674 lines
25 KiB
Python
674 lines
25 KiB
Python
# match.py
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import argparse
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import json
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import os
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from pathlib import Path
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image
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try:
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from torch.utils.tensorboard import SummaryWriter
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except ImportError: # pragma: no cover - fallback for environments without torch tensorboard
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from tensorboardX import SummaryWriter # type: ignore
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from models.rord import RoRD
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from utils.config_loader import load_config, to_absolute_path
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from utils.data_utils import get_transform
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# --- 新增：功能增强函数 ---
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def extract_rotation_angle(H):
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"""
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从单应性矩阵中提取旋转角度
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返回0°, 90°, 180°, 270°之一
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"""
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if H is None:
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return 0
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# 提取旋转分量
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cos_theta = H[0, 0] / np.sqrt(H[0, 0]**2 + H[1, 0]**2 + 1e-8)
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sin_theta = H[1, 0] / np.sqrt(H[0, 0]**2 + H[1, 0]**2 + 1e-8)
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# 计算角度（弧度转角度）
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angle = np.arctan2(sin_theta, cos_theta) * 180 / np.pi
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# 四舍五入到最近的90度倍数
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angles = [0, 90, 180, 270]
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nearest_angle = min(angles, key=lambda x: abs(x - angle))
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return nearest_angle
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def calculate_match_score(inlier_count, total_keypoints, H, inlier_ratio=None):
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"""
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计算匹配质量评分 (0-1)
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Args:
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inlier_count: 内点数量
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total_keypoints: 总关键点数量
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H: 单应性矩阵
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inlier_ratio: 内点比例（可选）
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"""
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if inlier_ratio is None:
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inlier_ratio = inlier_count / max(total_keypoints, 1)
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# 基于内点比例的基础分数
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base_score = inlier_ratio
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# 基于变换矩阵质量的分数（越接近单位矩阵分数越高）
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if H is not None:
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# 计算变换的"理想程度"
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det = np.linalg.det(H)
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ideal_det = 1.0
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det_score = 1.0 / (1.0 + abs(np.log(det + 1e-8)))
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# 综合评分
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final_score = base_score * 0.7 + det_score * 0.3
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else:
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final_score = base_score
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return min(max(final_score, 0.0), 1.0)
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def calculate_similarity(matches_count, template_kps_count, layout_kps_count):
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"""
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计算模板和版图之间的相似度
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Args:
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matches_count: 匹配对数量
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template_kps_count: 模板关键点数量
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layout_kps_count: 版图关键点数量
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"""
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# 匹配率
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template_match_rate = matches_count / max(template_kps_count, 1)
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# 覆盖率（简化计算）
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coverage_rate = min(matches_count / max(layout_kps_count, 1), 1.0)
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# 综合相似度
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similarity = (template_match_rate * 0.6 + coverage_rate * 0.4)
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return min(max(similarity, 0.0), 1.0)
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def generate_difference_description(H, inlier_count, total_matches, angle_diff=0):
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"""
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生成差异描述
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Args:
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H: 单应性矩阵
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inlier_count: 内点数量
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total_matches: 总匹配数
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angle_diff: 角度差异
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"""
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descriptions = []
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# 基于内点比例的描述
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if total_matches > 0:
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inlier_ratio = inlier_count / total_matches
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if inlier_ratio > 0.8:
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descriptions.append("高度匹配")
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elif inlier_ratio > 0.6:
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descriptions.append("良好匹配")
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elif inlier_ratio > 0.4:
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descriptions.append("中等匹配")
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else:
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descriptions.append("低质量匹配")
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# 基于旋转的描述
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if angle_diff != 0:
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descriptions.append(f"旋转{angle_diff}度")
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else:
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descriptions.append("无旋转")
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# 基于变换的描述
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if H is not None:
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# 检查缩放
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scale_x = np.sqrt(H[0,0]**2 + H[1,0]**2)
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scale_y = np.sqrt(H[0,1]**2 + H[1,1]**2)
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avg_scale = (scale_x + scale_y) / 2
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if abs(avg_scale - 1.0) > 0.1:
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if avg_scale > 1.0:
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descriptions.append(f"放大{avg_scale:.2f}倍")
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else:
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descriptions.append(f"缩小{1/avg_scale:.2f}倍")
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return ", ".join(descriptions) if descriptions else "无法评估差异"
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# --- 特征提取函数 (基本无变动) ---
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def extract_keypoints_and_descriptors(model, image_tensor, kp_thresh):
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with torch.no_grad():
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detection_map, desc = model(image_tensor)
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device = detection_map.device
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binary_map = (detection_map > kp_thresh).squeeze(0).squeeze(0)
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coords = torch.nonzero(binary_map).float() # y, x
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if len(coords) == 0:
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return torch.tensor([], device=device), torch.tensor([], device=device)
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# 描述子采样
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coords_for_grid = coords.flip(1).view(1, -1, 1, 2) # N, 2 -> 1, N, 1, 2 (x,y)
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# 归一化到 [-1, 1]
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coords_for_grid = coords_for_grid / torch.tensor([(desc.shape[3]-1)/2, (desc.shape[2]-1)/2], device=device) - 1
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descriptors = F.grid_sample(desc, coords_for_grid, align_corners=True).squeeze().T
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descriptors = F.normalize(descriptors, p=2, dim=1)
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# 将关键点坐标从特征图尺度转换回图像尺度
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# VGG到relu4_3的下采样率为8
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keypoints = coords.flip(1) * 8.0 # x, y
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return keypoints, descriptors
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# --- (新增) 简单半径 NMS 去重 ---
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def radius_nms(kps: torch.Tensor, scores: torch.Tensor, radius: float) -> torch.Tensor:
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if kps.numel() == 0:
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return torch.empty((0,), dtype=torch.long, device=kps.device)
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idx = torch.argsort(scores, descending=True)
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keep = []
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taken = torch.zeros(len(kps), dtype=torch.bool, device=kps.device)
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for i in idx:
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if taken[i]:
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continue
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keep.append(i.item())
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di = kps - kps[i]
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dist2 = (di[:, 0]**2 + di[:, 1]**2)
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taken |= dist2 <= (radius * radius)
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taken[i] = True
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return torch.tensor(keep, dtype=torch.long, device=kps.device)
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# --- (新增) 滑动窗口特征提取函数 ---
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def extract_features_sliding_window(model, large_image, transform, matching_cfg):
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"""
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使用滑动窗口从大图上提取所有关键点和描述子
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"""
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print("使用滑动窗口提取大版图特征...")
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device = next(model.parameters()).device
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W, H = large_image.size
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window_size = int(matching_cfg.inference_window_size)
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stride = int(matching_cfg.inference_stride)
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keypoint_threshold = float(matching_cfg.keypoint_threshold)
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all_kps = []
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all_descs = []
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for y in range(0, H, stride):
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for x in range(0, W, stride):
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# 确保窗口不越界
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x_end = min(x + window_size, W)
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y_end = min(y + window_size, H)
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# 裁剪窗口
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patch = large_image.crop((x, y, x_end, y_end))
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# 预处理
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patch_tensor = transform(patch).unsqueeze(0).to(device)
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# 提取特征
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kps, descs = extract_keypoints_and_descriptors(model, patch_tensor, keypoint_threshold)
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if len(kps) > 0:
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# 将局部坐标转换为全局坐标
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kps[:, 0] += x
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kps[:, 1] += y
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all_kps.append(kps)
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all_descs.append(descs)
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if not all_kps:
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return torch.tensor([], device=device), torch.tensor([], device=device)
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print(f"大版图特征提取完毕，共找到 {sum(len(k) for k in all_kps)} 个关键点。")
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return torch.cat(all_kps, dim=0), torch.cat(all_descs, dim=0)
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# --- (新增) FPN 路径的关键点与描述子抽取 ---
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def extract_from_pyramid(model, image_tensor, kp_thresh, nms_cfg):
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with torch.no_grad():
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pyramid = model(image_tensor, return_pyramid=True)
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all_kps = []
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all_desc = []
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for level_name, (det, desc, stride) in pyramid.items():
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binary = (det > kp_thresh).squeeze(0).squeeze(0)
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coords = torch.nonzero(binary).float() # y,x
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if len(coords) == 0:
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continue
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scores = det.squeeze()[binary]
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# 采样描述子
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coords_for_grid = coords.flip(1).view(1, -1, 1, 2)
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coords_for_grid = coords_for_grid / torch.tensor([(desc.shape[3]-1)/2, (desc.shape[2]-1)/2], device=desc.device) - 1
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descs = F.grid_sample(desc, coords_for_grid, align_corners=True).squeeze().T
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descs = F.normalize(descs, p=2, dim=1)
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# 映射回原图坐标
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kps = coords.flip(1) * float(stride)
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# NMS
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if nms_cfg and nms_cfg.get('enabled', False):
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keep = radius_nms(kps, scores, float(nms_cfg.get('radius', 4)))
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if len(keep) > 0:
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kps = kps[keep]
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descs = descs[keep]
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all_kps.append(kps)
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all_desc.append(descs)
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if not all_kps:
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return torch.tensor([], device=image_tensor.device), torch.tensor([], device=image_tensor.device)
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return torch.cat(all_kps, dim=0), torch.cat(all_desc, dim=0)
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# --- 互近邻匹配 (无变动) ---
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def mutual_nearest_neighbor(descs1, descs2):
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if len(descs1) == 0 or len(descs2) == 0:
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return torch.empty((0, 2), dtype=torch.int64)
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sim = descs1 @ descs2.T
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nn12 = torch.max(sim, dim=1)
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nn21 = torch.max(sim, dim=0)
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ids1 = torch.arange(0, sim.shape[0], device=sim.device)
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mask = (ids1 == nn21.indices[nn12.indices])
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matches = torch.stack([ids1[mask], nn12.indices[mask]], dim=1)
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return matches
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# --- (已修改) 多尺度、多实例匹配主函数 ---
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def match_template_multiscale(
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model,
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layout_image,
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template_image,
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transform,
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matching_cfg,
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log_writer: SummaryWriter | None = None,
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log_step: int = 0,
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return_detailed_info: bool = True,
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):
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"""
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在不同尺度下搜索模板，并检测多个实例
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Args:
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model: RoRD模型
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layout_image: 大版图图像
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template_image: 小版图图像
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transform: 图像预处理变换
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matching_cfg: 匹配配置
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log_writer: TensorBoard日志记录器
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log_step: 日志步数
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return_detailed_info: 是否返回详细信息
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Returns:
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匹配结果列表，包含坐标、旋转角度、置信度等信息
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"""
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# 1. 版图特征提取：根据配置选择 FPN 或滑窗
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device = next(model.parameters()).device
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if getattr(matching_cfg, 'use_fpn', False):
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layout_tensor = transform(layout_image).unsqueeze(0).to(device)
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layout_kps, layout_descs = extract_from_pyramid(model, layout_tensor, float(matching_cfg.keypoint_threshold), getattr(matching_cfg, 'nms', {}))
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else:
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layout_kps, layout_descs = extract_features_sliding_window(model, layout_image, transform, matching_cfg)
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if log_writer:
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log_writer.add_scalar("match/layout_keypoints", len(layout_kps), log_step)
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min_inliers = int(matching_cfg.min_inliers)
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if len(layout_kps) < min_inliers:
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print("从大版图中提取的关键点过少，无法进行匹配。")
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if log_writer:
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log_writer.add_scalar("match/instances_found", 0, log_step)
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return []
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found_instances = []
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active_layout_mask = torch.ones(len(layout_kps), dtype=bool, device=layout_kps.device)
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pyramid_scales = [float(s) for s in matching_cfg.pyramid_scales]
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keypoint_threshold = float(matching_cfg.keypoint_threshold)
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ransac_threshold = float(matching_cfg.ransac_reproj_threshold)
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# 2. 多实例迭代检测
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while True:
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current_active_indices = torch.nonzero(active_layout_mask).squeeze(1)
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# 如果剩余活动关键点过少，则停止
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if len(current_active_indices) < min_inliers:
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break
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current_layout_kps = layout_kps[current_active_indices]
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current_layout_descs = layout_descs[current_active_indices]
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best_match_info = {'inliers': 0, 'H': None, 'src_pts': None, 'dst_pts': None, 'mask': None}
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# 3. 图像金字塔：遍历模板的每个尺度
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print("在新尺度下搜索模板...")
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for scale in pyramid_scales:
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W, H = template_image.size
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new_W, new_H = int(W * scale), int(H * scale)
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# 缩放模板
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scaled_template = template_image.resize((new_W, new_H), Image.LANCZOS)
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template_tensor = transform(scaled_template).unsqueeze(0).to(layout_kps.device)
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# 提取缩放后模板的特征：FPN 或单尺度
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if getattr(matching_cfg, 'use_fpn', False):
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template_kps, template_descs = extract_from_pyramid(model, template_tensor, keypoint_threshold, getattr(matching_cfg, 'nms', {}))
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else:
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template_kps, template_descs = extract_keypoints_and_descriptors(model, template_tensor, keypoint_threshold)
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if len(template_kps) < 4: continue
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# 匹配当前尺度的模板和活动状态的版图特征
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matches = mutual_nearest_neighbor(template_descs, current_layout_descs)
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if len(matches) < 4: continue
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# RANSAC
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# 注意：模板关键点坐标需要还原到原始尺寸，才能计算正确的H
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src_pts = template_kps[matches[:, 0]].cpu().numpy() / scale
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dst_pts_indices = current_active_indices[matches[:, 1]]
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dst_pts = layout_kps[dst_pts_indices].cpu().numpy()
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H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, ransac_threshold)
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if H is not None and mask.sum() > best_match_info['inliers']:
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best_match_info = {'inliers': mask.sum(), 'H': H, 'mask': mask, 'scale': scale, 'dst_pts': dst_pts}
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# 4. 如果在所有尺度中找到了最佳匹配，则记录并屏蔽
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if best_match_info['inliers'] > min_inliers:
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print(f"找到一个匹配实例！内点数: {best_match_info['inliers']}, 使用的模板尺度: {best_match_info['scale']:.2f}x")
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if log_writer:
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instance_index = len(found_instances)
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log_writer.add_scalar("match/instance_inliers", int(best_match_info['inliers']), log_step + instance_index)
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log_writer.add_scalar("match/instance_scale", float(best_match_info['scale']), log_step + instance_index)
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inlier_mask = best_match_info['mask'].ravel().astype(bool)
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inlier_layout_kps = best_match_info['dst_pts'][inlier_mask]
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x_min, y_min = inlier_layout_kps.min(axis=0)
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x_max, y_max = inlier_layout_kps.max(axis=0)
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# 提取旋转角度
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rotation_angle = extract_rotation_angle(best_match_info['H'])
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# 计算匹配质量评分
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confidence = calculate_match_score(
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inlier_count=int(best_match_info['inliers']),
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total_keypoints=len(current_layout_kps),
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H=best_match_info['H']
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)
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# 计算相似度
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similarity = calculate_similarity(
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matches_count=int(best_match_info['inliers']),
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template_kps_count=len(template_kps),
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layout_kps_count=len(current_layout_kps)
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)
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# 生成差异描述
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diff_description = generate_difference_description(
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H=best_match_info['H'],
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inlier_count=int(best_match_info['inliers']),
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total_matches=len(matches),
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angle_diff=rotation_angle
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)
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# 构建详细实例信息
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if return_detailed_info:
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instance = {
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'bbox': {
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'x': int(x_min),
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'y': int(y_min),
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'width': int(x_max - x_min),
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'height': int(y_max - y_min)
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},
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'rotation': rotation_angle,
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'confidence': round(confidence, 3),
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'similarity': round(similarity, 3),
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'inliers': int(best_match_info['inliers']),
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'scale': best_match_info.get('scale', 1.0),
|
||
'homography': best_match_info['H'].tolist() if best_match_info['H'] is not None else None,
|
||
'description': diff_description
|
||
}
|
||
else:
|
||
# 兼容旧格式
|
||
instance = {
|
||
'x': int(x_min),
|
||
'y': int(y_min),
|
||
'width': int(x_max - x_min),
|
||
'height': int(y_max - y_min),
|
||
'homography': best_match_info['H']
|
||
}
|
||
|
||
found_instances.append(instance)
|
||
|
||
# 屏蔽已匹配区域的关键点，以便检测下一个实例
|
||
kp_x, kp_y = layout_kps[:, 0], layout_kps[:, 1]
|
||
region_mask = (kp_x >= x_min) & (kp_x <= x_max) & (kp_y >= y_min) & (kp_y <= y_max)
|
||
active_layout_mask[region_mask] = False
|
||
|
||
print(f"剩余活动关键点: {active_layout_mask.sum()}")
|
||
else:
|
||
# 如果在所有尺度下都找不到好的匹配，则结束搜索
|
||
print("在所有尺度下均未找到新的匹配实例，搜索结束。")
|
||
break
|
||
|
||
if log_writer:
|
||
log_writer.add_scalar("match/instances_found", len(found_instances), log_step)
|
||
|
||
return found_instances
|
||
|
||
|
||
def visualize_matches(layout_path, matches, output_path):
|
||
"""
|
||
可视化匹配结果，支持新的详细格式
|
||
|
||
Args:
|
||
layout_path: 大版图路径
|
||
matches: 匹配结果列表
|
||
output_path: 输出图像路径
|
||
"""
|
||
layout_img = cv2.imread(layout_path)
|
||
if layout_img is None:
|
||
print(f"错误：无法读取图像 {layout_path}")
|
||
return
|
||
|
||
for i, match in enumerate(matches):
|
||
# 支持新旧格式
|
||
if 'bbox' in match:
|
||
x, y, w, h = match['bbox']['x'], match['bbox']['y'], match['bbox']['width'], match['bbox']['height']
|
||
confidence = match.get('confidence', 0)
|
||
rotation = match.get('rotation', 0)
|
||
description = match.get('description', '')
|
||
else:
|
||
# 兼容旧格式
|
||
x, y, w, h = match['x'], match['y'], match['width'], match['height']
|
||
confidence = 0
|
||
rotation = 0
|
||
description = ''
|
||
|
||
# 绘制边界框
|
||
cv2.rectangle(layout_img, (x, y), (x + w, y + h), (0, 255, 0), 2)
|
||
|
||
# 准备标签文本
|
||
label_parts = [f"Match {i+1}"]
|
||
if confidence > 0:
|
||
label_parts.append(f"Conf: {confidence:.2f}")
|
||
if rotation != 0:
|
||
label_parts.append(f"Rot: {rotation}°")
|
||
if description:
|
||
label_parts.append(f"{description[:20]}...") # 截断长描述
|
||
|
||
label = " | ".join(label_parts)
|
||
|
||
# 绘制标签背景
|
||
(label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 2)
|
||
cv2.rectangle(layout_img, (x, y - label_height - 10), (x + label_width, y), (0, 255, 0), -1)
|
||
cv2.putText(layout_img, label, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
|
||
|
||
cv2.imwrite(output_path, layout_img)
|
||
print(f"可视化结果已保存至: {output_path}")
|
||
|
||
|
||
def save_matches_json(matches, output_path):
|
||
"""
|
||
保存匹配结果到JSON文件
|
||
|
||
Args:
|
||
matches: 匹配结果列表
|
||
output_path: 输出JSON文件路径
|
||
"""
|
||
result = {
|
||
'found_matches': len(matches) > 0,
|
||
'total_matches': len(matches),
|
||
'matches': matches
|
||
}
|
||
|
||
with open(output_path, 'w', encoding='utf-8') as f:
|
||
json.dump(result, f, indent=2, ensure_ascii=False)
|
||
|
||
print(f"匹配结果已保存至: {output_path}")
|
||
|
||
|
||
def print_detailed_results(matches):
|
||
"""
|
||
打印详细的匹配结果
|
||
|
||
Args:
|
||
matches: 匹配结果列表
|
||
"""
|
||
print("\n" + "="*60)
|
||
print("🎯 版图匹配结果详情")
|
||
print("="*60)
|
||
|
||
if not matches:
|
||
print("❌ 未找到任何匹配区域")
|
||
return
|
||
|
||
print(f"✅ 共找到 {len(matches)} 个匹配区域\n")
|
||
|
||
for i, match in enumerate(matches, 1):
|
||
print(f"📍 匹配区域 #{i}")
|
||
print("-" * 40)
|
||
|
||
# 支持新旧格式
|
||
if 'bbox' in match:
|
||
bbox = match['bbox']
|
||
print(f"📐 位置: ({bbox['x']}, {bbox['y']})")
|
||
print(f"📏 尺寸: {bbox['width']} × {bbox['height']} 像素")
|
||
|
||
if 'rotation' in match:
|
||
print(f"🔄 旋转角度: {match['rotation']}°")
|
||
if 'confidence' in match:
|
||
print(f"🎯 置信度: {match['confidence']:.3f}")
|
||
if 'similarity' in match:
|
||
print(f"📊 相似度: {match['similarity']:.3f}")
|
||
if 'inliers' in match:
|
||
print(f"🔗 内点数量: {match['inliers']}")
|
||
if 'scale' in match:
|
||
print(f"📈 匹配尺度: {match['scale']:.2f}x")
|
||
if 'description' in match:
|
||
print(f"📝 差异描述: {match['description']}")
|
||
else:
|
||
# 兼容旧格式
|
||
print(f"📐 位置: ({match['x']}, {match['y']})")
|
||
print(f"📏 尺寸: {match['width']} × {match['height']} 像素")
|
||
|
||
print() # 空行分隔
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser(description="使用 RoRD 进行多尺度模板匹配")
|
||
parser.add_argument('--config', type=str, default="configs/base_config.yaml", help="YAML 配置文件路径")
|
||
parser.add_argument('--model_path', type=str, default=None, help="模型权重路径，若未提供则使用配置文件中的路径")
|
||
parser.add_argument('--log_dir', type=str, default=None, help="TensorBoard 日志根目录，覆盖配置文件设置")
|
||
parser.add_argument('--experiment_name', type=str, default=None, help="TensorBoard 实验名称，覆盖配置文件设置")
|
||
parser.add_argument('--tb_log_matches', action='store_true', help="启用模板匹配过程的 TensorBoard 记录")
|
||
parser.add_argument('--disable_tensorboard', action='store_true', help="禁用 TensorBoard 记录")
|
||
parser.add_argument('--fpn_off', action='store_true', help="关闭 FPN 匹配路径（等同于 matching.use_fpn=false）")
|
||
parser.add_argument('--no_nms', action='store_true', help="关闭关键点去重（NMS）")
|
||
parser.add_argument('--layout', type=str, required=True, help="大版图图像路径")
|
||
parser.add_argument('--template', type=str, required=True, help="小版图（模板）图像路径")
|
||
parser.add_argument('--output', type=str, help="可视化结果保存路径")
|
||
parser.add_argument('--json_output', type=str, help="JSON结果保存路径")
|
||
parser.add_argument('--simple_format', action='store_true', help="使用简单的输出格式（兼容旧版本）")
|
||
args = parser.parse_args()
|
||
|
||
cfg = load_config(args.config)
|
||
config_dir = Path(args.config).resolve().parent
|
||
matching_cfg = cfg.matching
|
||
logging_cfg = cfg.get("logging", None)
|
||
model_path = args.model_path or str(to_absolute_path(cfg.paths.model_path, config_dir))
|
||
|
||
use_tensorboard = False
|
||
log_dir = None
|
||
experiment_name = None
|
||
if logging_cfg is not None:
|
||
use_tensorboard = bool(logging_cfg.get("use_tensorboard", False))
|
||
log_dir = logging_cfg.get("log_dir", "runs")
|
||
experiment_name = logging_cfg.get("experiment_name", "default")
|
||
|
||
if args.disable_tensorboard:
|
||
use_tensorboard = False
|
||
if args.log_dir is not None:
|
||
log_dir = args.log_dir
|
||
if args.experiment_name is not None:
|
||
experiment_name = args.experiment_name
|
||
|
||
should_log_matches = args.tb_log_matches and use_tensorboard and log_dir is not None
|
||
writer = None
|
||
if should_log_matches:
|
||
log_root = Path(log_dir).expanduser()
|
||
exp_folder = experiment_name or "default"
|
||
tb_path = log_root / "match" / exp_folder
|
||
tb_path.parent.mkdir(parents=True, exist_ok=True)
|
||
writer = SummaryWriter(tb_path.as_posix())
|
||
|
||
# CLI 快捷开关覆盖 YAML 配置
|
||
try:
|
||
if args.fpn_off:
|
||
matching_cfg.use_fpn = False
|
||
if args.no_nms and hasattr(matching_cfg, 'nms'):
|
||
matching_cfg.nms.enabled = False
|
||
except Exception:
|
||
# 若 OmegaConf 结构不可写，忽略并在后续逻辑中以 getattr 的方式读取
|
||
pass
|
||
|
||
transform = get_transform()
|
||
model = RoRD().cuda()
|
||
model.load_state_dict(torch.load(model_path))
|
||
model.eval()
|
||
|
||
layout_image = Image.open(args.layout).convert('L')
|
||
template_image = Image.open(args.template).convert('L')
|
||
|
||
# 执行匹配，根据参数选择详细或简单格式
|
||
detected_matches = match_template_multiscale(
|
||
model,
|
||
layout_image,
|
||
template_image,
|
||
transform,
|
||
matching_cfg,
|
||
log_writer=writer,
|
||
log_step=0,
|
||
return_detailed_info=not args.simple_format,
|
||
)
|
||
|
||
# 打印详细结果
|
||
print_detailed_results(detected_matches)
|
||
|
||
# 保存JSON结果
|
||
if args.json_output:
|
||
save_matches_json(detected_matches, args.json_output)
|
||
|
||
# 可视化结果
|
||
if args.output:
|
||
visualize_matches(args.layout, detected_matches, args.output)
|
||
|
||
if writer:
|
||
writer.add_scalar("match/output_instances", len(detected_matches), 0)
|
||
writer.add_text("match/layout_path", args.layout, 0)
|
||
writer.close()
|
||
|
||
print("\n🎉 匹配完成！")
|
||
if args.json_output:
|
||
print(f"📄 详细结果已保存到: {args.json_output}")
|
||
if args.output:
|
||
print(f"🖼️ 可视化结果已保存到: {args.output}") |