feat（新功能）: 模特编辑逻辑修改

fix（修复bug）:
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/service/mannequins_edit/service.py

@@ -1,5 +1,4 @@
 import cv2
-import mediapipe as mp
 import numpy as np
 from minio import Minio
 
@@ -13,6 +12,8 @@ minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET
 class MannequinEditService():
     def __init__(self, request_data):
         self.scale = request_data.scale
+        self.top = request_data.top
+        self.bottom = request_data.bottom
         self.image = oss_get_image(oss_client=minio_client, bucket=request_data.mannequins.split('/')[0], object_name=request_data.mannequins[request_data.mannequins.find('/') + 1:], data_type="cv2")
         self.mannequin_name = request_data.mannequin_name
         self.bucket_name = request_data.bucket_name
@@ -27,59 +28,33 @@ class MannequinEditService():
             self.alpha = None
 
     def __call__(self, *args, **kwargs):
-        leg_top, leg_bottom = self.attitude_detection()
-        if leg_top and leg_bottom:
-            new_mannequin = self.resize_leg(leg_top, leg_bottom)
-            _, encoded_image = cv2.imencode('.png', new_mannequin)
-            image_bytes = encoded_image.tobytes()
-            req = oss_upload_image(oss_client=minio_client, bucket=self.bucket_name, object_name=f"{self.mannequin_name}.png", image_bytes=image_bytes)
-            return req.bucket_name + "/" + req.object_name
-        else:
-            return "No leg detected"
+        new_mannequin = self.resize_leg(self.top, self.bottom)
+        _, encoded_image = cv2.imencode('.png', new_mannequin)
+        image_bytes = encoded_image.tobytes()
+        req = oss_upload_image(oss_client=minio_client, bucket=self.bucket_name, object_name=f"{self.mannequin_name}.png", image_bytes=image_bytes)
+        return req.bucket_name + "/" + req.object_name
 
-    def attitude_detection(self):
-        mp_pose = mp.solutions.pose
-        pose = mp_pose.Pose()
+    def resize_leg(self, top, bottom):
+        # 上部
+        top_part = self.bgr[:top, :]
+        top_part_alpha = self.alpha[:top, :]
 
-        # 将 BGR 图像转换为 RGB 格式
-        image_rgb = cv2.cvtColor(self.bgr, cv2.COLOR_BGR2RGB)
-        leg_top, leg_bottom = None, None
-        # 进行姿态检测
-        results = pose.process(image_rgb)
-        if results.pose_landmarks:
-            # 获取腿部关键点
-            landmarks = results.pose_landmarks.landmark
+        # 需要resize 部分
+        part_resize = self.bgr[top:bottom, :]
+        part_resize_alpha = self.alpha[top:bottom, :]
 
-            # 找到腿部上边界和下边界
-            leg_top = int(landmarks[mp_pose.PoseLandmark.LEFT_HIP].y * self.h)
-            leg_bottom = int(max(landmarks[mp_pose.PoseLandmark.LEFT_ANKLE].y,
-                                 landmarks[mp_pose.PoseLandmark.RIGHT_ANKLE].y) * self.h)
+        # 下部
+        part_bottom = self.bgr[bottom:, :]
+        part_bottom_alpha = self.alpha[bottom:, :]
 
-        return leg_top, leg_bottom
+        new_height = int((bottom - top) * self.scale)
 
-    def resize_leg(self, leg_top, leg_bottom):
-        # 上半身
-        top_part_bgr = self.bgr[:leg_top, :]
-        top_part_bgr_alpha = self.alpha[:leg_top, :]
+        resized_thigh = cv2.resize(part_resize, (self.w, new_height), interpolation=cv2.INTER_LINEAR)
+        resized_thigh_alpha = cv2.resize(part_resize_alpha, (self.w, new_height), interpolation=cv2.INTER_LINEAR)
 
-        # 小腿
-        part_thigh = self.bgr[leg_top:leg_bottom, :]
-        part_thigh_alpha = self.alpha[leg_top:leg_bottom, :]
-
-        # 大腿
-        part_calf = self.bgr[leg_bottom:, :]
-        part_calf_alpha = self.alpha[leg_bottom:, :]
-
-        new_thigh_height = int((leg_bottom - leg_top) * self.scale[0])
-        new_calf_height = int((self.h - leg_bottom) * self.scale[1])
-
-        resized_thigh = cv2.resize(part_thigh, (self.w, new_thigh_height), interpolation=cv2.INTER_LINEAR)
-        resized_thigh_alpha = cv2.resize(part_thigh_alpha, (self.w, new_thigh_height), interpolation=cv2.INTER_LINEAR)
-        resized_calf = cv2.resize(part_calf, (self.w, new_calf_height), interpolation=cv2.INTER_LINEAR)
-        resized_calf_alpha = cv2.resize(part_calf_alpha, (self.w, new_calf_height), interpolation=cv2.INTER_LINEAR)
-
-        new_bgr = np.vstack((top_part_bgr, resized_thigh, resized_calf))
-        new_bgr_alpha = np.vstack((top_part_bgr_alpha, resized_thigh_alpha, resized_calf_alpha))
+        # 组合
+        new_bgr = np.vstack((top_part, resized_thigh, part_bottom))
+        new_bgr_alpha = np.vstack((top_part_alpha, resized_thigh_alpha, part_bottom_alpha))
 
         if self.alpha is not None:
             # 拼接 alpha 通道
@@ -93,9 +68,11 @@ class MannequinEditService():
 if __name__ == '__main__':
     request_data = MannequinModel(
         mannequins="aida-sys-image/models/male/dc36ce58-46c3-4b6f-8787-5ca7d6fc26e6.png",
-        scale=[0.75, 0.75],
+        scale=0.1,
         bucket_name="test",
-        mannequin_name="mannequin_name"
+        mannequin_name="mannequin_name",
+        top=270,
+        bottom=432
     )
     service = MannequinEditService(request_data)
     print(service())