add midtern report and change data source

match.py

@@ -1,6 +1,7 @@
 # match.py
 
 import argparse
+import json
 import os
 from pathlib import Path
 
@@ -18,6 +19,127 @@ from models.rord import RoRD
 from utils.config_loader import load_config, to_absolute_path
 from utils.data_utils import get_transform
 
+# --- 新增：功能增强函数 ---
+def extract_rotation_angle(H):
+    """
+    从单应性矩阵中提取旋转角度
+    返回0°, 90°, 180°, 270°之一
+    """
+    if H is None:
+        return 0
+
+    # 提取旋转分量
+    cos_theta = H[0, 0] / np.sqrt(H[0, 0]**2 + H[1, 0]**2 + 1e-8)
+    sin_theta = H[1, 0] / np.sqrt(H[0, 0]**2 + H[1, 0]**2 + 1e-8)
+
+    # 计算角度（弧度转角度）
+    angle = np.arctan2(sin_theta, cos_theta) * 180 / np.pi
+
+    # 四舍五入到最近的90度倍数
+    angles = [0, 90, 180, 270]
+    nearest_angle = min(angles, key=lambda x: abs(x - angle))
+
+    return nearest_angle
+
+
+def calculate_match_score(inlier_count, total_keypoints, H, inlier_ratio=None):
+    """
+    计算匹配质量评分 (0-1)
+
+    Args:
+        inlier_count: 内点数量
+        total_keypoints: 总关键点数量
+        H: 单应性矩阵
+        inlier_ratio: 内点比例（可选）
+    """
+    if inlier_ratio is None:
+        inlier_ratio = inlier_count / max(total_keypoints, 1)
+
+    # 基于内点比例的基础分数
+    base_score = inlier_ratio
+
+    # 基于变换矩阵质量的分数（越接近单位矩阵分数越高）
+    if H is not None:
+        # 计算变换的"理想程度"
+        det = np.linalg.det(H)
+        ideal_det = 1.0
+        det_score = 1.0 / (1.0 + abs(np.log(det + 1e-8)))
+
+        # 综合评分
+        final_score = base_score * 0.7 + det_score * 0.3
+    else:
+        final_score = base_score
+
+    return min(max(final_score, 0.0), 1.0)
+
+
+def calculate_similarity(matches_count, template_kps_count, layout_kps_count):
+    """
+    计算模板和版图之间的相似度
+
+    Args:
+        matches_count: 匹配对数量
+        template_kps_count: 模板关键点数量
+        layout_kps_count: 版图关键点数量
+    """
+    # 匹配率
+    template_match_rate = matches_count / max(template_kps_count, 1)
+
+    # 覆盖率（简化计算）
+    coverage_rate = min(matches_count / max(layout_kps_count, 1), 1.0)
+
+    # 综合相似度
+    similarity = (template_match_rate * 0.6 + coverage_rate * 0.4)
+
+    return min(max(similarity, 0.0), 1.0)
+
+
+def generate_difference_description(H, inlier_count, total_matches, angle_diff=0):
+    """
+    生成差异描述
+
+    Args:
+        H: 单应性矩阵
+        inlier_count: 内点数量
+        total_matches: 总匹配数
+        angle_diff: 角度差异
+    """
+    descriptions = []
+
+    # 基于内点比例的描述
+    if total_matches > 0:
+        inlier_ratio = inlier_count / total_matches
+        if inlier_ratio > 0.8:
+            descriptions.append("高度匹配")
+        elif inlier_ratio > 0.6:
+            descriptions.append("良好匹配")
+        elif inlier_ratio > 0.4:
+            descriptions.append("中等匹配")
+        else:
+            descriptions.append("低质量匹配")
+
+    # 基于旋转的描述
+    if angle_diff != 0:
+        descriptions.append(f"旋转{angle_diff}度")
+    else:
+        descriptions.append("无旋转")
+
+    # 基于变换的描述
+    if H is not None:
+        # 检查缩放
+        scale_x = np.sqrt(H[0,0]**2 + H[1,0]**2)
+        scale_y = np.sqrt(H[0,1]**2 + H[1,1]**2)
+        avg_scale = (scale_x + scale_y) / 2
+
+        if abs(avg_scale - 1.0) > 0.1:
+            if avg_scale > 1.0:
+                descriptions.append(f"放大{avg_scale:.2f}倍")
+            else:
+                descriptions.append(f"缩小{1/avg_scale:.2f}倍")
+
+    return ", ".join(descriptions) if descriptions else "无法评估差异"
+
+
 # --- 特征提取函数 (基本无变动) ---
 def extract_keypoints_and_descriptors(model, image_tensor, kp_thresh):
     with torch.no_grad():
@@ -161,9 +283,23 @@ def match_template_multiscale(
     matching_cfg,
     log_writer: SummaryWriter | None = None,
     log_step: int = 0,
+    return_detailed_info: bool = True,
 ):
     """
     在不同尺度下搜索模板，并检测多个实例
+
+    Args:
+        model: RoRD模型
+        layout_image: 大版图图像
+        template_image: 小版图图像
+        transform: 图像预处理变换
+        matching_cfg: 匹配配置
+        log_writer: TensorBoard日志记录器
+        log_step: 日志步数
+        return_detailed_info: 是否返回详细信息
+
+    Returns:
+        匹配结果列表，包含坐标、旋转角度、置信度等信息
     """
     # 1. 版图特征提取：根据配置选择 FPN 或滑窗
     device = next(model.parameters()).device
@@ -248,8 +384,59 @@ def match_template_multiscale(
 
             x_min, y_min = inlier_layout_kps.min(axis=0)
             x_max, y_max = inlier_layout_kps.max(axis=0)
-            
-            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']}
+
+            # 提取旋转角度
+            rotation_angle = extract_rotation_angle(best_match_info['H'])
+
+            # 计算匹配质量评分
+            confidence = calculate_match_score(
+                inlier_count=int(best_match_info['inliers']),
+                total_keypoints=len(current_layout_kps),
+                H=best_match_info['H']
+            )
+
+            # 计算相似度
+            similarity = calculate_similarity(
+                matches_count=int(best_match_info['inliers']),
+                template_kps_count=len(template_kps),
+                layout_kps_count=len(current_layout_kps)
+            )
+
+            # 生成差异描述
+            diff_description = generate_difference_description(
+                H=best_match_info['H'],
+                inlier_count=int(best_match_info['inliers']),
+                total_matches=len(matches),
+                angle_diff=rotation_angle
+            )
+
+            # 构建详细实例信息
+            if return_detailed_info:
+                instance = {
+                    'bbox': {
+                        'x': int(x_min),
+                        'y': int(y_min),
+                        'width': int(x_max - x_min),
+                        'height': int(y_max - y_min)
+                    },
+                    'rotation': rotation_angle,
+                    'confidence': round(confidence, 3),
+                    'similarity': round(similarity, 3),
+                    'inliers': int(best_match_info['inliers']),
+                    '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
@@ -269,16 +456,124 @@ def match_template_multiscale(
     return found_instances
 
 
-def visualize_matches(layout_path, bboxes, output_path):
+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)
-    for i, bbox in enumerate(bboxes):
-        x, y, w, h = bbox['x'], bbox['y'], bbox['width'], bbox['height']
+    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)
-        cv2.putText(layout_img, f"Match {i+1}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (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")
@@ -289,9 +584,11 @@ if __name__ == "__main__":
     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)
-    parser.add_argument('--template', type=str, required=True)
-    parser.add_argument('--output', type=str)
+    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)
@@ -342,7 +639,8 @@ if __name__ == "__main__":
     layout_image = Image.open(args.layout).convert('L')
     template_image = Image.open(args.template).convert('L')
     
-    detected_bboxes = match_template_multiscale(
+    # 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,
@@ -350,16 +648,27 @@ if __name__ == "__main__":
         matching_cfg,
         log_writer=writer,
         log_step=0,
+        return_detailed_info=not args.simple_format,
     )
-    
-    print("\n检测到的边界框:")
-    for bbox in detected_bboxes:
-        print(bbox)
 
+    # 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_bboxes, args.output)
+        visualize_matches(args.layout, detected_matches, args.output)
 
     if writer:
-        writer.add_scalar("match/output_instances", len(detected_bboxes), 0)
+        writer.add_scalar("match/output_instances", len(detected_matches), 0)
         writer.add_text("match/layout_path", args.layout, 0)
-        writer.close()
+        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}")