feat: 新增design模式 merge,前端CV python 合成

app/api/api_design.py

@@ -27,6 +27,15 @@ def design(request_data: DesignModel):
     - **mask_url** 非空"mask_url" ->  区域透明
     - **transpose**  镜像模式 ,:"top_bottom"或"left_right"
     - **rotate** 45,
+
+    - ** design 参数变更:
+            design detail 请求参数中 basic -> preview_submit 替换为design_type  可选参数 default ,merge   (移除preview和submit)
+         design_type 参数说明:
+            defuault模式下 请求参数不变
+            merge模式下 items -> 每个item需要新增 merge_image_path , merge_image_path为前端处理 print  color等操作后的单件结果图
+
+    **
+
     - 创建一个具有以下参数的请求体:
     示例参数：
     ```json
@@ -61,7 +70,7 @@ def design(request_data: DesignModel):
                             ]
                         },
                         "layer_order": true,
-                        "preview_submit": "preview",
+                        "design_type": "preview",
                         "scale_bag": 0.7,
                         "scale_earrings": 0.16,
                         "self_template": true,

app/service/design_fast/design_generate.py

@@ -6,10 +6,10 @@ import requests
 from minio import Minio
 
 from app.core.config import settings
-from app.service.design_fast.item import BodyItem, TopItem, BottomItem, OthersItem
+from app.service.design_fast.item import BodyItem, TopItem, BottomItem, OthersItem, TopMergeItem, BottomMergeItem, OthersMergeItem
 from app.service.design_fast.utils.organize import organize_body, organize_clothing, organize_others
 from app.service.design_fast.utils.progress import final_progress, update_progress
-from app.service.design_fast.utils.synthesis_item import synthesis, synthesis_single, update_base_size_priority
+from app.service.design_fast.utils.synthesis_item import synthesis, synthesis_single, update_base_size_priority, merge
 from app.service.utils.decorator import RunTime
 
 id_lock = threading.Lock()
@@ -19,22 +19,46 @@ logger = logging.getLogger()
 minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
 
-def process_item(item, basic):
+def process_item(item, basic, design_type):
-    # 处理project中单个item
+    # 1. 定义映射配置
-    if item['type'] == "Body":
+    # key 为 item_type 的小写，value 为对应的处理类
-        body_server = BodyItem(data=item, basic=basic, minio_client=minio_client)
+    DESIGN_MAP = {
-        item_data = body_server.process()
+        'body': BodyItem,
-    elif item['type'].lower() in ['blouse', 'outwear', 'dress', 'tops']:
+        'blouse': TopItem, 'outwear': TopItem,
-        top_server = TopItem(data=item, basic=basic, minio_client=minio_client)
+        'dress': TopItem, 'tops': TopItem,
-        item_data = top_server.process()
+        'skirt': BottomItem, 'trousers': BottomItem,
-    elif item['type'].lower() in ['skirt', 'trousers', 'bottoms']:
+        'bottoms': BottomItem,
-        bottom_server = BottomItem(data=item, basic=basic, minio_client=minio_client)
+        'others': OthersItem
-        item_data = bottom_server.process()
+    }
-    elif item['type'].lower() in ['others']:
+
-        bottom_server = OthersItem(data=item, basic=basic, minio_client=minio_client)
+    MERGE_MAP = {
-        item_data = bottom_server.process()
+        'body_merge': BodyItem,
+        'blouse_merge': TopMergeItem, 'outwear_merge': TopMergeItem,
+        'dress_merge': TopMergeItem, 'tops_merge': TopMergeItem,
+        'skirt_merge': BottomMergeItem, 'trousers_merge': BottomMergeItem,
+        'bottoms_merge': BottomMergeItem,
+        'others_merge': OthersMergeItem
+    }
+
+    # 2. 根据 design_type 选择映射表
+    mapping = MERGE_MAP if design_type == 'merge' else DESIGN_MAP
+
+    if design_type == 'merge':
+        item_type_key = f"{item['type'].lower()}_merge"
+    elif design_type == 'default':
+        item_type_key = item['type'].lower()
     else:
-        raise NotImplementedError(f"Item type {item['type']} not implemented")
+        item_type_key = item['type'].lower()
+
+    handler_class = mapping.get(item_type_key)
+
+    if not handler_class:
+        raise NotImplementedError(f"Item type {item['type']} not implemented for design_type={design_type}")
+
+    # 4. 统一实例化并执行
+    # 注意：这里假设所有 Item 类构造函数签名一致
+    server = handler_class(data=item, basic=basic, minio_client=minio_client)
+    item_data = server.process()
     return item_data
 
 
@@ -44,7 +68,7 @@ def process_layer(item, layers):
         body_layer = organize_body(item)
         layers.append(body_layer)
         return item['body_image'].size
-    elif item['name'] == 'others':
+    elif item['name'] in ['others', 'others_merge']:
         front_layer, back_layer = organize_others(item)
         layers.append(front_layer)
         layers.append(back_layer)
@@ -70,10 +94,11 @@ def design_generate(request_data):
         nonlocal active_threads
         basic = object['basic']
         items_response = {'layers': [], 'objectSign': object['objectSign'] if 'objectSign' in object.keys() else ""}
+        design_type = basic.get('design_type', "default")
         if basic['single_overall'] == "overall":
             item_results = []
             for item in object['items']:
-                item_results.append(process_item(item, basic))
+                item_results.append(process_item(item, basic, design_type))
             layers = []
             for item in item_results:
                 process_layer(item, layers)
@@ -97,7 +122,13 @@ def design_generate(request_data):
                     'rotate': lay.get('rotate', None),
                     # 'back_perspective_url': lay['back_perspective_url'] if 'back_perspective_url' in lay.keys() else None,
                 })
-            items_response['synthesis_url'] = synthesis(layers, new_size, basic)
+            if basic.get('design_type') == 'default':
+                items_response['synthesis_url'] = synthesis(layers, new_size, basic)
+            elif basic.get('design_type') == 'merge':
+                items_response['synthesis_url'] = merge(layers, new_size, basic)
+            else:
+                items_response['synthesis_url'] = synthesis(layers, new_size, basic)
+
         else:
             item_result = process_item(object['items'][0], basic)
             items_response['layers'].append({

app/service/design_fast/item.py

@@ -7,6 +7,7 @@ class BaseItem:
         self.result['name'] = data['type'].lower()
         self.result.pop("type")
         self.result.update(basic)
+        self.result['design_type'] = basic.get('design_type', None)
 
 
 class OthersItem(BaseItem):
@@ -14,13 +15,7 @@ class OthersItem(BaseItem):
         super().__init__(data, basic)
         self.Others_pipeline = [
             LoadImage(minio_client),
-            # KeyPoint(),
-            # ContourDetection(),
             Segmentation(minio_client),
-            # BackPerspective(minio_client),
-            Color(minio_client),
-            NoSegPrintPainting(minio_client),
-            PrintPainting(minio_client),
             Scaling(),
             Split(minio_client)
         ]
@@ -74,6 +69,65 @@ class BottomItem(BaseItem):
         return self.result
 
 
+"""merge"""
+
+
+class OthersMergeItem(BaseItem):
+    def __init__(self, data, basic, minio_client):
+        super().__init__(data, basic)
+        self.Others_pipeline = [
+            LoadImage(minio_client),
+            # KeyPoint(),
+            # ContourDetection(),
+            Segmentation(minio_client),
+            # BackPerspective(minio_client),
+            Color(minio_client),
+            NoSegPrintPainting(minio_client),
+            PrintPainting(minio_client),
+            Scaling(),
+            Split(minio_client)
+        ]
+
+    def process(self):
+        for item in self.Others_pipeline:
+            self.result = item(self.result)
+        return self.result
+
+
+class TopMergeItem(BaseItem):
+    def __init__(self, data, basic, minio_client):
+        super().__init__(data, basic)
+        self.top_pipeline = [
+            LoadImage(minio_client),
+            KeyPoint(),
+            Segmentation(minio_client),
+            Scaling(),
+            Split(minio_client)
+        ]
+
+    def process(self):
+        for item in self.top_pipeline:
+            self.result = item(self.result)
+        return self.result
+
+
+class BottomMergeItem(BaseItem):
+    def __init__(self, data, basic, minio_client):
+        super().__init__(data, basic)
+        self.bottom_pipeline = [
+            LoadImage(minio_client),
+            KeyPoint(),
+            Segmentation(minio_client),
+            Scaling(),
+            Split(minio_client)
+        ]
+
+    def process(self):
+        for item in self.bottom_pipeline:
+            self.result = item(self.result)
+        return self.result
+
+
 class BodyItem(BaseItem):
     def __init__(self, data, basic, minio_client):
         super().__init__(data, basic)

app/service/design_fast/pipeline/loading.py

@@ -35,15 +35,9 @@ class LoadImage:
         return cls.name
 
     def __call__(self, result):
+        if result.get("merge_image_path"):
+            result['merge_image'], _ = self.read_image(result['merge_image_path'])
         result['image'], result['pre_mask'] = self.read_image(result['path'])
-        # if 'extract_lines' in result.keys():
-        #     if result['extract_lines']:
-        #         result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY), result['path'])
-        #     else:
-        #         result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
-        # else:
-        #     result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
-
         result['gray'] = self.get_lines(cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY))
         result['keypoint'] = self.get_keypoint(result['name'])
         result['img_shape'] = result['image'].shape
@@ -61,21 +55,6 @@ class LoadImage:
         mask = skeleton
         result = np.ones_like(img) * 255
         result[mask] = img[mask]
-
-        # 步骤2：细化边缘（可选，让线条更干净）
-        # kernel = np.ones((1, 1), np.uint8)
-        # clean = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
-
-        # thinned = cv2.ximgproc.thinning(binary, thinningType=cv2.ximgproc.THINNING_ZHANGSUEN)  # thinning算法细化线条
-        # mask = thinned > 0
-        # result = np.ones_like(img) * 255
-        # result[mask] = img[mask]
-
-        # 步骤3：反转回 白底黑线
-        # lines = cv2.bitwise_not(thinned)
-        # cv2.imwrite(os.path.join('/home/user/PycharmProjects/trinity_client_aida/test/lines_original_result_5', f"Original_{path.replace('/', '-')}.png"), img)
-        # cv2.imwrite(os.path.join('/home/user/PycharmProjects/trinity_client_aida/test/lines_original_result_5', f"Line_{path.replace('/', '-')}.png"), result)
-
         return result
 
     def read_image(self, image_path):
@@ -96,19 +75,19 @@ class LoadImage:
 
     @staticmethod
     def get_keypoint(name):
-        if name == 'blouse' or name == 'outwear' or name == 'dress' or name == 'tops':
+        if name in ['blouse', 'outwear', 'dress', 'tops', 'blouse_merge', 'outwear_merge', 'dress_merge', 'tops_merge']:
             keypoint = 'shoulder'
-        elif name == 'trousers' or name == 'skirt' or name == 'bottoms':
+        elif name in ['trousers', 'skirt', 'bottoms', 'trousers_merge', 'skirt_merge', 'bottoms_merge']:
             keypoint = 'waistband'
-        elif name == 'bag':
+        elif name in ['bag', 'bag_merge']:
             keypoint = 'hand_point'
-        elif name == 'shoes':
+        elif name in ['shoes', 'shoes_merge']:
             keypoint = 'toe'
-        elif name == 'hairstyle':
+        elif name in ['hairstyle', 'hairstyle_merge']:
             keypoint = 'head_point'
-        elif name == 'earring':
+        elif name in ['earring', 'earring_merge']:
             keypoint = 'ear_point'
-        elif name == 'others':
+        elif name in ['others', 'others_merge']:
             keypoint = "others"
         else:
             raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, "

app/service/design_fast/pipeline/segmentation.py

@@ -34,15 +34,15 @@ class Segmentation:
             result['mask'] = result['front_mask'] + result['back_mask']
         else:
             # preview 过模型 不缓存
-            if "preview_submit" in result.keys() and result['preview_submit'] == "preview":
+            if result.get("design_type", None) == "merge":
-                # 推理获得seg 结果
                 seg_result = get_seg_result(result['image'])
-            # submit 过模型 缓存
+            # 默认design 模式 - 过模型 缓存
-            elif "preview_submit" in result.keys() and result['preview_submit'] == "submit":
+            # elif result.get("design_type", None) == "submit":
-                # 推理获得seg 结果
+            # 推理获得seg 结果
-                seg_result = get_seg_result(result['image'])
+            # seg_result = get_seg_result(result['image'])
-                self.save_seg_result(seg_result, result['image_id'])
+            # 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
+
+            # 默认模式- 加载模型,找不到则过模型推理,推理后保存到本地
             else:
                 # 本地查询seg 缓存是否存在
                 _, seg_result = self.load_seg_result(result["image_id"])

app/service/design_fast/pipeline/split.py

@@ -4,6 +4,7 @@ import logging
 import cv2
 import numpy as np
 from PIL import Image
+from celery.bin.result import result
 
 from app.service.design_fast.utils.conversion_image import rgb_to_rgba
 from app.service.design_fast.utils.transparent import sketch_to_transparent
@@ -19,105 +20,106 @@ class Split(object):
     def __call__(self, result):
         try:
             if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms', 'others'):
-                ori_front_mask = result['front_mask'].copy()
+                if result.get('design_type', None) == 'merge':
-                ori_back_mask = result['back_mask'].copy()
+                    # merge 不需要返回mask (红绿图)
-
+                    if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
-                if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
+                        front_mask = result['front_mask']
-                    front_mask = result['front_mask']
+                        back_mask = result['back_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), interpolation=cv2.INTER_AREA)
-                    back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
-
-                rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
-                new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"]))
-                rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA)
-                result_front_image = np.zeros_like(rgba_image)
-                front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA)
-                result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
-                result_front_image_pil = Image.fromarray(cv2.cvtColor(result_front_image, cv2.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_AREA)
-                        # 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"])
+                        height, width = result['front_mask'].shape[:2]
-                result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None)
+                        new_width = int(width * result['resize_scale'][0])
+                        new_height = int(height * result['resize_scale'][1])
 
-                # 前片部分 (红图部分)
+                        front_mask = cv2.resize(result['front_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
-                # height, width = front_mask.shape
+                        back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
-                # mask_image = np.zeros((height, width, 3))
+                        result['merge_image'] = cv2.resize(result['merge_image'], (new_width, new_height), interpolation=cv2.INTER_AREA)
-                # mask_image[front_mask != 0] = [0, 0, 255]
 
-                # 切换为原始图片尺寸-------------------------------
+                    rgba_image = rgb_to_rgba(result['merge_image'], front_mask + back_mask)
-                height, width = ori_front_mask.shape
+                    new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"]))
-                mask_image = np.zeros((height, width, 3))
+                    rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA)
-                mask_image[ori_front_mask != 0] = [0, 0, 255]
+                    result_front_image = np.zeros_like(rgba_image)
-                # -----------------------------------------------
+                    front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA)
+                    result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
+                    result_front_image_pil = Image.fromarray(cv2.cvtColor(result_front_image, cv2.COLOR_BGR2RGBA))
+                    result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None)
 
-                # if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
+                    result_back_image = np.zeros_like(rgba_image)
-                #     result_back_image = np.zeros_like(rgba_image)
+                    back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA)
-                #     back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA)
+                    result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
-                #     result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
+                    result_back_image_pil = Image.fromarray(cv2.cvtColor(result_back_image, cv2.COLOR_BGR2RGBA))
-                #     result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
+                    result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
-                #     result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
+                    return result
-                #     mask_image[back_mask != 0] = [0, 255, 0]
+                else:
-                #
+                    ori_front_mask = result['front_mask'].copy()
-                #     rbga_mask = rgb_to_rgba(mask_image, front_mask + back_mask)
+                    ori_back_mask = result['back_mask'].copy()
-                #     mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA))
-                #     image_data = io.BytesIO()
-                #     mask_pil.save(image_data, format='PNG')
-                #     image_data.seek(0)
-                #     image_bytes = image_data.read()
-                #     req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
-                #     result['mask_url'] = req.bucket_name + "/" + req.object_name
-                # else:
-                #     rbga_mask = rgb_to_rgba(mask_image, front_mask)
-                #     mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA))
-                #     image_data = io.BytesIO()
-                #     mask_pil.save(image_data, format='PNG')
-                #     image_data.seek(0)
-                #     image_bytes = image_data.read()
-                #     req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
-                #     result['mask_url'] = req.bucket_name + "/" + req.object_name
-                #     result['back_image'] = None
-                #     result["back_image_url"] = None
-                #     # result["back_mask_url"] = None
-                #     # result['back_mask_image'] = None
 
-                result_back_image = np.zeros_like(rgba_image)
+                    if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
-                back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA)
+                        front_mask = result['front_mask']
-                result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
+                        back_mask = result['back_mask']
-                result_back_image_pil = Image.fromarray(cv2.cvtColor(result_back_image, cv2.COLOR_BGR2RGBA))
+                    else:
-                result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
+                        height, width = result['front_mask'].shape[:2]
+                        new_width = int(width * result['resize_scale'][0])
+                        new_height = int(height * result['resize_scale'][1])
 
-                # mask_image[back_mask != 0] = [0, 255, 0]
+                        front_mask = cv2.resize(result['front_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
-                mask_image[ori_back_mask != 0] = [0, 255, 0]
+                        back_mask = cv2.resize(result['back_mask'], (new_width, new_height), interpolation=cv2.INTER_AREA)
 
-                rbga_mask = rgb_to_rgba(mask_image, ori_front_mask + ori_back_mask)
+                    rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
-                mask_pil = Image.fromarray(cv2.cvtColor(rbga_mask.astype(np.uint8), cv2.COLOR_BGR2RGBA))
+                    new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"]))
-                image_data = io.BytesIO()
+                    rgba_image = cv2.resize(rgba_image, new_size, interpolation=cv2.INTER_AREA)
-                mask_pil.save(image_data, format='PNG')
+                    result_front_image = np.zeros_like(rgba_image)
-                image_data.seek(0)
+                    front_mask = cv2.resize(front_mask, new_size, interpolation=cv2.INTER_AREA)
-                image_bytes = image_data.read()
+                    result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
-                req = oss_upload_image(oss_client=self.minio_client, bucket="aida-clothing", object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
+                    result_front_image_pil = Image.fromarray(cv2.cvtColor(result_front_image, cv2.COLOR_BGR2RGBA))
-                result['mask_url'] = req.bucket_name + "/" + req.object_name
+                    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_AREA)
+                            # 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 = ori_front_mask.shape
+                    mask_image = np.zeros((height, width, 3))
+                    mask_image[ori_front_mask != 0] = [0, 0, 255]
+
+                    result_back_image = np.zeros_like(rgba_image)
+                    back_mask = cv2.resize(back_mask, new_size, interpolation=cv2.INTER_AREA)
+                    result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
+                    result_back_image_pil = Image.fromarray(cv2.cvtColor(result_back_image, cv2.COLOR_BGR2RGBA))
+                    result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
+
+                    # mask_image[back_mask != 0] = [0, 255, 0]
+                    mask_image[ori_back_mask != 0] = [0, 255, 0]
+
+                    rbga_mask = rgb_to_rgba(mask_image, ori_front_mask + ori_back_mask)
+                    mask_pil = Image.fromarray(cv2.cvtColor(rbga_mask.astype(np.uint8), cv2.COLOR_BGR2RGBA))
+                    image_data = io.BytesIO()
+                    mask_pil.save(image_data, format='PNG')
+                    image_data.seek(0)
+                    image_bytes = image_data.read()
+                    req = oss_upload_image(oss_client=self.minio_client, bucket="aida-clothing", object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
+                    result['mask_url'] = req.bucket_name + "/" + req.object_name
+
+                    # 创建中间图层(未分割图层) 1.color + overall_print  2.color + overall_print + print
+                    result_pattern_overall_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_overall'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA))
+                    result['pattern_overall_image'], result['pattern_overall_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_overall_image_pil, f'{generate_uuid()}')
+
+                    result_pattern_print_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_print'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA))
+                    result['pattern_print_image'], result['pattern_print_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_print_image_pil, f'{generate_uuid()}')
+                    return result
             else:
                 ori_front_mask, ori_back_mask = None, None
             # 创建中间图层(未分割图层) 1.color + overall_print  2.color + overall_print + print
@@ -127,5 +129,6 @@ class Split(object):
             result_pattern_print_image_pil = Image.fromarray(cv2.cvtColor(rgb_to_rgba(result['no_seg_sketch_print'], ori_front_mask + ori_back_mask), cv2.COLOR_BGR2RGBA))
             result['pattern_print_image'], result['pattern_print_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_print_image_pil, f'{generate_uuid()}')
             return result
+
         except Exception as e:
             logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")

app/service/design_fast/utils/organize.py

@@ -23,19 +23,20 @@ def organize_clothing(layer):
     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"],
+                       merge_image=layer["front_image"],
                        # mask_image=layer['front_mask_image'],
                        image_url=layer['front_image_url'],
-                       mask_url=layer['mask_url'],
+                       mask_url=layer.get("mask_url", None),
                        sacle=layer['scale'],
                        clothes_keypoint=layer['clothes_keypoint'],
                        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_overall_image_url=layer['pattern_overall_image_url'],
+                       pattern_overall_image_url=layer.get('pattern_overall_image_url', None),
-                       pattern_print_image_url=layer['pattern_print_image_url'],
+                       pattern_print_image_url=layer.get('pattern_print_image_url', None),
 
-                       pattern_image=layer['pattern_image'],
+                       pattern_image=layer.get('pattern_image', None),
                        # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
                        transpose=layer.get("transpose", [1, 1]),  # 默认为1, 1代表不镜像
                        rotate=layer.get('rotate', 0),
@@ -46,17 +47,17 @@ def organize_clothing(layer):
                       image=layer["back_image"],
                       # mask_image=layer['back_mask_image'],
                       image_url=layer['back_image_url'],
-                      mask_url=layer['mask_url'],
+                      mask_url=layer.get('mask_url', None),
                       sacle=layer['scale'],
                       clothes_keypoint=layer['clothes_keypoint'],
                       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_overall_image_url=layer['pattern_overall_image_url'],
+                      pattern_overall_image_url=layer.get('pattern_overall_image_url', None),
-                      pattern_print_image_url=layer['pattern_print_image_url'],
+                      pattern_print_image_url=layer.get('pattern_print_image_url', None),
                       # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
-                      transpose=layer.get("transpose", [1, 1]), # 默认为1, 1代表不镜像
+                      transpose=layer.get("transpose", [1, 1]),  # 默认为1, 1代表不镜像
                       rotate=layer.get('rotate', 0),
                       )
     return front_layer, back_layer
@@ -80,16 +81,16 @@ def organize_others(layer):
                        image=layer["front_image"],
                        # mask_image=layer['front_mask_image'],
                        image_url=layer['front_image_url'],
-                       mask_url=layer['mask_url'],
+                       mask_url=layer.get('mask_url', None),
                        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_overall_image_url=layer['pattern_overall_image_url'],
+                       pattern_overall_image_url=layer.get('pattern_overall_image_url', None),
-                       pattern_print_image_url=layer['pattern_print_image_url'],
+                       pattern_print_image_url=layer.get('pattern_print_image_url', None),
-                       pattern_image=layer['pattern_image'],
+                       pattern_image=layer.get('pattern_image', None),
                        # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
                        )
     # 后片数据
@@ -98,15 +99,15 @@ def organize_others(layer):
                       image=layer["back_image"],
                       # mask_image=layer['back_mask_image'],
                       image_url=layer['back_image_url'],
-                      mask_url=layer['mask_url'],
+                      mask_url=layer.get('mask_url', None),
                       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_overall_image_url=layer['pattern_overall_image_url'],
+                      pattern_overall_image_url=layer.get('pattern_overall_image_url', None),
-                      pattern_print_image_url=layer['pattern_print_image_url'],
+                      pattern_print_image_url=layer.get('pattern_print_image_url', None),
                       # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
                       )
     return front_layer, back_layer

app/service/design_fast/utils/synthesis_item.py

@@ -187,6 +187,111 @@ def synthesis(data, size, basic_info):
         logging.warning(f"synthesis runtime exception : {e}")
 
 
+def merge(data, size, basic_info):
+    # out_of_bounds_control: 是否允许服装越界  True 允许 False 不允许  默认情况允许
+    out_of_bounds_control = basic_info.get('out_of_bounds_control', True)
+    # 创建底图
+    base_image = Image.new('RGBA', size, (0, 0, 0, 0))
+    try:
+        all_mask_shape = (size[1], size[0])
+        body_mask = None
+        for d in data:
+            if d['name'] == 'body' or d['name'] == 'mannequin':
+                # 创建一个新的宽高透明图像， 把模特贴上去获取mask
+                transparent_image = Image.new("RGBA", size, (0, 0, 0, 0))
+                transparent_image.paste(d['image'], (d['adaptive_position'][1], d['adaptive_position'][0]), d['image'])  # 此处可变数组会被paste篡改值，所以使用下标获取position
+                body_mask = np.array(transparent_image.split()[3])
+
+                # 根据新的坐标获取新的肩点
+                left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
+                right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
+                body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255
+        _, 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)
+        others_outer_mask = np.array(binary_body_mask)
+
+        top = True
+        bottom = True
+        others = True
+        i = len(data)
+        while i:
+            i -= 1
+            if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
+                if out_of_bounds_control:
+                    top = True
+                else:
+                    top = 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
+                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]
+                top_outer_mask = background + top_outer_mask
+            elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]:
+                # 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
+                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]
+                bottom_outer_mask = background + bottom_outer_mask
+            elif others and data[i]['name'] in ['others_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]
+                others_outer_mask = background + others_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, others_outer_mask)
+
+        for layer in data:
+            if layer['image'] is not None:
+                if layer['name'] != "body":
+                    test_image = Image.new('RGBA', size, (0, 0, 0, 0))
+                    paste_img, position = transpose_rotate(layer, layer['image'])
+                    test_image.paste(paste_img, position, paste_img)
+                    mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
+                    mask_alpha = Image.fromarray(mask_data)
+                    mask_alpha.paste(paste_img.getchannel('A'), position, paste_img.getchannel('A'))
+                    cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha)
+                    base_image.paste(test_image, (0, 0), cropped_image)  # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00
+                else:
+                    base_image.paste(layer['merge_image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['merge_image'])
+
+        result_image = base_image
+
+        image_data = io.BytesIO()
+        result_image.save(image_data, format='PNG')
+        image_data.seek(0)
+
+        # oss upload
+        image_bytes = image_data.read()
+        bucket_name = "aida-results"
+        object_name = f'result_{generate_uuid()}.png'
+        oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+        return f"{bucket_name}/{object_name}"
+    except Exception as e:
+        logging.warning(f"synthesis runtime exception : {e}")
+
+
 def synthesis_single(front_image, back_image):
     result_image = None
     if front_image:

app/service/utils/new_oss_client.py

@@ -81,7 +81,7 @@ 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 = "lanecarford/lc_stylist_agent_outfit_items/141/ee25ec85-d504-4b42-9a18-db6682fe9e3b-6.jpg"
+    url = "aida-results/result_a7adcbd8-ef8d-11f0-8c92-0966ede33ab5.png"
 
     # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
     read_type = "2"