feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/service/design_pre_processing/service.py

@@ -1,19 +1,22 @@
 import logging
+import os
 import time
 
 import cv2
 import numpy as np
 import torch
 import tritonclient.grpc as grpcclient
+from minio import Minio
 from pymilvus import MilvusClient
 from urllib3.exceptions import ResponseError
 
-from app.core.config import *
+from app.core.config import settings, SR_MODEL_NAME, SR_TRITON_URL, MILVUS_TABLE_KEYPOINT, KEYPOINT_RESULT_TABLE_FIELD_SET
 from app.schemas.pre_processing import DesignPreProcessingModel
 from app.service.design_fast.utils.design_ensemble import get_seg_result, get_keypoint_result
-from app.service.utils.oss_client import oss_get_image, oss_upload_image
+from app.service.utils.new_oss_client import oss_get_image, oss_upload_image
 
 logger = logging.getLogger()
+minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
 
 
 class DesignPreprocessing:
@@ -46,11 +49,12 @@ class DesignPreprocessing:
                 del d['keypoint_result']
         return result
 
-    def read_image(self, image_list):
+    @staticmethod
+    def read_image(image_list):
         for obj in image_list:
             # file = self.minio_client.get_object(obj['image_url'].split("/", 1)[0], obj['image_url'].split("/", 1)[1]).data
             # image = cv2.imdecode(np.frombuffer(file, np.uint8), 1)
-            image = oss_get_image(bucket=obj['image_url'].split("/", 1)[0], object_name=obj['image_url'].split("/", 1)[1], data_type="cv2")
+            image = oss_get_image(oss_client=minio_client, bucket=obj['image_url'].split("/", 1)[0], object_name=obj['image_url'].split("/", 1)[1], data_type="cv2")
             if len(image.shape) == 2:
                 image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
             elif image.shape[2] == 4:  # 如果是四通道 mask
@@ -59,7 +63,8 @@ class DesignPreprocessing:
         return image_list
 
     # @ RunTime
-    def bounding_box(self, image_list):
+    @staticmethod
+    def bounding_box(image_list):
         for item in image_list:
             image = item['image_obj']
             height, width = image.shape[:2]
@@ -77,11 +82,6 @@ class DesignPreprocessing:
                 x_max = max(x_max, x + w)
                 y_max = max(y_max, y + h)
 
-            if IF_DEBUG_SHOW:
-                image_with_big_rect = cv2.rectangle(image.copy(), (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
-                cv2.imshow("bounding_box image", image_with_big_rect)
-                cv2.waitKey(0)
-
             # 根据大矩形的坐标来裁剪原始图像
             if len(contours) > 0:
                 cropped_image = image[y_min:y_max, x_min:x_max]
@@ -107,7 +107,8 @@ class DesignPreprocessing:
             item['obj'] = padded_image
         return image_list
 
-    def super_resolution(self, image_list):
+    @staticmethod
+    def super_resolution(image_list):
         for item in image_list:
             # 判断 两边是否同时都小于512 因为此处做四倍超分
             if item['obj'].shape[0] <= 512 and item['obj'].shape[1] <= 512:
@@ -136,7 +137,7 @@ class DesignPreprocessing:
                 # self.minio_client.put_object(item['image_url'].split("/", 1)[0], item['image_url'].split("/", 1)[1], io.BytesIO(image_bytes), len(image_bytes), content_type="image/jpeg", )
                 bucket_name = item['image_url'].split("/", 1)[0]
                 object_name = item['image_url'].split("/", 1)[1]
-                oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+                oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
                 logging.info(f"Object '{item['image_url'].split('/', 1)[1]}' overwritten successfully.")
             except ResponseError as err:
                 logging.warning(f"Error: {err}")
@@ -144,7 +145,6 @@ class DesignPreprocessing:
 
     # @ RunTime
     def infer_image(self, image_list):
-        seg_result = None
         for sketch in image_list:
             # 小写
             image_category = sketch['image_category'].lower()
@@ -156,36 +156,17 @@ class DesignPreprocessing:
                 _, seg_cache = self.load_seg_result(sketch['image_id'])
                 if not _:
                     # 推理获得seg 结果
-                    seg_result = get_seg_result(sketch["image_id"], sketch['obj'])[0]
+                    seg_result = get_seg_result(sketch['obj'])[0]
                     self.save_seg_result(seg_result, sketch['image_id'])
                     logger.info(f"{sketch['image_id']} image size is :{sketch['obj'].shape} , seg cache size is :{seg_result.shape}")
                 else:
                     logger.info(f"{sketch['image_id']} image size is :{sketch['obj'].shape} , seg cache size is :{seg_cache.shape}")
 
-            if IF_DEBUG_SHOW:
-                debug_show_image = sketch['obj'].copy()
-                points_list = []
-                point_size = 1
-                point_color = (0, 0, 255)  # BGR
-                thickness = 4  # 可以为 0 、4、8
-                for i in sketch['keypoint_result'].values():
-                    points_list.append((int(i[1]), int(i[0])))
-                for point in points_list:
-                    cv2.circle(debug_show_image, point, point_size, point_color, thickness)
-                cv2.imshow("seg_result", seg_result)
-                cv2.imshow("", debug_show_image)
-                cv2.waitKey(0)
-            # # 关键点在上部则推理seg
-            # if sketch["site"] == "up":
-            #     # 判断seg缓存是否存在,是否与当前图片shape一致
-            #     seg_result = self.search_seg_result(sketch["image_id"], sketch["obj"].shape)
-            #     if seg_result is False:
-            #         # 推理seg + 保存
-            #         seg_result = get_seg_result(sketch['image_id'], sketch['obj'])
         return image_list
 
     # @ RunTime
-    def composing_image(self, image_list):
+    @staticmethod
+    def composing_image(image_list):
         for image in image_list:
             ''' 比例相同 整合上下装代码'''
             image_width = image['obj'].shape[1]
@@ -194,21 +175,18 @@ class DesignPreprocessing:
             if waist_width / scale >= image_width:
                 add_width = int((waist_width / scale - image_width) / 2)
                 ret = cv2.copyMakeBorder(image['obj'], 0, 0, add_width, add_width, cv2.BORDER_CONSTANT, value=(256, 256, 256))
-                if IF_DEBUG_SHOW:
-                    cv2.imshow("composing_image", ret)
-                    cv2.waitKey(0)
                 image_bytes = cv2.imencode(".jpg", ret)[1].tobytes()
                 # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
                 bucket_name = image['image_url'].split('/', 1)[0]
                 object_name = image['image_url'].split('/', 1)[1].replace('.', '-show.')
-                oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+                oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
                 image['show_image_url'] = f"{bucket_name}/{object_name}"
             else:
                 image_bytes = cv2.imencode(".jpg", image['obj'])[1].tobytes()
                 # image['show_image_url'] = f"{image['image_url'].split('/', 1)[0]}/{self.minio_client.put_object(image['image_url'].split('/', 1)[0], image['image_url'].split('/', 1)[1].replace('.', '-show.'), io.BytesIO(image_bytes), len(image_bytes), content_type='image/jpeg').object_name}"
                 bucket_name = image['image_url'].split('/', 1)[0]
                 object_name = image['image_url'].split('/', 1)[1].replace('.', '-show.')
-                oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
+                oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
                 image['show_image_url'] = f"{bucket_name}/{object_name}"
 
             # if image['site'] == 'down':
@@ -261,7 +239,7 @@ class DesignPreprocessing:
 
     @staticmethod
     def load_seg_result(image_id):
-        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
+        file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy"
         try:
             seg_result = np.load(file_path)
             return True, seg_result
@@ -274,7 +252,7 @@ class DesignPreprocessing:
 
     @staticmethod
     def save_seg_result(seg_result, image_id):
-        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
+        file_path = f"{settings.SEG_CACHE_PATH}{image_id}.npy"
         try:
             np.save(file_path, seg_result)
             logging.debug(f"保存成功,{os.path.abspath(file_path)}")
@@ -283,7 +261,7 @@ class DesignPreprocessing:
 
     def keypoint_cache(self, sketch):
         try:
-            client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS)
+            client = MilvusClient(uri=settings.MILVUS_URL, token=settings.MILVUS_TOKEN, db_name=settings.MILVUS_ALIAS)
             keypoint_id = sketch['image_id']
             res = client.query(
                 collection_name=MILVUS_TABLE_KEYPOINT,
@@ -307,7 +285,8 @@ class DesignPreprocessing:
             return False
 
     # @ RunTime
-    def infer_keypoint_result(self, sketch):
+    @staticmethod
+    def infer_keypoint_result(sketch):
         keypoint_infer_result = get_keypoint_result(sketch["obj"], sketch['site'])  # 推理结果
         return keypoint_infer_result
 
@@ -320,14 +299,14 @@ class DesignPreprocessing:
         else:
             zeros = np.zeros(4, dtype=int)
             result = np.concatenate([keypoint_infer_result.flatten(), zeros])
-        data = [
-            [int(sketch['image_id'])],
-            [sketch['site']],
-            [result.tolist()]
-        ]
+        # [
+        #     [int(sketch['image_id'])],
+        #     [sketch['site']],
+        #     [result.tolist()]
+        # ]
         try:
             # connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
-            start_time = time.time()
+            time.time()
             # collection = Collection(MILVUS_TABLE_KEYPOINT)  # Get an existing collection.
             # mr = collection.insert(data)
             # logging.info(f"save keypoint time : {time.time() - start_time}")
@@ -344,11 +323,11 @@ class DesignPreprocessing:
         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 = [
-            [int(sketch['image_id'])],
-            ["all"],
-            [result.tolist()]
-        ]
+        # [
+        #     [int(sketch['image_id'])],
+        #     ["all"],
+        #     [result.tolist()]
+        # ]
         try:
             # connections.connect(alias=MILVUS_ALIAS, host=MILVUS_DB_HOST, port=MILVUS_PORT)
             # start_time = time.time()