feat（新功能）:

fix（修复bug）:  print 于 sketch拉伸导致的print比例不正确问题
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/service/design_fast/pipeline/loading.py

@@ -1,9 +1,6 @@
-import io
 import logging
 
 import cv2
-import numpy as np
-from PIL import Image
 
 from app.service.utils.new_oss_client import oss_get_image
 
@@ -38,6 +35,18 @@ class LoadImage:
 
     def __call__(self, result):
         result['image'], result['pre_mask'] = self.read_image(result['path'])
+
+        # 判断是否resize sketch 保留ori image 用于模型输入
+        result['ori_image'] = result['image']
+        if result['resize_scale'][0] != 0 and result['resize_scale'][1] != 0:
+            height, width = result['image'].shape[:2]
+            # 计算新的宽度和高度
+            new_width = int(width * result['resize_scale'][0])
+            new_height = int(height * result['resize_scale'][1])
+            # 使用cv2.resize()函数进行缩放
+            result['image'] = cv2.resize(result['image'], (new_width, new_height))
+            if result['pre_mask'] is not None:
+                result['pre_mask'] = cv2.resize(result['pre_mask'], (new_width, new_height))
         result['gray'] = cv2.cvtColor(result['image'], cv2.COLOR_BGR2GRAY)
         result['keypoint'] = self.get_keypoint(result['name'])
         result['img_shape'] = result['image'].shape

app/service/design_fast/pipeline/segmentation.py

@@ -36,12 +36,27 @@ class Segmentation:
             # preview 过模型 不缓存
             if "preview_submit" in result.keys() and result['preview_submit'] == "preview":
                 # 推理获得seg 结果
-                seg_result = get_seg_result(result["image_id"], result['image'])
+                seg_result = get_seg_result(result["image_id"], result['ori_image'])
+                if result['resize_scale'][0] != 0 and result['resize_scale'][1] != 0:
+                    height, width = seg_result.shape[:2]
+                    # 计算新的宽度和高度
+                    new_width = int(width * result['resize_scale'][0])
+                    new_height = int(height * result['resize_scale'][1])
+                    # 使用cv2.resize()函数进行缩放
+                    seg_result = cv2.resize(seg_result, (new_width, new_height))
             # submit 过模型 缓存
             elif "preview_submit" in result.keys() and result['preview_submit'] == "submit":
                 # 推理获得seg 结果
-                seg_result = get_seg_result(result["image_id"], result['image'])
+                seg_result = get_seg_result(result["image_id"], result['ori_image'])
-                self.save_seg_result(seg_result, result['image_id'])
+                seg_result_save = seg_result
+                if result['resize_scale'][0] != 0 and result['resize_scale'][1] != 0:
+                    height, width = seg_result.shape[:2]
+                    # 计算新的宽度和高度
+                    new_width = int(width * result['resize_scale'][0])
+                    new_height = int(height * result['resize_scale'][1])
+                    # 使用cv2.resize()函数进行缩放
+                    seg_result = cv2.resize(seg_result, (new_width, new_height))
+                self.save_seg_result(seg_result_save, result['image_id'])
             #  null  正常流程 加载本地缓存 无缓存则过模型
             else:
                 # 本地查询seg 缓存是否存在
@@ -49,8 +64,16 @@ class Segmentation:
                 # 判断缓存和实际图片size是否相同
                 if not _ or result["image"].shape[:2] != seg_result.shape:
                     # 推理获得seg 结果
-                    seg_result = get_seg_result(result["image_id"], result['image'])
+                    seg_result = get_seg_result(result["image_id"], result['ori_image'])
-                    self.save_seg_result(seg_result, result['image_id'])
+                    seg_result_save = seg_result
+                    if result['resize_scale'][0] != 0 and result['resize_scale'][1] != 0:
+                        height, width = seg_result.shape[:2]
+                        # 计算新的宽度和高度
+                        new_width = int(width * result['resize_scale'][0])
+                        new_height = int(height * result['resize_scale'][1])
+                        # 使用cv2.resize()函数进行缩放
+                        seg_result = cv2.resize(seg_result, (new_width, new_height))
+                    self.save_seg_result(seg_result_save, result['image_id'])
             result['seg_result'] = seg_result
 
             # 处理前片后片

app/service/design_fast/pipeline/split.py

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

app/service/utils/new_oss_client.py

@@ -82,7 +82,7 @@ if __name__ == '__main__':
     # url = "aida-users/89/sketchboard/female/Dress/e6724ab7-8d3f-4677-abe0-c3e42ab7af85.jpeg"
     # url = "aida-users/87/print/956614a2-7e75-4fbe-9ed0-c1831e37a2c9-4-87.png"
     # url = "aida-users/89/single_logo/123-89.png"
-    url = "aida-users/89/123-89.png"
+    url = "aida-results/result_40e527bf-e46d-11ef-813d-0826ae3ad6b3.png"
 
     # url = "aida-collection-element/12148/Sketchboard/95ea577b-305b-4a62-b30a-39c0dd3ddb3f.png"
     read_type = "2"