feat: AI套图分层方案 + Gemini集成 - 4种图案类型处理 + 正片叠底 + 宽高比 + 模型选择

Co-authored-by: Cursor <cursoragent@cursor.com>

PltService/main.py

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+# -*- 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)