# -*- coding: utf-8 -*- """ PLT 裁片处理微服务 独立部署到阿里云 SAE """ import os import io import base64 import json from typing import List, Optional from fastapi import FastAPI, UploadFile, File, Form, HTTPException from fastapi.middleware.cors import CORSMiddleware from pydantic import BaseModel import numpy as np import cv2 from PIL import Image from shapely import affinity from scipy.optimize import linear_sum_assignment from pltreader import PltReader app = FastAPI(title="PLT Processing Service") # CORS 配置 app.add_middleware( CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # ==================== 数据模型 ==================== class PieceInfo(BaseModel): """单个裁片信息""" size: str image_base64: str width_px: int height_px: int width_cm: float height_cm: float center_x_cm: float center_y_cm: float left_cm: float top_cm: float class GroupInfo(BaseModel): """裁片分组信息""" group_id: int pieces: List[PieceInfo] class ProcessPltResponse(BaseModel): """API 响应""" success: bool total_groups: int groups: List[GroupInfo] # ==================== 辅助函数 ==================== def parse_plt_file(file_content: str, tolerance: int = 10): """解析 PLT 文件内容""" reader = PltReader(io.StringIO(file_content)) output = reader.get_output(tolerance=tolerance) return output def apply_rotation_to_image(pil_img: Image.Image, rotation: int) -> Image.Image: """应用旋转变换""" if rotation == 90: return pil_img.rotate(-90, expand=True) elif rotation == -90: return pil_img.rotate(90, expand=True) elif rotation == 180: return pil_img.rotate(180, expand=True) return pil_img def calculate_piece_coordinates(polygon, bounds, plt_to_cm, rotation=0): """计算裁片坐标（厘米单位）""" min_x, min_y, max_x, max_y = bounds centroid = polygon.centroid orig_center_x = (centroid.x - min_x) * plt_to_cm orig_center_y = (centroid.y - min_y) * plt_to_cm piece_bounds = polygon.bounds orig_piece_width = (piece_bounds[2] - piece_bounds[0]) * plt_to_cm orig_piece_height = (piece_bounds[3] - piece_bounds[1]) * plt_to_cm orig_canvas_width = (max_x - min_x) * plt_to_cm orig_canvas_height = (max_y - min_y) * plt_to_cm if rotation == 90: center_x = orig_center_y center_y = orig_canvas_width - orig_center_x piece_width, piece_height = orig_piece_height, orig_piece_width elif rotation == -90: center_x = orig_canvas_height - orig_center_y center_y = orig_center_x piece_width, piece_height = orig_piece_height, orig_piece_width elif rotation == 180: center_x = orig_canvas_width - orig_center_x center_y = orig_canvas_height - orig_center_y piece_width, piece_height = orig_piece_width, orig_piece_height else: center_x, center_y = orig_center_x, orig_center_y piece_width, piece_height = orig_piece_width, orig_piece_height left_cm = center_x - piece_width / 2 top_cm = center_y - piece_height / 2 return { "center_x_cm": round(center_x, 2), "center_y_cm": round(center_y, 2), "left_cm": round(left_cm, 2), "top_cm": round(top_cm, 2), "width_cm": round(piece_width, 2), "height_cm": round(piece_height, 2) } # ==================== API 接口 ==================== @app.get("/health") async def health_check(): """健康检查""" return {"status": "healthy", "service": "plt-processor"} @app.post("/process", response_model=ProcessPltResponse) async def process_plt( file: UploadFile = File(..., description="PLT 文件"), size_labels: str = Form(..., description="尺码标签 JSON 数组"), dpi: int = Form(150, description="输出图片分辨率（DPI）"), rotation: int = Form(0, description="强制旋转角度 (0/90/-90/180)") ): """ PLT 裁片处理接口 """ try: # 1. 解析参数 try: size_labels_list = json.loads(size_labels) except: raise HTTPException(status_code=400, detail="size_labels 格式错误") size_num = len(size_labels_list) scale_factor = dpi / 1016 plt_to_cm = 2.54 / 1016 # 2. 读取文件 file_content = (await file.read()).decode('utf-8', errors='ignore') # 3. 解析 PLT print(f"[PLT] 正在处理文件: {file.filename}") output = parse_plt_file(file_content) # 4. 获取尺码聚类 clusters = output.get_single_size_info(size_num=size_num) # 5. 建立匹配关系 base_cluster = [piece for piece in clusters[0] if piece["parent"] is None] match_result = {piece["index"]: [piece["index"]] for piece in base_cluster} for size_index in range(1, len(clusters)): compare_cluster = [piece for piece in clusters[size_index] if piece["parent"] is None] cost_matrix = np.zeros((len(base_cluster), len(compare_cluster))) for i in range(len(base_cluster)): for j in range(len(compare_cluster)): poly1 = base_cluster[i]["data"] poly2 = compare_cluster[j]["data"] poly1 = affinity.translate(poly1, xoff=-poly1.centroid.x, yoff=-poly1.centroid.y) poly2 = affinity.translate(poly2, xoff=-poly2.centroid.x, yoff=-poly2.centroid.y) target_area = 10000 scale1 = (target_area / poly1.area) ** 0.5 scale2 = (target_area / poly2.area) ** 0.5 poly1 = affinity.scale(poly1, xfact=scale1, yfact=scale1, origin=(0, 0)) poly2 = affinity.scale(poly2, xfact=scale2, yfact=scale2, origin=(0, 0)) dist_min = float('inf') for angle in [0, 90, 180, 270]: rotated = affinity.rotate(poly2, angle, origin='centroid') dist = poly1.hausdorff_distance(rotated) if dist < dist_min: dist_min = dist cost_matrix[i, j] = dist_min row_ind, col_ind = linear_sum_assignment(cost_matrix) for i, j in zip(row_ind, col_ind): base_index = base_cluster[i]['index'] matched_index = compare_cluster[j]['index'] match_result[base_index].append(matched_index) # 6. 生成图片 all_nodes = {node["index"]: node for node in output.nodes} from shapely.geometry import MultiPolygon as ShapelyMultiPolygon all_polygons = [node["data"] for node in output.nodes] global_bounds = ShapelyMultiPolygon(all_polygons).bounds groups = [] for group_id, (base_index, matched_indices) in enumerate(match_result.items(), start=1): pieces = [] for size_idx, piece_index in enumerate(matched_indices): size_label = size_labels_list[size_idx] node = all_nodes[piece_index] nodes_to_draw = [node] + node['child'] img = output._draw_nodes(nodes_to_draw, scale_factor, show_id=False) img_rgba = cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA) pil_img = Image.fromarray(img_rgba) pil_img = apply_rotation_to_image(pil_img, rotation) buffer = io.BytesIO() pil_img.save(buffer, format='PNG', dpi=(dpi, dpi)) buffer.seek(0) base64_str = base64.b64encode(buffer.read()).decode('utf-8') image_base64 = f"data:image/png;base64,{base64_str}" coords = calculate_piece_coordinates( node["data"], global_bounds, plt_to_cm, rotation ) piece_info = PieceInfo( size=size_label, image_base64=image_base64, width_px=pil_img.width, height_px=pil_img.height, **coords ) pieces.append(piece_info) groups.append(GroupInfo(group_id=group_id, pieces=pieces)) print(f"[PLT] 处理完成，共 {len(groups)} 组裁片") return ProcessPltResponse( success=True, total_groups=len(groups), groups=groups ) except Exception as e: print(f"[PLT] 处理失败: {str(e)}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"处理失败: {str(e)}") if __name__ == "__main__": import uvicorn port = int(os.getenv("PORT", 8080)) uvicorn.run(app, host="0.0.0.0", port=port)