diff --git a/app/api/api_mannequins_edit.py b/app/api/api_mannequins_edit.py new file mode 100644 index 0000000..5cfaf3d --- /dev/null +++ b/app/api/api_mannequins_edit.py @@ -0,0 +1,40 @@ +import json +import logging + +from fastapi import APIRouter, HTTPException + +from app.schemas.mannequin_edit import MannequinModel +from app.schemas.response_template import ResponseModel +from app.service.mannequins_edit.service import MannequinEditService + +router = APIRouter() +logger = logging.getLogger() + + +@router.post("/mannequins_edit") +def mannequins_edit(request_data: MannequinModel): + """ + 模特腿长调整 + 创建一个具有以下参数的请求体: + - **mannequins**: mannequins url等信息 + - **scale**: 大腿小腿比例 + - **bucket_name**: bucket name + - **mannequin_name**: 模特名称 + + 示例参数： + - **{ + "mannequins": "aida-sys-image/models/male/dc36ce58-46c3-4b6f-8787-5ca7d6fc26e6.png", + "scale": [0.75, 0.75], + "bucket_name": "test", + "mannequin_name": "mannequin_name" + }** + """ + try: + logger.info(f"mannequins_edit request item is : @@@@@@:{json.dumps(request_data.dict())}") + service = MannequinEditService(request_data) + data = service() + logger.info(f"mannequins_edit response @@@@@@:{json.dumps(data)}") + except Exception as e: + logger.warning(f"mannequins_edit Run Exception @@@@@@:{e}") + raise HTTPException(status_code=404, detail=str(e)) + return ResponseModel(data=data) diff --git a/app/api/api_route.py b/app/api/api_route.py index 3890316..33e238e 100644 --- a/app/api/api_route.py +++ b/app/api/api_route.py @@ -8,9 +8,10 @@ from app.api import api_design from app.api import api_design_pre_processing from app.api import api_generate_image from app.api import api_image2sketch +from app.api import api_mannequins_edit from app.api import api_prompt_generation -from app.api import api_super_resolution from app.api import api_recommendation +from app.api import api_super_resolution from app.api import api_test router = APIRouter() @@ -28,3 +29,4 @@ router.include_router(api_brighten.router, tags=['api_brighten'], prefix="/api") router.include_router(api_query_image.router, tags=['api_query_image'], prefix="/api") router.include_router(api_brand_dna.router, tags=['api_brand_dna'], prefix="/api") router.include_router(api_recommendation.router, tags=['api_recommendation'], prefix="/api") +router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'], prefix="/api") diff --git a/app/schemas/mannequin_edit.py b/app/schemas/mannequin_edit.py new file mode 100644 index 0000000..c5514d6 --- /dev/null +++ b/app/schemas/mannequin_edit.py @@ -0,0 +1,8 @@ +from pydantic import BaseModel + + +class MannequinModel(BaseModel): + mannequins: str + scale: list[float, float] + bucket_name: str + mannequin_name: str diff --git a/app/service/mannequins_edit/service.py b/app/service/mannequins_edit/service.py new file mode 100644 index 0000000..bbb6cc5 --- /dev/null +++ b/app/service/mannequins_edit/service.py @@ -0,0 +1,101 @@ +import cv2 +import mediapipe as mp +import numpy as np +from minio import Minio + +from app.core.config import MINIO_URL, MINIO_ACCESS, MINIO_SECRET, MINIO_SECURE +from app.schemas.mannequin_edit import MannequinModel +from app.service.utils.new_oss_client import oss_get_image, oss_upload_image + +minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) + + +class MannequinEditService(): + def __init__(self, request_data): + self.scale = request_data.scale + 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 + if self.image.shape[2] == 4: + self.bgr = self.image[:, :, :3] + self.alpha = self.image[:, :, 3] + self.bgr = cv2.bitwise_and(self.bgr, self.bgr, mask=cv2.normalize(self.alpha, None, 0, 1, cv2.NORM_MINMAX)) + self.h, self.w, _ = self.bgr.shape + else: + self.bgr = self.image + self.h, self.w, _ = self.bgr.shape + 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" + + def attitude_detection(self): + mp_pose = mp.solutions.pose + pose = mp_pose.Pose() + + # 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 + + # 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) + + return leg_top, leg_bottom + + 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, :] + + # 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)) + + if self.alpha is not None: + # 拼接 alpha 通道 + # 合并 BGR 通道和 alpha 通道 + new_image = np.dstack((new_bgr, new_bgr_alpha)) + else: + new_image = new_bgr + return new_image + + +if __name__ == '__main__': + request_data = MannequinModel( + mannequins="aida-sys-image/models/male/dc36ce58-46c3-4b6f-8787-5ca7d6fc26e6.png", + scale=[0.75, 0.75], + bucket_name="test", + mannequin_name="mannequin_name" + ) + service = MannequinEditService(request_data) + print(service())