Dataset: add READMEs, organize scripts, update .gitignore
- Add README.md to all 16 dataset directories with script references - Centralize generation scripts under scripts/ with full README - Update .gitignore to cover all data directories, keep only metadata - Remove redundant CCPD/.gitignore (parent covers same rules) - Remove stale scripts/test/ (254MB pipeline residue) - Fix label parser reference: scripts are authoritative source of truth
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test/
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train/
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# CCPD_19K — 11类中国车牌数据集
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> **以脚本为准**：生成逻辑以 `ICE_Dataset/scripts/build_ccpd_19k.py` 为准。
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基于 CCPD 构建的 11 分类车牌数据集，涵盖蓝牌、绿牌及 9 种特殊车牌。
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## 数据来源
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| 来源 | 类型 | 采样数 | 说明 |
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|------|------|--------|------|
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| CCPD2019 | 蓝牌 (blue) | 5,000（随机） | 全蓝牌，从 341,369 张中随机抽取 |
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| CCPD2020 | 绿牌 (green) | 5,000（随机） | 全绿牌，从 11,776 张中随机抽取 |
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| CCPD_SPE | 特殊车牌 (9类) | 9,000（全部） | 9 类特殊车牌，每类 1,000 张 |
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## 11 类车牌
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| 序号 | 类别 | 目录名 | 说明 | 底板色 |
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|------|------|--------|------|--------|
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| 1 | 蓝牌 | `blue` | 普通小型汽车 | 蓝底白字 |
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| 2 | 绿牌 | `green` | 新能源小型汽车 | 绿底黑字 |
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| 3 | 警车 | `police` | 警用车牌 | 白底，末位"警"红字 |
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| 4 | 军车 | `army` | 军车/武警 | 白底 |
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| 5 | 港澳 | `gangao` | 港澳入出境 | 黑底，粤Z·XXXX港/澳 |
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| 6 | 使馆旧版 | `shiguan_old` | 使馆车牌（旧版） | 黑底，"使"红字 |
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| 7 | 使馆新版 | `shiguan_new` | 使馆车牌（新版） | 黑底 |
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| 8 | 领馆旧版 | `lingguan_old` | 领馆车牌（旧版） | 黑底，"领"红字 |
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| 9 | 领馆新版 | `lingguan_new` | 领馆车牌（新版） | 黑底 |
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| 10 | 教练车 | `coach` | 教练车牌 | 黄底，末位"学" |
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| 11 | 新能源大型 | `new_energy` | 大型新能源 | 黄绿双拼，8位 |
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## 目录结构
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```
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CCPD_19K/
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├── train/ # 训练集 (90%, 17,100 张)
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│ ├── blue/ # 4,500 张
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│ ├── green/ # 4,500 张
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│ ├── police/ # 900 张
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│ ├── army/ # 900 张
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│ ├── gangao/ # 900 张
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│ ├── shiguan_old/ # 900 张
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│ ├── shiguan_new/ # 900 张
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│ ├── lingguan_old/ # 900 张
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│ ├── lingguan_new/ # 900 张
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│ ├── coach/ # 900 张
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│ └── new_energy/ # 900 张
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├── test/ # 测试集 (10%, 1,900 张)
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│ ├── blue/ # 500 张
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│ ├── green/ # 500 张
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│ └── ... (同上 11 类)
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└── README.md
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```
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## 划分方式
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- 训练集:测试集 = 9:1
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- 分层抽样（stratified）：每类内部独立按 9:1 划分
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- 随机种子：42（可复现）
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## 总计
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- 训练集：17,100 张
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- 测试集：1,900 张
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- **总计：19,000 张**
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## 生成脚本
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```bash
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# 构建 CCPD_19K (从源数据采样)
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python build_ccpd_19k.py
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# 推理 → bbox拓展裁剪 → 角点重映射 → CCPD_19K_EXP_CUT
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# 脚本位于 CCPD_19K_EXP_CUT/build_ccpd_19k_exp_cut.py
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python ../CCPD_19K_EXP_CUT/build_ccpd_19k_exp_cut.py --full
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```
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#!/usr/bin/env python3
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"""
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Build CCPD_19K dataset from:
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- CCPD2019 (all blue plates): random 5,000 images -> class "blue"
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- CCPD2020 (all green plates): random 5,000 images -> class "green"
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- CCPD_SPE (9 special plate types): all 9,000 images -> 9 classes
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Output structure (under ICE_Dataset/CCPD_19K/):
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train/
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blue/ ~4500
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green/ ~4500
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police/ ~900
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army/ ~900
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gangao/ ~900
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shiguan_old/ ~900
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shiguan_new/ ~900
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lingguan_old/ ~900
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lingguan_new/ ~900
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coach/ ~900
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new_energy/ ~900
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test/
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(same 11 classes, ~10% each)
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Split ratio: 9:1 (train : test), stratified by class.
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"""
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import os
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import sys
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import random
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import shutil
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from pathlib import Path
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from collections import defaultdict
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# ---------------------------------------------------------------------------
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# Configuration
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# ---------------------------------------------------------------------------
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BASE_DIR = Path(r"C:\Users\Kirl\Desktop\ICEv2\ICE_Dataset")
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CCPD2019_DIR = BASE_DIR / "CCPD" / "CCPD2019"
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CCPD2020_DIR = BASE_DIR / "CCPD" / "CCPD2020"
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CCPD_SPE_DIR = BASE_DIR / "CCPD_SPE"
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OUTPUT_DIR = BASE_DIR / "CCPD_19K"
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SEED = 42
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TRAIN_RATIO = 0.9 # 9:1 split
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# Number of images to sample from CCPD2019 / CCPD2020
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N_CCPD2019 = 5000
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N_CCPD2020 = 5000
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# ---------------------------------------------------------------------------
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# Step 1: Collect all images from CCPD2019 (blue plates)
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# ---------------------------------------------------------------------------
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print("=" * 60)
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print("Step 1: Collecting CCPD2019 (blue) images ...")
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ccpd2019_images = []
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for jpg in CCPD2019_DIR.rglob("*.jpg"):
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ccpd2019_images.append(jpg)
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print(f" Found {len(ccpd2019_images)} total images")
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random.seed(SEED)
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sampled_2019 = random.sample(ccpd2019_images, min(N_CCPD2019, len(ccpd2019_images)))
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print(f" Sampled {len(sampled_2019)} images")
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# ---------------------------------------------------------------------------
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# Step 2: Collect all images from CCPD2020 (green plates)
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# ---------------------------------------------------------------------------
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print("\nStep 2: Collecting CCPD2020 (green) images ...")
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ccpd2020_images = []
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for jpg in CCPD2020_DIR.rglob("*.jpg"):
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ccpd2020_images.append(jpg)
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print(f" Found {len(ccpd2020_images)} total images")
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random.seed(SEED)
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sampled_2020 = random.sample(ccpd2020_images, min(N_CCPD2020, len(ccpd2020_images)))
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print(f" Sampled {len(sampled_2020)} images")
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# ---------------------------------------------------------------------------
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# Step 3: Collect all images from CCPD_SPE (9 special classes)
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# ---------------------------------------------------------------------------
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print("\nStep 3: Collecting CCPD_SPE (9 special plate classes) ...")
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# The 9 CCPD_SPE class names (subdirectory names)
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SPE_CLASSES = [
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"police",
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"army",
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"gangao",
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"shiguan_old",
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"shiguan_new",
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"lingguan_old",
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"lingguan_new",
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"coach",
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"new_energy",
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]
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spe_images = defaultdict(list)
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for jpg in CCPD_SPE_DIR.rglob("*.jpg"):
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# Path like: .../CCPD_SPE/ccpd_base/police/xxx.jpg
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# The class is the immediate parent directory name
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parent_dir = jpg.parent.name
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if parent_dir in SPE_CLASSES:
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spe_images[parent_dir].append(jpg)
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for cls in SPE_CLASSES:
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print(f" {cls}: {len(spe_images[cls])} images")
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total_spe = sum(len(v) for v in spe_images.values())
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print(f" Total CCPD_SPE: {total_spe} images")
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# ---------------------------------------------------------------------------
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# Step 4: Build class -> file list mapping
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# ---------------------------------------------------------------------------
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print("\nStep 4: Building class mappings ...")
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class_files = {}
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# Blue plates from CCPD2019
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class_files["blue"] = sampled_2019
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# Green plates from CCPD2020
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class_files["green"] = sampled_2020
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# 9 special plate classes
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for cls in SPE_CLASSES:
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class_files[cls] = spe_images[cls]
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# Validate
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all_11_classes = ["blue", "green"] + SPE_CLASSES
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print(f" 11 classes: {all_11_classes}")
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for cls in all_11_classes:
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print(f" {cls}: {len(class_files[cls])} images")
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# ---------------------------------------------------------------------------
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# Step 5: Stratified 9:1 train/test split and copy
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# ---------------------------------------------------------------------------
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print("\nStep 5: Splitting and copying files ...")
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random.seed(SEED)
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for cls in all_11_classes:
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files = class_files[cls]
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random.shuffle(files)
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n_train = max(1, int(len(files) * TRAIN_RATIO))
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train_files = files[:n_train]
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test_files = files[n_train:]
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# Create output directories
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train_dir = OUTPUT_DIR / "train" / cls
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test_dir = OUTPUT_DIR / "test" / cls
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train_dir.mkdir(parents=True, exist_ok=True)
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test_dir.mkdir(parents=True, exist_ok=True)
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# Copy train files (handle duplicate filenames by adding suffix)
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for src in train_files:
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dst = train_dir / src.name
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counter = 1
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stem, suffix = dst.stem, dst.suffix
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while dst.exists():
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dst = train_dir / f"{stem}_{counter}{suffix}"
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counter += 1
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shutil.copy2(src, dst)
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# Copy test files (handle duplicate filenames by adding suffix)
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for src in test_files:
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dst = test_dir / src.name
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counter = 1
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stem, suffix = dst.stem, dst.suffix
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while dst.exists():
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dst = test_dir / f"{stem}_{counter}{suffix}"
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counter += 1
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shutil.copy2(src, dst)
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print(f" {cls}: train={len(train_files)}, test={len(test_files)}")
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# ---------------------------------------------------------------------------
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# Step 6: Summary
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# ---------------------------------------------------------------------------
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print("\n" + "=" * 60)
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print("CCPD_19K dataset built successfully!")
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print(f" Output: {OUTPUT_DIR}")
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total_train = 0
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total_test = 0
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for cls in all_11_classes:
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train_cnt = len(list((OUTPUT_DIR / "train" / cls).glob("*.jpg")))
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test_cnt = len(list((OUTPUT_DIR / "test" / cls).glob("*.jpg")))
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total_train += train_cnt
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total_test += test_cnt
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print(f" {cls:15s} train: {train_cnt:5d} test: {test_cnt:5d}")
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print(f" {'TOTAL':15s} train: {total_train:5d} test: {total_test:5d}")
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print(f" Grand total: {total_train + total_test}")
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print("=" * 60)
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