feat design 功能迁移

app/service/design/items/pipelines/__init__.py

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+from .compose import Compose
+from .loading import LoadImageFromFile, LoadBodyImageFromFile, ImageShow
+from .keypoints import KeypointDetection
+from .segmentation import Segmentation
+from .painting import Painting, PrintPainting
+from .scale import Scaling
+from .contour_detection import ContourDetection
+from .split import Split
+
+__all__ = [
+    'Compose',
+    'LoadImageFromFile', 'LoadBodyImageFromFile', 'ImageShow',
+    'KeypointDetection',
+    'Segmentation',
+    'Painting', 'PrintPainting',
+    'Scaling',
+    'ContourDetection',
+    'split',
+]

app/service/design/items/pipelines/compose.py

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+import collections
+
+from mmcv.utils import build_from_cfg
+
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class Compose(object):
+    def __init__(self, transforms):
+        assert isinstance(transforms, collections.abc.Sequence)
+        self.transforms = []
+        for transform in transforms:
+            if isinstance(transform, dict):
+                transform = build_from_cfg(transform, PIPELINES)
+                self.transforms.append(transform)
+            elif callable(transform):
+                self.transforms.append(transform)
+            else:
+                raise TypeError('transform must be callable or a dict')
+
+    def __call__(self, data):
+        """Call function to apply transforms sequentially.
+
+        Args:
+            data (dict): A result dict contains the data to transform.
+
+        Returns:
+           dict: Transformed data.
+        """
+
+        for t in self.transforms:
+            data = t(data)
+            if data is None:
+                return None
+        return data

app/service/design/items/pipelines/contour_detection.py

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+import logging
+
+from ..builder import PIPELINES
+import cv2
+import numpy as np
+
+
+@PIPELINES.register_module()
+class ContourDetection(object):
+    def __init__(self):
+        # logging.info("ContourDetection run ")
+        pass
+
+    #@ RunTime
+    def __call__(self, result):
+        # shoe diff
+        if result['name'] == 'shoes':
+            Contour = self.get_contours(result['image'])
+            Mask = np.zeros(result['image'].shape[:2], np.uint8)
+            for i in range(2):
+                Max_contour = Contour[i]
+                Epsilon = 0.001 * cv2.arcLength(Max_contour, True)
+                Approx = cv2.approxPolyDP(Max_contour, Epsilon, True)
+                cv2.drawContours(Mask, [Approx], -1, 255, -1)
+            if result['pre_mask'] is None:
+                result['mask'] = Mask
+            else:
+                result['mask'] = cv2.bitwise_and(Mask, result['pre_mask'])
+        else:
+            Contour = self.get_contours(result['image'])
+            Mask = np.zeros(result['image'].shape[:2], np.uint8)
+            if len(Contour):
+                Max_contour = Contour[0]
+                Epsilon = 0.001 * cv2.arcLength(Max_contour, True)
+                Approx = cv2.approxPolyDP(Max_contour, Epsilon, True)
+                cv2.drawContours(Mask, [Approx], -1, 255, -1)
+            else:
+                Mask = np.ones(result['image'].shape[:2], np.uint8) * 255
+            # TODO 修复部分图片出现透明的情况 下版本上线
+            # img2gray = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
+            # ret, Mask = cv2.threshold(img2gray, 126, 255, cv2.THRESH_BINARY)
+            # Mask = cv2.bitwise_not(Mask)
+            if result['pre_mask'] is None:
+                result['mask'] = Mask
+            else:
+                result['mask'] = cv2.bitwise_and(Mask, result['pre_mask'])
+
+        return result
+
+    @staticmethod
+    def get_contours(image):
+        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+        Edge = cv2.Canny(gray, 10, 150)
+        kernel = np.ones((5, 5), np.uint8)
+        Edge = cv2.dilate(Edge, kernel=kernel, iterations=1)
+        Edge = cv2.erode(Edge, kernel=kernel, iterations=1)
+        Contour, _ = cv2.findContours(Edge, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+        Contour = sorted(Contour, key=cv2.contourArea, reverse=True)
+        return Contour

app/service/design/items/pipelines/keypoints.py

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+import logging
+import time
+import numpy as np
+from pymilvus import MilvusClient
+
+from app.core.config import *
+from ..builder import PIPELINES
+from ...utils.design_ensemble import get_keypoint_result
+
+
+@PIPELINES.register_module()
+class KeypointDetection(object):
+    """
+    path here: abstract path
+    """
+
+    def __init__(self):
+        self.client = MilvusClient(
+            uri="http://10.1.1.240:19530",
+            token="root:Milvus",
+            db_name=MILVUS_ALIAS
+        )
+
+    def __del__(self):
+        # start_time = time.time()
+        self.client.close()
+        # print(f"client close time : {time.time() - start_time}")
+
+    # @ RunTime
+    def __call__(self, result):
+        # logging.info("KeypointDetection run ")
+        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)
+
+            # 取消向量查询 直接过模型推理
+            keypoint_cache = self.keypoint_cache(result, site)
+            # 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)
+            else:
+                result['clothes_keypoint'] = keypoint_cache
+        return result
+
+    @staticmethod
+    def infer_keypoint_result(result):
+        site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down'
+        start_time = time.time()
+        keypoint_infer_result = get_keypoint_result(result["image"], site)  # 推理结果
+        # logging.info(f"infer keypoint time : {time.time() - start_time}")
+        return keypoint_infer_result, site
+
+    @staticmethod
+    # @ RunTime
+    def save_keypoint_cache(keypoint_id, cache, site, KEYPOINT_RESULT_TABLE_FIELD_SET=None):
+        if site == "down":
+            zeros = np.zeros(20, dtype=int)
+            result = np.concatenate([zeros, cache.flatten()])
+        else:
+            zeros = np.zeros(4, dtype=int)
+            result = np.concatenate([cache.flatten(), zeros])
+        # 取消向量保存 直接拿结果
+        data = [
+            {"keypoint_id": keypoint_id,
+             "keypoint_site": site,
+             "keypoint_vector": result.tolist()
+             }
+        ]
+        client = MilvusClient(
+            uri="http://10.1.1.240:19530",
+            token="root:Milvus",
+            db_name=MILVUS_ALIAS
+        )
+        try:
+            start_time = time.time()
+            res = client.upsert(
+                collection_name=MILVUS_TABLE_KEYPOINT,
+                data=data,
+            )
+            # logging.info(f"save keypoint time : {time.time() - start_time}")
+            return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
+        except Exception as e:
+            logging.info(f"save keypoint cache milvus error : {e}")
+            return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
+        finally:
+            client.close()
+
+    @staticmethod
+    def update_keypoint_cache(keypoint_id, infer_result, search_result, site):
+        if site == "up":
+            # 需要的是up 即推理出来的是up 那么查询的就是down
+            result = np.concatenate([infer_result.flatten(), search_result[-4:]])
+        else:
+            # 需要的是down 即推理出来的是down 那么查询的就是up
+            result = np.concatenate([search_result[:20], infer_result.flatten()])
+        data = [
+            {"keypoint_id": keypoint_id,
+             "keypoint_site": "all",
+             "keypoint_vector": result.tolist()
+             }
+        ]
+        client = MilvusClient(
+            uri="http://10.1.1.240:19530",
+            token="root:Milvus",
+            db_name=MILVUS_ALIAS
+        )
+        try:
+            # connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
+            start_time = time.time()
+            # collection = Collection(MILVUS_TABLE_KEYPOINT)  # Get an existing collection.
+            # mr = collection.upsert(data)
+            client.upsert(
+                collection_name=MILVUS_TABLE_KEYPOINT,
+                data=data
+            )
+            # logging.info(f"save keypoint time : {time.time() - start_time}")
+            return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
+        except Exception as e:
+            logging.info(f"save keypoint cache milvus error : {e}")
+            return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist()))
+
+    # @ RunTime
+    def keypoint_cache(self, result, site):
+        try:
+            keypoint_id = result['image_id']
+            res = self.client.query(
+                collection_name=MILVUS_TABLE_KEYPOINT,
+                # ids=[keypoint_id],
+                filter=f"keypoint_id == {keypoint_id}",
+                output_fields=['keypoint_vector', 'keypoint_site']
+            )
+            if len(res) == 0:
+                # 没有结果 直接推理拿结果 并保存
+                keypoint_infer_result, site = self.infer_keypoint_result(result)
+                return self.save_keypoint_cache(result['image_id'], keypoint_infer_result, site)
+            elif res[0]["keypoint_site"] == "all" or res[0]["keypoint_site"] == site:
+                # 需要的类型和查询的类型一致，或者查询的类型为all 则直接返回查询的结果
+                return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, np.array(res[0]['keypoint_vector']).astype(int).reshape(12, 2).tolist()))
+            elif res[0]["keypoint_site"] != site:
+                # 需要的类型和查询到的不一致，则更新类型为all
+                keypoint_infer_result, site = self.infer_keypoint_result(result)
+                return self.update_keypoint_cache(result["image_id"], keypoint_infer_result, res[0]['keypoint_vector'], site)
+        except Exception as e:
+            logging.info(f"search keypoint cache milvus error {e}")
+            return False

app/service/design/items/pipelines/loading.py

@@ -0,0 +1,143 @@
+import io
+import logging
+import time
+
+import cv2
+import numpy as np
+from PIL import Image
+from minio import Minio
+
+from app.core.config import *
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class LoadImageFromFile(object):
+    def __init__(self, path, color=None, print_dict=None):
+        self.path = path
+        self.color = color
+        self.print_dict = print_dict
+        self.minio_client = Minio(
+            f"{MINIO_URL}",
+            access_key=MINIO_ACCESS,
+            secret_key=MINIO_SECRET,
+            secure=MINIO_SECURE)
+
+    def __call__(self, result):
+        result['image'], result['pre_mask'] = self.read_image(self.path)
+        result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
+        result['keypoint'] = self.get_keypoint(result['name'])
+        result['path'] = self.path
+        result['img_shape'] = result['image'].shape
+        result['ori_shape'] = result['image'].shape
+        result['color'] = self.color if self.color is not None else None
+        result['print_dict'] = self.print_dict
+        return result
+
+    @staticmethod
+    def get_keypoint(name):
+        if name == 'blouse' or name == 'outwear' or name == 'dress' or name == 'tops':
+            keypoint = 'shoulder'
+        elif name == 'trousers' or name == 'skirt' or name == 'bottoms':
+            keypoint = 'waistband'
+        elif name == 'bag':
+            keypoint = 'hand_point'
+        elif name == 'shoes':
+            keypoint = 'toe'
+        elif name == 'hairstyle':
+            keypoint = 'head_point'
+        elif name == 'earring':
+            keypoint = 'ear_point'
+        else:
+            raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, "
+                           f"bag, shoes, hairstyle, earring.")
+        return keypoint
+
+    def read_image(self, image_path):
+        image_mask = None
+        file = self.minio_client.get_object(image_path.split("/", 1)[0], image_path.split("/", 1)[1]).data
+        image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
+
+        if len(image.shape) == 2:
+            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+        if image.shape[2] == 4:  # 如果是四通道 mask
+            image_mask = image[:, :, 3]
+            image = image[:, :, :3]
+        return image, image_mask
+
+
+@PIPELINES.register_module()
+class LoadBodyImageFromFile(object):
+    def __init__(self, body_path):
+        self.body_path = body_path
+        self.minioClient = Minio(
+            f"{MINIO_URL}",
+            access_key=MINIO_ACCESS,
+            secret_key=MINIO_SECRET,
+            secure=MINIO_SECURE)
+
+        # response = self.minioClient.get_object("aida-mannequins", "model_1693218345.2714431.png")
+
+    # @ RunTime
+    def __call__(self, result):
+        result["image_url"] = result['body_path'] = self.body_path
+        result["name"] = "mannequin"
+        if not result['image_url'].lower().endswith(".png"):
+            logging.info(1)
+            bucket = self.body_path.split("/", 1)[0]
+            object_name = self.body_path.split("/", 1)[1]
+            new_object_name = f'{object_name[:object_name.rfind(".")]}.png'
+            image = self.minioClient.get_object(bucket, object_name)
+            image = Image.open(io.BytesIO(image.data))
+            image = image.convert("RGBA")
+            data = image.getdata()
+            #
+            new_data = []
+            for item in data:
+                if item[0] >= 230 and item[1] >= 230 and item[2] >= 230:
+                    new_data.append((255, 255, 255, 0))
+                else:
+                    new_data.append(item)
+            image.putdata(new_data)
+            image_data = io.BytesIO()
+            image.save(image_data, format='PNG')
+            image_data.seek(0)
+            image_bytes = image_data.read()
+            image_path = f"{bucket}/{self.minioClient.put_object(bucket, new_object_name, io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
+            self.body_path = image_path
+            result["image_url"] = result['body_path'] = self.body_path
+        response = self.minioClient.get_object(self.body_path.split("/", 1)[0], self.body_path.split("/", 1)[1])
+        # put_image_time = time.time()
+        result['body_image'] = Image.open(io.BytesIO(response.read()))
+        # logging.info(f"Image.open time is : {time.time() - put_image_time}")
+        return result
+
+
+@PIPELINES.register_module()
+class ImageShow(object):
+    def __init__(self, key):
+        self.key = key
+
+    # @ RunTime
+    def __call__(self, result):
+        import matplotlib.pyplot as plt
+        if isinstance(self.key, list):
+            for key in self.key:
+                plt.imshow(result[key])
+                plt.title(key)
+                plt.show()
+        elif isinstance(self.key, str):
+            img = self._resize_img(result[self.key])
+            cv2.imshow(self.key, img)
+            cv2.waitKey(0)
+        else:
+            raise TypeError(f'key should be string but got type {type(self.key)}.')
+        return result
+
+    @staticmethod
+    def _resize_img(img):
+        shape = img.shape
+        if shape[0] > 400 or shape[1] > 400:
+            ratio = min(400 / shape[0], 400 / shape[1])
+            img = cv2.resize(img, (int(ratio * shape[1]), int(ratio * shape[0])))
+        return img

app/service/design/items/pipelines/painting.py

@@ -0,0 +1,498 @@
+import random
+from io import BytesIO
+import boto3
+import cv2
+import numpy as np
+from PIL import Image
+from ..builder import PIPELINES
+
+# minio_client = Minio(
+#     f"{MINIO_IP}:{MINIO_PORT}",
+#     access_key=MINIO_ACCESS,
+#     secret_key=MINIO_SECRET,
+#     secure=MINIO_SECURE)
+s3 = boto3.client(
+    's3',
+    aws_access_key_id="AKIAVD3OJIMF6UJFLSHZ",
+    aws_secret_access_key="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",
+    region_name="ap-east-1"
+)
+
+
+@PIPELINES.register_module()
+class Painting(object):
+    def __init__(self, painting_flag=True):
+        self.painting_flag = painting_flag
+
+    # @ RunTime
+    def __call__(self, result):
+        if result['name'] not in ['hairstyle', 'earring'] and self.painting_flag and result['color'] != 'none':
+            dim_image_h, dim_image_w = result['image'].shape[0:2]
+            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)
+            else:
+                pattern = self.get_pattern(result['color'])
+                resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA)
+            closed_mo = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
+            gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2)
+            get_image_fir = resize_pattern * (closed_mo / 255) * (gray_mo / 255)
+            result['pattern_image'] = get_image_fir.astype(np.uint8)
+            result['final_image'] = result['pattern_image']
+            canvas = np.full_like(result['final_image'], 255)
+            temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2)
+            tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8)
+            temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
+            tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
+            result['single_image'] = cv2.add(tmp1, tmp2)
+            result['alpha'] = 100 / 255.0
+        else:
+            closed_mo = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
+            get_image_fir = result['image'] * (closed_mo / 255)
+            result['pattern_image'] = get_image_fir.astype(np.uint8)
+            result['final_image'] = result['pattern_image']
+        return result
+
+    @staticmethod
+    def get_gradient(bucket_name, object_name):
+        # image_data = minio_client.get_object(bucket_name, object_name)
+        image_data = s3.get_object(Bucket=bucket_name, Key=object_name)['Body']
+
+        # 从数据流中读取图像
+        image_bytes = image_data.read()
+
+        # 将图像数据转换为numpy数组
+        image_array = np.asarray(bytearray(image_bytes), dtype=np.uint8)
+
+        # 使用OpenCV解码图像数组
+        image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+        return image
+
+    @staticmethod
+    def crop_image(image, image_size_h, image_size_w):
+        x_offset = np.random.randint(low=0, high=int(image_size_h / 5) - 6)
+        y_offset = np.random.randint(low=0, high=int(image_size_w / 5) - 6)
+        image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :]
+        return image
+
+    @staticmethod
+    def get_pattern(single_color):
+        if single_color is None:
+            raise False
+        R, G, B = single_color.split(' ')
+        pattern = np.zeros([1, 1, 3], np.uint8)
+        pattern[0, 0, 0] = int(B)
+        pattern[0, 0, 1] = int(G)
+        pattern[0, 0, 2] = int(R)
+        return pattern
+
+    @staticmethod
+    def gradient(image, angle_degrees, start_color, end_color):
+        height, width = image.shape[0], image.shape[1]
+
+        # 创建一个空白的图像
+        gradient_image = np.zeros((height, width, 3), dtype=np.uint8)
+
+        # 将角度限制在 0 到 360 度之间
+        angle_degrees = np.clip(angle_degrees, 0, 360)
+
+        # 将角度转换为弧度
+        angle_radians = np.radians(angle_degrees)
+
+        # 计算渐变的方向
+        dx = np.cos(angle_radians)
+        dy = np.sin(angle_radians)
+
+        # 创建网格
+        x_grid, y_grid = np.meshgrid(np.arange(width), np.arange(height))
+
+        # 计算每个像素在渐变方向上的位置
+        distance_along_gradient = (x_grid * dx + y_grid * dy) / np.sqrt(dx ** 2 + dy ** 2)
+
+        # 计算渐变的权重
+        weight = np.clip(distance_along_gradient / max(width, height), 0, 1)
+
+        # 计算渐变的颜色
+        gradient_image[:, :, 0] = (1 - weight) * start_color[0] + weight * end_color[0]
+        gradient_image[:, :, 1] = (1 - weight) * start_color[1] + weight * end_color[1]
+        gradient_image[:, :, 2] = (1 - weight) * start_color[2] + weight * end_color[2]
+
+        return gradient_image
+
+
+@PIPELINES.register_module()
+class PrintPainting(object):
+    def __init__(self, print_flag=True):
+        self.print_flag = print_flag
+
+    # @ RunTime
+    def __call__(self, result):
+
+        if "location" not in result['print'].keys():
+            result['print']["location"] = [[0, 0]]
+        elif result['print']["location"] == [] or result['print']["location"] is None:
+            result['print']["location"] = [[0, 0]]
+        if result['print']['IfSingle']:
+            if len(result['print']['print_path_list']) == 0:
+                raise ValueError('When there is no printing, ifsingle must be false')
+
+            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)
+            # print_background = np.full((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), 255, dtype=np.uint8)
+            for i in range(len(result['print']['print_path_list'])):
+                image, image_mode = self.read_image(result['print']['print_path_list'][i])
+                if image_mode == "RGBA":
+                    new_size = (int(image.width * result['print']['print_scale_list'][i]), int(image.height * result['print']['print_scale_list'][i]))
+
+                    mask = image.split()[3]
+                    resized_source = image.resize(new_size)
+                    resized_source_mask = mask.resize(new_size)
+
+                    rotated_resized_source = resized_source.rotate(result['print']['print_angle_list'][i])
+                    rotated_resized_source_mask = resized_source_mask.rotate(result['print']['print_angle_list'][i])
+
+                    source_image_pil = Image.fromarray(print_background)
+                    source_image_pil_mask = Image.fromarray(mask_background)
+
+                    source_image_pil.paste(rotated_resized_source, (int(result['print']['location'][i][0]), int(result['print']['location'][i][1])), rotated_resized_source)
+                    source_image_pil_mask.paste(rotated_resized_source_mask, (int(result['print']['location'][i][0]), int(result['print']['location'][i][1])), rotated_resized_source_mask)
+
+                    print_background = np.array(source_image_pil)
+                    mask_background = np.array(source_image_pil_mask)
+
+                    print(1)
+                else:
+                    mask = self.get_mask_inv(image)
+                    mask = np.expand_dims(mask, axis=2)
+                    mask = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)
+                    mask = cv2.bitwise_not(mask)
+                    # 旋转后的坐标需要重新算
+                    rotate_mask, _ = self.img_rotate(mask, result['print']['print_angle_list'][i], result['print']['print_scale_list'][i])
+                    rotate_image, rotated_new_size = self.img_rotate(image, result['print']['print_angle_list'][i], result['print']['print_scale_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(result['print']['location'][i][0] - rotated_new_size[0]), int(result['print']['location'][i][1] - rotated_new_size[1])
+
+                    image_x = print_background.shape[1]
+                    image_y = print_background.shape[0]
+                    print_x = rotate_image.shape[1]
+                    print_y = rotate_image.shape[0]
+
+                    #  有bug
+                    # if x + print_x > image_x:
+                    #     rotate_image = rotate_image[:, :x + print_x - image_x]
+                    #     rotate_mask = rotate_mask[:, :x + print_x - image_x]
+                    # #
+                    # if y + print_y > image_y:
+                    #     rotate_image = rotate_image[:y + print_y - image_y]
+                    #     rotate_mask = rotate_mask[:y + print_y - image_y]
+
+                    #  不能是并行
+                    # 当前第一轮的if （108以及115）是判断有没有过下界和右界。第二轮的是判断左上有没有超出。 如果这个样子的话，先裁了右边，再左移，region就会有问题
+                    # 先挪 再判断 最后裁剪
+
+                    # 如果print旋转了 或者 print贴边了 则需要判断 判断左界和上界是否小于0
+                    if x <= 0:
+                        rotate_image = rotate_image[:, -x:]
+                        rotate_mask = rotate_mask[:, -x:]
+                        start_x = x = 0
+                    else:
+                        start_x = x
+
+                    if y <= 0:
+                        rotate_image = rotate_image[-y:, :]
+                        rotate_mask = rotate_mask[-y:, :]
+                        start_y = y = 0
+                    else:
+                        start_y = y
+
+                    # ------------------
+                    # 如果print-size大于image-size 则需要裁剪print
+
+                    if x + print_x > image_x:
+                        rotate_image = rotate_image[:, :image_x - x]
+                        rotate_mask = rotate_mask[:, :image_x - x]
+
+                    if y + print_y > image_y:
+                        rotate_image = rotate_image[:image_y - y, :]
+                        rotate_mask = rotate_mask[:image_y - y, :]
+
+                    # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = cv2.bitwise_xor(mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]], rotate_mask)
+                    # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = cv2.add(print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]], rotate_image)
+
+                    # mask_background[start_y:y + rotate_mask.shape[0], start_x:x + rotate_mask.shape[1]] = rotate_mask
+                    # print_background[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image
+                    mask_background = self.stack_prin(mask_background, result['pattern_image'], rotate_mask, start_y, y, start_x, x)
+                    print_background = self.stack_prin(print_background, result['pattern_image'], rotate_image, start_y, y, start_x, x)
+
+            # gray_image = cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY)
+            # print_background = cv2.bitwise_and(print_background, print_background, mask=gray_image)
+
+            print_mask = cv2.bitwise_and(result['mask'], cv2.cvtColor(mask_background, cv2.COLOR_BGR2GRAY))
+            img_fg = cv2.bitwise_or(print_background, print_background, mask=print_mask)
+            img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=cv2.bitwise_not(print_mask))
+            mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2)
+            gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2)
+            img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8)
+            result['final_image'] = cv2.add(img_bg, img_fg)
+            canvas = np.full_like(result['final_image'], 255)
+            temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2)
+            tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8)
+            temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
+            tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
+            result['single_image'] = cv2.add(tmp1, tmp2)
+            return result
+        else:
+            painting_dict = {}
+            painting_dict['dim_image_h'], painting_dict['dim_image_w'] = result['pattern_image'].shape[0:2]
+
+            # no print
+            if len(result['print_dict']['print_path_list']) == 0 or not self.print_flag:
+                result['print_image'] = result['pattern_image']
+            # print
+            else:
+                painting_dict = self.painting_collection(painting_dict, result, print_trigger=True)
+                result['print_image'] = self.printpaint(result, painting_dict, print_=True)
+            result['final_image'] = result['print_image']
+            canvas = np.full_like(result['final_image'], 255)
+            temp_bg = np.expand_dims(cv2.bitwise_not(result['mask']), axis=2).repeat(3, axis=2)
+            tmp1 = (canvas * (temp_bg / 255)).astype(np.uint8)
+            temp_fg = np.expand_dims(result['mask'], axis=2).repeat(3, axis=2)
+            tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
+            result['single_image'] = cv2.add(tmp1, tmp2)
+            return result
+
+    @staticmethod
+    def stack_prin(print_background, pattern_image, rotate_image, start_y, y, start_x, x):
+        temp_print = np.zeros((pattern_image.shape[0], pattern_image.shape[1], 3), dtype=np.uint8)
+
+        temp_print[start_y:y + rotate_image.shape[0], start_x:x + rotate_image.shape[1]] = rotate_image
+
+        img2gray = cv2.cvtColor(print_background, cv2.COLOR_BGR2GRAY)
+
+        ret, mask_ = cv2.threshold(img2gray, 1, 255, cv2.THRESH_BINARY)
+
+        mask_inv = cv2.bitwise_not(mask_)
+
+        img1_bg = cv2.bitwise_and(print_background, print_background, mask=mask_)
+
+        img2_fg = cv2.bitwise_and(temp_print, temp_print, mask=mask_inv)
+
+        print_background = img1_bg + img2_fg
+
+        return print_background
+
+    def painting_collection(self, painting_dict, result, print_trigger=False):
+        if print_trigger:
+            print_ = self.get_print(result['print_dict'])
+            painting_dict['Trigger'] = not print_['IfSingle']
+            painting_dict['location'] = print_['location'] if 'location' in print_.keys() else None
+            single_mask_inv_print = self.get_mask_inv(print_['image'])
+            dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w'])
+            dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5))
+            if not print_['IfSingle']:
+                self.random_seed = random.randint(0, 1000)
+                painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True)
+                painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'], trigger=True)
+            else:
+                painting_dict['mask_inv_print'] = self.tile_image(single_mask_inv_print, dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'])
+                painting_dict['tile_print'] = self.tile_image(print_['image'], dim_pattern, print_['scale'], painting_dict['dim_image_h'], painting_dict['dim_image_w'], painting_dict['location'])
+                painting_dict['dim_print_h'], painting_dict['dim_print_w'] = dim_pattern
+        return painting_dict
+
+    def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False):
+        if not trigger:
+            tile = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA)
+        else:
+            resize_pattern = cv2.resize(pattern, dim, interpolation=cv2.INTER_AREA)
+            if len(pattern.shape) == 2:
+                tile = np.tile(resize_pattern, (int((5 + 1) / scale) + 4, int((5 + 1) / scale) + 4))
+            if len(pattern.shape) == 3:
+                tile = np.tile(resize_pattern, (int((5 + 1) / scale) + 4, int((5 + 1) / scale) + 4, 1))
+            tile = self.crop_image(tile, dim_image_h, dim_image_w, location, resize_pattern.shape)
+        return tile
+
+    def get_mask_inv(self, print_):
+        if print_[0][0][0] == 255 and print_[0][0][1] == 255 and print_[0][0][2] == 255:
+            bg_color = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)[0][0]
+            print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)
+            bg_l, bg_a, bg_b = bg_color[0], bg_color[1], bg_color[2]
+            bg_L_high, bg_L_low = self.get_low_high_lab(bg_l, L=True)
+            bg_a_high, bg_a_low = self.get_low_high_lab(bg_a)
+            bg_b_high, bg_b_low = self.get_low_high_lab(bg_b)
+            lower = np.array([bg_L_low, bg_a_low, bg_b_low])
+            upper = np.array([bg_L_high, bg_a_high, bg_b_high])
+            mask_inv = cv2.inRange(print_tile, lower, upper)
+            return mask_inv
+        else:
+            # bg_color = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)[0][0]
+            # print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)
+            # bg_l, bg_a, bg_b = bg_color[0], bg_color[1], bg_color[2]
+            # bg_L_high, bg_L_low = self.get_low_high_lab(bg_l, L=True)
+            # bg_a_high, bg_a_low = self.get_low_high_lab(bg_a)
+            # bg_b_high, bg_b_low = self.get_low_high_lab(bg_b)
+            # lower = np.array([bg_L_low, bg_a_low, bg_b_low])
+            # upper = np.array([bg_L_high, bg_a_high, bg_b_high])
+
+            # print_tile = cv2.cvtColor(print_, cv2.COLOR_BGR2LAB)
+            # mask_inv = cv2.cvtColor(print_tile, cv2.COLOR_BGR2GRAY)
+
+            # mask_inv = cv2.cvtColor(print_, cv2.COLOR_BGR2GRAY)
+            mask_inv = np.zeros(print_.shape[:2], dtype=np.uint8)
+            return mask_inv
+
+    @staticmethod
+    def printpaint(result, painting_dict, print_=False):
+
+        if print_ and painting_dict['Trigger']:
+            print_mask = cv2.bitwise_and(result['mask'], cv2.bitwise_not(painting_dict['mask_inv_print']))
+            img_fg = cv2.bitwise_and(painting_dict['tile_print'], painting_dict['tile_print'], mask=print_mask)
+        else:
+            print_mask = result['mask']
+            img_fg = result['final_image']
+        if print_ and not painting_dict['Trigger']:
+            try:
+                index_ = len(painting_dict['location'])
+            except:
+                assert f'there must be parameter of location if choose IfSingle'
+
+            for i in range(index_):
+                start_h, start_w = int(painting_dict['location'][i][1]), int(painting_dict['location'][i][0])
+
+                length_h = min(start_h + painting_dict['dim_print_h'], img_fg.shape[0])
+                length_w = min(start_w + painting_dict['dim_print_w'], img_fg.shape[1])
+
+                change_region = img_fg[start_h: length_h, start_w: length_w, :]
+                # problem in change_mask
+                change_mask = print_mask[start_h: length_h, start_w: length_w]
+                # get real part into change mask
+                _, change_mask = cv2.threshold(change_mask, 220, 255, cv2.THRESH_BINARY)
+                mask = cv2.bitwise_not(painting_dict['mask_inv_print'])
+                img_fg[start_h:start_h + painting_dict['dim_print_h'], start_w:start_w + painting_dict['dim_print_w'], :] = change_region
+
+        clothes_mask_print = cv2.bitwise_not(print_mask)
+
+        img_bg = cv2.bitwise_and(result['pattern_image'], result['pattern_image'], mask=clothes_mask_print)
+        mask_mo = np.expand_dims(print_mask, axis=2).repeat(3, axis=2)
+        gray_mo = np.expand_dims(result['gray'], axis=2).repeat(3, axis=2)
+        img_fg = (img_fg * (mask_mo / 255) * (gray_mo / 255)).astype(np.uint8)
+        print_image = cv2.add(img_bg, img_fg)
+        return print_image
+
+    @staticmethod
+    def get_print(print_dict):
+        if not 'print_scale_list' in print_dict.keys() or print_dict['print_scale_list'][0] < 0.3:
+            print_dict['scale'] = 0.3
+        else:
+            print_dict['scale'] = print_dict['print_scale_list'][0]
+
+        if not 'IfSingle' in print_dict.keys():
+            print_dict['IfSingle'] = False
+
+        # data = minio_client.get_object(print_dict['print_path_list'][0].split("/", 1)[0], print_dict['print_path_list'][0].split("/", 1)[1])
+        data = s3.get_object(Bucket=print_dict['print_path_list'][0].split("/", 1)[0], Key=print_dict['print_path_list'][0].split("/", 1)[1])['Body']
+
+        data_bytes = BytesIO(data.read())
+        image = Image.open(data_bytes)
+        image_mode = image.mode
+        # 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑
+        if image_mode == "RGBA":
+            new_background = Image.new('RGB', image.size, (255, 255, 255))
+            new_background.paste(image, mask=image.split()[3])
+            image = new_background
+        print_dict['image'] = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+
+        # file = minio_client.get_object(print_dict['print_path_list'][0].split("/", 1)[0], print_dict['print_path_list'][0].split("/", 1)[1]).data
+        # print_dict['image'] = cv2.imdecode(np.fromstring(file, np.uint8), 1)
+
+        # image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
+        # return image
+
+        return print_dict
+
+    def crop_image(self, image, image_size_h, image_size_w, location, print_shape):
+        print_w = print_shape[1]
+        print_h = print_shape[0]
+
+        random.seed(self.random_seed)
+        # logging.info(f'overall print location : {location}')
+        # x_offset = random.randint(0, image.shape[0] - image_size_h)
+        # y_offset = random.randint(0, image.shape[1] - image_size_w)
+
+        # 1.拿到偏移量后和resize后的print宽高取余 得到真正偏移量
+        x_offset = print_w - int(location[0][1] % print_w)
+        y_offset = print_w - int(location[0][0] % print_h)
+
+        # y_offset = int(location[0][0])
+        # x_offset = int(location[0][1])
+
+        if len(image.shape) == 2:
+            image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w]
+        elif len(image.shape) == 3:
+            image = image[x_offset: x_offset + image_size_h, y_offset: y_offset + image_size_w, :]
+        return image
+
+    @staticmethod
+    def get_low_high_lab(Lab_value, L=False):
+        if L:
+            high = Lab_value + 30 if Lab_value + 30 < 255 else 255
+            low = Lab_value - 30 if Lab_value - 30 > 0 else 0
+        else:
+            high = Lab_value + 30 if Lab_value + 30 < 255 else 255
+            low = Lab_value - 30 if Lab_value - 30 > 0 else 0
+        return high, low
+
+    @staticmethod
+    def img_rotate(image, angel, scale):
+        """顺时针旋转图像任意角度
+
+        Args:
+            image (np.array): [原始图像]
+            angel (float): [逆时针旋转的角度]
+
+        Returns:
+            [array]: [旋转后的图像]
+        """
+
+        h, w = image.shape[:2]
+        center = (w // 2, h // 2)
+        # if type(angel) is not int:
+        #     angel = 0
+        M = cv2.getRotationMatrix2D(center, -angel, scale)
+        # 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]))))
+        M[0, 2] += (rotated_w - w) // 2
+        M[1, 2] += (rotated_h - h) // 2
+        # 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, (0, 0)
+
+    @staticmethod
+    def read_image(image_url):
+        # data = minio_client.get_object(image_url.split("/", 1)[0], image_url.split("/", 1)[1])
+        data = s3.get_object(Bucket=image_url.split("/", 1)[0], Key=image_url.split("/", 1)[1])['Body']
+
+        data_bytes = BytesIO(data.read())
+        image = Image.open(data_bytes)
+        image_mode = image.mode
+        # 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑
+        if image_mode == "RGBA":
+            # new_background = Image.new('RGB', image.size, (255, 255, 255))
+            # new_background.paste(image, mask=image.split()[3])
+            # image = new_background
+            return image, image_mode
+        image = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        return image, image_mode
+
+    # @staticmethod
+    # def read_image(image_url):
+    #     response = requests.get(image_url)
+    #     image_data = np.frombuffer(response.content, np.uint8)
+    #
+    #     # 解码图像
+    #     image = cv2.imdecode(image_data, 3)
+    #     return image

app/service/design/items/pipelines/scale.py

@@ -0,0 +1,54 @@
+from ..builder import PIPELINES
+import math
+import cv2
+
+
+@PIPELINES.register_module()
+class Scaling(object):
+    def __init__(self):
+        pass
+
+    # @ RunTime
+    def __call__(self, result):
+        if result['keypoint'] in ['waistband', 'shoulder', 'head_point']:
+            # milvus_db_keypoint_cache
+            distance_clo = math.sqrt(
+                (int(result['clothes_keypoint'][result['keypoint'] + '_left'][0]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'][0])) ** 2
+                +
+                (int(result['clothes_keypoint'][result['keypoint'] + '_left'][1]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'][1])) ** 2)
+
+            distance_bdy = math.sqrt((int(result['body_point_test'][result['keypoint'] + '_left'][0]) - int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1)
+            # distance_clo = math.sqrt(
+            #     (int(result['clothes_keypoint'][result['keypoint'] + '_left'].split("_")[0]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'].split("_")[0])) ** 2
+            #     +
+            #     (int(result['clothes_keypoint'][result['keypoint'] + '_left'].split("_")[1]) - int(result['clothes_keypoint'][result['keypoint'] + '_right'].split("_")[1])) ** 2)
+            #
+            # distance_bdy = math.sqrt((int(result['body_point_test'][result['keypoint'] + '_left'][0]) - int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1)
+            if distance_clo == 0:
+                result['scale'] = 10
+            else:
+                result['scale'] = distance_bdy / distance_clo
+        elif result['keypoint'] == 'toe':
+            distance_bdy = math.sqrt(
+                (int(result['body_point_test']['foot_length'][0]) - int(result['body_point_test']['foot_length'][2])) ** 2
+                +
+                (int(result['body_point_test']['foot_length'][1]) - int(result['body_point_test']['foot_length'][3])) ** 2
+            )
+
+            Blur = cv2.GaussianBlur(result['gray'], (3, 3), 0)
+            Edge = cv2.Canny(Blur, 10, 200)
+            Edge = cv2.dilate(Edge, None)
+            Edge = cv2.erode(Edge, None)
+            Contour, _ = cv2.findContours(Edge, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            Contours = sorted(Contour, key=cv2.contourArea, reverse=True)
+
+            Max_contour = Contours[0]
+            x, y, w, h = cv2.boundingRect(Max_contour)
+            width = w
+            distance_clo = width
+            result['scale'] = distance_bdy / distance_clo
+        elif result['keypoint'] == 'hand_point':
+            result['scale'] = result['scale_bag']
+        elif result['keypoint'] == 'ear_point':
+            result['scale'] = result['scale_earrings']
+        return result

app/service/design/items/pipelines/segmentation.py

@@ -0,0 +1,14 @@
+from ..builder import PIPELINES
+from ...utils.design_ensemble import get_seg_result
+
+
+@PIPELINES.register_module()
+class Segmentation(object):
+    def __init__(self, device='cpu', show=False, debug=None):
+        self.show = show
+        self.device = device
+        self.debug = debug
+
+    def __call__(self, result):
+        result['seg_result'] = get_seg_result(result["image_id"], result['image'])
+        return result

app/service/design/items/pipelines/split.py

@@ -0,0 +1,115 @@
+import logging
+import cv2
+import numpy as np
+from cv2 import cvtColor, COLOR_BGR2RGBA
+from app.service.utils.generate_uuid import generate_uuid
+from ..builder import PIPELINES
+from PIL import Image
+from ...utils.conversion_image import rgb_to_rgba
+from ...utils.upload_image import upload_png_mask
+
+
+@PIPELINES.register_module()
+class Split(object):
+    """
+    Split image into front and back layer according to the segmentation result
+    """
+
+    # KNet
+    def __call__(self, result):
+        try:
+            if 'mask' not in result.keys():
+                raise KeyError(f'Cannot find mask in result dict, please check ContourDetection is included in process pipelines.')
+            if 'seg_result' not in result.keys():  # 没过seg模型
+                result['front_mask'] = result['mask'].copy()
+                result['back_mask'] = np.zeros_like(result['mask'])
+            else:
+                temp_front = result['seg_result'] == 1
+                result['front_mask'] = (result['mask'] * (temp_front + 0).astype(np.uint8))
+                temp_back = result['seg_result'] == 2
+                result['back_mask'] = (result['mask'] * (temp_back + 0).astype(np.uint8))
+
+            if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'):
+                if len(result['front_mask'].shape) > 2:
+                    front_mask = result['front_mask'][0]
+                else:
+                    front_mask = result['front_mask']
+
+                if len(result['back_mask'].shape) > 2:
+                    back_mask = result['back_mask'][0]
+                else:
+                    back_mask = result['back_mask']
+
+                rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], result['mask'])
+                result_front_image = np.zeros_like(rgba_image)
+                result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
+
+                result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
+                front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"]))
+                result_front_image_pil = result_front_image_pil.resize(front_new_size, Image.LANCZOS)
+                front_mask = cv2.resize(front_mask, front_new_size)
+                result['front_image'], result["front_image_url"], result["front_mask_url"] = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=front_mask)
+
+                if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
+                    result_back_image = np.zeros_like(rgba_image)
+                    result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
+
+                    result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
+                    back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"]))
+                    result_back_image_pil = result_back_image_pil.resize(back_new_size, Image.LANCZOS)
+                    back_mask = cv2.resize(back_mask, back_new_size)
+                    result['back_image'], result["back_image_url"], result["back_mask_url"] = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=back_mask)
+                else:
+                    result['back_image'] = None
+                    result["back_image_url"] = None
+                    result["back_mask_url"] = None
+            return result
+        except Exception as e:
+            logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")
+
+    # @ RunTime
+    # def __call__(self, result):
+    #     try:
+    #         if 'mask' not in result.keys():
+    #             raise KeyError(f'Cannot find mask in result dict, please check ContourDetection is included in process pipelines.')
+    #         if 'seg_result' not in result.keys():  # 没过seg模型
+    #             result['front_mask'] = result['mask'].copy()
+    #             result['back_mask'] = np.zeros_like(result['mask'])
+    #         else:
+    #             temp_front = result['seg_result'] == 1
+    #             result['front_mask'] = (result['mask'] * (temp_front + 0).astype(np.uint8))
+    #             temp_back = result['seg_result'] == 2
+    #             result['back_mask'] = (result['mask'] * (temp_back + 0).astype(np.uint8))
+    #
+    #         if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'):
+    #             if len(result['front_mask'].shape) > 2:
+    #                 front_mask = result['front_mask'][0]
+    #             else:
+    #                 front_mask = result['front_mask']
+    #
+    #             rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], result['mask'])
+    #             result_front_image = np.zeros_like(rgba_image)
+    #             result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
+    #
+    #             result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
+    #             front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"]))
+    #             result_front_image_pil = result_front_image_pil.resize(front_new_size, Image.LANCZOS)
+    #             front_mask = cv2.resize(front_mask, front_new_size)
+    #             result['front_image'], result["front_image_url"], result["front_mask_url"] = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=front_mask)
+    #
+    #             if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
+    #                 result_back_image = np.zeros_like(rgba_image)
+    #                 result_back_image[result['back_mask'] != 0] = rgba_image[result['back_mask'] != 0]
+    #
+    #                 result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
+    #                 back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"]))
+    #                 result_back_image_pil = result_back_image_pil.resize(back_new_size, Image.LANCZOS)
+    #                 back_mask = cv2.resize(result['back_mask'], back_new_size)
+    #                 result['back_image'], result["back_image_url"], result["back_mask_url"] = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=back_mask)
+    #             else:
+    #                 result['back_image'] = None
+    #                 result["back_image_url"] = None
+    #                 result["back_mask_url"] = None
+    #         return result
+    #     except Exception as e:
+    #         logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")