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/generate_image/utils/image_processing.py

@@ -7,7 +7,7 @@ import numpy as np
 import torch
 import tritonclient.http as httpclient
 
-from app.core.config import *
+from app.core.config import settings, DESIGN_MODEL_URL, DESIGN_MODEL_NAME
 from app.service.generate_image.utils.upload_sd_image import upload_stain_png_sd, upload_face_png_sd
 
 logger = logging.getLogger()
@@ -65,40 +65,40 @@ def get_contours(image):
 #     transformed_img = image.astype(np.float32)
 #     # 输入集
 #     inputs = [
-#         httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32")
+#         httpclient.InferInput(DESIGN_MODEL_NAME, transformed_img.shape, datatype="FP32")
 #     ]
 #     inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
 #     # 输出集
 #     outputs = [
-#         httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
+#         httpclient.InferRequestedOutput("seg_input__0", binary_data=True),
 #     ]
 #     results = client.infer(model_name=SEGMENTATION['name'], inputs=inputs, outputs=outputs)
 #     # 推理
 #     # 取结果
-#     inference_output1 = torch.from_numpy(results.as_numpy(SEGMENTATION['output']))
+#     inference_output1 = torch.from_numpy(results.as_numpy("seg_input__0"))
 #     seg_result = seg_postprocess(inference_output1, ori_shape)
 #     return seg_result
 
 def seg_infer_image(image_obj):
     image, ori_shape = seg_preprocess(image_obj)
-    client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}")
+    client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
     transformed_img = image.astype(np.float32)
     # 输入集
     inputs = [
-        httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32")
+        httpclient.InferInput("seg_input__0", transformed_img.shape, datatype="FP32")
     ]
     inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
     # 输出集
     outputs = [
-        httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
+        httpclient.InferRequestedOutput("seg_output__0", binary_data=True),
     ]
     start_time = time.time()
-    results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs)
+    results = client.infer(model_name=DESIGN_MODEL_NAME, inputs=inputs, outputs=outputs)
     print(f"KNet infer time is :{time.time() - start_time}")
     # 推理
     # 取结果
-    inference_output1 = results.as_numpy(SEGMENTATION['output'])
-    seg_result = seg_postprocess(inference_output1, ori_shape)
+    inference_output1 = results.as_numpy("seg_output__0")
+    seg_result = seg_postprocess(inference_output1)
     return seg_result
 
 
@@ -110,7 +110,7 @@ def seg_infer_image(image_obj):
 #     return seg_pred
 
 # KNet
-def seg_postprocess(output, ori_shape):
+def seg_postprocess(output):
     # seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
     # seg_logit = F.softmax(seg_logit, dim=1)
     # seg_pred = seg_logit.argmax(dim=1)
@@ -201,7 +201,7 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):
     # 如果有连续的纯白区域存在
     if filtered_contours:
         # 将纯白区域替换为灰色
-        if DEBUG:
+        if settings.DEBUG:
             for cnt in filtered_contours:
                 x, y, w, h = cv2.boundingRect(cnt)
                 # 在原始图像上进行替换
@@ -216,7 +216,7 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):
 
     if is_pure_white:
         return False, None
-    if DEBUG:
+    if settings.DEBUG:
         for corner_coords in [
             (0, 0),
             # (0, width - spot_size),
@@ -236,7 +236,7 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):
     ]:
         cv2.rectangle(dst, corner_coords, (corner_coords[0] + spot_size, corner_coords[1] + spot_size), (0, 0, 255), 2)
     cv2.rectangle(dst, (center_x - spot_size // 2, center_y - spot_size // 2), (center_x + spot_size // 2, center_y + spot_size // 2), (0, 255, 0), 2)  # 在原始图像上绘制矩形框
-    image_url = upload_stain_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png")
+    upload_stain_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png")
     return True, image
 
 
@@ -262,10 +262,10 @@ def generate_category_recognition(image, gender):
     scores = inference_output.detach().numpy()
     import pandas as pd
 
-    attr_type = pd.read_csv(CATEGORY_PATH)
+    attr_type = pd.read_csv(settings.CATEGORY_PATH)
     colattr = list(attr_type['labelName'])
 
-    task = attr_type['taskName'][0]
+    # attr_type['taskName'][0]
 
     maxsc = np.max(scores[0][:5])
     indexs = np.argwhere(scores == maxsc)[:, 1]
@@ -321,12 +321,13 @@ def face_detect_pic(image, user_id, category, tasks_id):
     # cv2.imshow("gray", gray)
 
     # 2、训练一组人脸
+    FACE_CLASSIFIER = ""
     face_detector = cv2.CascadeClassifier(FACE_CLASSIFIER)
 
     # 3、检测人脸（用灰度图检测，返回人脸矩形坐标(4个角)）
     faces_rect = face_detector.detectMultiScale(gray, 1.05, 3)
 
-    if DEBUG:
+    if settings.DEBUG:
         dst = image.copy()
         for x, y, w, h in faces_rect:
             cv2.rectangle(dst, (x, y), (x + w, y + h), (0, 0, 255), 3)  # 画出矩形框
@@ -336,7 +337,7 @@ def face_detect_pic(image, user_id, category, tasks_id):
     dst = image.copy()
     for x, y, w, h in faces_rect:
         cv2.rectangle(dst, (x, y), (x + w, y + h), (0, 0, 255), 3)  # 画出矩形框
-    image_url = upload_face_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png")
+    upload_face_png_sd(dst, user_id=user_id, category=f"{category}", object_name=f"{tasks_id}.png")
     return len(faces_rect)