419 lines
18 KiB
Python
419 lines
18 KiB
Python
import cv2
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import numpy as np
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import os
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import time
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from PIL import Image
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from pltreader import PltReader
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from concurrent.futures import ThreadPoolExecutor
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from shapely import affinity
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from scipy.optimize import linear_sum_assignment
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def get_single_size_info(filepath, size_num, english_name):
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with open(filepath, 'r') as f:
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reader = PltReader(f)
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output = reader.get_output(tolerance=10)
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# 整幅输出
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full_sheet_path = english_name + '_full_sheet.png'
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cv2.imwrite(full_sheet_path, output.debug_full_sheet(scale_factor=72/1016))
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#cv2.imwrite(full_sheet_path, output.debug_full_sheet(scale_factor=0.1))
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print(f"已保存整幅裁片图像到: {full_sheet_path}")
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# 获取单片输出信息
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return output.get_single_size_info(size_num=size_num)
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def get_principal_angle(polygon):
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'''计算主轴角度'''
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coords = np.array(polygon.exterior.coords)
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# 计算协方差矩阵
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cov = np.cov(coords.T)
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# 特征值分解
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eig_vals, eig_vecs = np.linalg.eig(cov)
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# 找到最大特征值对应的特征向量
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principal_vec = eig_vecs[:, np.argmax(eig_vals)]
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# 计算角度
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angle = np.degrees(np.arctan2(principal_vec[1], principal_vec[0]))
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return angle
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def get_different_size_match_result(clusters):
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base_cluster = [piece for piece in clusters[0] if piece["parent"] == None]
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result = {piece["index"]:[] for piece in base_cluster}
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for size_index in range(1, len(clusters)):
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compare_cluster = [piece for piece in clusters[size_index] if piece["parent"] == None]
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assert len(base_cluster) == len(compare_cluster)
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# 初始化成本矩阵
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cost_matrix = np.zeros((len(base_cluster), len(base_cluster)))
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# 初始化旋转角度矩阵，表示每个裁片的最佳旋转角度
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rotation_matrix = np.zeros((len(base_cluster), len(base_cluster)))
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# 把每个裁片对应上
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for i in range(len(base_cluster)):
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for j in range(len(compare_cluster)):
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poly1 = base_cluster[i]["data"]
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poly2 = compare_cluster[j]["data"]
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# 质心对齐
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poly1 = affinity.translate(poly1, xoff=-poly1.centroid.x, yoff=-poly1.centroid.y)
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poly2 = affinity.translate(poly2, xoff=-poly2.centroid.x, yoff=-poly2.centroid.y)
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# 缩放到相同面积
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target_area = 10000
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scale_factor1 = (target_area / poly1.area) ** 0.5
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scale_factor2 = (target_area / poly2.area) ** 0.5
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# 绕原点缩放
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poly1 = affinity.scale(poly1, xfact=scale_factor1, yfact=scale_factor1, origin=(0, 0))
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poly2 = affinity.scale(poly2, xfact=scale_factor2, yfact=scale_factor2, origin=(0, 0))
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candidates = [0, 90, 180, 270]
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dist_min = float('inf')
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best_angle = 0
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for angle in candidates:
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rotated_back = affinity.rotate(poly2, angle, origin='centroid')
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dist = poly1.hausdorff_distance(rotated_back)
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if dist < dist_min:
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dist_min = dist
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best_angle = angle
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cost_matrix[i, j] = dist_min
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rotation_matrix[i, j] = best_angle
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row_ind, col_ind = linear_sum_assignment(cost_matrix)
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for i, j in zip(row_ind, col_ind):
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result[base_cluster[i]['index']].append((compare_cluster[j]['index'], cost_matrix[i, j], rotation_matrix[i, j]))
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for base_index, matches in result.items():
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print(f"{base_index} <->", end=" ")
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for match in matches:
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print(f"{match[0]} (d={match[1]:.2f} r={match[2]}°)", end="; ")
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print()
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print("\n")
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return result
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def export_pieces_by_size_group(filepath, size_labels, output_dir="output", dpi=150, rotation=0):
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"""
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按尺码和组号导出裁片PNG
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Args:
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filepath: PLT文件路径
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size_labels: 尺码标签列表，如 ["S", "M", "L", "XL", "2XL", "3XL", "4XL"]
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output_dir: 输出目录
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dpi: 输出分辨率 (DPI)
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rotation: 旋转角度，可选值:
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0 - 不旋转
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90 - 顺时针旋转90°
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-90 - 逆时针旋转90°
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180 - 旋转180°
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输出文件命名: S-1.png, M-1.png, L-1.png, ...
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"""
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start_time = time.time()
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# 根据 DPI 计算 scale_factor (1016 绘图仪单位 = 1 英寸)
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scale_factor = dpi / 1016
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size_num = len(size_labels)
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print(f"==========开始读取PLT文件: {filepath}==========")
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with open(filepath, 'r') as f:
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reader = PltReader(f)
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output = reader.get_output(tolerance=10)
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# 获取尺码聚类信息
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clusters = output.get_single_size_info(size_num=size_num)
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# 获取不同尺码间的匹配关系
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print("==========开始匹配不同尺码的相同裁片==========")
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base_cluster = [piece for piece in clusters[0] if piece["parent"] == None]
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match_result = {piece["index"]: [piece["index"]] for piece in base_cluster} # 基准尺码的index作为key
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for size_index in range(1, len(clusters)):
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compare_cluster = [piece for piece in clusters[size_index] if piece["parent"] == None]
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# 初始化成本矩阵
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cost_matrix = np.zeros((len(base_cluster), len(compare_cluster)))
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for i in range(len(base_cluster)):
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for j in range(len(compare_cluster)):
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poly1 = base_cluster[i]["data"]
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poly2 = compare_cluster[j]["data"]
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# 质心对齐
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poly1 = affinity.translate(poly1, xoff=-poly1.centroid.x, yoff=-poly1.centroid.y)
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poly2 = affinity.translate(poly2, xoff=-poly2.centroid.x, yoff=-poly2.centroid.y)
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# 缩放到相同面积
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target_area = 10000
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scale_factor1 = (target_area / poly1.area) ** 0.5
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scale_factor2 = (target_area / poly2.area) ** 0.5
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poly1 = affinity.scale(poly1, xfact=scale_factor1, yfact=scale_factor1, origin=(0, 0))
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poly2 = affinity.scale(poly2, xfact=scale_factor2, yfact=scale_factor2, origin=(0, 0))
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# 尝试不同旋转角度
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dist_min = float('inf')
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for angle in [0, 90, 180, 270]:
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rotated = affinity.rotate(poly2, angle, origin='centroid')
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dist = poly1.hausdorff_distance(rotated)
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if dist < dist_min:
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dist_min = dist
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cost_matrix[i, j] = dist_min
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# 匈牙利算法匹配
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row_ind, col_ind = linear_sum_assignment(cost_matrix)
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for i, j in zip(row_ind, col_ind):
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base_index = base_cluster[i]['index']
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matched_index = compare_cluster[j]['index']
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match_result[base_index].append(matched_index)
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# 创建输出目录
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os.makedirs(output_dir, exist_ok=True)
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# 构建 index -> node 的映射（包含所有节点，不只是parent为None的）
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all_nodes = {node["index"]: node for node in output.nodes}
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# 导出图片
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rotation_desc = {0: "不旋转", 90: "顺时针90°", -90: "逆时针90°", 180: "旋转180°"}.get(rotation, f"旋转{rotation}°")
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print(f"==========开始导出裁片图片到: {output_dir}/ ({rotation_desc})==========")
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group_id = 1
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for base_index, matched_indices in match_result.items():
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print(f"组 {group_id}: ", end="")
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for size_idx, piece_index in enumerate(matched_indices):
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size_label = size_labels[size_idx]
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# 找到对应的node（包括其子节点）
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node = all_nodes[piece_index]
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nodes_to_draw = [node] + node['child']
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# 绘制图像
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img = output._draw_nodes(nodes_to_draw, scale_factor, show_id=False)
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# OpenCV 的 BGRA 转 Pillow 的 RGBA
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img_rgba = cv2.cvtColor(img, cv2.COLOR_BGRA2RGBA)
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pil_img = Image.fromarray(img_rgba)
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# 应用旋转
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if rotation == 90:
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pil_img = pil_img.rotate(-90, expand=True) # Pillow的rotate是逆时针，所以用负值实现顺时针
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elif rotation == -90:
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pil_img = pil_img.rotate(90, expand=True) # 逆时针90°
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elif rotation == 180:
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pil_img = pil_img.rotate(180, expand=True)
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# 保存文件（写入 DPI 元数据）
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filename = f"{size_label}-{group_id}.png"
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filepath_out = os.path.join(output_dir, filename)
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pil_img.save(filepath_out, dpi=(dpi, dpi))
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print(f"{filename}", end=" ")
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print()
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group_id += 1
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print(f"\n导出完成！共 {group_id - 1} 组裁片，每组 {len(size_labels)} 个尺码")
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print(f"文件保存在: {output_dir}/")
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# ========== 输出每个尺码的裁片中心坐标 ==========
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SAFETY_MARGIN = 10 # 安全边距 10cm
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print("\n" + "=" * 70)
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print(f"裁片中心坐标（单位: cm，原点: 左下角，安全边距: {SAFETY_MARGIN}cm，{rotation_desc}）")
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print("=" * 70)
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# PLT单位转厘米的系数 (1016单位 = 1英寸 = 2.54cm)
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plt_to_cm = 2.54 / 1016
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# 按尺码分组输出坐标
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for size_idx, size_label in enumerate(size_labels):
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# 收集该尺码下所有裁片
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size_pieces = []
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for base_index, matched_indices in match_result.items():
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piece_index = matched_indices[size_idx]
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node = all_nodes[piece_index]
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polygon = node["data"]
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size_pieces.append({
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"group_id": list(match_result.keys()).index(base_index) + 1,
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"polygon": polygon,
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"centroid": polygon.centroid
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})
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# 计算该尺码的边界框（用于重置原点）
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all_polygons = [p["polygon"] for p in size_pieces]
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from shapely.geometry import MultiPolygon as ShapelyMultiPolygon
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multi_poly = ShapelyMultiPolygon(all_polygons)
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min_x, min_y, max_x, max_y = multi_poly.bounds
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# 原始画布尺寸（厘米）
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orig_width_cm = (max_x - min_x) * plt_to_cm
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orig_height_cm = (max_y - min_y) * plt_to_cm
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# 根据旋转调整画布宽高
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if rotation in [90, -90]:
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width_cm, height_cm = orig_height_cm, orig_width_cm # 宽高互换
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else:
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width_cm, height_cm = orig_width_cm, orig_height_cm
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# 含安全边距的画布尺寸
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total_width_cm = width_cm + 2 * SAFETY_MARGIN
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total_height_cm = height_cm + 2 * SAFETY_MARGIN
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print(f"\n【尺码 {size_label}】")
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print(f" 裁片区域尺寸: 宽 {width_cm:.2f} cm, 高 {height_cm:.2f} cm")
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print(f" 含安全边距尺寸: 宽 {total_width_cm:.2f} cm, 高 {total_height_cm:.2f} cm")
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print("-" * 70)
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print(f" {'裁片名称':<10} {'原始中心坐标':<22} {'含安全边距坐标':<22} {'裁片尺寸'}")
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print("-" * 70)
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for piece in size_pieces:
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# 原始坐标（以左下角为原点，Y轴向上）
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orig_center_x = (piece["centroid"].x - min_x) * plt_to_cm
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orig_center_y = (piece["centroid"].y - min_y) * plt_to_cm
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# 原始裁片尺寸
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piece_bounds = piece["polygon"].bounds
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orig_piece_width = (piece_bounds[2] - piece_bounds[0]) * plt_to_cm
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orig_piece_height = (piece_bounds[3] - piece_bounds[1]) * plt_to_cm
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# 根据旋转变换坐标
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if rotation == 90: # 顺时针90°
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center_x = orig_center_y
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center_y = orig_width_cm - orig_center_x
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piece_width, piece_height = orig_piece_height, orig_piece_width
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elif rotation == -90: # 逆时针90°
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center_x = orig_height_cm - orig_center_y
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center_y = orig_center_x
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piece_width, piece_height = orig_piece_height, orig_piece_width
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elif rotation == 180: # 旋转180°
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center_x = orig_width_cm - orig_center_x
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center_y = orig_height_cm - orig_center_y
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piece_width, piece_height = orig_piece_width, orig_piece_height
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else: # 不旋转
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center_x, center_y = orig_center_x, orig_center_y
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piece_width, piece_height = orig_piece_width, orig_piece_height
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# 含安全边距的坐标（加上10cm偏移）
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safe_x = center_x + SAFETY_MARGIN
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safe_y = center_y + SAFETY_MARGIN
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# 裁片名称与PNG文件名一致
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piece_name = f"{size_label}-{piece['group_id']}"
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print(f" {piece_name:<10} ({center_x:7.2f}, {center_y:7.2f}) cm ({safe_x:7.2f}, {safe_y:7.2f}) cm {piece_width:.2f} x {piece_height:.2f} cm")
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print("\n" + "=" * 70)
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# 计算并显示处理时间
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elapsed_time = time.time() - start_time
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if elapsed_time >= 60:
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minutes = int(elapsed_time // 60)
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seconds = elapsed_time % 60
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print(f"处理耗时: {minutes}分{seconds:.2f}秒")
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else:
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print(f"处理耗时: {elapsed_time:.2f}秒")
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def get_contour_match_result(standard_clusters, rotated_clusters):
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for standard_cluster, rotated_cluster in zip(standard_clusters, rotated_clusters):
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# 仅保留父节点
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standard_cluster = [piece for piece in standard_cluster if piece["parent"] == None]
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rotated_cluster = [piece for piece in rotated_cluster if piece["parent"] == None]
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assert len(standard_cluster) == len(rotated_cluster)
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# 初始化成本矩阵
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cost_matrix = np.zeros((len(standard_cluster), len(standard_cluster)))
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# 初始化旋转角度矩阵，表示每个裁片的最佳旋转角度
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rotation_matrix = np.zeros((len(standard_cluster), len(standard_cluster)))
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# 把旋转后的每个裁片和标准的裁片对应上
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for i in range(len(standard_cluster)):
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for j in range(len(rotated_cluster)):
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poly1 = standard_cluster[i]["data"]
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poly2 = rotated_cluster[j]["data"]
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#poly1 = poly1.simplify(0)
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#poly2 = poly2.simplify(0)
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# 质心对齐
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poly1 = affinity.translate(poly1, xoff=-poly1.centroid.x, yoff=-poly1.centroid.y)
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poly2 = affinity.translate(poly2, xoff=-poly2.centroid.x, yoff=-poly2.centroid.y)
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# 计算主轴角度
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#angle1 = get_principal_angle(poly1)
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#angle2 = get_principal_angle(poly2)
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# 计算角度差
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#angle_diff = angle1 - angle2
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#candidates = [angle_diff, angle_diff + 90, angle_diff + 180, angle_diff + 270]
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candidates = [0, 90, 180, 270]
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dist_min = float('inf')
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best_angle = 0
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for angle in candidates:
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rotated_back = affinity.rotate(poly2, angle, origin='centroid')
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dist = poly1.hausdorff_distance(rotated_back)
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if dist < dist_min:
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dist_min = dist
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best_angle = angle
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cost_matrix[i, j] = dist_min
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rotation_matrix[i, j] = best_angle
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row_ind, col_ind = linear_sum_assignment(cost_matrix)
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print("\n标准裁片与旋转后裁片的最佳匹配结果:")
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for i, j in zip(row_ind, col_ind):
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print(f"标准裁片ID: {standard_cluster[i]['index']}, 旋转后裁片ID: {rotated_cluster[j]['index']}, 距离: {cost_matrix[i, j]:.4f}, 最佳旋转角度: {rotation_matrix[i, j]}度")
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# 打印成本矩阵
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#print(cost_matrix)
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def main():
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STANDARD_FILE_PATH = "默认方案-POLO衫长袖-A料-标准.plt"
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ROTATED_FILE_PATH = "默认方案-POLO衫长袖-A料-已旋转.plt"
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SIZE_NUM = 5
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# ========== 新功能：按尺码分组导出裁片 ==========
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# 尺码标签列表（根据你的实际情况修改）
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SIZE_LABELS = ["S", "M", "L", "XL", "2XL"]
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# 导出标准文件的裁片（按 S-1, M-1, L-1... 命名）
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export_pieces_by_size_group(
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filepath=STANDARD_FILE_PATH,
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size_labels=SIZE_LABELS,
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output_dir="standard_pieces",
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dpi=150, # 输出分辨率，Photoshop 会正确识别
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rotation=90 # 顺时针旋转90°
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)
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# 如果需要导出旋转后文件的裁片，取消下面的注释
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# export_pieces_by_size_group(
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# filepath=ROTATED_FILE_PATH,
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# size_labels=SIZE_LABELS,
|
||
# output_dir="rotated_pieces",
|
||
# scale_factor=72/1016
|
||
# )
|
||
|
||
# ========== 以下是原有的匹配功能 ==========
|
||
# print("==========开始读取轮廓数据==========")
|
||
# with ThreadPoolExecutor(max_workers=2) as executor:
|
||
# standard_future = executor.submit(get_single_size_info, STANDARD_FILE_PATH, SIZE_NUM, "standard")
|
||
# rotated_future = executor.submit(get_single_size_info, ROTATED_FILE_PATH, SIZE_NUM, "rotated")
|
||
#
|
||
# standard_clusters = standard_future.result()
|
||
# rotated_clusters = rotated_future.result()
|
||
#
|
||
# print("==========开始获取两个文件的轮廓匹配结果==========")
|
||
# get_contour_match_result(standard_clusters, rotated_clusters)
|
||
#
|
||
# print("==========开始获取一个文件内不同尺寸的匹配结果==========")
|
||
# print(f"{STANDARD_FILE_PATH}:")
|
||
# get_different_size_match_result(standard_clusters)
|
||
# print(f"{ROTATED_FILE_PATH}:")
|
||
# get_different_size_match_result(rotated_clusters)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|