整理git文件

.gitignore

@@ -8,3 +8,12 @@
 /faiss_vector_db/faiss_index*/
 /faiss_vector_db/WholeIngredientRecognition/faiss_index*/
 /data_management/download_history.db
+faiss_vector_db
+*.jpg
+*.png
+*.pt
+*.xlsx
+SegFormer
+*.bin
+.vscode
+.agents

data_management/count_delete_images.py

@@ -0,0 +1,227 @@
+"""
+统计每个类别的图片数量（包含train/val/test），并可选择删除样本不足的类别
+"""
+import os
+import shutil
+from pathlib import Path
+from collections import defaultdict
+
+
+def count_images_by_class(dataset_root: str, output_file: str = None, delete_threshold: int = None, auto_confirm: bool = False):
+    """
+    统计每个类别的图片数量（包含train/val/test），并可选择删除样本不足的类别
+
+    Args:
+        dataset_root: 数据集根目录（包含train/val/test目录）
+        output_file: 输出文件路径（可选，如果提供则保存到文件）
+        delete_threshold: 删除阈值（可选，如果提供则删除总数少于该值的类别）
+        auto_confirm: 是否自动确认删除（默认False，需要用户确认）
+    """
+    dataset_root = Path(dataset_root)
+    train_dir = dataset_root / 'train'
+    val_dir = dataset_root / 'val'
+    test_dir = dataset_root / 'test'
+
+    if not train_dir.exists():
+        print(f"错误: 训练目录不存在: {train_dir}")
+        return
+
+    print("="*80)
+    print("每个类别的图片数量统计（包含train/val/test）")
+    print("="*80)
+
+    class_counts = {}
+    class_counts_detail = {}  # 详细统计：train/val/test分别的数量
+    total_images = 0
+    total_train = 0
+    total_val = 0
+    total_test = 0
+
+    # 获取所有类别文件夹（从train目录）
+    class_folders = sorted([f for f in train_dir.iterdir() if f.is_dir()])
+
+    # 也检查val和test目录中是否有train中没有的类别
+    all_class_names = set()
+    for folder in class_folders:
+        all_class_names.add(folder.name)
+
+    if val_dir.exists():
+        for folder in val_dir.iterdir():
+            if folder.is_dir():
+                all_class_names.add(folder.name)
+
+    if test_dir.exists():
+        for folder in test_dir.iterdir():
+            if folder.is_dir():
+                all_class_names.add(folder.name)
+
+    # 统计每个类别的图片数量
+    for class_name in sorted(all_class_names):
+        train_count = 0
+        val_count = 0
+        test_count = 0
+
+        # 统计train目录
+        train_class_dir = train_dir / class_name
+        if train_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                train_count += len(list(train_class_dir.glob(ext)))
+
+        # 统计val目录
+        val_class_dir = val_dir / class_name
+        if val_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                val_count += len(list(val_class_dir.glob(ext)))
+
+        # 统计test目录
+        test_class_dir = test_dir / class_name
+        if test_class_dir.exists():
+            for ext in ['*.jpg', '*.jpeg', '*.png', '*.JPG', '*.JPEG', '*.PNG']:
+                test_count += len(list(test_class_dir.glob(ext)))
+
+        total = train_count + val_count + test_count
+        class_counts[class_name] = total
+        class_counts_detail[class_name] = {
+            'train': train_count,
+            'val': val_count,
+            'test': test_count,
+            'total': total
+        }
+        total_images += total
+        total_train += train_count
+        total_val += val_count
+        total_test += test_count
+
+    # 按数量排序
+    sorted_classes = sorted(class_counts.items(), key=lambda x: x[1], reverse=True)
+
+    # 修复文件开头的注释
+    if output_file:
+        # 确保sorted_classes已定义
+        pass
+
+    # 打印结果
+    print(f"\n总类别数: {len(class_counts)}")
+    print(f"总图片数: {total_images} (训练集: {total_train}, 验证集: {total_val}, 测试集: {total_test})")
+    print(f"平均每类: {total_images // len(class_counts) if len(class_counts) > 0 else 0} 张\n")
+    print("-"*100)
+    print(f"{'类别名称':<50} {'总计':<8} {'训练':<8} {'验证':<8} {'测试':<8} {'状态':<10}")
+    print("-"*100)
+
+    # 统计样本不足的类别（假设阈值是10）
+    min_threshold = 10
+    insufficient_count = 0
+
+    for class_name, count in sorted_classes:
+        detail = class_counts_detail[class_name]
+        status = "⚠样本不足" if count < min_threshold else "✓"
+        if count < min_threshold:
+            insufficient_count += 1
+        print(f"{class_name:<50} {detail['total']:<8} {detail['train']:<8} {detail['val']:<8} {detail['test']:<8} {status:<10}")
+
+    print("-"*100)
+    print(f"\n样本不足的类别数（<{min_threshold}张）: {insufficient_count}")
+    print(f"样本充足的类别数（>={min_threshold}张）: {len(class_counts) - insufficient_count}")
+
+    # 删除样本不足的类别（如果指定了删除阈值）
+    if delete_threshold is not None:
+        classes_to_delete = [class_name for class_name, count in class_counts.items() if count < delete_threshold]
+
+        if classes_to_delete:
+            print("\n" + "="*100)
+            print(f"发现 {len(classes_to_delete)} 个类别的总图片数少于 {delete_threshold} 张:")
+            print("="*100)
+            for class_name in classes_to_delete:
+                detail = class_counts_detail[class_name]
+                print(f"  - {class_name}: {detail['total']} 张 (训练:{detail['train']}, 验证:{detail['val']}, 测试:{detail['test']})")
+
+            # 确认删除
+            if not auto_confirm:
+                confirm = input(f"\n是否删除这 {len(classes_to_delete)} 个类别? (yes/no): ").strip().lower()
+                if confirm not in ['yes', 'y']:
+                    print("取消删除操作")
+                    return
+
+            # 执行删除
+            deleted_count = 0
+            for class_name in classes_to_delete:
+                try:
+                    # 删除train目录中的类别文件夹
+                    train_class_dir = train_dir / class_name
+                    if train_class_dir.exists():
+                        shutil.rmtree(train_class_dir)
+                        print(f"✓ 已删除: {train_class_dir}")
+
+                    # 删除val目录中的类别文件夹
+                    val_class_dir = val_dir / class_name
+                    if val_class_dir.exists():
+                        shutil.rmtree(val_class_dir)
+                        print(f"✓ 已删除: {val_class_dir}")
+
+                    # 删除test目录中的类别文件夹
+                    test_class_dir = test_dir / class_name
+                    if test_class_dir.exists():
+                        shutil.rmtree(test_class_dir)
+                        print(f"✓ 已删除: {test_class_dir}")
+
+                    deleted_count += 1
+                except Exception as e:
+                    print(f"✗ 删除失败 {class_name}: {e}")
+
+            print(f"\n删除完成: 成功删除 {deleted_count}/{len(classes_to_delete)} 个类别")
+        else:
+            print(f"\n没有找到总数少于 {delete_threshold} 张的类别")
+
+    # 保存到文件（如果指定）
+    if output_file:
+        output_path = Path(output_file)
+        with open(output_path, 'w', encoding='utf-8') as f:
+            f.write("="*100 + "\n")
+            f.write("每个类别的图片数量统计（包含train/val/test）\n")
+            f.write("="*100 + "\n\n")
+            f.write(f"总类别数: {len(class_counts)}\n")
+            f.write(f"总图片数: {total_images} (训练集: {total_train}, 验证集: {total_val}, 测试集: {total_test})\n")
+            f.write(f"平均每类: {total_images // len(class_counts) if len(class_counts) > 0 else 0} 张\n\n")
+            f.write("-"*100 + "\n")
+            f.write(f"{'类别名称':<50} {'总计':<8} {'训练':<8} {'验证':<8} {'测试':<8} {'状态':<10}\n")
+            f.write("-"*100 + "\n")
+
+            for class_name, count in sorted_classes:
+                detail = class_counts_detail[class_name]
+                status = "⚠样本不足" if count < min_threshold else "✓"
+                f.write(f"{class_name:<50} {detail['total']:<8} {detail['train']:<8} {detail['val']:<8} {detail['test']:<8} {status:<10}\n")
+
+            f.write("-"*100 + "\n")
+            f.write(f"\n样本不足的类别数（<{min_threshold}张）: {insufficient_count}\n")
+            f.write(f"样本充足的类别数（>={min_threshold}张）: {len(class_counts) - insufficient_count}\n")
+
+        print(f"\n✓ 统计结果已保存到: {output_path}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description='统计每个类别的图片数量，并可选择删除样本不足的类别')
+    parser.add_argument('--dataset', type=str,
+                       default='dataset/WholeIngredientRecognition',
+                       help='数据集根目录（默认: dataset/WholeIngredientRecognition）')
+    parser.add_argument('--output', type=str, default=None,
+                       help='输出文件路径（可选，保存统计结果到文件）')
+    parser.add_argument('--delete', type=int, default=None,
+                       help='删除阈值（可选，删除总数少于该值的类别，例如: --delete 20）')
+    parser.add_argument('--yes', action='store_true',
+                       help='自动确认删除，不需要手动输入yes（谨慎使用）')
+
+    args = parser.parse_args()
+
+    # 获取项目根目录
+    script_dir = Path(__file__).parent
+    project_root = script_dir.parent
+
+    # 处理相对路径
+    if not os.path.isabs(args.dataset):
+        dataset_path = project_root / args.dataset
+    else:
+        dataset_path = Path(args.dataset)
+
+    count_images_by_class(str(dataset_path), args.output, args.delete, args.yes)