feat（新功能）:

fix（修复bug）: design 分割预处理新增25padding，后处理取消插值处理
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/service/design_fast/utils/design_ensemble.py

@@ -13,7 +13,6 @@ import cv2
 import mmcv
 import numpy as np
 import torch
-import torch.nn.functional as F
 import tritonclient.http as httpclient
 
 from app.core.config import *
@@ -85,6 +84,9 @@ def seg_preprocess(img_path):
     if ori_shape != (img_scale_w, img_scale_h):
         # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
         img = cv2.resize(img, (img_scale_h, img_scale_w))
+
+    # 扩充25的白边
+    img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
     # img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
     preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
     return preprocessed_img, ori_shape
@@ -114,9 +116,9 @@ def get_seg_result(image_id, image):
 
 # no cache
 def seg_postprocess(image_id, output, ori_shape):
-    seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
-    seg_pred = seg_logit.cpu().numpy()
-    return seg_pred[0]
+    seg_logit = cv2.resize(output[0][0].astype(np.uint8), (ori_shape[1] + 50, ori_shape[0] + 50))
+    seg_logit = seg_logit[25: - 25, 25: - 25]
+    return seg_logit
 
 
 def key_point_show(image_path, key_point_result=None):