From 1ff540045d48cf12bbc9cca054606ad86cc78d4f Mon Sep 17 00:00:00 2001 From: zhangpu <1250681871@qq.com> Date: Wed, 4 Mar 2026 16:42:19 +0800 Subject: [PATCH] =?UTF-8?q?=E7=BB=9F=E8=AE=A1=E8=AE=AD=E7=BB=83=E5=9B=BE?= =?UTF-8?q?=E7=89=87=E6=95=B0=E9=87=8F=E8=84=9A=E6=9C=AC=E4=B8=AD=EF=BC=8C?= =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=AF=BC=E5=87=BAexcel=E5=8A=9F=E8=83=BD?= =?UTF-8?q?=EF=BC=8C=E4=BE=BF=E4=BA=8E=E5=9C=A8=E9=A4=90=E5=8E=85=E7=9C=8B?= =?UTF-8?q?=E5=90=84=E4=B8=AA=E5=93=81=E7=B1=BB=E7=9A=84=E6=95=B0=E9=87=8F?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- classifier/embedding_food_classifier_app.py | 6 +- data_management/count_images.py | 347 ++++++++++++++------ 2 files changed, 256 insertions(+), 97 deletions(-) diff --git a/classifier/embedding_food_classifier_app.py b/classifier/embedding_food_classifier_app.py index 82fb85e..844e5a8 100644 --- a/classifier/embedding_food_classifier_app.py +++ b/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 diff --git a/data_management/count_images.py b/data_management/count_images.py index 5566231..0c32477 100644 --- a/data_management/count_images.py +++ b/data_management/count_images.py @@ -4,130 +4,265 @@ 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） + 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 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' - val_dir = dataset_root / 'val' - test_dir = dataset_root / 'test' - + 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 + + class_counts = {} + class_counts_detail = {} + total_images = total_train = total_val = total_test = 0 + + # 获取所有类别（以 train 为主，兼容 val/test 独有类别） all_class_names = set() - for folder in class_folders: - all_class_names.add(folder.name) - + 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 + + image_exts = {'.jpg', '.jpeg', '.png'} + 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 - val_class_dir = val_dir / class_name - if val_class_dir.exists(): - val_count = sum( - 1 for f in val_class_dir.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 - ) - - total = train_count + val_count + test_count - class_counts[class_name] = total + def _count(base_dir): + d = base_dir / class_name + if not d.exists(): + return 0 + return sum(1 for f in d.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, - 'val': val_count, - 'test': test_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 + total_train += train_count + total_val += val_count + total_test += test_count + + # 按数量降序排列 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 + min_threshold = 10 + insufficient_count = 0 + + # ── 控制台输出 ────────────────────────────────────────────── 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(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: @@ -140,39 +275,59 @@ 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, - 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( + '--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 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() - + # 获取项目根目录 - script_dir = Path(__file__).parent + 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) + + count_images_by_class(str(dataset_path), args.output, export_excel=args.excel)