统计训练图片数量脚本中，增加导出excel功能，便于在餐厅看各个品类的数量

classifier/embedding_food_classifier_app.py

@@ -1151,7 +1151,11 @@ class EmbeddingFoodClassifierApp:
             
             # 提取查询图片的特征向量
             query_embedding = self.model.extract_embedding(pil_image, normalize=True)
-            # print('特征向量:',  query_embedding)
+            # 打印前50位向量，用于与秤端结果对比
+            # print("=" * 60)
+            # print(f"[PC端] 模型输出向量（前50位，共{len(query_embedding)}维）:")
+            # print(", ".join(f"{v:.6f}" for v in query_embedding[:50]))
+            # print("=" * 60)
             query_embedding = query_embedding.reshape(1, -1).astype(np.float32)
             
             # 在FAISS索引中搜索最相似的k张图片

data_management/count_images.py

@@ -4,15 +4,180 @@
 import os
 from pathlib import Path
 from collections import defaultdict
+from datetime import datetime
 
 
-def count_images_by_class(dataset_root: str, output_file: str = None):
+def export_to_excel(sorted_classes, class_counts_detail, class_counts,
+                    total_images, total_train, total_val, total_test,
+                    min_threshold, dataset_root: Path, output_dir: Path = None):
+    """
+    将统计结果导出为 Excel 文件（带格式，方便粘贴到飞书）
+
+    Args:
+        sorted_classes: 按数量排序的类别列表
+        class_counts_detail: 每类的详细统计
+        class_counts: 每类的总数
+        total_images/train/val/test: 各分区汇总
+        min_threshold: 样本不足阈值
+        dataset_root: 数据集根目录（用于生成文件名）
+        output_dir: Excel 输出目录（默认和脚本同目录）
+    """
+    try:
+        from openpyxl import Workbook
+        from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
+        from openpyxl.utils import get_column_letter
+    except ImportError:
+        print("❌ 未找到 openpyxl，请运行: pip install openpyxl")
+        return
+
+    wb = Workbook()
+    ws = wb.active
+    ws.title = "图片数量统计"
+
+    # ── 颜色 & 样式定义 ──────────────────────────────────────────
+    COLOR_HEADER_BG   = "2563EB"   # 深蓝色表头背景
+    COLOR_HEADER_FONT = "FFFFFF"   # 白色表头字
+    COLOR_WARN_BG     = "FEF08A"   # 淡黄色：样本不足行
+    COLOR_SUMMARY_BG  = "F1F5F9"   # 浅灰：汇总行
+    COLOR_TOTAL_BG    = "DBEAFE"   # 浅蓝：总计行
+
+    def make_font(bold=False, color="000000", size=11):
+        return Font(bold=bold, color=color, size=size, name="微软雅黑")
+
+    def make_fill(hex_color):
+        return PatternFill("solid", fgColor=hex_color)
+
+    def make_border():
+        thin = Side(style="thin", color="CBD5E1")
+        return Border(left=thin, right=thin, top=thin, bottom=thin)
+
+    center = Alignment(horizontal="center", vertical="center")
+    left   = Alignment(horizontal="left",   vertical="center")
+
+    # ── 第1行：大标题 ────────────────────────────────────────────
+    ws.merge_cells("A1:F1")
+    title_cell = ws["A1"]
+    title_cell.value = f"📊 图片数量统计 — {dataset_root.name}"
+    title_cell.font      = make_font(bold=True, color="1E3A5F", size=14)
+    title_cell.alignment = center
+    title_cell.fill      = make_fill("EFF6FF")
+    ws.row_dimensions[1].height = 32
+
+    # ── 第2行：生成时间 ──────────────────────────────────────────
+    ws.merge_cells("A2:F2")
+    time_cell = ws["A2"]
+    time_cell.value = f"生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}    数据集: {dataset_root}"
+    time_cell.font      = make_font(color="64748B", size=9)
+    time_cell.alignment = center
+    time_cell.fill      = make_fill("EFF6FF")
+    ws.row_dimensions[2].height = 18
+
+    # ── 第3行：空行分隔 ──────────────────────────────────────────
+    ws.row_dimensions[3].height = 8
+
+    # ── 第4行：汇总信息 ──────────────────────────────────────────
+    summary_labels = [
+        ("总类别数",   len(class_counts)),
+        ("总图片数",   total_images),
+        ("训练集",     total_train),
+        ("验证集",     total_val),
+        ("测试集",     total_test),
+        ("平均每类",   total_images // len(class_counts) if len(class_counts) > 0 else 0),
+    ]
+    insufficient_count = sum(1 for _, cnt in sorted_classes if cnt < min_threshold)
+
+    # 汇总用两行：标签行 + 数值行
+    for col_idx, (label, _) in enumerate(summary_labels, start=1):
+        cell = ws.cell(row=4, column=col_idx, value=label)
+        cell.font      = make_font(bold=True, color="475569", size=10)
+        cell.alignment = center
+        cell.fill      = make_fill(COLOR_SUMMARY_BG)
+        cell.border    = make_border()
+        ws.row_dimensions[4].height = 20
+
+    for col_idx, (_, value) in enumerate(summary_labels, start=1):
+        cell = ws.cell(row=5, column=col_idx, value=value)
+        cell.font      = make_font(bold=True, size=12)
+        cell.alignment = center
+        cell.fill      = make_fill(COLOR_TOTAL_BG)
+        cell.border    = make_border()
+        ws.row_dimensions[5].height = 22
+
+    # ── 第6行：空行 ───────────────────────────────────────────────
+    ws.row_dimensions[6].height = 8
+
+    # ── 第7行：列表头 ────────────────────────────────────────────
+    headers = ["类别名称", "总计", "训练集", "验证集", "测试集", "状态"]
+    for col_idx, header in enumerate(headers, start=1):
+        cell = ws.cell(row=7, column=col_idx, value=header)
+        cell.font      = make_font(bold=True, color=COLOR_HEADER_FONT, size=11)
+        cell.fill      = make_fill(COLOR_HEADER_BG)
+        cell.alignment = center
+        cell.border    = make_border()
+        ws.row_dimensions[7].height = 24
+
+    # ── 数据行（从第8行开始）────────────────────────────────────
+    for row_offset, (class_name, count) in enumerate(sorted_classes):
+        row = 8 + row_offset
+        detail = class_counts_detail[class_name]
+        is_warn = count < min_threshold
+        status  = "⚠ 样本不足" if is_warn else "✓"
+        row_data = [class_name, detail['total'], detail['train'],
+                    detail['val'], detail['test'], status]
+
+        for col_idx, value in enumerate(row_data, start=1):
+            cell = ws.cell(row=row, column=col_idx, value=value)
+            cell.border    = make_border()
+            cell.alignment = left if col_idx == 1 else center
+            cell.font      = make_font(size=10)
+            if is_warn:
+                cell.fill = make_fill(COLOR_WARN_BG)
+            elif row_offset % 2 == 1:
+                cell.fill = make_fill("F8FAFC")   # 斑马条纹
+
+        ws.row_dimensions[row].height = 18
+
+    # ── 最后一行：样本汇总说明 ───────────────────────────────────
+    last_row = 8 + len(sorted_classes)
+    ws.merge_cells(f"A{last_row}:F{last_row}")
+    note_cell = ws[f"A{last_row}"]
+    note_cell.value = (f"样本不足（< {min_threshold} 张）: {insufficient_count} 类   ｜   "
+                       f"样本充足（≥ {min_threshold} 张）: {len(class_counts) - insufficient_count} 类")
+    note_cell.font      = make_font(bold=True, color="475569", size=10)
+    note_cell.alignment = center
+    note_cell.fill      = make_fill(COLOR_SUMMARY_BG)
+    note_cell.border    = make_border()
+    ws.row_dimensions[last_row].height = 22
+
+    # ── 列宽自适应 ───────────────────────────────────────────────
+    col_widths = [40, 8, 8, 8, 8, 12]
+    for col_idx, width in enumerate(col_widths, start=1):
+        ws.column_dimensions[get_column_letter(col_idx)].width = width
+
+    # ── 冻结表头（冻结到第8行，滚动时列头始终可见）──────────────
+    ws.freeze_panes = "A8"
+
+    # ── 生成文件名 & 保存 ────────────────────────────────────────
+    timestamp  = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename   = f"image_count_{dataset_root.name}_{timestamp}.xlsx"
+    save_dir   = output_dir if output_dir else Path(__file__).parent
+    save_path  = save_dir / filename
+
+    wb.save(save_path)
+    print(f"\n✅ Excel 已导出: {save_path}")
+    print(f"   （直接打开后全选复制，粘贴到飞书云文档即可保留格式）")
+    return save_path
+
+
+def count_images_by_class(dataset_root: str, output_file: str = None,
+                           export_excel: bool = False):
     """
     统计每个类别的图片数量（包含train/val/test）
 
     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）
+        output_file:   输出文件路径（可选，如果提供则保存到 txt 文件）
+        export_excel:  是否同时导出 Excel 文件（默认 False）
     """
     dataset_root = Path(dataset_root)
     train_dir = dataset_root / 'train'
@@ -28,62 +193,38 @@ def count_images_by_class(dataset_root: str, output_file: str = None):
     print("="*80)
 
     class_counts        = {}
-    class_counts_detail = {}  # 详细统计：train/val/test分别的数量
+    class_counts_detail = {}
-    total_images = 0
+    total_images = total_train = total_val = total_test = 0
-    total_train = 0
-    total_val = 0
-    total_test = 0
 
-    # 获取所有类别文件夹（从train目录）
+    # 获取所有类别（以 train 为主，兼容 val/test 独有类别）
-    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:
+    for folder in train_dir.iterdir():
+        if folder.is_dir():
             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
     image_exts = {'.jpg', '.jpeg', '.png'}
-        train_class_dir = train_dir / class_name
-        if train_class_dir.exists():
-            train_count = sum(
-                1 for f in train_class_dir.iterdir()
-                if f.is_file() and f.suffix.lower() in image_exts
-            )
 
-        # 统计val目录
+    for class_name in sorted(all_class_names):
-        val_class_dir = val_dir / class_name
+        def _count(base_dir):
-        if val_class_dir.exists():
+            d = base_dir / class_name
-            val_count = sum(
+            if not d.exists():
-                1 for f in val_class_dir.iterdir()
+                return 0
-                if f.is_file() and f.suffix.lower() in image_exts
+            return sum(1 for f in d.iterdir()
-            )
+                       if f.is_file() and f.suffix.lower() in image_exts)
-        
-        # 统计test目录
-        test_class_dir = test_dir / class_name
-        if test_class_dir.exists():
-            test_count = sum(
-                1 for f in test_class_dir.iterdir()
-                if f.is_file() and f.suffix.lower() in image_exts
-            )
 
+        train_count = _count(train_dir)
+        val_count   = _count(val_dir)
+        test_count  = _count(test_dir)
         total       = train_count + val_count + test_count
+
         class_counts[class_name]        = total
         class_counts_detail[class_name] = {
             'train': train_count,
@@ -96,15 +237,12 @@ def count_images_by_class(dataset_root: str, output_file: str = None):
         total_val    += val_count
         total_test   += test_count
 
-    # 按数量排序
+    # 按数量降序排列
     sorted_classes = sorted(class_counts.items(), key=lambda x: x[1], reverse=True)
+    min_threshold  = 10
+    insufficient_count = 0
 
-    # 修复文件开头的注释
+    # ── 控制台输出 ──────────────────────────────────────────────
-    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")
@@ -112,22 +250,19 @@ def count_images_by_class(dataset_root: str, output_file: str = None):
     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(f"{class_name:<50} {detail['total']:<8} {detail['train']:<8} "
+              f"{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}")
 
-    # 保存到文件（如果指定）
+    # ── 可选：保存到 txt 文件 ────────────────────────────────────
     if output_file:
         output_path = Path(output_file)
         with open(output_path, 'w', encoding='utf-8') as f:
@@ -140,28 +275,48 @@ def count_images_by_class(dataset_root: str, output_file: str = None):
             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(f"{class_name:<50} {detail['total']:<8} {detail['train']:<8} "
-            
+                        f"{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}")
 
+    # ── 可选：导出 Excel ─────────────────────────────────────────
+    if export_excel:
+        export_to_excel(
+            sorted_classes      = sorted_classes,
+            class_counts_detail = class_counts_detail,
+            class_counts        = class_counts,
+            total_images        = total_images,
+            total_train         = total_train,
+            total_val           = total_val,
+            total_test          = total_test,
+            min_threshold       = min_threshold,
+            dataset_root        = dataset_root,
+        )
+
 
 if __name__ == "__main__":
     import argparse
 
     parser = argparse.ArgumentParser(description='统计每个类别的图片数量')
-    parser.add_argument('--dataset', type=str, 
+    parser.add_argument(
+        '--dataset', type=str,
         default='dataset/WholeIngredientRecognition',
-                       help='数据集根目录（默认: 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(
+        '--output', type=str, default=None,
+        help='输出 txt 文件路径（可选）'
+    )
+    parser.add_argument(
+        '--excel', action='store_true',
+        help='是否同时导出 Excel 文件（可直接复制粘贴到飞书云文档）'
+    )
 
     args = parser.parse_args()
 
@@ -175,4 +330,4 @@ if __name__ == "__main__":
     else:
         dataset_path = Path(args.dataset)
 
-    count_images_by_class(str(dataset_path), args.output)
+    count_images_by_class(str(dataset_path), args.output, export_excel=args.excel)