': self.tasks_id, 'status': 'PENDING', 'message': "pending", 'image_url': ''}
@@ -55,10 +247,13 @@ class GenerateProductImage:
             self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
         else:
             # pil图像转成numpy数组
-            image = result.as_numpy("generated_inpaint_image")
+            if self.product_type == "single":
-            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8))).resize(self.image_size)
+                image = result.as_numpy("generated_cnet_image")
-            cropped_image = post_processing_image(image_result, self.left, self.top)
+            else:
-            image_url = upload_SDXL_image(cropped_image, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
+                image = result.as_numpy("generated_inpaint_image")
+            image_result = Image.fromarray(np.squeeze(image.astype(np.uint8)))
+            # cropped_image = post_processing_image(image_result, self.left, self.top)
+            image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
             self.gen_product_data['status'] = "SUCCESS"
             self.gen_product_data['message'] = "success"
             self.gen_product_data['image_url'] = str(image_url)
@@ -71,17 +266,18 @@ class GenerateProductImage:
     def get_result(self):
         try:
             prompts = [self.prompt] * self.batch_size
-            self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+
-            self.image = cv2.resize(self.image, (1024, 1024))
+            self.image = cv2.cvtColor(self.image, cv2.COLOR_RGBA2RGB)
+            # self.image = cv2.resize(self.image, (1024, 1024))
             images = [self.image.astype(np.uint8)] * self.batch_size
 
             if self.product_type == "single":
-                text_obj = np.array(prompts, dtype="object").reshape(-1, 1)
+                text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
-                image_obj = np.array(images, dtype=np.uint8).reshape((-1, 1024, 1024, 3))
+                image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-                image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape(-1, 1)
+                image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((-1, 1))
             else:
                 text_obj = np.array(prompts, dtype="object").reshape((1))
-                image_obj = np.array(images, dtype=np.uint8).reshape((1024, 1024, 3))
+                image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
                 image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((1))
 
             # 假设 prompts、images 和 self.image_strength 已经定义
@@ -97,7 +293,7 @@ class GenerateProductImage:
             inputs = [input_text, input_image, input_image_strength]
 
             if self.product_type == "single":
-                ctx = self.grpc_client.async_infer(model_name="stable_diffusion_xl_cnet_inpaint", inputs=inputs, callback=self.callback)
+                ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_SINGLE, inputs=inputs, callback=self.callback)
             else:
                 ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
 
@@ -135,33 +331,41 @@ def infer_cancel(tasks_id):
 
 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")
-    # resize 原图至1024*1024
+    # 目标图片的尺寸
-    image = image.resize((int(1024 / image.height * image.width), 1024))
+    target_width = 512
+    target_height = 768
 
     # 原始图片的尺寸
-    width, height = image.size
+    original_width, original_height = image.size
 
-    new_height, new_width = 1024, 1024
+    # 计算宽度和高度的缩放比例
-    # 创建一个新的画布，大小为添加 padding 后的尺寸，并设置为白色背景
+    width_ratio = target_width / original_width
-    pad_image = Image.new('RGBA', (new_width, new_height), (0, 0, 0, 0))
+    height_ratio = target_height / original_height
 
-    # 将原始图片粘贴到新的画布中心
+    # 选择较小的缩放比例，确保图片能完整放入目标图片中
-    left = (new_width - width) // 2
+    scale_ratio = min(width_ratio, height_ratio)
-    top = (new_height - height) // 2
-    pad_image.paste(image, (left, top))
 
-    # 将画布 resize 成宽度 1024，长度 1024
+    # 计算调整后的尺寸
-    resized_image = pad_image.resize((1024, 1024))
+    new_width = int(original_width * scale_ratio)
-    image_size = (1024, 1024)
+    new_height = int(original_height * scale_ratio)
 
-    if resized_image.mode in ('RGBA', 'LA') or (resized_image.mode == 'P' and 'transparency' in resized_image.info):
+    # 调整图片大小
-        # 创建白色背景
+    resized_image = image.resize((new_width, new_height))
-        background = Image.new("RGB", image_size, (255, 255, 255))
+
-        # 将图片粘贴到白色背景上
+    # 创建一个 512x768 的透明图片
-        background.paste(resized_image, mask=resized_image.split()[3])
+    result_image = Image.new("RGBA", (target_width, target_height), (255, 255, 255, 0))
-        image = np.array(background)
+
-        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    # 计算需要粘贴的位置，使图片居中
-    return image, image_size, left, top
+    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
+    result_image.paste(resized_image, (x_offset, y_offset), mask=resized_image.split()[3])
+
+    image = np.array(result_image)
+
+    # image = cv2.cvtColor(image, cv2.COLOR_BGRA2RGBA)
+    return image
 
 
 def post_processing_image(image, left, top):
@@ -182,9 +386,9 @@ if __name__ == '__main__':
         tasks_id="123-89",
         # prompt="",
         image_strength=0.7,
-        prompt="The best quality, masterpiece,outwear, 8K realistic, HUD",
+        prompt=" The best quality, masterpiece, real image.Outwear,high quality clothing details,8K realistic,HDR",
-        image_url="aida-results/result_53381ada-ac64-11ef-ae9d-0242ac150002.png",
+        image_url="aida-results/result_40b1a2fe-e220-11ef-9bfa-0242ac150003.png",
-        product_type="overall"
+        product_type="single"
     )
     server = GenerateProductImage(rd)
     print(server.get_result())

app/service/prompt_generation/chatgpt_for_translation.py

@@ -95,10 +95,10 @@ def get_translation_from_llama3(text):
     # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
 
     # 先获取用户输入文本的语言
-    language = get_language(text)
+    # language = get_language(text)
 
-    if 'English' in language:
+    # if 'English' in language:
-        return text
+    #     return text
 
     # 创建请求的负载 translator是自定义的翻译模型
     payload = {
@@ -106,7 +106,6 @@ def get_translation_from_llama3(text):
         "prompt": f"[{text}]",
         "stream": False
     }
-
     # 将负载转换为 JSON 格式
     headers = {'Content-Type': 'application/json'}
     response = requests.post(url, data=json.dumps(payload), headers=headers)

app/service/utils/new_oss_client.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
     # url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
     # url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png"
     # url = "aida-users/89/single_logo/123-89.png"
-    url = "aida-users/89/123-89.png"
+    url = "aida-results/result_4185cc1c-e476-11ef-b8e1-0826ae3ad6b3.png"
 
     # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
     read_type = "2"