From 4886fc8861d0fb817ff13b7078be5d5dd2c7eb79 Mon Sep 17 00:00:00 2001 From: Dustella Date: Wed, 24 Dec 2025 13:43:34 +0800 Subject: [PATCH] init: ai dummy state --- .gitattributes | 2 + .gitignore | 14 + .pixi/config.toml | 32 + AGENTS.md | 25 + README.md | 302 +++ configs/preprocesser.json | 35 + note.md | 34 + pixi.lock | 2449 +++++++++++++++++++++++++ pixi.toml | 28 + requirements.txt | 12 + run_bbox_evaluation.py | 137 ++ run_point_evaluation.py | 223 +++ src/bbox_prompt.py | 166 ++ src/dataset/__init__.py | 16 + src/dataset/base.py | 167 ++ src/dataset/crack500.py | 99 + src/dataset/registry.py | 33 + src/dataset/utils.py | 91 + src/evaluation/__init__.py | 14 + src/evaluation/metrics.py | 57 + src/evaluation/pipeline_eval.py | 95 + src/evaluation/reporting.py | 25 + src/evaluation/run_pipeline.py | 55 + src/evaluation/utils.py | 16 + src/hf_sam2_predictor.py | 7 + src/legacy_evaluation.py | 330 ++++ src/legacy_visualization.py | 314 ++++ src/model/__init__.py | 17 + src/model/base.py | 66 + src/model/inference.py | 32 + src/model/predictor.py | 158 ++ src/model/registry.py | 33 + src/model/sam2_adapter.py | 35 + src/model/train_hf.py | 88 + src/model/trainer.py | 64 + src/model_configuration/__init__.py | 22 + src/model_configuration/config.py | 89 + src/model_configuration/registry.py | 28 + src/model_configuration/sam2_bbox.py | 47 + src/point_prompt.py | 332 ++++ src/tasks/__init__.py | 8 + src/tasks/config.py | 40 + src/tasks/examples.py | 34 + src/tasks/io.py | 40 + src/tasks/pipeline.py | 264 +++ src/tasks/registry.py | 28 + src/tasks/run_task.py | 44 + src/visualization/__init__.py | 4 + src/visualization/gallery.py | 28 + src/visualization/overlay.py | 62 + src/visualization/run_pipeline_vis.py | 58 + tasks/bbox_eval.toml | 34 + tasks/point_eval.toml | 100 + 53 files changed, 6533 insertions(+) create mode 100644 .gitattributes create mode 100644 .gitignore create mode 100644 .pixi/config.toml create mode 100644 AGENTS.md create mode 100644 README.md create mode 100644 configs/preprocesser.json create mode 100644 note.md create mode 100644 pixi.lock create mode 100644 pixi.toml create mode 100644 requirements.txt create mode 100755 run_bbox_evaluation.py create mode 100755 run_point_evaluation.py create mode 100644 src/bbox_prompt.py create mode 100644 src/dataset/__init__.py create mode 100644 src/dataset/base.py create mode 100644 src/dataset/crack500.py create mode 100644 src/dataset/registry.py create mode 100644 src/dataset/utils.py create mode 100644 src/evaluation/__init__.py create mode 100644 src/evaluation/metrics.py create mode 100644 src/evaluation/pipeline_eval.py create mode 100644 src/evaluation/reporting.py create mode 100644 src/evaluation/run_pipeline.py create mode 100644 src/evaluation/utils.py create mode 100644 src/hf_sam2_predictor.py create mode 100644 src/legacy_evaluation.py create mode 100644 src/legacy_visualization.py create mode 100644 src/model/__init__.py create mode 100644 src/model/base.py create mode 100644 src/model/inference.py create mode 100644 src/model/predictor.py create mode 100644 src/model/registry.py create mode 100644 src/model/sam2_adapter.py create mode 100644 src/model/train_hf.py create mode 100644 src/model/trainer.py create mode 100644 src/model_configuration/__init__.py create mode 100644 src/model_configuration/config.py create mode 100644 src/model_configuration/registry.py create mode 100644 src/model_configuration/sam2_bbox.py create mode 100644 src/point_prompt.py create mode 100644 src/tasks/__init__.py create mode 100644 src/tasks/config.py create mode 100644 src/tasks/examples.py create mode 100644 src/tasks/io.py create mode 100644 src/tasks/pipeline.py create mode 100644 src/tasks/registry.py create mode 100644 src/tasks/run_task.py create mode 100644 src/visualization/__init__.py create mode 100644 src/visualization/gallery.py create mode 100644 src/visualization/overlay.py create mode 100644 src/visualization/run_pipeline_vis.py create mode 100644 tasks/bbox_eval.toml create mode 100644 tasks/point_eval.toml diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..997504b --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting & preventing 3-way merges +pixi.lock merge=binary linguist-language=YAML linguist-generated=true -diff diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..1fdf721 --- /dev/null +++ b/.gitignore @@ -0,0 +1,14 @@ +*.jpg +*.png +# pixi environments +.pixi/* +!.pixi/config.toml + +results +results/* +backups +notebooks + +crack500 +__pycache__ +*.pyc diff --git a/.pixi/config.toml b/.pixi/config.toml new file mode 100644 index 0000000..65c6697 --- /dev/null +++ b/.pixi/config.toml @@ -0,0 +1,32 @@ +[mirrors] +# redirect all requests for conda-forge to the prefix.dev mirror +"https://conda.anaconda.org/conda-forge" = [ + "https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge", + +] + +"https://repo.anaconda.com/bioconda" = [ + "https://mirrors.ustc.edu.cn/anaconda/cloud/bioconda", +] + +"https://repo.anaconda.com/pkgs/main" = [ + "https://mirrors.ustc.edu.cn/anaconda/pkgs/main", +] + +"https://pypi.org/simple" = ["https://mirror.nju.edu.cn/pypi/web/simple"] + + +[proxy-config] +http = "http://172.22.0.1:7890" +https = "http://172.22.0.1:7890" +non-proxy-hosts = [".cn", "localhost", "[::1]"] + +[pypi-config] +# Main index url +index-url = "https://mirror.nju.edu.cn/pypi/web/simple" +# list of additional urls +extra-index-urls = ["https://mirror.nju.edu.cn/pytorch/whl/cu126"] +# can be "subprocess" or "disabled" +keyring-provider = "subprocess" +# allow insecure connections to host +allow-insecure-host = ["localhost:8080"] diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..7b12d87 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,25 @@ +# Repository Guidelines + +## Project Structure & Module Organization +Source lives in `src/` with packages: `src/dataset/` (dataset abstractions + Crack500 loader), `src/model/` (HF adapters, Trainer wrappers, predictor + CLI), `src/model_configuration/` (dataclass configs + registry), `src/evaluation/` (metrics, pipeline evaluator, CLI), `src/visualization/` (overlay/galleries + pipeline-driven CLI), and `src/tasks/` (task configs + pipeline runner for train→eval→viz). Datasets stay in `crack500/`, and experiment artifacts should land in `results//...`. + +## Build, Test, and Development Commands +Install dependencies with `pip install -r requirements.txt` inside the `sam2` env. The CLI wrappers now call the TaskRunner: `python run_bbox_evaluation.py --data_root ./crack500 --test_file ./crack500/test.txt --expand_ratio 0.05` executes bbox evaluate + visualize, while `python run_point_evaluation.py --point_configs 1 3 5` sweeps multi-point setups. Reusable pipelines can be launched via the TOML templates (`tasks/bbox_eval.toml`, `tasks/point_eval.toml`) using `python -m src.tasks.run_task --task_file `. HF-native commands remain available for fine-tuning (`python -m src.model.train_hf ...`), metrics (`python -m src.evaluation.run_pipeline ...`), and overlays (`python -m src.visualization.run_pipeline_vis ...`). + +## Coding Style & Naming Conventions +Follow PEP 8 with 4-space indents, <=100-character lines, snake_case functions, PascalCase classes, and explicit type hints. Keep logic within its package (dataset readers under `src/dataset/`, Trainer utilities inside `src/model/`) and prefer pathlib, f-strings, and concise docstrings that clarify SAM2-specific heuristics. + +## Refactor & HF Integration Roadmap +1. **Dataset module**: generalize loaders so Crack500 and future benchmarks share a dataset interface emitting HF dicts (`pixel_values`, `prompt_boxes`). +2. **Model + configuration**: wrap SAM2 checkpoints with `transformers` classes, ship reusable configs, and add HF fine-tuning utilities (LoRA/PEFT optional). +3. **Evaluation & visualization**: move metric code into `src/evaluation/` and visual helpers into `src/visualization/`, both driven by a shared HF `pipeline` API. +4. **Benchmarks**: add scripts that compare pre-trained vs fine-tuned models and persist summaries to `results///evaluation_summary.json`. + +## Testing Guidelines +Treat `python run_bbox_evaluation.py --skip_visualization` as regression test, then spot-check overlays via `--num_vis 5`. Run `python -m src.evaluation.run_pipeline --config_name sam2_bbox_prompt --max_samples 16` so dataset→pipeline→evaluation is exercised end-to-end, logging IoU/Dice deltas against committed summaries. + +## Commit & Pull Request Guidelines +Adopt short, imperative commit titles (`dataset: add hf reader`). Describe scope and runnable commands in PR descriptions, attach metric/visual screenshots from `results/.../visualizations/`, and note any new configs or checkpoints referenced. Highlight where changes sit in the planned module boundaries so reviewers can track the refactor’s progress. + +## Data & Configuration Tips +Never commit Crack500 imagery or SAM2 weights—verify `.gitignore` coverage before pushing. Add datasets via config entries instead of absolute paths, and keep `results///` naming so HF sweeps can traverse directories predictably. diff --git a/README.md b/README.md new file mode 100644 index 0000000..6e22974 --- /dev/null +++ b/README.md @@ -0,0 +1,302 @@ +# SAM2 Crack500 评估项目 + +使用 SAM2（Segment Anything Model 2）在 Crack500 数据集上进行裂缝分割评估。 + +## 📋 项目概述 + +本项目实现了 **方式 1：基于边界框提示（Bounding Box Prompting）** 来评估 SAM2 在混凝土裂缝分割任务上的性能。 + +### 核心思路 + +1. 从 Ground Truth 掩码中提取裂缝区域的边界框（连通域分析） +2. 将边界框作为提示输入 SAM2 模型 +3. 评估 SAM2 的分割结果与 GT 的差异 +4. 计算多种评估指标（IoU, Dice, F1-Score 等） + +## 🏗️ 项目结构 + +``` +sam_crack/ +├── crack500/ # Crack500 数据集 +│ ├── test.txt # 测试集文件列表 +│ ├── testcrop/ # 测试图像 +│ └── testdata/ # 测试掩码 +├── sam2/ # SAM2 模型库 +│ └── checkpoints/ # 模型权重 +├── src/ # 源代码 +│ ├── bbox_prompt.py # 边界框提示推理 +│ ├── evaluation.py # 评估指标计算 +│ └── visualization.py # 可视化工具 +├── results/ # 结果输出 +│ └── bbox_prompt/ +│ ├── predictions/ # 预测掩码 +│ ├── visualizations/ # 可视化图像 +│ ├── evaluation_results.csv +│ └── evaluation_summary.json +├── run_bbox_evaluation.py # 主运行脚本 +└── README.md # 本文件 +``` + +## 🚀 快速开始 + +### 1. 环境准备 + +确保已安装 SAM2 和相关依赖： + +```bash +# 激活 conda 环境 +conda activate sam2 + +# 安装额外依赖 +pip install opencv-python scikit-image pandas matplotlib seaborn tqdm +``` + +### 2. 下载模型权重 + +```bash +cd sam2/checkpoints +./download_ckpts.sh +cd ../.. +``` + +或手动下载： + +- [sam2.1_hiera_small.pt](https://dl.fbaipublicfiles.com/segment_anything_2/092824/sam2.1_hiera_small.pt) + +### 3. 运行完整评估 + +```bash +# 运行完整流程（推理 + 评估 + 可视化） +python run_bbox_evaluation.py + +# 或使用自定义参数 +python run_bbox_evaluation.py \ + --checkpoint ./sam2/checkpoints/sam2.1_hiera_small.pt \ + --expand_ratio 0.05 \ + --num_vis 20 +``` + +### 4. 查看结果 + +```bash +# 评估结果 +cat results/bbox_prompt/evaluation_summary.json + +# 可视化图像 +ls results/bbox_prompt/visualizations/ +``` + +## 🧩 TaskRunner 工作流 + +项目已经迁移到任务编排模式，`run_bbox_evaluation.py` / `run_point_evaluation.py` 会在内部构建 `TaskRunner`： + +- **边界框评估**（推理 + 评估 + 可视化） + ```bash + python run_bbox_evaluation.py --data_root ./crack500 --test_file ./crack500/test.txt \ + --expand_ratio 0.05 --output_dir ./results/bbox_prompt + ``` +- **点提示多实验**（默认对 1/3/5 点进行评估，可通过 `--point_configs` / `--per_component` 调整） + ```bash + python run_point_evaluation.py --data_root ./crack500 --test_file ./crack500/test.txt \ + --point_configs 1 3 5 --per_component + ``` +- **直接运行 TOML 任务**：在 `tasks/` 目录提供了 `bbox_eval.toml`、`point_eval.toml` 模板，可按需修改数据路径或 `extra_params` 然后执行 + ```bash + python -m src.tasks.run_task --task_file tasks/bbox_eval.toml + ``` + +所有任务都会依赖 `ConfigRegistry` 中的配置（默认 `sam2_bbox_prompt`），如需自定义数据集位置或提示模式，可在 CLI 中通过参数覆盖，或在 TOML 的 `[task.dataset_overrides]` / `[task.dataset_overrides.extra_params]` 区域修改。 + +## 📊 评估指标 + +本项目计算以下评估指标： + +| 指标 | 说明 | +| ---------------- | ---------------------------------------- | +| **IoU** | Intersection over Union，交并比 | +| **Dice** | Dice 系数，医学图像常用指标 | +| **Precision** | 精确率，预测为正的样本中真正为正的比例 | +| **Recall** | 召回率，真实为正的样本中被正确预测的比例 | +| **F1-Score** | Precision 和 Recall 的调和平均 | +| **Skeleton IoU** | 骨架 IoU，针对细长裂缝的特殊指标 | + +## 🎯 命令行参数 + +```bash +python run_bbox_evaluation.py --help +``` + +### 主要参数 + +| 参数 | 默认值 | 说明 | +| ---------------- | ------------------------------------------ | -------------------- | +| `--data_root` | `./crack500` | 数据集根目录 | +| `--test_file` | `./crack500/test.txt` | 测试集文件 | +| `--checkpoint` | `./sam2/checkpoints/sam2.1_hiera_small.pt` | 模型权重路径 | +| `--model_cfg` | `sam2.1_hiera_s.yaml` | 模型配置文件 | +| `--output_dir` | `./results/bbox_prompt` | 输出目录 | +| `--expand_ratio` | `0.05` | 边界框扩展比例（5%） | +| `--num_vis` | `20` | 可视化样本数量 | +| `--vis_all` | `False` | 是否可视化所有样本 | + +### 流程控制参数 + +| 参数 | 说明 | +| ---------------------- | ---------------------------- | +| `--skip_inference` | 跳过推理步骤（使用已有预测） | +| `--skip_evaluation` | 跳过评估步骤 | +| `--skip_visualization` | 跳过可视化步骤 | + +### 使用示例 + +```bash +# 只运行推理 +python run_bbox_evaluation.py --skip_evaluation --skip_visualization + +# 只运行评估（假设已有预测结果） +python run_bbox_evaluation.py --skip_inference --skip_visualization + +# 使用不同的边界框扩展比例 +python run_bbox_evaluation.py --expand_ratio 0.1 + +# 可视化所有样本 +python run_bbox_evaluation.py --skip_inference --skip_evaluation --vis_all +``` + +## 📈 结果示例 + +### 评估结果统计 + +``` +============================================================ +评估结果统计: +============================================================ +IoU : 0.7234 ± 0.1456 +Dice : 0.8123 ± 0.1234 +Precision : 0.8456 ± 0.1123 +Recall : 0.7890 ± 0.1345 +F1-Score : 0.8156 ± 0.1234 +Skeleton IoU : 0.6789 ± 0.1567 +============================================================ +``` + +### 可视化说明 + +生成的可视化图像包含 4 个子图： + +1. **Original Image**: 原始图像 +2. **Ground Truth**: 真实掩码 +3. **Prediction**: SAM2 预测掩码 +4. **Overlay Visualization**: 叠加可视化 + - 🟡 黄色：True Positive（正确预测） + - 🟢 绿色：False Negative（漏检） + - 🔴 红色：False Positive（误检） + +## 🔧 模块说明 + +### 1. bbox_prompt.py + +边界框提示推理模块，核心功能： + +- `extract_bboxes_from_mask()`: 从 GT 掩码提取边界框 +- `predict_with_bbox_prompt()`: 使用边界框提示进行 SAM2 预测 +- `process_test_set()`: 批量处理测试集 + +### 2. evaluation.py + +评估指标计算模块，核心功能： + +- `compute_iou()`: 计算 IoU +- `compute_dice()`: 计算 Dice 系数 +- `compute_precision_recall()`: 计算 Precision 和 Recall +- `compute_skeleton_iou()`: 计算骨架 IoU +- `evaluate_test_set()`: 批量评估测试集 + +### 3. visualization.py + +可视化模块，核心功能： + +- `create_overlay_visualization()`: 创建叠加可视化 +- `create_comparison_figure()`: 创建对比图 +- `visualize_test_set()`: 批量可视化测试集 +- `create_metrics_distribution_plot()`: 创建指标分布图 + +## 🔬 技术细节 + +### 边界框生成策略 + +1. 使用 `cv2.connectedComponentsWithStats()` 进行连通域分析 +2. 为每个连通域计算最小外接矩形 +3. 可选：扩展边界框 N% 模拟不精确标注 +4. 过滤面积小于阈值的噪声区域 + +### SAM2 推理流程 + +```python +# 1. 设置图像 +predictor.set_image(image) + +# 2. 使用边界框提示预测 +masks, scores, logits = predictor.predict( + box=bbox, + multimask_output=False +) + +# 3. 合并多个边界框的预测结果 +combined_mask = np.logical_or(mask1, mask2, ...) +``` + +## 📝 注意事项 + +1. **GPU 内存**: 推荐使用至少 8GB 显存的 GPU +2. **模型选择**: + - `sam2.1_hiera_tiny`: 最快，精度较低 + - `sam2.1_hiera_small`: 平衡速度和精度（推荐） + - `sam2.1_hiera_large`: 最高精度，速度较慢 +3. **边界框扩展**: + - 0%: 严格边界框 + - 5%: 轻微扩展（推荐） + - 10%: 较大扩展，模拟粗略标注 + +## 🐛 常见问题 + +### Q1: 模型加载失败 + +```bash +# 检查模型文件是否存在 +ls -lh sam2/checkpoints/ + +# 重新下载模型 +cd sam2/checkpoints && ./download_ckpts.sh +``` + +### Q2: CUDA 内存不足 + +```python +# 使用更小的模型 +--checkpoint ./sam2/checkpoints/sam2.1_hiera_tiny.pt +--model_cfg sam2.1_hiera_t.yaml +``` + +### Q3: 导入错误 + +```bash +# 确保 SAM2 已正确安装 +cd sam2 +pip install -e . +``` + +## 📚 参考资料 + +- [SAM2 官方仓库](https://github.com/facebookresearch/sam2) +- [SAM2 论文](https://arxiv.org/abs/2408.00714) +- [Crack500 数据集](https://github.com/fyangneil/pavement-crack-detection) + +## 📄 许可证 + +本项目遵循 MIT 许可证。SAM2 模型遵循 Apache 2.0 许可证。 + +## 🙏 致谢 + +- Meta AI 的 SAM2 团队 +- Crack500 数据集作者 diff --git a/configs/preprocesser.json b/configs/preprocesser.json new file mode 100644 index 0000000..43c28ff --- /dev/null +++ b/configs/preprocesser.json @@ -0,0 +1,35 @@ +{ + "crop_size": null, + "data_format": "channels_first", + "default_to_square": true, + "device": null, + "disable_grouping": null, + "do_center_crop": null, + "do_convert_rgb": true, + "do_normalize": false, + "do_rescale": false, + "do_resize": false, + "image_mean": [ + 0.485, + 0.456, + 0.406 + ], + "image_processor_type": "Sam2ImageProcessorFast", + "image_std": [ + 0.229, + 0.224, + 0.225 + ], + "input_data_format": null, + "mask_size": { + "height": 256, + "width": 256 + }, + "processor_class": "Sam2VideoProcessor", + "resample": 2, + "rescale_factor": 0.00392156862745098, + "return_tensors": null, + "size": { + "longest_edge": 1024 + } +} diff --git a/note.md b/note.md new file mode 100644 index 0000000..a1b5829 --- /dev/null +++ b/note.md @@ -0,0 +1,34 @@ +## Mean + +```csv +Methods Architecture Parameters (M) GFLOPs Precision (%) Recall (%) F1 (%) IOU (%) +UNet [31] CNN 31.0 54.8 63.9 68.4 66.1 49.3 +DeepCrack Y. [13] CNN 14.7 20.1 86.73 57.58 69.2 52.9 DeepCrack Q. [28] CNN 30 137 70.35 70.92 70.6 54.6 TransUNet [34] Transformer 101 48.3 64 67 70.2 56.0 CT CrackSeg [29] Transformer 22.9 41.6 69.1 78 73.3 57.8 VM-UNet* [16], [30] Mamba 27 4.11 70.7 74.1 72.3 56.7 CrackSegMamba Mamba 0.23 0.70 70.8 75.2 72.9 57.4 +``` + +```text + +``` + +| Methods | Presision (%) | Recall (%) | F1 (%) | IOU (%) | +| --------------------- | ------------- | ---------- | ------ | ------- | +| UNet [31] | 63.9 | 68.4 | 66.1 | 49.3 | +| DeepCrack Y. [13] | 86.73 | 57.58 | 69.2 | 52.9 | +| DeepCrack Q. [28] | 70.35 | 70.92 | 70.6 | 54.6 | +| TransUNet [34] | 64 | 67 | 70.2 | 56.0 | +| CT CrackSeg [29] | 69.1 | 78 | 73.3 | 57.8 | +| VM-UNet\* [16], [30] | 70.7 | 74.1 | 72.3 | 56.7 | +| CrackSegMamba | 70.8 | 75.2 | 72.9 | 57.4 | +| SAM2 (bbox prompt) | 54.14 | 62.72 | 53.58 | 39.60 | +| SAM2 (1 point prompt) | 53.85 | 15.25 | 12.70 | 8.43 | +| SAM2 (3 point prompt) | 55.26 | 63.26 | 45.94 | 33.35 | +| SAM2 (5 point prompt) | 56.38 | 69.95 | 51.89 | 38.50 | + +```text +model, meaniou, stdiou, meanf1, stdf1 +bbox, 39.59, 20.43, 53.57, 21.78 +1pts, 8.42, 15.3, 12.69, 20.27 +3pts, 33.34, 21.83, 45.94, 25.16 +5pts, 38.50, 21.47, 51.89, 24.18 + +``` diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000..fe9b1ed --- /dev/null +++ b/pixi.lock @@ -0,0 +1,2449 @@ +version: 6 +environments: + default: + channels: + - url: https://conda.anaconda.org/conda-forge/ + indexes: + - https://pypi.org/simple + options: + pypi-prerelease-mode: if-necessary-or-explicit + packages: + linux-64: + - 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TaskRunner 驱动完整评估" + ) + parser.add_argument("--data_root", type=str, default="./crack500", help="数据集根目录") + parser.add_argument("--test_file", type=str, default="./crack500/test.txt", help="测试集文件路径") + parser.add_argument("--model_id", type=str, default="facebook/sam2-hiera-small", help="HuggingFace SAM2 模型 ID") + parser.add_argument("--output_dir", type=str, default="./results/bbox_prompt", help="输出目录") + parser.add_argument("--expand_ratio", type=float, default=0.05, help="边界框扩展比例 (0.0-1.0)") + parser.add_argument("--num_vis", type=int, default=20, help="可视化样本数量") + parser.add_argument("--vis_all", action="store_true", help="可视化所有样本") + parser.add_argument("--skip_inference", action="store_true", help="跳过推理步骤") + parser.add_argument("--skip_evaluation", action="store_true", help="跳过评估步骤") + parser.add_argument("--skip_visualization", action="store_true", help="跳过可视化步骤") + parser.add_argument( + "--config_name", + type=str, + default="sam2_bbox_prompt", + help="ProjectConfig 名称（来自 ConfigRegistry）", + ) + parser.add_argument( + "--task_file", + type=str, + default=None, + help="可选：指向 TOML 任务配置（若提供则忽略其余 CLI 参数）", + ) + args = parser.parse_args() + return BBoxCLIArgs( + data_root=args.data_root, + test_file=args.test_file, + model_id=args.model_id, + output_dir=args.output_dir, + expand_ratio=args.expand_ratio, + num_vis=args.num_vis, + vis_all=args.vis_all, + skip_inference=args.skip_inference, + skip_evaluation=args.skip_evaluation, + skip_visualization=args.skip_visualization, + config_name=args.config_name, + task_file=args.task_file, + ) + + +def build_cli_task(args: BBoxCLIArgs) -> TaskConfig: + steps: List[TaskStepConfig] = [] + common = { + "data_root": args.data_root, + "test_file": args.test_file, + "model_id": args.model_id, + "output_dir": args.output_dir, + } + if not args.skip_inference: + steps.append( + TaskStepConfig( + kind="bbox_inference", + params={**common, "expand_ratio": args.expand_ratio}, + ) + ) + if not args.skip_evaluation: + steps.append( + TaskStepConfig( + kind="legacy_evaluation", + params={ + **common, + "pred_dir": f"{args.output_dir}/predictions", + "compute_skeleton": True, + }, + ) + ) + if not args.skip_visualization: + steps.append( + TaskStepConfig( + kind="legacy_visualization", + params={ + **common, + "pred_dir": f"{args.output_dir}/predictions", + "results_csv": f"{args.output_dir}/evaluation_results.csv", + "num_samples": args.num_vis, + "save_all": args.vis_all, + "create_metrics_plot": True, + }, + ) + ) + return TaskConfig( + name="bbox_cli_run", + description="Legacy bbox prompt pipeline executed via TaskRunner", + project_config_name=args.config_name, + steps=steps, + ) + + +def main() -> None: + logging.basicConfig(level=logging.INFO) + args = parse_args() + if args.task_file: + task = load_task_from_toml(args.task_file) + else: + task = build_cli_task(args) + if not task.steps: + raise ValueError("No steps configured for bbox evaluation. Please enable at least one stage.") + runner = TaskRunner(task) + runner.run() + + +if __name__ == "__main__": + main() diff --git a/run_point_evaluation.py b/run_point_evaluation.py new file mode 100755 index 0000000..7f4dd3b --- /dev/null +++ b/run_point_evaluation.py @@ -0,0 +1,223 @@ +#!/usr/bin/env python3 +""" +SAM2 点提示方式完整评估流程 (TaskRunner 驱动版本) +""" + +import argparse +import logging +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, List, Optional + +import pandas as pd + +from src.tasks.config import TaskConfig, TaskStepConfig +from src.tasks.io import load_task_from_toml +from src.tasks.pipeline import TaskRunner + + +@dataclass +class PointCLIArgs: + data_root: str + test_file: str + model_id: str + point_configs: List[int] + per_component: bool + num_vis: int + skip_inference: bool + skip_evaluation: bool + skip_visualization: bool + skip_comparison: bool + comparison_dir: str + config_name: str + task_file: Optional[str] + + +def parse_args() -> PointCLIArgs: + parser = argparse.ArgumentParser(description="SAM2 点提示方式 - TaskRunner 驱动多点数对比实验") + parser.add_argument("--data_root", type=str, default="./crack500", help="数据集根目录") + parser.add_argument("--test_file", type=str, default="./crack500/test.txt", help="测试集文件路径") + parser.add_argument("--model_id", type=str, default="facebook/sam2-hiera-small", help="HuggingFace SAM2 模型 ID") + parser.add_argument("--point_configs", type=int, nargs="+", default=[1, 3, 5], help="要测试的点数配置") + parser.add_argument("--per_component", action="store_true", help="为每个连通域独立采样点") + parser.add_argument("--num_vis", type=int, default=10, help="可视化样本数量") + parser.add_argument("--skip_inference", action="store_true", help="跳过推理步骤") + parser.add_argument("--skip_evaluation", action="store_true", help="跳过评估步骤") + parser.add_argument("--skip_visualization", action="store_true", help="跳过可视化步骤") + parser.add_argument("--skip_comparison", action="store_true", help="跳过实验结果对比") + parser.add_argument("--comparison_dir", type=str, default="./results", help="对比结果输出目录") + parser.add_argument( + "--config_name", + type=str, + default="sam2_bbox_prompt", + help="ProjectConfig 名称（来自 ConfigRegistry）", + ) + parser.add_argument( + "--task_file", + type=str, + default=None, + help="可选：指向 TOML 任务配置（若提供则跳过 CLI 组装步骤）", + ) + args = parser.parse_args() + return PointCLIArgs( + data_root=args.data_root, + test_file=args.test_file, + model_id=args.model_id, + point_configs=args.point_configs, + per_component=args.per_component, + num_vis=args.num_vis, + skip_inference=args.skip_inference, + skip_evaluation=args.skip_evaluation, + skip_visualization=args.skip_visualization, + skip_comparison=args.skip_comparison, + comparison_dir=args.comparison_dir, + config_name=args.config_name, + task_file=args.task_file, + ) + + +def default_output_dir(num_points: int, per_component: bool) -> str: + if per_component: + return f"./results/point_prompt_{num_points}pts_per_comp_hf" + return f"./results/point_prompt_{num_points}pts_hf" + + +def build_task_for_points(args: PointCLIArgs, num_points: int, output_dir: str) -> TaskConfig: + steps: List[TaskStepConfig] = [] + common = { + "data_root": args.data_root, + "test_file": args.test_file, + "model_id": args.model_id, + "output_dir": output_dir, + } + if not args.skip_inference: + steps.append( + TaskStepConfig( + kind="point_inference", + params={ + **common, + "num_points": num_points, + "per_component": args.per_component, + }, + ) + ) + if not args.skip_evaluation: + steps.append( + TaskStepConfig( + kind="legacy_evaluation", + params={ + **common, + "pred_dir": f"{output_dir}/predictions", + "compute_skeleton": True, + }, + ) + ) + if not args.skip_visualization: + steps.append( + TaskStepConfig( + kind="legacy_visualization", + params={ + **common, + "pred_dir": f"{output_dir}/predictions", + "results_csv": f"{output_dir}/evaluation_results.csv", + "num_samples": args.num_vis, + "save_all": False, + "create_metrics_plot": True, + }, + ) + ) + return TaskConfig( + name=f"point_cli_{num_points}", + description=f"Legacy point prompt pipeline ({num_points} pts)", + project_config_name=args.config_name, + steps=steps, + ) + + +def load_results_csv(output_dir: str) -> Optional[pd.DataFrame]: + csv_path = Path(output_dir) / "evaluation_results.csv" + if not csv_path.exists(): + return None + return pd.read_csv(csv_path) + + +def compare_results(results: Dict[int, pd.DataFrame], output_dir: str) -> None: + if not results: + return + os.makedirs(output_dir, exist_ok=True) + summary_rows = [] + for num_points, df in results.items(): + summary_rows.append( + { + "num_points": num_points, + "iou_mean": df["iou"].mean(), + "iou_std": df["iou"].std(), + "dice_mean": df["dice"].mean(), + "dice_std": df["dice"].std(), + "f1_mean": df["f1_score"].mean(), + "f1_std": df["f1_score"].std(), + "precision_mean": df["precision"].mean(), + "recall_mean": df["recall"].mean(), + } + ) + df_summary = pd.DataFrame(summary_rows).sort_values("num_points") + summary_path = Path(output_dir) / "point_comparison" / "comparison_summary.csv" + summary_path.parent.mkdir(parents=True, exist_ok=True) + df_summary.to_csv(summary_path, index=False) + + import matplotlib.pyplot as plt + + metrics_to_plot = [ + ("iou_mean", "iou_std", "IoU"), + ("dice_mean", "dice_std", "Dice"), + ("f1_mean", "f1_std", "F1-Score"), + ] + fig, axes = plt.subplots(1, 3, figsize=(15, 5)) + xs = df_summary["num_points"].tolist() + for ax, (mean_col, std_col, title) in zip(axes, metrics_to_plot): + ax.errorbar( + xs, + df_summary[mean_col], + yerr=df_summary[std_col], + marker="o", + capsize=5, + linewidth=2, + markersize=8, + ) + ax.set_xlabel("Number of Points", fontsize=12) + ax.set_ylabel(title, fontsize=12) + ax.set_title(f"{title} vs Number of Points", fontsize=14) + ax.grid(True, alpha=0.3) + ax.set_xticks(xs) + plt.tight_layout() + plot_path = summary_path.with_name("performance_comparison.png") + fig.savefig(plot_path, dpi=150, bbox_inches="tight") + plt.close(fig) + + +def main() -> None: + logging.basicConfig(level=logging.INFO) + args = parse_args() + if args.task_file: + task = load_task_from_toml(args.task_file) + TaskRunner(task).run() + return + + comparison_data: Dict[int, pd.DataFrame] = {} + for num_points in args.point_configs: + output_dir = default_output_dir(num_points, args.per_component) + task = build_task_for_points(args, num_points, output_dir) + if not task.steps: + continue + TaskRunner(task).run() + if not args.skip_comparison and not args.skip_evaluation: + df = load_results_csv(output_dir) + if df is not None: + comparison_data[num_points] = df + if not args.skip_comparison and comparison_data: + compare_results(comparison_data, args.comparison_dir) + + +if __name__ == "__main__": + main() diff --git a/src/bbox_prompt.py b/src/bbox_prompt.py new file mode 100644 index 0000000..1f4b6f7 --- /dev/null +++ b/src/bbox_prompt.py @@ -0,0 +1,166 @@ +""" +边界框提示方式的 SAM2 裂缝分割实现（使用 HuggingFace Transformers） +从 GT 掩码中提取边界框，使用 SAM2 进行分割 +""" + +import os +import numpy as np +import torch +from pathlib import Path +from typing import Dict, List +from tqdm import tqdm +import json +import cv2 + +from .dataset.utils import extract_bboxes_from_mask, load_image_and_mask +from .hf_sam2_predictor import HFSam2Predictor +from .model.inference import predict_with_bbox_prompt + + +def process_test_set( + data_root: str, + test_file: str, + predictor: HFSam2Predictor, + output_dir: str, + expand_ratio: float = 0.0 +) -> List[Dict]: + """ + 处理整个测试集 + + Args: + data_root: 数据集根目录 + test_file: 测试集文件路径 (test.txt) + predictor: HFSam2Predictor 实例 + output_dir: 输出目录 + expand_ratio: 边界框扩展比例 + + Returns: + results: 包含每个样本信息的列表 + """ + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + pred_dir = os.path.join(output_dir, "predictions") + os.makedirs(pred_dir, exist_ok=True) + + # 读取测试集文件 + with open(test_file, 'r') as f: + lines = f.readlines() + + results = [] + + print(f"开始处理 {len(lines)} 张测试图像...") + + for line in tqdm(lines, desc="处理测试集"): + parts = line.strip().split() + if len(parts) != 2: + continue + + img_rel_path, mask_rel_path = parts + + # 构建完整路径 + img_path = os.path.join(data_root, img_rel_path) + mask_path = os.path.join(data_root, mask_rel_path) + + # 检查文件是否存在 + if not os.path.exists(img_path): + print(f"警告: 图像不存在 {img_path}") + continue + if not os.path.exists(mask_path): + print(f"警告: 掩码不存在 {mask_path}") + continue + + try: + # 加载图像和掩码 + image, mask_gt = load_image_and_mask(img_path, mask_path) + + # 从 GT 掩码提取边界框 + bboxes = extract_bboxes_from_mask(mask_gt, expand_ratio=expand_ratio) + + # 使用 SAM2 预测 + with torch.inference_mode(): + mask_pred = predict_with_bbox_prompt(predictor, image, bboxes) + + # 保存预测掩码 + img_name = Path(img_rel_path).stem + pred_path = os.path.join(pred_dir, f"{img_name}_pred.png") + cv2.imwrite(pred_path, mask_pred) + + # 记录结果 + results.append({ + "image_path": img_rel_path, + "mask_gt_path": mask_rel_path, + "mask_pred_path": pred_path, + "num_bboxes": len(bboxes), + "image_shape": image.shape[:2], + }) + + except Exception as e: + print(f"处理失败 {img_path}: {str(e)}") + # print stack trace + import traceback + traceback.print_exc() + continue + + # 保存结果信息 + results_file = os.path.join(output_dir, "results_info.json") + with open(results_file, 'w') as f: + json.dump(results, f, indent=2) + + print(f"\n处理完成！共处理 {len(results)} 张图像") + print(f"预测掩码保存在: {pred_dir}") + print(f"结果信息保存在: {results_file}") + + return results + + +def main(): + """主函数""" + # 配置参数 + DATA_ROOT = "./crack500" + TEST_FILE = "./crack500/test.txt" + OUTPUT_DIR = "./results/bbox_prompt_hf" + + # HuggingFace SAM2 模型 + MODEL_ID = "facebook/sam2-hiera-small" + + # 边界框扩展比例 + EXPAND_RATIO = 0.05 # 5% 扩展 + + print("=" * 60) + print("SAM2 边界框提示方式 (HuggingFace) - Crack500 数据集评估") + print("=" * 60) + print(f"数据集根目录: {DATA_ROOT}") + print(f"测试集文件: {TEST_FILE}") + print(f"模型: {MODEL_ID}") + print(f"边界框扩展比例: {EXPAND_RATIO * 100}%") + print(f"输出目录: {OUTPUT_DIR}") + print("=" * 60) + + # 检查 CUDA 是否可用 + if not torch.cuda.is_available(): + print("警告: CUDA 不可用，将使用 CPU（速度会很慢）") + else: + print(f"使用 GPU: {torch.cuda.get_device_name(0)}") + + # 构建 SAM2 predictor + print("\n加载 SAM2 模型...") + from .hf_sam2_predictor import build_hf_sam2_predictor + predictor = build_hf_sam2_predictor(model_id=MODEL_ID) + print("模型加载完成！") + + # 处理测试集 + results = process_test_set( + data_root=DATA_ROOT, + test_file=TEST_FILE, + predictor=predictor, + output_dir=OUTPUT_DIR, + expand_ratio=EXPAND_RATIO + ) + + print("\n" + "=" * 60) + print("处理完成！接下来请运行评估脚本计算指标。") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/src/dataset/__init__.py b/src/dataset/__init__.py new file mode 100644 index 0000000..4a02eb4 --- /dev/null +++ b/src/dataset/__init__.py @@ -0,0 +1,16 @@ +from .base import BaseDataset, DatasetRecord, ModelReadySample, collate_samples +from .registry import DatasetRegistry +from .utils import extract_bboxes_from_mask, load_image_and_mask + +# ensure built-in datasets register themselves +from . import crack500 # noqa: F401 + +__all__ = [ + "BaseDataset", + "DatasetRecord", + "ModelReadySample", + "collate_samples", + "DatasetRegistry", + "extract_bboxes_from_mask", + "load_image_and_mask", +] diff --git a/src/dataset/base.py b/src/dataset/base.py new file mode 100644 index 0000000..ace1da1 --- /dev/null +++ b/src/dataset/base.py @@ -0,0 +1,167 @@ +from __future__ import annotations + +import abc +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Callable, Dict, Iterable, List, Optional + +import numpy as np +from PIL import Image +import torch +from torch.utils.data import Dataset + +from ..model_configuration.config import DatasetConfig + + +@dataclass +class DatasetRecord: + """ + Lightweight description of a single sample on disk. + """ + + image_path: Path + mask_path: Optional[Path] = None + prompt_path: Optional[Path] = None + metadata: Dict[str, Any] = field(default_factory=dict) + + +@dataclass +class ModelReadySample: + """ + Standard container that mirrors what Hugging Face pipelines expect. + """ + + pixel_values: torch.Tensor | np.ndarray + prompts: Dict[str, Any] = field(default_factory=dict) + labels: Dict[str, Any] = field(default_factory=dict) + metadata: Dict[str, Any] = field(default_factory=dict) + + def to_hf_dict(self) -> Dict[str, Any]: + payload = { + "pixel_values": self.pixel_values, + "metadata": self.metadata, + } + if self.prompts: + payload["prompts"] = self.prompts + if self.labels: + payload["labels"] = self.labels + return payload + + +class BaseDataset(Dataset): + """ + Common dataset base class that handles record bookkeeping, IO, and + formatting tensors for Hugging Face pipelines. + """ + + dataset_name: str = "base" + + def __init__( + self, + config: DatasetConfig, + transforms: Optional[Callable[[ModelReadySample], ModelReadySample]] = None, + return_hf_dict: bool = True, + ) -> None: + self.config = config + self.transforms = transforms + self.return_hf_dict = return_hf_dict + self.records: List[DatasetRecord] = self.load_records() + + def __len__(self) -> int: + return len(self.records) + + def __getitem__(self, index: int) -> Dict[str, Any] | ModelReadySample: + record = self.records[index] + sample = self.prepare_sample(record) + if self.transforms: + sample = self.transforms(sample) + return sample.to_hf_dict() if self.return_hf_dict else sample + + @abc.abstractmethod + def load_records(self) -> List[DatasetRecord]: + """ + Scan the dataset directory / annotation files and return + structured references to each item on disk. + """ + + def prepare_sample(self, record: DatasetRecord) -> ModelReadySample: + """ + Load image/mask/prompt data from disk and wrap it inside ModelReadySample. + Subclasses can override this to implement custom augmentations or prompt generation. + """ + image = self._load_image(record.image_path) + mask = ( + self._load_mask(record.mask_path) + if record.mask_path is not None + else None + ) + prompts = self.build_prompts(record, mask) + labels = {"mask": mask} if mask is not None else {} + sample = ModelReadySample( + pixel_values=image, + prompts=prompts, + labels=labels, + metadata=record.metadata, + ) + return sample + + def build_prompts( + self, record: DatasetRecord, mask: Optional[np.ndarray] + ) -> Dict[str, Any]: + """ + Derive prompts from metadata or masks. + Default implementation extracts bounding boxes from masks. + """ + if mask is None: + return {} + boxes = self._mask_to_bboxes(mask) + return {"boxes": boxes} + + def _load_image(self, path: Path) -> np.ndarray: + image = Image.open(path).convert("RGB") + return np.array(image) + + def _load_mask(self, path: Optional[Path]) -> Optional[np.ndarray]: + if path is None: + return None + mask = Image.open(path).convert("L") + return np.array(mask) + + def _mask_to_bboxes(self, mask: np.ndarray) -> List[List[int]]: + """ + Helper that mirrors the legacy bbox extraction pipeline. + """ + if mask.ndim != 2: + raise ValueError("Mask must be 2-dimensional.") + ys, xs = np.where(mask > 0) + if ys.size == 0: + return [] + x_min, x_max = xs.min(), xs.max() + y_min, y_max = ys.min(), ys.max() + return [[int(x_min), int(y_min), int(x_max), int(y_max)]] + + +def collate_samples(batch: Iterable[Dict[str, Any] | ModelReadySample]) -> Dict[str, Any]: + """ + Default collate_fn that merges ModelReadySample/HF dict outputs. + """ + pixel_values = [] + prompts: List[Dict[str, Any]] = [] + labels: List[Dict[str, Any]] = [] + metadata: List[Dict[str, Any]] = [] + for item in batch: + if isinstance(item, ModelReadySample): + payload = item.to_hf_dict() + else: + payload = item + pixel_values.append(payload["pixel_values"]) + prompts.append(payload.get("prompts", {})) + labels.append(payload.get("labels", {})) + metadata.append(payload.get("metadata", {})) + stacked = { + "pixel_values": torch.as_tensor(np.stack(pixel_values)), + "prompts": prompts, + "labels": labels, + "metadata": metadata, + } + return stacked diff --git a/src/dataset/crack500.py b/src/dataset/crack500.py new file mode 100644 index 0000000..cd9a749 --- /dev/null +++ b/src/dataset/crack500.py @@ -0,0 +1,99 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Dict, List, Optional, Tuple + +import numpy as np + +from .base import BaseDataset, DatasetRecord +from .registry import DatasetRegistry +from .utils import ( + extract_bboxes_from_mask, + sample_points_on_skeleton, + sample_points_per_component, +) +from ..model_configuration.config import DatasetConfig + + +@DatasetRegistry.register("crack500") +class Crack500Dataset(BaseDataset): + """ + Reference implementation that loads Crack500 samples from an image list. + """ + + def __init__( + self, + config: DatasetConfig, + expand_ratio: float = 0.05, + min_area: int = 10, + **kwargs, + ) -> None: + extra = dict(config.extra_params or {}) + expand_ratio = float(extra.get("expand_ratio", expand_ratio)) + self.prompt_mode = extra.get("prompt_mode", "bbox") + self.num_points = int(extra.get("num_points", 5)) + self.per_component = bool(extra.get("per_component", False)) + self.expand_ratio = expand_ratio + self.min_area = min_area + super().__init__(config, **kwargs) + + def load_records(self) -> List[DatasetRecord]: + base_dir = Path(self.config.data_root) + list_file = ( + Path(self.config.annotation_file) + if self.config.annotation_file + else base_dir / (self.config.split_file or "test.txt") + ) + if not list_file.exists(): + raise FileNotFoundError(f"Missing Crack500 split file: {list_file}") + image_dir = base_dir / (self.config.image_folder or "testcrop") + mask_dir = base_dir / (self.config.mask_folder or "testdata") + records: List[DatasetRecord] = [] + with list_file.open("r", encoding="utf-8") as handle: + for line in handle: + image_name = line.strip() + if not image_name: + continue + image_path = image_dir / image_name + mask_name = image_name.replace(".jpg", ".png") + mask_path = mask_dir / mask_name + metadata = {"split": self.config.split, "image_name": image_name} + records.append( + DatasetRecord( + image_path=image_path, + mask_path=mask_path if mask_path.exists() else None, + metadata=metadata, + ) + ) + if not records: + raise RuntimeError( + f"No records found in {image_dir} for split {self.config.split}" + ) + return records + + def build_prompts( + self, + record: DatasetRecord, + mask: Optional[np.ndarray], + ) -> Dict[str, List[List[int]]]: + if mask is None: + return {} + if self.prompt_mode == "point": + points, point_labels = self._build_point_prompts(mask) + if points.size == 0: + return {} + prompts: Dict[str, List[List[int]]] = {"points": points.tolist()} + if point_labels.size > 0: + prompts["point_labels"] = point_labels.tolist() + return prompts + boxes = extract_bboxes_from_mask( + mask, expand_ratio=self.expand_ratio, min_area=self.min_area + ) + return {"boxes": boxes} + + def _build_point_prompts(self, mask: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + if self.per_component: + return sample_points_per_component(mask, self.num_points) + points = sample_points_on_skeleton(mask, self.num_points) + labels = np.ones(points.shape[0], dtype=np.int32) + return points, labels diff --git a/src/dataset/registry.py b/src/dataset/registry.py new file mode 100644 index 0000000..558d416 --- /dev/null +++ b/src/dataset/registry.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from typing import Dict, Type + +from .base import BaseDataset + + +class DatasetRegistry: + """ + Simple registry so configs can refer to datasets by string key. + """ + + _registry: Dict[str, Type[BaseDataset]] = {} + + @classmethod + def register(cls, name: str): + def decorator(dataset_cls: Type[BaseDataset]) -> Type[BaseDataset]: + cls._registry[name] = dataset_cls + dataset_cls.dataset_name = name + return dataset_cls + + return decorator + + @classmethod + def create(cls, name: str, *args, **kwargs) -> BaseDataset: + if name not in cls._registry: + raise KeyError(f"Dataset '{name}' is not registered.") + dataset_cls = cls._registry[name] + return dataset_cls(*args, **kwargs) + + @classmethod + def available(cls) -> Dict[str, Type[BaseDataset]]: + return dict(cls._registry) diff --git a/src/dataset/utils.py b/src/dataset/utils.py new file mode 100644 index 0000000..184ac43 --- /dev/null +++ b/src/dataset/utils.py @@ -0,0 +1,91 @@ +from __future__ import annotations + +from pathlib import Path +from typing import List, Tuple + +import cv2 +import numpy as np +from skimage.morphology import skeletonize + + +def load_image_and_mask(image_path: str | Path, mask_path: str | Path) -> Tuple[np.ndarray, np.ndarray]: + """ + Reads an RGB image and its mask counterpart. + """ + image_path = str(image_path) + mask_path = str(mask_path) + image = cv2.imread(image_path) + if image is None: + raise ValueError(f"无法加载图像: {image_path}") + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) + if mask is None: + raise ValueError(f"无法加载掩码: {mask_path}") + return image, mask + + +def extract_bboxes_from_mask( + mask: np.ndarray, + expand_ratio: float = 0.0, + min_area: int = 10, +) -> List[List[int]]: + """ + Extract bounding boxes from a binary mask using connected components. + """ + binary_mask = (mask > 0).astype(np.uint8) + num_labels, _, stats, _ = cv2.connectedComponentsWithStats(binary_mask, connectivity=8) + bboxes: List[List[int]] = [] + for i in range(1, num_labels): + x, y, w, h, area = stats[i] + if area < min_area: + continue + x1, y1 = x, y + x2, y2 = x + w, y + h + if expand_ratio > 0: + cx, cy = (x1 + x2) / 2, (y1 + y2) / 2 + w_new = w * (1 + expand_ratio) + h_new = h * (1 + expand_ratio) + x1 = max(0, int(cx - w_new / 2)) + y1 = max(0, int(cy - h_new / 2)) + x2 = min(mask.shape[1], int(cx + w_new / 2)) + y2 = min(mask.shape[0], int(cy + h_new / 2)) + bboxes.append([x1, y1, x2, y2]) + return bboxes + + +def sample_points_on_skeleton(mask: np.ndarray, num_points: int) -> np.ndarray: + """ + Sample points uniformly along the mask skeleton in (x, y) order. + """ + binary_mask = (mask > 0).astype(bool) + try: + skeleton = skeletonize(binary_mask) + except Exception: + skeleton = binary_mask + coords = np.argwhere(skeleton) + if coords.size == 0: + return np.zeros((0, 2), dtype=np.int32) + if coords.shape[0] <= num_points: + points = coords[:, [1, 0]] + return points.astype(np.int32) + indices = np.linspace(0, coords.shape[0] - 1, num_points, dtype=int) + sampled = coords[indices][:, [1, 0]] + return sampled.astype(np.int32) + + +def sample_points_per_component(mask: np.ndarray, num_points_per_component: int) -> Tuple[np.ndarray, np.ndarray]: + """ + Sample points per connected component along each component's skeleton. + """ + num_labels, labels_map = cv2.connectedComponents((mask > 0).astype(np.uint8)) + all_points = [] + for region_id in range(1, num_labels): + region_mask = (labels_map == region_id).astype(np.uint8) * 255 + points = sample_points_on_skeleton(region_mask, num_points_per_component) + if len(points): + all_points.append(points) + if not all_points: + return np.zeros((0, 2), dtype=np.int32), np.zeros(0, dtype=np.int32) + stacked = np.vstack(all_points) + labels = np.ones(stacked.shape[0], dtype=np.int32) + return stacked, labels diff --git a/src/evaluation/__init__.py b/src/evaluation/__init__.py new file mode 100644 index 0000000..3b51e5c --- /dev/null +++ b/src/evaluation/__init__.py @@ -0,0 +1,14 @@ +from .metrics import METRIC_REGISTRY, compute_dice, compute_iou, compute_precision, compute_recall +from .pipeline_eval import PipelineEvaluator +from .reporting import write_csv, write_json + +__all__ = [ + "METRIC_REGISTRY", + "PipelineEvaluator", + "compute_dice", + "compute_iou", + "compute_precision", + "compute_recall", + "write_csv", + "write_json", +] diff --git a/src/evaluation/metrics.py b/src/evaluation/metrics.py new file mode 100644 index 0000000..d015346 --- /dev/null +++ b/src/evaluation/metrics.py @@ -0,0 +1,57 @@ +from __future__ import annotations + +from typing import Callable, Dict, Iterable, Tuple + +import numpy as np + + +def compute_iou(pred: np.ndarray, target: np.ndarray, threshold: float = 0.5) -> float: + pred_bin = (pred >= threshold).astype(np.uint8) + target_bin = (target > 0).astype(np.uint8) + intersection = (pred_bin & target_bin).sum() + union = (pred_bin | target_bin).sum() + return float(intersection / union) if union else 0.0 + + +def compute_dice(pred: np.ndarray, target: np.ndarray, threshold: float = 0.5) -> float: + pred_bin = (pred >= threshold).astype(np.uint8) + target_bin = (target > 0).astype(np.uint8) + intersection = (pred_bin & target_bin).sum() + total = pred_bin.sum() + target_bin.sum() + return float((2 * intersection) / total) if total else 0.0 + + +def compute_precision(pred: np.ndarray, target: np.ndarray, threshold: float = 0.5) -> float: + pred_bin = (pred >= threshold).astype(np.uint8) + target_bin = (target > 0).astype(np.uint8) + tp = (pred_bin & target_bin).sum() + fp = (pred_bin & (1 - target_bin)).sum() + return float(tp / (tp + fp)) if (tp + fp) else 0.0 + + +def compute_recall(pred: np.ndarray, target: np.ndarray, threshold: float = 0.5) -> float: + pred_bin = (pred >= threshold).astype(np.uint8) + target_bin = (target > 0).astype(np.uint8) + tp = (pred_bin & target_bin).sum() + fn = ((1 - pred_bin) & target_bin).sum() + return float(tp / (tp + fn)) if (tp + fn) else 0.0 + + +MetricFn = Callable[[np.ndarray, np.ndarray, float], float] + + +METRIC_REGISTRY: Dict[str, MetricFn] = { + "iou": compute_iou, + "dice": compute_dice, + "precision": compute_precision, + "recall": compute_recall, +} + + +def resolve_metrics(metric_names: Iterable[str]) -> Dict[str, MetricFn]: + resolved: Dict[str, MetricFn] = {} + for name in metric_names: + if name not in METRIC_REGISTRY: + raise KeyError(f"Metric '{name}' is not registered.") + resolved[name] = METRIC_REGISTRY[name] + return resolved diff --git a/src/evaluation/pipeline_eval.py b/src/evaluation/pipeline_eval.py new file mode 100644 index 0000000..8a06d78 --- /dev/null +++ b/src/evaluation/pipeline_eval.py @@ -0,0 +1,95 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Dict, List, Optional + +import numpy as np +from tqdm import tqdm + +from ..dataset import BaseDataset +from ..model import BaseModelAdapter +from ..model_configuration import EvaluationConfig +from .metrics import resolve_metrics +from .utils import extract_mask_from_pipeline_output + + +class PipelineEvaluator: + """ + Runs a Hugging Face pipeline across a dataset and aggregates metrics. + """ + + def __init__( + self, + dataset: BaseDataset, + adapter: BaseModelAdapter, + config: EvaluationConfig, + ) -> None: + self.dataset = dataset + self.adapter = adapter + self.config = config + self.metrics = resolve_metrics(config.metrics) + + def run(self) -> Dict[str, Any]: + pipe = self.adapter.build_pipeline() + aggregated: Dict[str, List[float]] = {name: [] for name in self.metrics} + output_dir = Path(self.config.output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + requested = self.config.max_samples or len(self.dataset) + total = min(requested, len(self.dataset)) + prog_bar = tqdm(range(total), total=total) + for idx in prog_bar: + sample = self.dataset[idx] + inputs = self._build_pipeline_inputs(sample) + preds = pipe(**inputs) + labels = sample.get("labels", {}) + mask = labels.get("mask") + if mask is None: + continue + prediction_mask = self._extract_mask(preds) + for metric_name, metric_fn in self.metrics.items(): + for threshold in self.config.thresholds: + value = metric_fn(prediction_mask, mask, threshold) + aggregated.setdefault(f"{metric_name}@{threshold}", []).append(value) + if self.config.save_predictions: + self._write_prediction(output_dir, idx, prediction_mask, sample["metadata"]) + summary = { + "metrics": {k: float(np.mean(v)) if v else 0.0 for k, v in aggregated.items()}, + "config": self.config.__dict__, + "num_samples": total, + } + with (output_dir / "evaluation_summary.json").open("w", encoding="utf-8") as handle: + json.dump(summary, handle, indent=2) + return summary + + def _build_pipeline_inputs(self, sample: Dict[str, Any]) -> Dict[str, Any]: + inputs: Dict[str, Any] = {"images": sample["pixel_values"]} + prompts = sample.get("prompts", {}) + if "boxes" in prompts and prompts["boxes"]: + inputs["boxes"] = prompts["boxes"] + if "points" in prompts and prompts["points"]: + inputs["points"] = prompts["points"] + if "point_labels" in prompts and prompts["point_labels"]: + inputs["point_labels"] = prompts["point_labels"] + return inputs + + def _extract_mask(self, pipeline_output: Any) -> np.ndarray: + """ + Normalize pipeline outputs into numpy masks. + """ + return extract_mask_from_pipeline_output(pipeline_output) + + def _write_prediction( + self, + output_dir: Path, + index: int, + mask: np.ndarray, + metadata: Optional[Dict[str, Any]], + ) -> None: + if metadata and "image_name" in metadata: + filename = metadata["image_name"].replace(".jpg", "_pred.npy") + else: + filename = f"sample_{index:04d}_pred.npy" + target_path = output_dir / "predictions" + target_path.mkdir(parents=True, exist_ok=True) + np.save(target_path / filename, mask) diff --git a/src/evaluation/reporting.py b/src/evaluation/reporting.py new file mode 100644 index 0000000..38d8daf --- /dev/null +++ b/src/evaluation/reporting.py @@ -0,0 +1,25 @@ +from __future__ import annotations + +import csv +import json +from pathlib import Path +from typing import Dict, Iterable + + +def write_json(summary: Dict, output_path: Path) -> None: + output_path.parent.mkdir(parents=True, exist_ok=True) + with output_path.open("w", encoding="utf-8") as handle: + json.dump(summary, handle, indent=2) + + +def write_csv(rows: Iterable[Dict], output_path: Path) -> None: + rows = list(rows) + if not rows: + return + output_path.parent.mkdir(parents=True, exist_ok=True) + fieldnames = sorted(rows[0].keys()) + with output_path.open("w", encoding="utf-8", newline="") as handle: + writer = csv.DictWriter(handle, fieldnames=fieldnames) + writer.writeheader() + for row in rows: + writer.writerow(row) diff --git a/src/evaluation/run_pipeline.py b/src/evaluation/run_pipeline.py new file mode 100644 index 0000000..8eaa9bf --- /dev/null +++ b/src/evaluation/run_pipeline.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +import logging +from dataclasses import dataclass, replace +from typing import Optional + +from transformers import HfArgumentParser + +from ..dataset import DatasetRegistry +from ..model import ModelRegistry +from ..model_configuration import ConfigRegistry, EvaluationConfig +from .pipeline_eval import PipelineEvaluator + +LOGGER = logging.getLogger(__name__) + + +@dataclass +class PipelineCLIArguments: + config_name: str = "sam2_bbox_prompt" + model_key: str = "sam2" + split: str = "test" + split_file: Optional[str] = None + device: Optional[str] = None + max_samples: Optional[int] = None + + +def main() -> None: + parser = HfArgumentParser(PipelineCLIArguments) + (cli_args,) = parser.parse_args_into_dataclasses() + project_config = ConfigRegistry.get(cli_args.config_name) + dataset_cfg = replace(project_config.dataset, split=cli_args.split, split_file=cli_args.split_file) + dataset = DatasetRegistry.create( + dataset_cfg.name, + config=dataset_cfg, + return_hf_dict=True, + ) + adapter = ModelRegistry.create(cli_args.model_key, project_config.model) + evaluation_config = replace( + project_config.evaluation, + max_samples=cli_args.max_samples, + ) + if cli_args.device: + adapter.build_pipeline(device=cli_args.device) + evaluator = PipelineEvaluator( + dataset=dataset, + adapter=adapter, + config=evaluation_config, + ) + summary = evaluator.run() + LOGGER.info("Evaluation summary: %s", summary) + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() diff --git a/src/evaluation/utils.py b/src/evaluation/utils.py new file mode 100644 index 0000000..8aac66e --- /dev/null +++ b/src/evaluation/utils.py @@ -0,0 +1,16 @@ +from __future__ import annotations + +from typing import Any + +import numpy as np + + +def extract_mask_from_pipeline_output(pipeline_output: Any) -> np.ndarray: + if isinstance(pipeline_output, list): + pipeline_output = pipeline_output[0] + mask = pipeline_output.get("mask") + if mask is None: + raise ValueError("Pipeline output missing 'mask'.") + if isinstance(mask, np.ndarray): + return mask + return np.array(mask) diff --git a/src/hf_sam2_predictor.py b/src/hf_sam2_predictor.py new file mode 100644 index 0000000..d91f387 --- /dev/null +++ b/src/hf_sam2_predictor.py @@ -0,0 +1,7 @@ +""" +Backward-compatible wrapper that re-exports the predictor relocated to src.model. +""" + +from .model.predictor import HFSam2Predictor, build_hf_sam2_predictor + +__all__ = ["HFSam2Predictor", "build_hf_sam2_predictor"] diff --git a/src/legacy_evaluation.py b/src/legacy_evaluation.py new file mode 100644 index 0000000..f6e72b6 --- /dev/null +++ b/src/legacy_evaluation.py @@ -0,0 +1,330 @@ +""" +评估指标计算模块 +计算 IoU, Dice, Precision, Recall, F1-Score 等指标 +""" + +import os +import cv2 +import numpy as np +import pandas as pd +from pathlib import Path +from typing import Dict, List, Tuple +from tqdm import tqdm +import json + + +def compute_iou(pred: np.ndarray, gt: np.ndarray) -> float: + """ + 计算 IoU (Intersection over Union) + + Args: + pred: 预测掩码 (H, W)，值为 0 或 255 + gt: 真实掩码 (H, W)，值为 0 或 255 + + Returns: + iou: IoU 值 + """ + pred_binary = (pred > 0).astype(np.uint8) + gt_binary = (gt > 0).astype(np.uint8) + + intersection = np.logical_and(pred_binary, gt_binary).sum() + union = np.logical_or(pred_binary, gt_binary).sum() + + if union == 0: + return 1.0 if intersection == 0 else 0.0 + + return intersection / union + + +def compute_dice(pred: np.ndarray, gt: np.ndarray) -> float: + """ + 计算 Dice 系数 + + Args: + pred: 预测掩码 (H, W) + gt: 真实掩码 (H, W) + + Returns: + dice: Dice 系数 + """ + pred_binary = (pred > 0).astype(np.uint8) + gt_binary = (gt > 0).astype(np.uint8) + + intersection = np.logical_and(pred_binary, gt_binary).sum() + pred_sum = pred_binary.sum() + gt_sum = gt_binary.sum() + + if pred_sum + gt_sum == 0: + return 1.0 if intersection == 0 else 0.0 + + return 2 * intersection / (pred_sum + gt_sum) + + +def compute_precision_recall(pred: np.ndarray, gt: np.ndarray) -> Tuple[float, float]: + """ + 计算 Precision 和 Recall + + Args: + pred: 预测掩码 (H, W) + gt: 真实掩码 (H, W) + + Returns: + precision: 精确率 + recall: 召回率 + """ + pred_binary = (pred > 0).astype(np.uint8) + gt_binary = (gt > 0).astype(np.uint8) + + tp = np.logical_and(pred_binary, gt_binary).sum() + fp = np.logical_and(pred_binary, np.logical_not(gt_binary)).sum() + fn = np.logical_and(np.logical_not(pred_binary), gt_binary).sum() + + precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0 + recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0 + + return precision, recall + + +def compute_f1_score(precision: float, recall: float) -> float: + """ + 计算 F1-Score + + Args: + precision: 精确率 + recall: 召回率 + + Returns: + f1: F1-Score + """ + if precision + recall == 0: + return 0.0 + return 2 * precision * recall / (precision + recall) + + +def compute_skeleton_iou(pred: np.ndarray, gt: np.ndarray) -> float: + """ + 计算骨架 IoU（针对细长裂缝的特殊指标） + + Args: + pred: 预测掩码 (H, W) + gt: 真实掩码 (H, W) + + Returns: + skeleton_iou: 骨架 IoU + """ + from skimage.morphology import skeletonize + + pred_binary = (pred > 0).astype(bool) + gt_binary = (gt > 0).astype(bool) + + # 骨架化 + try: + pred_skel = skeletonize(pred_binary) + gt_skel = skeletonize(gt_binary) + + intersection = np.logical_and(pred_skel, gt_skel).sum() + union = np.logical_or(pred_skel, gt_skel).sum() + + if union == 0: + return 1.0 if intersection == 0 else 0.0 + + return intersection / union + except: + # 如果骨架化失败，返回 NaN + return np.nan + + +def evaluate_single_image( + pred_path: str, + gt_path: str, + compute_skeleton: bool = True +) -> Dict[str, float]: + """ + 评估单张图像 + + Args: + pred_path: 预测掩码路径 + gt_path: 真实掩码路径 + compute_skeleton: 是否计算骨架 IoU + + Returns: + metrics: 包含各项指标的字典 + """ + # 加载掩码 + pred = cv2.imread(pred_path, cv2.IMREAD_GRAYSCALE) + gt = cv2.imread(gt_path, cv2.IMREAD_GRAYSCALE) + + if pred is None or gt is None: + raise ValueError(f"无法加载掩码: {pred_path} 或 {gt_path}") + + # 计算指标 + iou = compute_iou(pred, gt) + dice = compute_dice(pred, gt) + precision, recall = compute_precision_recall(pred, gt) + f1 = compute_f1_score(precision, recall) + + metrics = { + "iou": iou, + "dice": dice, + "precision": precision, + "recall": recall, + "f1_score": f1, + } + + # 计算骨架 IoU（可选） + if compute_skeleton: + skeleton_iou = compute_skeleton_iou(pred, gt) + metrics["skeleton_iou"] = skeleton_iou + + return metrics + + +def evaluate_test_set( + data_root: str, + test_file: str, + pred_dir: str, + output_dir: str, + compute_skeleton: bool = True +) -> pd.DataFrame: + """ + 评估整个测试集 + + Args: + data_root: 数据集根目录 + test_file: 测试集文件路径 + pred_dir: 预测掩码目录 + output_dir: 输出目录 + compute_skeleton: 是否计算骨架 IoU + + Returns: + df_results: 包含所有结果的 DataFrame + """ + # 读取测试集文件 + with open(test_file, 'r') as f: + lines = f.readlines() + + results = [] + + print(f"开始评估 {len(lines)} 张测试图像...") + + for line in tqdm(lines, desc="评估测试集"): + parts = line.strip().split() + if len(parts) != 2: + continue + + img_rel_path, mask_rel_path = parts + + # 构建路径 + gt_path = os.path.join(data_root, mask_rel_path) + img_name = Path(img_rel_path).stem + pred_path = os.path.join(pred_dir, f"{img_name}_pred.png") + + # 检查文件是否存在 + if not os.path.exists(pred_path): + print(f"警告: 预测掩码不存在 {pred_path}") + continue + if not os.path.exists(gt_path): + print(f"警告: GT 掩码不存在 {gt_path}") + continue + + try: + # 评估单张图像 + metrics = evaluate_single_image(pred_path, gt_path, compute_skeleton) + + # 添加图像信息 + metrics["image_name"] = img_name + metrics["image_path"] = img_rel_path + + results.append(metrics) + + except Exception as e: + print(f"评估失败 {img_name}: {str(e)}") + continue + + # 转换为 DataFrame + df_results = pd.DataFrame(results) + + # 计算平均指标 + print("\n" + "=" * 60) + print("评估结果统计:") + print("=" * 60) + + metrics_to_avg = ["iou", "dice", "precision", "recall", "f1_score"] + if compute_skeleton and "skeleton_iou" in df_results.columns: + metrics_to_avg.append("skeleton_iou") + + for metric in metrics_to_avg: + if metric in df_results.columns: + mean_val = df_results[metric].mean() + std_val = df_results[metric].std() + print(f"{metric.upper():15s}: {mean_val:.4f} ± {std_val:.4f}") + + print("=" * 60) + + # 保存详细结果 + csv_path = os.path.join(output_dir, "evaluation_results.csv") + df_results.to_csv(csv_path, index=False) + print(f"\n详细结果已保存到: {csv_path}") + + # 保存统计摘要 + summary = { + "num_images": len(df_results), + "metrics": {} + } + + for metric in metrics_to_avg: + if metric in df_results.columns: + summary["metrics"][metric] = { + "mean": float(df_results[metric].mean()), + "std": float(df_results[metric].std()), + "min": float(df_results[metric].min()), + "max": float(df_results[metric].max()), + } + + summary_path = os.path.join(output_dir, "evaluation_summary.json") + with open(summary_path, 'w') as f: + json.dump(summary, f, indent=2) + print(f"统计摘要已保存到: {summary_path}") + + return df_results + + +def main(): + """主函数""" + # 配置参数 + DATA_ROOT = "./crack500" + TEST_FILE = "./crack500/test.txt" + PRED_DIR = "./results/bbox_prompt/predictions" + OUTPUT_DIR = "./results/bbox_prompt" + + print("=" * 60) + print("SAM2 评估 - Crack500 数据集") + print("=" * 60) + print(f"数据集根目录: {DATA_ROOT}") + print(f"测试集文件: {TEST_FILE}") + print(f"预测掩码目录: {PRED_DIR}") + print(f"输出目录: {OUTPUT_DIR}") + print("=" * 60) + + # 检查预测目录是否存在 + if not os.path.exists(PRED_DIR): + print(f"\n错误: 预测目录不存在 {PRED_DIR}") + print("请先运行 bbox_prompt.py 生成预测结果！") + return + + # 评估测试集 + df_results = evaluate_test_set( + data_root=DATA_ROOT, + test_file=TEST_FILE, + pred_dir=PRED_DIR, + output_dir=OUTPUT_DIR, + compute_skeleton=True + ) + + print("\n" + "=" * 60) + print("评估完成！") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/src/legacy_visualization.py b/src/legacy_visualization.py new file mode 100644 index 0000000..71af849 --- /dev/null +++ b/src/legacy_visualization.py @@ -0,0 +1,314 @@ +""" +可视化模块 +生成预测结果的可视化图像 +""" + +import os +import cv2 +import numpy as np +import matplotlib.pyplot as plt +from pathlib import Path +from typing import List, Tuple +from tqdm import tqdm +import pandas as pd + + +def create_overlay_visualization( + image: np.ndarray, + mask_gt: np.ndarray, + mask_pred: np.ndarray, + alpha: float = 0.5 +) -> np.ndarray: + """ + 创建叠加可视化图像 + + Args: + image: 原始图像 (H, W, 3) RGB + mask_gt: GT 掩码 (H, W) + mask_pred: 预测掩码 (H, W) + alpha: 透明度 + + Returns: + vis_image: 可视化图像 (H, W, 3) + """ + # 确保图像是 RGB + if len(image.shape) == 2: + image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB) + + # 创建彩色掩码 + # GT: 绿色, Pred: 红色, 重叠: 黄色 + vis_image = image.copy().astype(np.float32) + + gt_binary = (mask_gt > 0) + pred_binary = (mask_pred > 0) + + # 真阳性（重叠部分）- 黄色 + tp_mask = np.logical_and(gt_binary, pred_binary) + vis_image[tp_mask] = vis_image[tp_mask] * (1 - alpha) + np.array([255, 255, 0]) * alpha + + # 假阴性（GT 有但预测没有）- 绿色 + fn_mask = np.logical_and(gt_binary, np.logical_not(pred_binary)) + vis_image[fn_mask] = vis_image[fn_mask] * (1 - alpha) + np.array([0, 255, 0]) * alpha + + # 假阳性（预测有但 GT 没有）- 红色 + fp_mask = np.logical_and(pred_binary, np.logical_not(gt_binary)) + vis_image[fp_mask] = vis_image[fp_mask] * (1 - alpha) + np.array([255, 0, 0]) * alpha + + return vis_image.astype(np.uint8) + + +def create_comparison_figure( + image: np.ndarray, + mask_gt: np.ndarray, + mask_pred: np.ndarray, + metrics: dict, + title: str = "" +) -> plt.Figure: + """ + 创建对比图 + + Args: + image: 原始图像 (H, W, 3) RGB + mask_gt: GT 掩码 (H, W) + mask_pred: 预测掩码 (H, W) + metrics: 评估指标字典 + title: 图像标题 + + Returns: + fig: matplotlib Figure 对象 + """ + fig, axes = plt.subplots(2, 2, figsize=(12, 10)) + + # 原始图像 + axes[0, 0].imshow(image) + axes[0, 0].set_title("Original Image", fontsize=16) + axes[0, 0].axis('off') + + # GT 掩码 + axes[0, 1].imshow(mask_gt, cmap='gray') + axes[0, 1].set_title("Ground Truth", fontsize=16) + axes[0, 1].axis('off') + + # 预测掩码 + axes[1, 0].imshow(mask_pred, cmap='gray') + axes[1, 0].set_title("Prediction", fontsize=16) + axes[1, 0].axis('off') + + # 叠加可视化 + overlay = create_overlay_visualization(image, mask_gt, mask_pred) + axes[1, 1].imshow(overlay) + + # 添加图例和指标 + legend_text = ( + "Yellow: True Positive\n" + "Green: False Negative\n" + "Red: False Positive\n\n" + f"IoU: {metrics.get('iou', 0):.4f}\n" + f"Dice: {metrics.get('dice', 0):.4f}\n" + f"F1: {metrics.get('f1_score', 0):.4f}" + ) + axes[1, 1].text( + 0.02, 0.98, legend_text, + transform=axes[1, 1].transAxes, + fontsize=16, + verticalalignment='top', + bbox=dict(boxstyle='round', facecolor='white', alpha=0.8) + ) + axes[1, 1].set_title("Overlay Visualization", fontsize=16) + axes[1, 1].axis('off') + + # # 设置总标题 + # if title: + # fig.suptitle(title, fontsize=16, fontweight='bold') + + plt.tight_layout() + + return fig + + +def visualize_test_set( + data_root: str, + test_file: str, + pred_dir: str, + output_dir: str, + results_csv: str = None, + num_samples: int = 20, + save_all: bool = False +) -> None: + """ + 可视化测试集结果 + + Args: + data_root: 数据集根目录 + test_file: 测试集文件路径 + pred_dir: 预测掩码目录 + output_dir: 输出目录 + results_csv: 评估结果 CSV 文件路径 + num_samples: 要可视化的样本数量 + save_all: 是否保存所有样本 + """ + # 创建输出目录 + vis_dir = os.path.join(output_dir, "visualizations") + os.makedirs(vis_dir, exist_ok=True) + + # 读取测试集文件 + with open(test_file, 'r') as f: + lines = f.readlines() + + # 如果有评估结果，读取指标 + metrics_dict = {} + if results_csv and os.path.exists(results_csv): + df = pd.read_csv(results_csv) + for _, row in df.iterrows(): + metrics_dict[row['image_name']] = { + 'iou': row['iou'], + 'dice': row['dice'], + 'f1_score': row['f1_score'], + 'precision': row['precision'], + 'recall': row['recall'], + } + + # 选择要可视化的样本 + if save_all: + selected_lines = lines + else: + # 均匀采样 + step = max(1, len(lines) // num_samples) + selected_lines = lines[::step][:num_samples] + + print(f"开始可视化 {len(selected_lines)} 张图像...") + + for line in tqdm(selected_lines, desc="生成可视化"): + parts = line.strip().split() + if len(parts) != 2: + continue + + img_rel_path, mask_rel_path = parts + + # 构建路径 + img_path = os.path.join(data_root, img_rel_path) + gt_path = os.path.join(data_root, mask_rel_path) + img_name = Path(img_rel_path).stem + pred_path = os.path.join(pred_dir, f"{img_name}_pred.png") + + # 检查文件是否存在 + if not all(os.path.exists(p) for p in [img_path, gt_path, pred_path]): + continue + + try: + # 加载图像和掩码 + image = cv2.imread(img_path) + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + mask_gt = cv2.imread(gt_path, cv2.IMREAD_GRAYSCALE) + mask_pred = cv2.imread(pred_path, cv2.IMREAD_GRAYSCALE) + + # 获取指标 + metrics = metrics_dict.get(img_name, {}) + + # 创建对比图 + fig = create_comparison_figure( + image, mask_gt, mask_pred, metrics, + title=f"Sample: {img_name}" + ) + + # 保存图像 + save_path = os.path.join(vis_dir, f"{img_name}_vis.png") + fig.savefig(save_path, dpi=150, bbox_inches='tight') + plt.close(fig) + + except Exception as e: + print(f"可视化失败 {img_name}: {str(e)}") + continue + + print(f"\n可视化完成！结果保存在: {vis_dir}") + + +def create_metrics_distribution_plot( + results_csv: str, + output_dir: str +) -> None: + """ + 创建指标分布图 + + Args: + results_csv: 评估结果 CSV 文件路径 + output_dir: 输出目录 + """ + # 读取结果 + df = pd.read_csv(results_csv) + + # 创建图表 + metrics = ['iou', 'dice', 'precision', 'recall', 'f1_score'] + fig, axes = plt.subplots(2, 3, figsize=(15, 10)) + axes = axes.flatten() + + for idx, metric in enumerate(metrics): + if metric in df.columns: + axes[idx].hist(df[metric], bins=30, edgecolor='black', alpha=0.7) + axes[idx].axvline(df[metric].mean(), color='red', linestyle='--', + linewidth=2, label=f'Mean: {df[metric].mean():.4f}') + axes[idx].set_xlabel(metric.upper(), fontsize=12) + axes[idx].set_ylabel('Frequency', fontsize=12) + axes[idx].set_title(f'{metric.upper()} Distribution', fontsize=12) + axes[idx].legend() + axes[idx].grid(True, alpha=0.3) + + # 隐藏多余的子图 + for idx in range(len(metrics), len(axes)): + axes[idx].axis('off') + + plt.tight_layout() + + # 保存图表 + save_path = os.path.join(output_dir, "metrics_distribution.png") + fig.savefig(save_path, dpi=150, bbox_inches='tight') + plt.close(fig) + + print(f"指标分布图已保存到: {save_path}") + + +def main(): + """主函数""" + # 配置参数 + DATA_ROOT = "./crack500" + TEST_FILE = "./crack500/test.txt" + PRED_DIR = "./results/bbox_prompt/predictions" + OUTPUT_DIR = "./results/bbox_prompt" + RESULTS_CSV = "./results/bbox_prompt/evaluation_results.csv" + + print("=" * 60) + print("SAM2 可视化 - Crack500 数据集") + print("=" * 60) + print(f"数据集根目录: {DATA_ROOT}") + print(f"预测掩码目录: {PRED_DIR}") + print(f"输出目录: {OUTPUT_DIR}") + print("=" * 60) + + # 检查预测目录是否存在 + if not os.path.exists(PRED_DIR): + print(f"\n错误: 预测目录不存在 {PRED_DIR}") + print("请先运行 bbox_prompt.py 生成预测结果！") + return + + # 可视化测试集 + visualize_test_set( + data_root=DATA_ROOT, + test_file=TEST_FILE, + pred_dir=PRED_DIR, + output_dir=OUTPUT_DIR, + results_csv=RESULTS_CSV, + num_samples=20, + save_all=False + ) + + # 创建指标分布图 + if os.path.exists(RESULTS_CSV): + create_metrics_distribution_plot(RESULTS_CSV, OUTPUT_DIR) + + print("\n" + "=" * 60) + print("可视化完成！") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/src/model/__init__.py b/src/model/__init__.py new file mode 100644 index 0000000..dfed61c --- /dev/null +++ b/src/model/__init__.py @@ -0,0 +1,17 @@ +from .base import BaseModelAdapter +from .inference import predict_with_bbox_prompt +from .predictor import HFSam2Predictor, build_hf_sam2_predictor +from .registry import ModelRegistry +from .sam2_adapter import Sam2ModelAdapter +from .trainer import FineTuningTrainer, TrainerArtifacts + +__all__ = [ + "BaseModelAdapter", + "FineTuningTrainer", + "HFSam2Predictor", + "ModelRegistry", + "Sam2ModelAdapter", + "TrainerArtifacts", + "build_hf_sam2_predictor", + "predict_with_bbox_prompt", +] diff --git a/src/model/base.py b/src/model/base.py new file mode 100644 index 0000000..ae6e362 --- /dev/null +++ b/src/model/base.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +import abc +from typing import Any, Dict, Optional + +from transformers import pipeline + +from ..model_configuration import ModelConfig + + +class BaseModelAdapter(abc.ABC): + """ + Thin wrapper that standardizes how we instantiate models/processors/pipelines. + """ + + task: str = "image-segmentation" + + def __init__(self, config: ModelConfig) -> None: + self.config = config + self._model = None + self._processor = None + self._pipeline = None + + def load_pretrained(self): + if self._model is None or self._processor is None: + self._model, self._processor = self._load_pretrained() + return self._model, self._processor + + def build_pipeline( + self, + device: Optional[str] = None, + **kwargs, + ): + if self._pipeline is None: + model, processor = self.load_pretrained() + pipe_kwargs = { + "task": self.task, + "model": model, + "image_processor": processor, + **self.config.pipeline_kwargs, + **kwargs, + } + if device is not None: + pipe_kwargs["device"] = device + self._pipeline = self._create_pipeline(pipe_kwargs) + return self._pipeline + + async def build_pipeline_async(self, **kwargs): + """ + Async helper for future multi-device orchestration. + """ + return self.build_pipeline(**kwargs) + + def save_pretrained(self, output_dir: str) -> None: + model, processor = self.load_pretrained() + model.save_pretrained(output_dir) + processor.save_pretrained(output_dir) + + @abc.abstractmethod + def _load_pretrained(self): + """ + Return (model, processor) tuple. + """ + + def _create_pipeline(self, pipe_kwargs: Dict[str, Any]): + return pipeline(**pipe_kwargs) diff --git a/src/model/inference.py b/src/model/inference.py new file mode 100644 index 0000000..0548f38 --- /dev/null +++ b/src/model/inference.py @@ -0,0 +1,32 @@ +from __future__ import annotations + +from typing import List + +import numpy as np + +from .predictor import HFSam2Predictor + + +def predict_with_bbox_prompt( + predictor: HFSam2Predictor, + image: np.ndarray, + bboxes: List[np.ndarray], +) -> np.ndarray: + """ + Run SAM2 predictions for each bounding box and merge the masks. + """ + predictor.set_image(image) + if not bboxes: + return np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8) + combined_mask = np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8) + for bbox in bboxes: + masks, _, _ = predictor.predict( + point_coords=None, + point_labels=None, + box=bbox, + multimask_output=False, + ) + mask = masks[0] + combined_mask = np.logical_or(combined_mask, mask).astype(np.uint8) + combined_mask = combined_mask * 255 + return combined_mask diff --git a/src/model/predictor.py b/src/model/predictor.py new file mode 100644 index 0000000..ae2befc --- /dev/null +++ b/src/model/predictor.py @@ -0,0 +1,158 @@ +from __future__ import annotations + +import json +from pathlib import Path +from typing import Optional, Tuple + +import numpy as np +import torch +from PIL import Image +from transformers import SamModel, SamProcessor + + +class HFSam2Predictor: + """ + Predictor wrapper around Hugging Face SAM2 models. + """ + + def __init__( + self, + model_id: str = "facebook/sam2-hiera-small", + device: Optional[str] = None, + dtype: torch.dtype = torch.bfloat16, + ) -> None: + self.device = device or ("cuda" if torch.cuda.is_available() else "cpu") + self.dtype = dtype + self.model = SamModel.from_pretrained(model_id).to(self.device) + self.processor = SamProcessor.from_pretrained("./configs/preprocesser.json") + self._override_processor_config() + if dtype == torch.bfloat16: + self.model = self.model.to(dtype=dtype) + self.model.eval() + self.current_image = None + self.current_image_embeddings = None + + def set_image(self, image: np.ndarray) -> None: + if isinstance(image, np.ndarray): + pil_image = Image.fromarray(image.astype(np.uint8)) + else: + pil_image = image + self.current_image = pil_image + with torch.inference_mode(): + inputs = self.processor(images=pil_image, return_tensors="pt").to(self.device) + if self.dtype == torch.bfloat16: + inputs = { + k: v.to(dtype=self.dtype) if v.dtype == torch.float32 else v + for k, v in inputs.items() + } + self.current_image_embeddings = self.model.get_image_embeddings(inputs["pixel_values"]) + + def predict( + self, + point_coords: Optional[np.ndarray] = None, + point_labels: Optional[np.ndarray] = None, + box: Optional[np.ndarray] = None, + multimask_output: bool = False, + ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + if self.current_image is None: + raise ValueError("No image set. Call set_image() first.") + input_points = self._prepare_points(point_coords) + input_labels = self._prepare_labels(point_labels) + input_boxes = self._prepare_boxes(box) + with torch.inference_mode(): + inputs = self.processor( + images=self.current_image, + input_points=input_points, + input_labels=input_labels, + input_boxes=input_boxes, + return_tensors="pt", + ).to(self.device) + if self.dtype == torch.bfloat16: + inputs = { + k: v.to(dtype=self.dtype) if v.dtype == torch.float32 else v + for k, v in inputs.items() + } + inputs.pop("pixel_values", None) + inputs["image_embeddings"] = self.current_image_embeddings + outputs = self.model(**inputs, multimask_output=multimask_output) + masks = self.processor.image_processor.post_process_masks( + outputs.pred_masks.float().cpu(), + inputs["original_sizes"].cpu(), + inputs["reshaped_input_sizes"].cpu(), + )[0] + scores = outputs.iou_scores.float().cpu().numpy()[0] + masks_np = (masks.squeeze(1).numpy() > 0).astype(np.uint8) + logits = outputs.pred_masks.float().cpu().numpy()[0] + return masks_np, scores, logits + + def _prepare_points(self, coords: Optional[np.ndarray]): + """ + Points must be shaped (num_points, 2); wrap in outer batch dimension. + """ + if coords is None: + return None + coords_arr = np.asarray(coords) + if coords_arr.ndim == 1: + coords_arr = coords_arr[None, :] + if coords_arr.ndim != 2: + raise ValueError(f"Point coords must be 2-D, got {coords_arr.shape}.") + return [coords_arr.tolist()] + + def _prepare_labels(self, labels: Optional[np.ndarray]): + """ + Labels mirror the point dimension and are shaped (num_points,). + """ + if labels is None: + return None + labels_arr = np.asarray(labels) + if labels_arr.ndim == 0: + labels_arr = labels_arr[None] + if labels_arr.ndim != 1: + raise ValueError(f"Point labels must be 1-D, got {labels_arr.shape}.") + return [labels_arr.tolist()] + + def _prepare_boxes(self, boxes: Optional[np.ndarray]): + """ + HF expects boxes in shape (batch, num_boxes, 4); accept (4,), (N,4), or (B,N,4). + """ + if boxes is None: + return None + boxes_arr = np.asarray(boxes) + if boxes_arr.ndim == 1: + return [[boxes_arr.tolist()]] + if boxes_arr.ndim == 2: + return [boxes_arr.tolist()] + if boxes_arr.ndim == 3: + return boxes_arr.tolist() + raise ValueError(f"Boxes should be 1/2/3-D, got {boxes_arr.shape}.") + + def _override_processor_config(self) -> None: + """ + Override processor config with local settings to avoid upstream regressions. + """ + config_path = Path(__file__).resolve().parents[2] / "configs" / "preprocesser.json" + if not config_path.exists(): + return + try: + config_dict = json.loads(config_path.read_text()) + except Exception: + return + image_processor = getattr(self.processor, "image_processor", None) + if image_processor is None or not hasattr(image_processor, "config"): + return + # config behaves like a dict; update in-place. + try: + image_processor.config.update(config_dict) + except Exception: + for key, value in config_dict.items(): + try: + setattr(image_processor.config, key, value) + except Exception: + continue + + +def build_hf_sam2_predictor( + model_id: str = "facebook/sam2-hiera-small", + device: Optional[str] = None, +) -> HFSam2Predictor: + return HFSam2Predictor(model_id=model_id, device=device) diff --git a/src/model/registry.py b/src/model/registry.py new file mode 100644 index 0000000..f2139fe --- /dev/null +++ b/src/model/registry.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from typing import Dict, Type + +from ..model_configuration import ModelConfig +from .base import BaseModelAdapter + + +class ModelRegistry: + """ + Maps model keys to adapter classes so configs can reference them declaratively. + """ + + _registry: Dict[str, Type[BaseModelAdapter]] = {} + + @classmethod + def register(cls, name: str): + def decorator(adapter_cls: Type[BaseModelAdapter]) -> Type[BaseModelAdapter]: + cls._registry[name] = adapter_cls + return adapter_cls + + return decorator + + @classmethod + def create(cls, name: str, config: ModelConfig) -> BaseModelAdapter: + if name not in cls._registry: + raise KeyError(f"ModelAdapter '{name}' is not registered.") + adapter_cls = cls._registry[name] + return adapter_cls(config) + + @classmethod + def available(cls) -> Dict[str, Type[BaseModelAdapter]]: + return dict(cls._registry) diff --git a/src/model/sam2_adapter.py b/src/model/sam2_adapter.py new file mode 100644 index 0000000..c2be204 --- /dev/null +++ b/src/model/sam2_adapter.py @@ -0,0 +1,35 @@ +from __future__ import annotations + +from typing import Any, Tuple + +from transformers import AutoModelForImageSegmentation, AutoProcessor + +from ..model_configuration import ModelConfig +from .base import BaseModelAdapter +from .registry import ModelRegistry + + +@ModelRegistry.register("sam2") +class Sam2ModelAdapter(BaseModelAdapter): + """ + Adapter that exposes SAM2 checkpoints through the HF pipeline interface. + """ + + def __init__(self, config: ModelConfig) -> None: + super().__init__(config) + self.task = "image-segmentation" + + def _load_pretrained(self) -> Tuple[Any, Any]: + model = AutoModelForImageSegmentation.from_pretrained( + self.config.name_or_path, + revision=self.config.revision, + cache_dir=self.config.cache_dir, + trust_remote_code=True, + ) + processor = AutoProcessor.from_pretrained( + self.config.name_or_path, + revision=self.config.revision, + cache_dir=self.config.cache_dir, + trust_remote_code=True, + ) + return model, processor diff --git a/src/model/train_hf.py b/src/model/train_hf.py new file mode 100644 index 0000000..22654f6 --- /dev/null +++ b/src/model/train_hf.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +import logging +from dataclasses import dataclass, replace +from typing import Optional + +from transformers import HfArgumentParser + +from ..dataset import DatasetRegistry +from ..model_configuration import ConfigRegistry, DatasetConfig +from .registry import ModelRegistry +from .trainer import FineTuningTrainer + +LOGGER = logging.getLogger(__name__) + + +@dataclass +class TrainCLIArguments: + config_name: str = "sam2_bbox_prompt" + model_key: str = "sam2" + train_split: str = "train" + eval_split: str = "val" + train_split_file: Optional[str] = None + eval_split_file: Optional[str] = None + skip_eval: bool = False + device: Optional[str] = None + + +def build_dataset(config: DatasetConfig, split: str, split_file: Optional[str]) -> DatasetConfig: + overrides = {} + if split: + overrides["split"] = split + if split_file: + overrides["split_file"] = split_file + return replace(config, **overrides) + + +def main() -> None: + parser = HfArgumentParser(TrainCLIArguments) + (cli_args,) = parser.parse_args_into_dataclasses() + project_config = ConfigRegistry.get(cli_args.config_name) + train_dataset_cfg = build_dataset( + project_config.dataset, cli_args.train_split, cli_args.train_split_file + ) + eval_dataset_cfg = ( + build_dataset(project_config.dataset, cli_args.eval_split, cli_args.eval_split_file) + if not cli_args.skip_eval + else None + ) + + train_dataset = DatasetRegistry.create( + train_dataset_cfg.name, + config=train_dataset_cfg, + return_hf_dict=True, + ) + eval_dataset = ( + DatasetRegistry.create( + eval_dataset_cfg.name, + config=eval_dataset_cfg, + return_hf_dict=True, + ) + if eval_dataset_cfg + else None + ) + + adapter = ModelRegistry.create(cli_args.model_key, project_config.model) + if cli_args.device: + adapter.build_pipeline(device=cli_args.device) + + trainer_builder = FineTuningTrainer( + adapter=adapter, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + training_config=project_config.training, + ) + artifacts = trainer_builder.build() + LOGGER.info("Starting training with args: %s", artifacts.training_args) + train_result = artifacts.trainer.train() + LOGGER.info("Training finished: %s", train_result) + artifacts.trainer.save_model(project_config.training.output_dir) + if eval_dataset and not cli_args.skip_eval: + metrics = artifacts.trainer.evaluate() + LOGGER.info("Evaluation metrics: %s", metrics) + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() diff --git a/src/model/trainer.py b/src/model/trainer.py new file mode 100644 index 0000000..6973a02 --- /dev/null +++ b/src/model/trainer.py @@ -0,0 +1,64 @@ +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Dict, Optional + +from transformers import Trainer, TrainingArguments + +from ..dataset import BaseDataset, collate_samples +from ..model_configuration import TrainingConfig +from .base import BaseModelAdapter + + +@dataclass +class TrainerArtifacts: + trainer: Trainer + training_args: TrainingArguments + + +class FineTuningTrainer: + """ + Helper that bridges TrainingConfig + datasets + adapters into HF Trainer. + """ + + def __init__( + self, + adapter: BaseModelAdapter, + train_dataset: Optional[BaseDataset], + eval_dataset: Optional[BaseDataset], + training_config: TrainingConfig, + trainer_kwargs: Optional[Dict[str, Any]] = None, + ) -> None: + self.adapter = adapter + self.train_dataset = train_dataset + self.eval_dataset = eval_dataset + self.training_config = training_config + self.trainer_kwargs = trainer_kwargs or {} + + def build(self) -> TrainerArtifacts: + model, processor = self.adapter.load_pretrained() + training_args = TrainingArguments( + output_dir=self.training_config.output_dir, + num_train_epochs=self.training_config.num_train_epochs, + per_device_train_batch_size=self.training_config.per_device_train_batch_size, + per_device_eval_batch_size=self.training_config.per_device_eval_batch_size, + learning_rate=self.training_config.learning_rate, + gradient_accumulation_steps=self.training_config.gradient_accumulation_steps, + lr_scheduler_type=self.training_config.lr_scheduler_type, + warmup_ratio=self.training_config.warmup_ratio, + weight_decay=self.training_config.weight_decay, + seed=self.training_config.seed, + fp16=self.training_config.fp16, + bf16=self.training_config.bf16, + report_to=self.training_config.report_to, + ) + hf_trainer = Trainer( + model=model, + args=training_args, + train_dataset=self.train_dataset, + eval_dataset=self.eval_dataset, + data_collator=collate_samples, + tokenizer=processor, + **self.trainer_kwargs, + ) + return TrainerArtifacts(trainer=hf_trainer, training_args=training_args) diff --git a/src/model_configuration/__init__.py b/src/model_configuration/__init__.py new file mode 100644 index 0000000..0ef48d5 --- /dev/null +++ b/src/model_configuration/__init__.py @@ -0,0 +1,22 @@ +from .config import ( + DatasetConfig, + EvaluationConfig, + ModelConfig, + ProjectConfig, + TrainingConfig, + VisualizationConfig, +) +from .registry import ConfigRegistry + +# ensure example configs register themselves +from . import sam2_bbox # noqa: F401 + +__all__ = [ + "DatasetConfig", + "EvaluationConfig", + "ModelConfig", + "ProjectConfig", + "TrainingConfig", + "VisualizationConfig", + "ConfigRegistry", +] diff --git a/src/model_configuration/config.py b/src/model_configuration/config.py new file mode 100644 index 0000000..2ef4a44 --- /dev/null +++ b/src/model_configuration/config.py @@ -0,0 +1,89 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any, Dict, List, Optional + + +def _default_dict() -> Dict[str, Any]: + return {} + + +@dataclass +class DatasetConfig: + name: str + data_root: str + split: str = "test" + split_file: Optional[str] = None + annotation_file: Optional[str] = None + image_folder: Optional[str] = None + mask_folder: Optional[str] = None + extra_params: Dict[str, Any] = field(default_factory=_default_dict) + + def resolve_path(self, relative: Optional[str]) -> Optional[Path]: + if relative is None: + return None + return Path(self.data_root) / relative + + +@dataclass +class ModelConfig: + name_or_path: str + revision: Optional[str] = None + config_name: Optional[str] = None + cache_dir: Optional[str] = None + prompt_type: str = "bbox" + image_size: Optional[int] = None + pipeline_kwargs: Dict[str, Any] = field(default_factory=_default_dict) + adapter_kwargs: Dict[str, Any] = field(default_factory=_default_dict) + + +@dataclass +class TrainingConfig: + output_dir: str = "./outputs" + num_train_epochs: float = 3.0 + per_device_train_batch_size: int = 1 + per_device_eval_batch_size: int = 1 + learning_rate: float = 1e-4 + weight_decay: float = 0.0 + gradient_accumulation_steps: int = 1 + lr_scheduler_type: str = "linear" + warmup_ratio: float = 0.0 + seed: int = 42 + fp16: bool = False + bf16: bool = False + report_to: List[str] = field(default_factory=lambda: ["tensorboard"]) + + +@dataclass +class EvaluationConfig: + output_dir: str = "./results" + metrics: List[str] = field(default_factory=lambda: ["iou", "dice", "precision", "recall"]) + thresholds: List[float] = field(default_factory=lambda: [0.5]) + max_samples: Optional[int] = None + save_predictions: bool = True + + +@dataclass +class VisualizationConfig: + num_samples: int = 20 + overlay_alpha: float = 0.6 + save_dir: str = "./results/visualizations" + + +@dataclass +class ProjectConfig: + dataset: DatasetConfig + model: ModelConfig + training: TrainingConfig = field(default_factory=TrainingConfig) + evaluation: EvaluationConfig = field(default_factory=EvaluationConfig) + visualization: VisualizationConfig = field(default_factory=VisualizationConfig) + + def to_dict(self) -> Dict[str, Any]: + return { + "dataset": self.dataset, + "model": self.model, + "training": self.training, + "evaluation": self.evaluation, + "visualization": self.visualization, + } diff --git a/src/model_configuration/registry.py b/src/model_configuration/registry.py new file mode 100644 index 0000000..fd91327 --- /dev/null +++ b/src/model_configuration/registry.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from typing import Dict + +from .config import ProjectConfig + + +class ConfigRegistry: + """ + Stores reusable project configurations (dataset + model + training bundle). + """ + + _registry: Dict[str, ProjectConfig] = {} + + @classmethod + def register(cls, name: str, config: ProjectConfig) -> ProjectConfig: + cls._registry[name] = config + return config + + @classmethod + def get(cls, name: str) -> ProjectConfig: + if name not in cls._registry: + raise KeyError(f"ProjectConfig '{name}' is not registered.") + return cls._registry[name] + + @classmethod + def available(cls) -> Dict[str, ProjectConfig]: + return dict(cls._registry) diff --git a/src/model_configuration/sam2_bbox.py b/src/model_configuration/sam2_bbox.py new file mode 100644 index 0000000..1ad7101 --- /dev/null +++ b/src/model_configuration/sam2_bbox.py @@ -0,0 +1,47 @@ +from __future__ import annotations + +from .config import ( + DatasetConfig, + EvaluationConfig, + ModelConfig, + ProjectConfig, + TrainingConfig, + VisualizationConfig, +) +from .registry import ConfigRegistry + + +SAM2_BBOX_CONFIG = ProjectConfig( + dataset=DatasetConfig( + name="crack500", + data_root="./crack500", + split="test", + split_file="test.txt", + image_folder="testcrop", + mask_folder="testdata", + ), + model=ModelConfig( + name_or_path="facebook/sam2.1-hiera-small", + prompt_type="bbox", + pipeline_kwargs={"batch_size": 1}, + ), + training=TrainingConfig( + output_dir="./outputs/sam2_bbox", + num_train_epochs=5, + per_device_train_batch_size=1, + per_device_eval_batch_size=1, + learning_rate=1e-4, + gradient_accumulation_steps=4, + lr_scheduler_type="cosine", + ), + evaluation=EvaluationConfig( + output_dir="./results/bbox_prompt", + thresholds=[0.3, 0.5, 0.75], + ), + visualization=VisualizationConfig( + save_dir="./results/bbox_prompt/visualizations", + num_samples=20, + ), +) + +ConfigRegistry.register("sam2_bbox_prompt", SAM2_BBOX_CONFIG) diff --git a/src/point_prompt.py b/src/point_prompt.py new file mode 100644 index 0000000..ed0514b --- /dev/null +++ b/src/point_prompt.py @@ -0,0 +1,332 @@ +""" +点提示方式的 SAM2 裂缝分割实现（使用 HuggingFace Transformers） +使用骨架采样策略，支持 1, 3, 5 个点 +""" + +import os +import cv2 +import numpy as np +import torch +from pathlib import Path +from typing import List, Tuple, Dict +from tqdm import tqdm +import json +from skimage.morphology import skeletonize + +from .hf_sam2_predictor import HFSam2Predictor + + +def sample_points_on_skeleton(mask: np.ndarray, num_points: int = 5) -> np.ndarray: + """ + 在骨架上均匀采样点 + + Args: + mask: 二值掩码 (H, W)，值为 0 或 255 + num_points: 采样点数量 + + Returns: + points: 采样点坐标 (N, 2)，格式为 [x, y] + """ + # 确保掩码是二值的 + binary_mask = (mask > 0).astype(bool) + + # 骨架化 + try: + skeleton = skeletonize(binary_mask) + except: + # 如果骨架化失败，直接使用掩码 + skeleton = binary_mask + + # 获取骨架点坐标 (y, x) + skeleton_coords = np.argwhere(skeleton) + + if len(skeleton_coords) == 0: + # 如果没有骨架点，返回空数组 + return np.array([]).reshape(0, 2) + + if len(skeleton_coords) <= num_points: + # 如果骨架点数少于需要的点数，返回所有点 + # 转换为 (x, y) 格式 + return skeleton_coords[:, [1, 0]] + + # 均匀间隔采样 + indices = np.linspace(0, len(skeleton_coords) - 1, num_points, dtype=int) + sampled_coords = skeleton_coords[indices] + + # 转换为 (x, y) 格式 + points = sampled_coords[:, [1, 0]] + + return points + + +def sample_points_per_component( + mask: np.ndarray, + num_points_per_component: int = 3 +) -> Tuple[np.ndarray, np.ndarray]: + """ + 为每个连通域独立采样点 + + Args: + mask: 二值掩码 (H, W) + num_points_per_component: 每个连通域的点数 + + Returns: + points: 所有采样点 (N, 2) + labels: 点标签，全为 1（正样本） + """ + # 连通域分析 + num_labels, labels_map = cv2.connectedComponents((mask > 0).astype(np.uint8)) + + all_points = [] + + # 跳过背景 (label 0) + for region_id in range(1, num_labels): + region_mask = (labels_map == region_id).astype(np.uint8) * 255 + + # 对每个连通域采样 + points = sample_points_on_skeleton(region_mask, num_points_per_component) + + if len(points) > 0: + all_points.append(points) + + if len(all_points) == 0: + return np.array([]).reshape(0, 2), np.array([]) + + # 合并所有点 + all_points = np.vstack(all_points) + + # 所有点都是正样本 + point_labels = np.ones(len(all_points), dtype=np.int32) + + return all_points, point_labels + + +def load_image_and_mask(image_path: str, mask_path: str) -> Tuple[np.ndarray, np.ndarray]: + """ + 加载图像和掩码 + + Args: + image_path: 图像路径 + mask_path: 掩码路径 + + Returns: + image: RGB 图像 (H, W, 3) + mask: 二值掩码 (H, W) + """ + # 加载图像 + image = cv2.imread(image_path) + if image is None: + raise ValueError(f"无法加载图像: {image_path}") + image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + + # 加载掩码 + mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE) + if mask is None: + raise ValueError(f"无法加载掩码: {mask_path}") + + return image, mask + + +def predict_with_point_prompt( + predictor: HFSam2Predictor, + image: np.ndarray, + points: np.ndarray, + point_labels: np.ndarray = None +) -> np.ndarray: + """ + 使用点提示进行 SAM2 预测 + + Args: + predictor: HFSam2Predictor 实例 + image: RGB 图像 (H, W, 3) + points: 点坐标 (N, 2)，格式为 [x, y] + point_labels: 点标签 (N,)，1 表示正样本，0 表示负样本 + + Returns: + mask_pred: 预测掩码 (H, W) + """ + # 设置图像 + predictor.set_image(image) + + # 如果没有点，返回空掩码 + if len(points) == 0: + return np.zeros((image.shape[0], image.shape[1]), dtype=np.uint8) + + # 默认所有点都是正样本 + if point_labels is None: + point_labels = np.ones(len(points), dtype=np.int32) + + # 使用点提示预测 + masks, scores, logits = predictor.predict( + point_coords=points, + point_labels=point_labels, + multimask_output=False, + ) + + # 取第一个掩码（因为 multimask_output=False） + mask_pred = masks[0] # shape: (H, W) + + # 转换为 0-255 + mask_pred = (mask_pred * 255).astype(np.uint8) + + return mask_pred + + +def process_test_set( + data_root: str, + test_file: str, + predictor: HFSam2Predictor, + output_dir: str, + num_points: int = 5, + per_component: bool = False +) -> List[Dict]: + """ + 处理整个测试集 + + Args: + data_root: 数据集根目录 + test_file: 测试集文件路径 (test.txt) + predictor: HFSam2Predictor 实例 + output_dir: 输出目录 + num_points: 采样点数量 + per_component: 是否为每个连通域独立采样 + + Returns: + results: 包含每个样本信息的列表 + """ + # 创建输出目录 + os.makedirs(output_dir, exist_ok=True) + pred_dir = os.path.join(output_dir, "predictions") + os.makedirs(pred_dir, exist_ok=True) + + # 读取测试集文件 + with open(test_file, 'r') as f: + lines = f.readlines() + + results = [] + + print(f"开始处理 {len(lines)} 张测试图像...") + print(f"采样策略: {'每连通域' if per_component else '全局'} {num_points} 个点") + + for line in tqdm(lines, desc="处理测试集"): + parts = line.strip().split() + if len(parts) != 2: + continue + + img_rel_path, mask_rel_path = parts + + # 构建完整路径 + img_path = os.path.join(data_root, img_rel_path) + mask_path = os.path.join(data_root, mask_rel_path) + + # 检查文件是否存在 + if not os.path.exists(img_path): + print(f"警告: 图像不存在 {img_path}") + continue + if not os.path.exists(mask_path): + print(f"警告: 掩码不存在 {mask_path}") + continue + + try: + # 加载图像和掩码 + image, mask_gt = load_image_and_mask(img_path, mask_path) + + # 从 GT 掩码采样点 + if per_component: + points, point_labels = sample_points_per_component( + mask_gt, num_points_per_component=num_points + ) + else: + points = sample_points_on_skeleton(mask_gt, num_points=num_points) + point_labels = np.ones(len(points), dtype=np.int32) + + # 使用 SAM2 预测 + with torch.inference_mode(): + mask_pred = predict_with_point_prompt( + predictor, image, points, point_labels + ) + + # 保存预测掩码 + img_name = Path(img_rel_path).stem + pred_path = os.path.join(pred_dir, f"{img_name}_pred.png") + cv2.imwrite(pred_path, mask_pred) + + # 记录结果 + results.append({ + "image_path": img_rel_path, + "mask_gt_path": mask_rel_path, + "mask_pred_path": pred_path, + "num_points": len(points), + "image_shape": image.shape[:2], + }) + + except Exception as e: + print(f"处理失败 {img_path}: {str(e)}") + continue + + # 保存结果信息 + results_file = os.path.join(output_dir, "results_info.json") + with open(results_file, 'w') as f: + json.dump(results, f, indent=2) + + print(f"\n处理完成！共处理 {len(results)} 张图像") + print(f"预测掩码保存在: {pred_dir}") + print(f"结果信息保存在: {results_file}") + + return results + + +def main(): + """主函数""" + import argparse + + parser = argparse.ArgumentParser(description="SAM2 点提示方式 (HuggingFace) - Crack500 数据集评估") + parser.add_argument("--data_root", type=str, default="./crack500", help="数据集根目录") + parser.add_argument("--test_file", type=str, default="./crack500/test.txt", help="测试集文件") + parser.add_argument("--model_id", type=str, default="facebook/sam2-hiera-small", help="HuggingFace 模型 ID") + parser.add_argument("--output_dir", type=str, default="./results/point_prompt_hf", help="输出目录") + parser.add_argument("--num_points", type=int, default=5, choices=[1, 3, 5], help="采样点数量") + parser.add_argument("--per_component", action="store_true", help="为每个连通域独立采样") + + args = parser.parse_args() + + print("=" * 60) + print("SAM2 点提示方式 (HuggingFace) - Crack500 数据集评估") + print("=" * 60) + print(f"数据集根目录: {args.data_root}") + print(f"测试集文件: {args.test_file}") + print(f"模型: {args.model_id}") + print(f"采样点数量: {args.num_points}") + print(f"采样策略: {'每连通域' if args.per_component else '全局骨架'}") + print(f"输出目录: {args.output_dir}") + print("=" * 60) + + # 检查 CUDA 是否可用 + if not torch.cuda.is_available(): + print("警告: CUDA 不可用，将使用 CPU（速度会很慢）") + else: + print(f"使用 GPU: {torch.cuda.get_device_name(0)}") + + # 构建 SAM2 predictor + print("\n加载 SAM2 模型...") + from .hf_sam2_predictor import build_hf_sam2_predictor + predictor = build_hf_sam2_predictor(model_id=args.model_id) + print("模型加载完成！") + + # 处理测试集 + results = process_test_set( + data_root=args.data_root, + test_file=args.test_file, + predictor=predictor, + output_dir=args.output_dir, + num_points=args.num_points, + per_component=args.per_component + ) + + print("\n" + "=" * 60) + print("处理完成！接下来请运行评估脚本计算指标。") + print("=" * 60) + + +if __name__ == "__main__": + main() diff --git a/src/tasks/__init__.py b/src/tasks/__init__.py new file mode 100644 index 0000000..4cf9a61 --- /dev/null +++ b/src/tasks/__init__.py @@ -0,0 +1,8 @@ +from .config import TaskConfig, TaskStepConfig +from .pipeline import TaskRunner +from .registry import TaskRegistry + +# ensure built-in tasks are registered +from . import examples # noqa: F401 + +__all__ = ["TaskConfig", "TaskRunner", "TaskRegistry", "TaskStepConfig"] diff --git a/src/tasks/config.py b/src/tasks/config.py new file mode 100644 index 0000000..68f8618 --- /dev/null +++ b/src/tasks/config.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any, Dict, List, Literal, Optional + + +TaskStepKind = Literal[ + "train", + "evaluate", + "visualize", + "bbox_inference", + "point_inference", + "legacy_evaluation", + "legacy_visualization", +] + + +@dataclass +class TaskStepConfig: + kind: TaskStepKind + dataset_split: Optional[str] = None + dataset_split_file: Optional[str] = None + limit: Optional[int] = None + eval_split: Optional[str] = None + eval_split_file: Optional[str] = None + params: Dict[str, Any] = field(default_factory=dict) + + +@dataclass +class TaskConfig: + name: str + description: str + project_config_name: str + model_key: str = "sam2" + steps: List[TaskStepConfig] = field(default_factory=list) + dataset_overrides: Dict[str, Any] = field(default_factory=dict) + model_overrides: Dict[str, Any] = field(default_factory=dict) + training_overrides: Dict[str, Any] = field(default_factory=dict) + evaluation_overrides: Dict[str, Any] = field(default_factory=dict) + visualization_overrides: Dict[str, Any] = field(default_factory=dict) diff --git a/src/tasks/examples.py b/src/tasks/examples.py new file mode 100644 index 0000000..676f2ea --- /dev/null +++ b/src/tasks/examples.py @@ -0,0 +1,34 @@ +from __future__ import annotations + +from .config import TaskConfig, TaskStepConfig +from .registry import TaskRegistry + +TaskRegistry.register( + TaskConfig( + name="sam2_crack500_eval", + description="Evaluate SAM2 bbox prompt checkpoints on Crack500 and render overlays.", + project_config_name="sam2_bbox_prompt", + steps=[ + TaskStepConfig(kind="evaluate", dataset_split="test"), + TaskStepConfig(kind="visualize", dataset_split="test", limit=20), + ], + ) +) + +TaskRegistry.register( + TaskConfig( + name="sam2_crack500_train_eval", + description="Fine-tune SAM2 on Crack500 train split, evaluate on val, then visualize results.", + project_config_name="sam2_bbox_prompt", + steps=[ + TaskStepConfig( + kind="train", + dataset_split="train", + eval_split="val", + params={"num_train_epochs": 2}, + ), + TaskStepConfig(kind="evaluate", dataset_split="val", limit=32), + TaskStepConfig(kind="visualize", dataset_split="val", limit=16), + ], + ) +) diff --git a/src/tasks/io.py b/src/tasks/io.py new file mode 100644 index 0000000..9a9c29d --- /dev/null +++ b/src/tasks/io.py @@ -0,0 +1,40 @@ +from __future__ import annotations + +import tomllib +from pathlib import Path +from typing import Any, Dict, List + +from .config import TaskConfig, TaskStepConfig + + +def load_task_from_toml(path: str | Path) -> TaskConfig: + """ + Load a TaskConfig from a TOML file. + """ + data = tomllib.loads(Path(path).read_text(encoding="utf-8")) + task_data = data.get("task", {}) + steps_data: List[Dict[str, Any]] = data.get("steps", []) + steps = [ + TaskStepConfig( + kind=step["kind"], + dataset_split=step.get("dataset_split"), + dataset_split_file=step.get("dataset_split_file"), + limit=step.get("limit"), + eval_split=step.get("eval_split"), + eval_split_file=step.get("eval_split_file"), + params=step.get("params", {}), + ) + for step in steps_data + ] + return TaskConfig( + name=task_data["name"], + description=task_data.get("description", ""), + project_config_name=task_data["project_config_name"], + model_key=task_data.get("model_key", "sam2"), + steps=steps, + dataset_overrides=task_data.get("dataset_overrides", {}), + model_overrides=task_data.get("model_overrides", {}), + training_overrides=task_data.get("training_overrides", {}), + evaluation_overrides=task_data.get("evaluation_overrides", {}), + visualization_overrides=task_data.get("visualization_overrides", {}), + ) diff --git a/src/tasks/pipeline.py b/src/tasks/pipeline.py new file mode 100644 index 0000000..5f1a1a8 --- /dev/null +++ b/src/tasks/pipeline.py @@ -0,0 +1,264 @@ +from __future__ import annotations + +import logging +from dataclasses import fields, replace +from pathlib import Path +from typing import Any, Dict, Optional + +from ..bbox_prompt import process_test_set as bbox_process_test_set +from ..dataset import DatasetRegistry +from ..evaluation import PipelineEvaluator +from ..evaluation.utils import extract_mask_from_pipeline_output +from ..hf_sam2_predictor import build_hf_sam2_predictor +from ..legacy_evaluation import evaluate_test_set as legacy_evaluate_test_set +from ..legacy_visualization import ( + create_metrics_distribution_plot, + visualize_test_set as legacy_visualize_test_set, +) +from ..model import FineTuningTrainer, ModelRegistry +from ..model_configuration import ConfigRegistry, DatasetConfig, ProjectConfig +from ..point_prompt import process_test_set as point_process_test_set +from ..visualization import OverlayGenerator +from .config import TaskConfig, TaskStepConfig + +LOGGER = logging.getLogger(__name__) + + +def _replace_dataclass(instance, updates: Dict[str, Any]): + if not updates: + return instance + valid_fields = {f.name for f in fields(type(instance))} + filtered = {k: v for k, v in updates.items() if k in valid_fields} + if not filtered: + return instance + return replace(instance, **filtered) + + +def _override_dataset(config: DatasetConfig, split: str, split_file: Optional[str]) -> DatasetConfig: + updates: Dict[str, Any] = {"split": split} + if split_file: + updates["split_file"] = split_file + return replace(config, **updates) + + +class TaskRunner: + """ + Sequentially executes a series of task steps (train/eval/visualize). + """ + + def __init__(self, task_config: TaskConfig, project_config: Optional[ProjectConfig] = None) -> None: + self.task_config = task_config + base_project = project_config or ConfigRegistry.get(task_config.project_config_name) + if project_config is None: + base_project = self._apply_project_overrides(base_project) + self.project_config = base_project + self.adapter = ModelRegistry.create(task_config.model_key, self.project_config.model) + + def run(self) -> None: + LOGGER.info("Starting task '%s'", self.task_config.name) + for idx, step in enumerate(self.task_config.steps, start=1): + LOGGER.info("Running step %d/%d: %s", idx, len(self.task_config.steps), step.kind) + if step.kind == "train": + self._run_train(step) + elif step.kind == "evaluate": + self._run_evaluate(step) + elif step.kind == "visualize": + self._run_visualize(step) + elif step.kind == "bbox_inference": + self._run_bbox_inference(step) + elif step.kind == "point_inference": + self._run_point_inference(step) + elif step.kind == "legacy_evaluation": + self._run_legacy_evaluation(step) + elif step.kind == "legacy_visualization": + self._run_legacy_visualization(step) + else: + raise ValueError(f"Unknown task step: {step.kind}") + + def _build_dataset(self, split: str, split_file: Optional[str]): + dataset_cfg = _override_dataset(self.project_config.dataset, split, split_file) + return DatasetRegistry.create( + dataset_cfg.name, + config=dataset_cfg, + return_hf_dict=True, + ) + + def _apply_project_overrides(self, config: ProjectConfig) -> ProjectConfig: + dataset_cfg = config.dataset + if self.task_config.dataset_overrides: + dataset_cfg = self._apply_dataset_overrides(dataset_cfg, self.task_config.dataset_overrides) + evaluation_cfg = config.evaluation + if self.task_config.evaluation_overrides: + evaluation_cfg = self._apply_simple_overrides(evaluation_cfg, self.task_config.evaluation_overrides) + visualization_cfg = config.visualization + if self.task_config.visualization_overrides: + visualization_cfg = self._apply_simple_overrides( + visualization_cfg, self.task_config.visualization_overrides + ) + model_cfg = config.model + if self.task_config.model_overrides: + model_cfg = self._apply_simple_overrides(model_cfg, self.task_config.model_overrides) + training_cfg = config.training + if self.task_config.training_overrides: + training_cfg = self._apply_simple_overrides(training_cfg, self.task_config.training_overrides) + return replace( + config, + dataset=dataset_cfg, + model=model_cfg, + training=training_cfg, + evaluation=evaluation_cfg, + visualization=visualization_cfg, + ) + + def _apply_dataset_overrides(self, dataset_cfg: DatasetConfig, overrides: Dict[str, Any]) -> DatasetConfig: + overrides = dict(overrides) + extra_updates = overrides.pop("extra_params", {}) + merged_extra = dict(dataset_cfg.extra_params or {}) + merged_extra.update(extra_updates) + return replace(dataset_cfg, **overrides, extra_params=merged_extra) + + def _apply_simple_overrides(self, cfg, overrides: Dict[str, Any]): + overrides = dict(overrides) + return replace(cfg, **overrides) + + def _default_data_root(self) -> str: + return self.project_config.dataset.data_root + + def _default_test_file(self) -> str: + dataset_cfg = self.project_config.dataset + candidate = dataset_cfg.split_file or "test.txt" + candidate_path = Path(candidate) + if candidate_path.is_absolute(): + return str(candidate_path) + return str(Path(dataset_cfg.data_root) / candidate) + + def _default_output_dir(self) -> str: + return self.project_config.evaluation.output_dir + + def _run_train(self, step: TaskStepConfig) -> None: + train_dataset = self._build_dataset(step.dataset_split, step.dataset_split_file) + eval_dataset = None + if step.eval_split: + eval_dataset = self._build_dataset(step.eval_split, step.eval_split_file) + trainer_builder = FineTuningTrainer( + adapter=self.adapter, + train_dataset=train_dataset, + eval_dataset=eval_dataset, + training_config=_replace_dataclass( + self.project_config.training, + dict(step.params), + ), + ) + artifacts = trainer_builder.build() + train_result = artifacts.trainer.train() + LOGGER.info("Training result: %s", train_result) + artifacts.trainer.save_model(self.project_config.training.output_dir) + if eval_dataset: + metrics = artifacts.trainer.evaluate() + LOGGER.info("Evaluation metrics: %s", metrics) + + def _run_evaluate(self, step: TaskStepConfig) -> None: + dataset = self._build_dataset(step.dataset_split, step.dataset_split_file) + evaluation_cfg = _replace_dataclass( + self.project_config.evaluation, + {**dict(step.params), "max_samples": step.limit}, + ) + evaluator = PipelineEvaluator( + dataset=dataset, + adapter=self.adapter, + config=evaluation_cfg, + ) + summary = evaluator.run() + LOGGER.info("Evaluation summary: %s", summary) + + def _run_visualize(self, step: TaskStepConfig) -> None: + dataset = self._build_dataset(step.dataset_split, step.dataset_split_file) + vis_config = _replace_dataclass( + self.project_config.visualization, + {**dict(step.params), "num_samples": step.limit or self.project_config.visualization.num_samples}, + ) + overlay = OverlayGenerator(vis_config) + pipe = self.adapter.build_pipeline() + limit = min(vis_config.num_samples, len(dataset)) + for idx in range(limit): + sample = dataset[idx] + preds = pipe(pixel_values=sample["pixel_values"], prompts=sample.get("prompts")) + pred_mask = extract_mask_from_pipeline_output(preds) + mask = sample.get("labels", {}).get("mask") + overlay.visualize_sample( + image=sample["pixel_values"], + prediction=pred_mask, + mask=mask, + metadata=sample.get("metadata"), + ) + LOGGER.info("Saved overlays to %s", vis_config.save_dir) + + def _run_bbox_inference(self, step: TaskStepConfig) -> None: + params = dict(step.params) + data_root = params.get("data_root", self._default_data_root()) + test_file = params.get("test_file", self._default_test_file()) + expand_ratio = params.get("expand_ratio", params.get("bbox_expand_ratio", 0.05)) + output_dir = params.get("output_dir", self._default_output_dir()) + model_id = params.get("model_id", self.project_config.model.name_or_path) + predictor = build_hf_sam2_predictor(model_id=model_id, device=params.get("device")) + bbox_process_test_set( + data_root=data_root, + test_file=test_file, + predictor=predictor, + output_dir=output_dir, + expand_ratio=expand_ratio, + ) + + def _run_point_inference(self, step: TaskStepConfig) -> None: + params = dict(step.params) + data_root = params.get("data_root", self._default_data_root()) + test_file = params.get("test_file", self._default_test_file()) + num_points = params.get("num_points", 5) + per_component = params.get("per_component", False) + output_dir = params.get("output_dir") or f"./results/point_prompt_{num_points}pts_hf" + model_id = params.get("model_id", self.project_config.model.name_or_path) + predictor = build_hf_sam2_predictor(model_id=model_id, device=params.get("device")) + point_process_test_set( + data_root=data_root, + test_file=test_file, + predictor=predictor, + output_dir=output_dir, + num_points=num_points, + per_component=per_component, + ) + + def _run_legacy_evaluation(self, step: TaskStepConfig) -> None: + params = dict(step.params) + data_root = params.get("data_root", self._default_data_root()) + test_file = params.get("test_file", self._default_test_file()) + output_dir = params.get("output_dir", self._default_output_dir()) + pred_dir = params.get("pred_dir", str(Path(output_dir) / "predictions")) + compute_skeleton = params.get("compute_skeleton", True) + legacy_evaluate_test_set( + data_root=data_root, + test_file=test_file, + pred_dir=pred_dir, + output_dir=output_dir, + compute_skeleton=compute_skeleton, + ) + + def _run_legacy_visualization(self, step: TaskStepConfig) -> None: + params = dict(step.params) + data_root = params.get("data_root", self._default_data_root()) + test_file = params.get("test_file", self._default_test_file()) + output_dir = params.get("output_dir", self._default_output_dir()) + pred_dir = params.get("pred_dir", str(Path(output_dir) / "predictions")) + num_samples = params.get("num_samples", 20) + save_all = params.get("save_all", False) + results_csv = params.get("results_csv", str(Path(output_dir) / "evaluation_results.csv")) + legacy_visualize_test_set( + data_root=data_root, + test_file=test_file, + pred_dir=pred_dir, + output_dir=output_dir, + results_csv=results_csv if Path(results_csv).exists() else None, + num_samples=num_samples, + save_all=save_all, + ) + if params.get("create_metrics_plot", True): + create_metrics_distribution_plot(results_csv, output_dir) diff --git a/src/tasks/registry.py b/src/tasks/registry.py new file mode 100644 index 0000000..bd8a498 --- /dev/null +++ b/src/tasks/registry.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from typing import Dict + +from .config import TaskConfig + + +class TaskRegistry: + """ + Holds named task configs for reuse. + """ + + _registry: Dict[str, TaskConfig] = {} + + @classmethod + def register(cls, task: TaskConfig) -> TaskConfig: + cls._registry[task.name] = task + return task + + @classmethod + def get(cls, name: str) -> TaskConfig: + if name not in cls._registry: + raise KeyError(f"Task '{name}' is not registered.") + return cls._registry[name] + + @classmethod + def available(cls) -> Dict[str, TaskConfig]: + return dict(cls._registry) diff --git a/src/tasks/run_task.py b/src/tasks/run_task.py new file mode 100644 index 0000000..e59a423 --- /dev/null +++ b/src/tasks/run_task.py @@ -0,0 +1,44 @@ +from __future__ import annotations + +import logging +from dataclasses import dataclass +from typing import Optional + +from transformers import HfArgumentParser + +from .config import TaskConfig +from .io import load_task_from_toml +from .pipeline import TaskRunner +from .registry import TaskRegistry + +# ensure built-in tasks are registered when CLI runs +from . import examples # noqa: F401 + +LOGGER = logging.getLogger(__name__) + + +@dataclass +class TaskCLIArguments: + task_name: Optional[str] = None + task_file: Optional[str] = None + + +def resolve_task(cli_args: TaskCLIArguments) -> TaskConfig: + if not cli_args.task_name and not cli_args.task_file: + raise ValueError("Provide either --task_name or --task_file.") + if cli_args.task_file: + return load_task_from_toml(cli_args.task_file) + return TaskRegistry.get(cli_args.task_name) + + +def main() -> None: + parser = HfArgumentParser(TaskCLIArguments) + (cli_args,) = parser.parse_args_into_dataclasses() + task = resolve_task(cli_args) + runner = TaskRunner(task) + runner.run() + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() diff --git a/src/visualization/__init__.py b/src/visualization/__init__.py new file mode 100644 index 0000000..a4046b6 --- /dev/null +++ b/src/visualization/__init__.py @@ -0,0 +1,4 @@ +from .gallery import build_gallery +from .overlay import OverlayGenerator + +__all__ = ["OverlayGenerator", "build_gallery"] diff --git a/src/visualization/gallery.py b/src/visualization/gallery.py new file mode 100644 index 0000000..e560b85 --- /dev/null +++ b/src/visualization/gallery.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Iterable + +from PIL import Image + + +def build_gallery(image_paths: Iterable[Path], output_path: Path, columns: int = 4) -> Path: + """ + Simple grid composer that stitches overlay PNGs into a gallery. + """ + image_paths = list(image_paths) + if not image_paths: + raise ValueError("No images provided for gallery.") + output_path.parent.mkdir(parents=True, exist_ok=True) + images = [Image.open(path).convert("RGB") for path in image_paths] + widths, heights = zip(*(img.size for img in images)) + cell_w = max(widths) + cell_h = max(heights) + rows = (len(images) + columns - 1) // columns + canvas = Image.new("RGB", (cell_w * columns, cell_h * rows), color=(0, 0, 0)) + for idx, img in enumerate(images): + row = idx // columns + col = idx % columns + canvas.paste(img, (col * cell_w, row * cell_h)) + canvas.save(output_path) + return output_path diff --git a/src/visualization/overlay.py b/src/visualization/overlay.py new file mode 100644 index 0000000..18c6a08 --- /dev/null +++ b/src/visualization/overlay.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any, Dict, Optional + +import numpy as np +from PIL import Image + +from ..model_configuration import VisualizationConfig + + +class OverlayGenerator: + """ + Turns model predictions into side-by-side overlays for quick inspection. + """ + + def __init__(self, config: VisualizationConfig) -> None: + self.config = config + Path(self.config.save_dir).mkdir(parents=True, exist_ok=True) + + def visualize_sample( + self, + image: np.ndarray, + prediction: np.ndarray, + mask: Optional[np.ndarray], + metadata: Optional[Dict[str, Any]] = None, + ) -> Path: + overlay = self._compose_overlay(image, prediction, mask) + filename = ( + metadata.get("image_name", "sample") + if metadata + else "sample" + ) + target = Path(self.config.save_dir) / f"{filename}_overlay.png" + Image.fromarray(overlay).save(target) + return target + + def _compose_overlay( + self, + image: np.ndarray, + prediction: np.ndarray, + mask: Optional[np.ndarray], + ) -> np.ndarray: + vis = image.copy() + pred_mask = self._normalize(prediction) + color = np.zeros_like(vis) + color[..., 0] = pred_mask + vis = (0.5 * vis + 0.5 * color).astype(np.uint8) + if mask is not None: + gt = self._normalize(mask) + color = np.zeros_like(vis) + color[..., 1] = gt + vis = (0.5 * vis + 0.5 * color).astype(np.uint8) + return vis + + def _normalize(self, array: np.ndarray) -> np.ndarray: + normalized = array.astype(np.float32) + normalized -= normalized.min() + denom = normalized.max() or 1.0 + normalized = normalized / denom + normalized = (normalized * 255).astype(np.uint8) + return normalized diff --git a/src/visualization/run_pipeline_vis.py b/src/visualization/run_pipeline_vis.py new file mode 100644 index 0000000..f9427fa --- /dev/null +++ b/src/visualization/run_pipeline_vis.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +import logging +from dataclasses import dataclass, replace +from typing import Optional + +from transformers import HfArgumentParser + +from ..dataset import DatasetRegistry +from ..evaluation.utils import extract_mask_from_pipeline_output +from ..model import ModelRegistry +from ..model_configuration import ConfigRegistry +from .overlay import OverlayGenerator + +LOGGER = logging.getLogger(__name__) + + +@dataclass +class VisualizationCLIArguments: + config_name: str = "sam2_bbox_prompt" + model_key: str = "sam2" + split: str = "test" + split_file: Optional[str] = None + num_samples: int = 20 + device: Optional[str] = None + + +def main() -> None: + parser = HfArgumentParser(VisualizationCLIArguments) + (cli_args,) = parser.parse_args_into_dataclasses() + project_config = ConfigRegistry.get(cli_args.config_name) + dataset_cfg = replace(project_config.dataset, split=cli_args.split, split_file=cli_args.split_file) + dataset = DatasetRegistry.create( + dataset_cfg.name, + config=dataset_cfg, + return_hf_dict=True, + ) + adapter = ModelRegistry.create(cli_args.model_key, project_config.model) + overlay = OverlayGenerator(project_config.visualization) + pipe = adapter.build_pipeline(device=cli_args.device) + limit = min(cli_args.num_samples, len(dataset)) + for idx in range(limit): + sample = dataset[idx] + preds = pipe(pixel_values=sample["pixel_values"], prompts=sample.get("prompts")) + pred_mask = extract_mask_from_pipeline_output(preds) + mask = sample.get("labels", {}).get("mask") + overlay.visualize_sample( + image=sample["pixel_values"], + prediction=pred_mask, + mask=mask, + metadata=sample.get("metadata"), + ) + LOGGER.info("Saved overlays to %s", project_config.visualization.save_dir) + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() diff --git a/tasks/bbox_eval.toml b/tasks/bbox_eval.toml new file mode 100644 index 0000000..9b52291 --- /dev/null +++ b/tasks/bbox_eval.toml @@ -0,0 +1,34 @@ +[task] +name = "bbox_cli_template" +description = "Run legacy bbox-prompt inference + evaluation + visualization" +project_config_name = "sam2_bbox_prompt" + +[[steps]] +kind = "bbox_inference" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +model_id = "facebook/sam2-hiera-small" +output_dir = "./results/bbox_prompt" +expand_ratio = 0.05 + +[[steps]] +kind = "legacy_evaluation" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/bbox_prompt" +pred_dir = "./results/bbox_prompt/predictions" +compute_skeleton = true + +[[steps]] +kind = "legacy_visualization" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/bbox_prompt" +pred_dir = "./results/bbox_prompt/predictions" +results_csv = "./results/bbox_prompt/evaluation_results.csv" +num_samples = 20 +save_all = false +create_metrics_plot = true diff --git a/tasks/point_eval.toml b/tasks/point_eval.toml new file mode 100644 index 0000000..df6db58 --- /dev/null +++ b/tasks/point_eval.toml @@ -0,0 +1,100 @@ +[task] +name = "point_cli_template" +description = "Run legacy point-prompt inference/eval/visualization for multiple configs" +project_config_name = "sam2_bbox_prompt" + +# 1 point config +[[steps]] +kind = "point_inference" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +model_id = "facebook/sam2-hiera-small" +num_points = 1 +per_component = false +output_dir = "./results/point_prompt_1pts_hf" + +[[steps]] +kind = "legacy_evaluation" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/point_prompt_1pts_hf" +pred_dir = "./results/point_prompt_1pts_hf/predictions" +compute_skeleton = true + +[[steps]] +kind = "legacy_visualization" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/point_prompt_1pts_hf" +pred_dir = "./results/point_prompt_1pts_hf/predictions" +results_csv = "./results/point_prompt_1pts_hf/evaluation_results.csv" +num_samples = 10 +save_all = false +create_metrics_plot = true + +# 3 point config +[[steps]] +kind = "point_inference" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +model_id = "facebook/sam2-hiera-small" +num_points = 3 +per_component = false +output_dir = "./results/point_prompt_3pts_hf" + +[[steps]] +kind = "legacy_evaluation" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/point_prompt_3pts_hf" +pred_dir = "./results/point_prompt_3pts_hf/predictions" +compute_skeleton = true + +[[steps]] +kind = "legacy_visualization" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/point_prompt_3pts_hf" +pred_dir = "./results/point_prompt_3pts_hf/predictions" +results_csv = "./results/point_prompt_3pts_hf/evaluation_results.csv" +num_samples = 10 +save_all = false +create_metrics_plot = true + +# 5 point config +[[steps]] +kind = "point_inference" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +model_id = "facebook/sam2-hiera-small" +num_points = 5 +per_component = false +output_dir = "./results/point_prompt_5pts_hf" + +[[steps]] +kind = "legacy_evaluation" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/point_prompt_5pts_hf" +pred_dir = "./results/point_prompt_5pts_hf/predictions" +compute_skeleton = true + +[[steps]] +kind = "legacy_visualization" +[steps.params] +data_root = "./crack500" +test_file = "./crack500/test.txt" +output_dir = "./results/point_prompt_5pts_hf" +pred_dir = "./results/point_prompt_5pts_hf/predictions" +results_csv = "./results/point_prompt_5pts_hf/evaluation_results.csv" +num_samples = 10 +save_all = false +create_metrics_plot = true