design design batch

app/core/config.py

@@ -20,7 +20,7 @@ class Settings(BaseSettings):
 
 
 OSS = "minio"
-DEBUG = False
+DEBUG = True
 if DEBUG:
     LOGS_PATH = "logs/"
     CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv"

app/service/design_batch/design_batch_celery.py

@@ -12,7 +12,7 @@ from app.service.design_batch.utils.save_json import oss_upload_json
 from app.service.design_batch.utils.synthesis_item import update_base_size_priority, synthesis, synthesis_single
 
 id_lock = threading.Lock()
-celery_app = Celery('tasks', broker='amqp://guest:guest@10.1.2.213:5672//', backend='rpc://')
+celery_app = Celery('tasks', broker='amqp://guest:guest@10.1.2.190:5672//', backend='rpc://')
 celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s'
 celery_app.conf.worker_hijack_root_logger = False
 logging.getLogger('pika').setLevel(logging.WARNING)
@@ -46,7 +46,7 @@ def process_layer(item, layers):
         layers.append(back_layer)
 
 
-@celery_app.task
+# @celery_app.task
 def batch_design(objects_data, tasks_id, json_name):
     object_response = []
     threads = []
@@ -108,6 +108,7 @@ def batch_design(objects_data, tasks_id, json_name):
 
         with lock:
             object_response.append(items_response)
+            logger.info(items_response)
             publish_status(tasks_id, step + 1, items_response)
             active_threads -= 1
 

app/service/design_batch/item.py

@@ -1,4 +1,4 @@
-from app.service.design_batch.pipeline import *
+from app.service.design_fast.pipeline import LoadImage, KeyPoint, Segmentation, Color, PrintPainting, Scaling, Split, LoadBodyImage, ContourDetection
 
 
 class BaseItem:
@@ -9,6 +9,27 @@ class BaseItem:
         self.result.update(basic)
 
 
+class AccessoriesItem(BaseItem):
+    def __init__(self, data, basic, minio_client):
+        super().__init__(data, basic)
+        self.Accessories_pipeline = [
+            LoadImage(minio_client),
+            # KeyPoint(),
+            ContourDetection(),
+            # Segmentation(minio_client),
+            # BackPerspective(minio_client),
+            Color(minio_client),
+            PrintPainting(minio_client),
+            Scaling(),
+            Split(minio_client)
+        ]
+
+    def process(self):
+        for item in self.Accessories_pipeline:
+            self.result = item(self.result)
+        return self.result
+
+
 class TopItem(BaseItem):
     def __init__(self, data, basic, minio_client):
         super().__init__(data, basic)
@@ -16,6 +37,7 @@ class TopItem(BaseItem):
             LoadImage(minio_client),
             KeyPoint(),
             Segmentation(minio_client),
+            # BackPerspective(minio_client),
             Color(minio_client),
             PrintPainting(minio_client),
             Scaling(),
@@ -36,6 +58,7 @@ class BottomItem(BaseItem):
             KeyPoint(),
             ContourDetection(),
             # Segmentation(),
+            # BackPerspective(minio_client),
             Color(minio_client),
             PrintPainting(minio_client),
             Scaling(),

app/service/design_batch/pipeline/__init__.py

@@ -1,3 +1,4 @@
+from .back_perspective import BackPerspective
 from .color import Color
 from .contour_detection import ContourDetection
 from .keypoint import KeyPoint
@@ -13,6 +14,7 @@ __all__ = [
     'KeyPoint',
     'ContourDetection',
     'Segmentation',
+    'BackPerspective',
     'Color',
     'PrintPainting',
     'Scaling',

app/service/design_batch/pipeline/back_perspective.py

@@ -0,0 +1,79 @@
+import cv2
+import numpy as np
+
+from app.service.design_fast.utils.design_ensemble import get_seg_result
+from app.service.utils.new_oss_client import oss_upload_image
+
+
+class BackPerspective:
+    def __init__(self, minio_client):
+        self.minio_client = minio_client
+
+    def __call__(self, result):
+
+        # 如果sketch为系统图 查看是否有对应的 背后视角图
+        if result['path'].split('/')[0] == 'aida-sys-image':
+            file_path = result['path'].replace("images", 'images_back', 1)
+            if self.is_file_exists(bucket_name='aida-sys-image', file_name=file_path[file_path.find('/') + 1:]):
+                result['back_perspective_url'] = file_path
+                return result
+            else:
+                seg_result = get_seg_result("1", result['image'])[0]
+        elif result['name'] in ['blouse', 'outwear', 'dress', 'tops']:
+            seg_result = result['seg_result']
+        else:
+            seg_result = get_seg_result("1", result['image'])[0]
+
+        m = self.thicken_contours_and_display(seg_result, thickness=10, color=(0, 0, 0))
+        back_sketch = result['image'].copy()
+        back_sketch[m > 100] = 255
+        # 上传背后视角图
+        _, img_encoded = cv2.imencode(".jpg", back_sketch)
+
+        resp = oss_upload_image(self.minio_client, bucket='test', object_name=result['path'], image_bytes=img_encoded.tobytes())
+        result['back_perspective_url'] = f"{resp.bucket_name}/{resp.object_name}"
+        return result
+
+    def thicken_contours_and_display(self, mask, thickness=10, color=(0, 0, 0)):
+        mask = mask.astype(np.uint8) * 255
+        # 查找轮廓
+        contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        # 创建一个彩色副本用于绘制轮廓
+        mask_color = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
+
+        def thicken_contour_inward(contour, thick):
+            # 创建一个空白的黑色图像与原始掩码大小相同
+            blank = np.zeros_like(mask)
+            # 在空白图像上绘制白色的轮廓
+            cv2.drawContours(blank, [contour], -1, 255, thickness=thick)
+            # 找到轮廓的中心（可以用重心等方法近似）
+            M = cv2.moments(contour)
+            cx = int(M['m10'] / M['m00'])
+            cy = int(M['m01'] / M['m00'])
+            # 进行距离变换，离中心越近的值越小
+            dist_transform = cv2.distanceTransform(255 - blank, cv2.DIST_L2, 5)
+            # 根据距离变换的值来决定是否保留像素，离中心近的像素更容易被保留
+            result = np.zeros_like(mask)
+            for i in range(dist_transform.shape[0]):
+                for j in range(dist_transform.shape[1]):
+                    if dist_transform[i, j] < thick:
+                        result[i, j] = 255
+            return result
+
+        for contour in contours:
+            thickened_contour = thicken_contour_inward(contour, thickness)
+            mask_color[thickened_contour > 0] = color
+
+        _, binary_result = cv2.threshold(mask_color, 127, 255, cv2.THRESH_BINARY)
+
+        # 转换为掩码形式
+        mask_result = cv2.cvtColor(binary_result, cv2.COLOR_BGR2GRAY)
+        return mask_result
+
+    def is_file_exists(self, bucket_name, file_name):
+        try:
+            self.minio_client.stat_object(bucket_name, file_name)
+            return True
+        except Exception:
+            return False

app/service/design_batch/pipeline/color.py

@@ -14,11 +14,18 @@ class Color:
 
     def __call__(self, result):
         dim_image_h, dim_image_w = result['image'].shape[0:2]
+        # 渐变色
         if "gradient" in result.keys() and result['gradient'] != "":
             bucket_name = result['gradient'].split('/')[0]
             object_name = result['gradient'][result['gradient'].find('/') + 1:]
             pattern = self.get_gradient(bucket_name=bucket_name, object_name=object_name)
             resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA)
+        # 无色
+        elif "color" not in result.keys() or result['color'] == "":
+            result['final_image'] = result['pattern_image'] = result['single_image'] = result['image']
+            result['alpha'] = 100 / 255.0
+            return result
+        # 正常颜色
         else:
             pattern = self.get_pattern(result['color'])
             resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA)

app/service/design_batch/pipeline/keypoint.py

@@ -4,7 +4,8 @@ import numpy as np
 from pymilvus import MilvusClient
 
 from app.core.config import *
-from app.service.design_batch.utils.design_ensemble import get_keypoint_result
+from app.service.design_fast.utils.design_ensemble import get_keypoint_result
+from app.service.utils.decorator import ClassCallRunTime, RunTime
 
 logger = logging.getLogger(__name__)
 
@@ -16,14 +17,15 @@ class KeyPoint:
     def get_name(cls):
         return cls.name
 
+    @ClassCallRunTime
     def __call__(self, result):
         if result['name'] in ['blouse', 'skirt', 'dress', 'outwear', 'trousers', 'tops', 'bottoms']:  # 查询是否有数据 且类别相同 相同则直接读 不同则推理后更新
             # result['clothes_keypoint'] = self.infer_keypoint_result(result)
             site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down'
             # keypoint_cache = search_keypoint_cache(result["image_id"], site)
-            keypoint_cache = self.keypoint_cache(result, site)
+            # keypoint_cache = self.keypoint_cache(result, site)
+            keypoint_cache = False
             # 取消向量查询 直接过模型推理
-            # keypoint_cache = False
             if keypoint_cache is False:
                 keypoint_infer_result, site = self.infer_keypoint_result(result)
                 result['clothes_keypoint'] = self.save_keypoint_cache(result["image_id"], keypoint_infer_result, site)
@@ -87,7 +89,7 @@ class KeyPoint:
             logger.info(f"save keypoint cache milvus error : {e}")
             return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
 
-    # @ RunTime
+    @RunTime
     def keypoint_cache(self, result, site):
         try:
             client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS)

app/service/design_batch/pipeline/loading.py

@@ -1,6 +1,9 @@
+import io
 import logging
 
 import cv2
+import numpy as np
+from PIL import Image
 
 from app.service.utils.new_oss_client import oss_get_image
 
@@ -71,6 +74,8 @@ class LoadImage:
             keypoint = 'head_point'
         elif name == 'earring':
             keypoint = 'ear_point'
+        elif name == 'accessories':
+            keypoint = "accessories"
         else:
             raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, "
                            f"bag, shoes, hairstyle, earring.")

app/service/design_batch/pipeline/scale.py

@@ -46,4 +46,16 @@ class Scaling:
             result['scale'] = result['scale_bag']
         elif result['keypoint'] == 'ear_point':
             result['scale'] = result['scale_earrings']
+        elif result['keypoint'] == 'accessories':
+            # 由于没有识别配饰keypoint的模型  所以统一将配饰的两个关键点设定为 (0,0) (0,img.width)
+            # 模特的关键点设定为(0,0) (0,320/2) 距离比例简写为 160 / img.width
+            distance_clo = result['img_shape'][1]
+            distance_bdy = 320 / 2
+
+            if distance_clo == 0:
+                result['scale'] = 1
+            else:
+                result['scale'] = distance_bdy / distance_clo
+        else:
+            result['scale'] = 1
         return result

app/service/design_batch/pipeline/segmentation.py

@@ -5,7 +5,8 @@ import cv2
 import numpy as np
 
 from app.core.config import SEG_CACHE_PATH
-from app.service.design_batch.utils.design_ensemble import get_seg_result
+from app.service.design_fast.utils.design_ensemble import get_seg_result
+from app.service.utils.decorator import ClassCallRunTime
 from app.service.utils.new_oss_client import oss_get_image
 
 logger = logging.getLogger()
@@ -15,6 +16,7 @@ class Segmentation:
     def __init__(self, minio_client):
         self.minio_client = minio_client
 
+    @ClassCallRunTime
     def __call__(self, result):
         if "seg_mask_url" in result.keys() and result['seg_mask_url'] != "":
             seg_mask = oss_get_image(oss_client=self.minio_client, bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2")
@@ -31,13 +33,26 @@ class Segmentation:
             result['back_mask'] = np.array(green_mask, dtype=np.uint8) * 255
             result['mask'] = result['front_mask'] + result['back_mask']
         else:
-            # 本地查询seg 缓存是否存在
+            # preview 过模型 不缓存
-            _, seg_result = self.load_seg_result(result["image_id"])
+            if "preview_submit" in result.keys() and result['preview_submit'] == "preview":
-            result['seg_result'] = seg_result
+                # 推理获得seg 结果
-            if not _:
+                seg_result = get_seg_result(result["image_id"], result['image'])[0]
+            # submit 过模型 缓存
+            elif "preview_submit" in result.keys() and result['preview_submit'] == "submit":
                 # 推理获得seg 结果
                 seg_result = get_seg_result(result["image_id"], result['image'])[0]
                 self.save_seg_result(seg_result, result['image_id'])
+            #  null  正常流程 加载本地缓存 无缓存则过模型
+            else:
+                # 本地查询seg 缓存是否存在
+                _, seg_result = self.load_seg_result(result["image_id"])
+                # 判断缓存和实际图片size是否相同
+                if not _ or result["image"].shape[:2] != seg_result.shape:
+                    # 推理获得seg 结果
+                    seg_result = get_seg_result(result["image_id"], result['image'])[0]
+                    self.save_seg_result(seg_result, result['image_id'])
+            result['seg_result'] = seg_result
+
             # 处理前片后片
             temp_front = seg_result == 1.0
             result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8))
@@ -48,7 +63,7 @@ class Segmentation:
 
     @staticmethod
     def save_seg_result(seg_result, image_id):
-        file_path = f"seg_cache/{image_id}.npy"
+        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
         try:
             np.save(file_path, seg_result)
             logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
@@ -57,7 +72,7 @@ class Segmentation:
 
     @staticmethod
     def load_seg_result(image_id):
-        file_path = f"seg_cache/{image_id}.npy"
+        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
         logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
         try:
             seg_result = np.load(file_path)

app/service/design_batch/pipeline/split.py

@@ -7,10 +7,11 @@ from PIL import Image
 from cv2 import cvtColor, COLOR_BGR2RGBA
 
 from app.core.config import AIDA_CLOTHING
-from app.service.design_batch.utils.conversion_image import rgb_to_rgba
+from app.service.design_fast.utils.conversion_image import rgb_to_rgba
-from app.service.design_batch.utils.upload_image import upload_png_mask
+from app.service.design_fast.utils.transparent import sketch_to_transparent
+from app.service.design_fast.utils.upload_image import upload_png_mask
 from app.service.utils.generate_uuid import generate_uuid
-from app.service.utils.new_oss_client import oss_upload_image
+from app.service.utils.new_oss_client import oss_upload_image, oss_get_image
 
 
 class Split(object):
@@ -20,7 +21,7 @@ class Split(object):
     def __call__(self, result):
         try:
 
-            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'):
+            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms','accessories'):
                 front_mask = result['front_mask']
                 back_mask = result['back_mask']
                 rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
@@ -30,6 +31,24 @@ class Split(object):
                 front_mask = cv2.resize(front_mask, new_size)
                 result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
                 result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
+                if 'transparent' in result.keys():
+                    #  用户自选区域transparent
+                    transparent = result['transparent']
+                    if transparent['mask_url'] is not None and transparent['mask_url'] != "":
+                        # 预处理用户自选区mask
+                        seg_mask = oss_get_image(oss_client=self.minio_client, bucket=transparent['mask_url'].split('/')[0], object_name=transparent['mask_url'][transparent['mask_url'].find('/') + 1:], data_type="cv2")
+                        seg_mask = cv2.resize(seg_mask, new_size, interpolation=cv2.INTER_NEAREST)
+                        # 转换颜色空间为 RGB（OpenCV 默认是 BGR）
+                        image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB)
+
+                        r, g, b = cv2.split(image_rgb)
+                        blue_mask = b > r
+
+                        # 创建红色和绿色掩码
+                        transparent_mask = np.array(blue_mask, dtype=np.uint8) * 255
+                        result_front_image_pil = sketch_to_transparent(result_front_image_pil, transparent_mask, transparent["scale"])
+                    else:
+                        result_front_image_pil = sketch_to_transparent(result_front_image_pil, front_mask, transparent["scale"])
                 result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None)
 
                 height, width = front_mask.shape

app/service/design_batch/service.py

@@ -5,7 +5,7 @@ from app.service.design_batch.utils.MQ import publish_status
 
 
 async def start_design_batch_generate(data, file):
-    generate_clothes_task = batch_design.delay(json.loads(file.decode())['objects'], data.total, data.tasks_id)
+    generate_clothes_task = batch_design(json.loads(file.decode())['objects'], data.total, data.file_name)
     print(generate_clothes_task)
     publish_status(data.tasks_id, "0/100", "")
     return {"task_id": data.tasks_id}

app/service/design_batch/utils/MQ.py

@@ -2,9 +2,12 @@ import json
 
 import pika
 
+from app.core.config import RABBITMQ_PARAMS
+
 
 def publish_status(task_id, progress, result):
-    connection = pika.BlockingConnection(pika.ConnectionParameters('10.1.2.213'))
+    connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS))
+    # connection = pika.BlockingConnection(pika.ConnectionParameters('10.1.2.190'))
     channel = connection.channel()
     channel.queue_declare(queue='DesignBatch', durable=True)
     message = {'task_id': task_id, 'progress': progress, "result": result}
@@ -15,3 +18,7 @@ def publish_status(task_id, progress, result):
                               delivery_mode=2,
                           ))
     connection.close()
+
+
+if __name__ == '__main__':
+    publish_status("1", "1", "1")

app/service/design_batch/utils/design_ensemble.py

@@ -85,7 +85,7 @@ def seg_preprocess(img_path):
     if ori_shape != (img_scale_w, img_scale_h):
         # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
         img = cv2.resize(img, (img_scale_h, img_scale_w))
-    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    # img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
     preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
     return preprocessed_img, ori_shape
 

app/service/design_batch/utils/organize.py

@@ -33,8 +33,8 @@ def organize_clothing(layer):
                        mask=cv2.resize(layer['mask'], layer["front_image"].size),
                        gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
                        pattern_image_url=layer['pattern_image_url'],
-                       pattern_image=layer['pattern_image']
+                       pattern_image=layer['pattern_image'],
-
+                       # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
                        )
     # 后片数据
     back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
@@ -50,6 +50,46 @@ def organize_clothing(layer):
                       mask=cv2.resize(layer['mask'], layer["front_image"].size),
                       gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
                       pattern_image_url=layer['pattern_image_url'],
+                      # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
+                      )
+    return front_layer, back_layer
+
+
+def organize_accessories(layer):
+    # 起始坐标
+    start_point = (0, 0)
+    # 前片数据
+    front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
+                       name=f'{layer["name"].lower()}_front',
+                       image=layer["front_image"],
+                       # mask_image=layer['front_mask_image'],
+                       image_url=layer['front_image_url'],
+                       mask_url=layer['mask_url'],
+                       sacle=layer['scale'],
+                       clothes_keypoint=(0, 0),
+                       position=start_point,
+                       resize_scale=layer["resize_scale"],
+                       mask=cv2.resize(layer['mask'], layer["front_image"].size),
+                       gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
+                       pattern_image_url=layer['pattern_image_url'],
+                       pattern_image=layer['pattern_image'],
+                       # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
+                       )
+    # 后片数据
+    back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
+                      name=f'{layer["name"].lower()}_back',
+                      image=layer["back_image"],
+                      # mask_image=layer['back_mask_image'],
+                      image_url=layer['back_image_url'],
+                      mask_url=layer['mask_url'],
+                      sacle=layer['scale'],
+                      clothes_keypoint=(0, 0),
+                      position=start_point,
+                      resize_scale=layer["resize_scale"],
+                      mask=cv2.resize(layer['mask'], layer["front_image"].size),
+                      gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
+                      pattern_image_url=layer['pattern_image_url'],
+                      # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
                       )
     return front_layer, back_layer
 

app/service/design_batch/utils/synthesis_item.py

@@ -79,9 +79,11 @@ def synthesis(data, size, basic_info):
         _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
         top_outer_mask = np.array(binary_body_mask)
         bottom_outer_mask = np.array(binary_body_mask)
+        accessories_outer_mask = np.array(binary_body_mask)
 
         top = True
         bottom = True
+        accessories = True
         i = len(data)
         while i:
             i -= 1
@@ -98,7 +100,7 @@ def synthesis(data, size, basic_info):
                 background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
                 top_outer_mask = background + top_outer_mask
             elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]:
-                bottom = False
+                # bottom = False
                 mask_shape = data[i]['mask'].shape
                 y_offset, x_offset = data[i]['adaptive_position']
                 # 初始化叠加区域的起始和结束位置
@@ -109,10 +111,23 @@ def synthesis(data, size, basic_info):
                 background = np.zeros_like(top_outer_mask)
                 background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
                 bottom_outer_mask = background + bottom_outer_mask
+            elif accessories and data[i]['name'] in ['accessories_front']:
+                mask_shape = data[i]['mask'].shape
+                y_offset, x_offset = data[i]['adaptive_position']
+                # 初始化叠加区域的起始和结束位置
+                all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
+                all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
+                # 将叠加区域赋值为相应的像素值
+                _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
+                background = np.zeros_like(top_outer_mask)
+                background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
+                accessories_outer_mask = background + accessories_outer_mask
+                pass
             elif bottom is False and top is False:
                 break
 
         all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask)
+        all_mask = cv2.bitwise_or(all_mask, accessories_outer_mask)
 
         for layer in data:
             if layer['image'] is not None:
@@ -185,12 +200,14 @@ def update_base_size_priority(layers, size):
     # 计算透明背景图片的宽度
     min_x = min(info['position'][1] for info in layers)
     x_list = []
+    new_height = 700
     for info in layers:
         if info['image'] is not None:
             x_list.append(info['position'][1] + info['image'].width)
+        if info['name'] == 'mannequin':
+            new_height = info['image'].height
     max_x = max(x_list)
     new_width = max_x - min_x
-    new_height = 700
     # 更新坐标
     for info in layers:
         info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x)

app/service/design_batch/utils/transparent.py

@@ -0,0 +1,26 @@
+from PIL import Image
+
+
+def sketch_to_transparent(image, mask, transparency):
+    # 打开原始图片
+    image = image.convert("RGBA")
+    # 打开mask图片，假设mask图片是灰度图，白色区域为要处理的区域，黑色区域为保留的区域
+    mask = Image.fromarray(mask)
+
+    # 根据透明度调整因子，将透明度转换为0-255之间的值
+    alpha_value = int((1 - transparency) * 255.0)
+
+    # 获取图片的像素数据
+    image_pixels = image.load()
+    mask_pixels = mask.load()
+
+    width, height = image.size
+
+    for y in range(height):
+        for x in range(width):
+            # 如果mask区域对应的像素为白色（值大于128，这里假设白色为要处理的区域，可根据实际情况调整）
+            if mask_pixels[x, y] > 128:
+                r, g, b, a = image_pixels[x, y]
+                image_pixels[x, y] = (r, g, b, alpha_value)
+
+    return image