Add pytest suite with 40 unit and integration tests

Coverage: - test_model: SimpleNet forward (parametrized over batch sizes and both unsqueezed and flat input shapes), layer dimensions, differentiability, and ONNX round-trip - test_inference: load_model resolution order (bundled, cwd override, explicit path, missing path), and predict shape/dtype/determinism plus endpoint sanity across 8 cardinal/diagonal targets - test_train: _load_csv parsing, TrajectoryDataset indexing, full train() pipeline producing a single-file ONNX, plus a smoke test against the real data shipped under data/ - test_cli: --help for the three console scripts and a real run of mouse-visualize via both the entry point and python -m Wire up pytest via dependency-groups and tool.pytest.ini_options.
Refactor into standard Python package with uv
2026-05-12 00:33:15 +08:00 · 2026-05-12 00:30:08 +08:00 · 2025-01-21 14:17:27 +08:00 · 2025-01-19 18:49:23 +08:00 · 2025-01-19 18:28:14 +08:00 · 2024-02-10 15:31:18 +08:00
24 changed files with 2601 additions and 278 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1,22 @@
 # Byte-compiled / build artifacts
 __pycache__/
 *.py[cod]
 *.egg-info/
 build/
 dist/
 # Virtual envs / tooling
 .venv/
 .uv/
 # Generated outputs (only at repo root; bundled assets must stay)
 /trajectories.png
 /mouse.onnx
 /mouse.onnx.data
 # IDE
 .idea/
 .vscode/
 # pytest
 .pytest_cache/
--- a/README.md
+++ b/README.md
@@ -1,26 +1,148 @@
 # mouse_control
-一种基于神经网络来模拟人手移动鼠标的方法  
+
 一种基于神经网络来模拟人手移动鼠标的方法。
 ## 简介
-本项目源于该死的轨迹检测。经过初步验证，本项目是有效的。  
+
 本项目源于该死的轨迹检测。我们用一个简单的三层全连接神经网络去拟合真人鼠标移动轨迹。
 <img src="./imgs/Figure_1.png">
-上图为真人轨迹移动得到的散点图。这里我们用10个点来拟合轨迹。  
+
-可以看到，人手的移动其实和pid，以及其他的曲线是有一定区别的。  
+上图为真人轨迹移动得到的散点图——用 10 个关键点拟合一条轨迹。可以看到，人手移动和 PID、贝塞尔等曲线有明显区别，因此用神经网络来拟合更合适。
-因此，我们选用神经网络来拟合真人鼠标移动轨迹。  
+
-该神经网络其实很简单，只有三个全连接层。  
+**网络结构**：
-输入为目标距离当前位置的dx，dy。  
+
-输出为10个点用来模拟真人的轨迹。  
+- 输入：`(dx, dy)` —— 目标点相对当前位置的偏移
-## 收集鼠标轨迹
+- 三层全连接：`Linear(2 → 64) → ReLU → Linear(64 → 32) → ReLU → Linear(32 → 20)`
-首先运行collect_data.py。运行后，我们会看到这样一个界面。  
+- 输出：`(10, 2)` —— 10 个轨迹点
 仓库内含 `data/` 下的 400 条采集数据和 `src/mouse_control/assets/mouse.onnx` 预训练模型，clone 之后可以直接 `mouse-visualize` 看效果。
 ## 安装
 使用 [uv](https://docs.astral.sh/uv/) 管理：
 ```bash
 uv sync
 ```
 或从构建产物安装：
 ```bash
 uv pip install dist/mouse_control-0.1.0-py3-none-any.whl
 ```
 要求 Python ≥ 3.12。
 ## 项目结构
 ```
 mouse_control/
 ├── pyproject.toml
 ├── README.md
 ├── data/                       # 训练 / 测试数据 (380 + 20 条)
 │   ├── mouse_data.csv
 │   └── mouse_data_test.csv
 ├── imgs/                       # README 图片
 └── src/mouse_control/          # 包源码 (src layout)
    ├── __init__.py             # 公共 API
    ├── model.py                # SimpleNet 网络定义
    ├── collect.py              # 数据采集 GUI
    ├── train.py                # 训练 + ONNX 导出
    ├── visualize.py            # 轨迹可视化
    ├── inference.py            # 模型加载 / 推理辅助
    └── assets/
        └── mouse.onnx          # 随包发布的预训练模型
 ```
 ## 命令行用法
 安装后可以直接调用三个命令（也可以用 `uv run <cmd>`）：
 ### 1. 采集真人鼠标轨迹
 ```bash
 uv run mouse-collect -o data/mouse_data.csv -n 100
 ```
 <img src="./imgs/collect.png">
-我们需要点击红球，就会开始记录鼠标轨迹，点击蓝球，结束记录。这样我们就成功收集到一条鼠标轨迹的数据。  
+
-重复这样，每收集100次程序会退出。我们一共要收集约300条数据。  
+全屏界面：点**红球**开始记录、移动到**蓝球**点击结束记录，每条轨迹保存 10 个关键点到 CSV。
-这样，我们就收集好了数据。  
+
-## 划分数据集（可选）
+- 默认采集 100 条后自动退出，按 `Esc` 提前退出
-我们将mouse_data.csv用vscode打开，选择一些数据剪切到mouse_data_test.csv中。  
+- 推荐采集 ≥ 300 条以获得较好的训练效果
-## 训练模型
+
-运行train.py，程序就会开始训练模型。最终我们能看到控制台打印出的一条test数据。是一个dx，dy拟合出的十个点。  
+### 2. 划分训练 / 测试集
-## 验证
+
 把 `mouse_data.csv` 里**剪切**若干行（10-20 条）到 `mouse_data_test.csv` 即可。
 ### 3. 训练模型
 ```bash
 uv run mouse-train \
  --train-csv data/mouse_data.csv \
  --test-csv data/mouse_data_test.csv \
  --output mouse.onnx \
  --epochs 1000
 ```
 训练结束后在当前目录生成 `mouse.onnx`。其它参数 `--batch-size` `--lr` 可调。
 ### 4. 可视化效果
 ```bash
 uv run mouse-visualize --n-trajectories 10 --seed 42
 ```
 <img src="./imgs/Figure_2.png">
-我们将刚刚得到的十个点放到show.py中，观察散点图，发现其轨迹类似于本人鼠标移动轨迹。  
+
-todo  
+随机生成若干目标点，每条轨迹包含 10 个模型输出点 + 80 个三次样条插值点。`--no-show` 跳过 GUI 窗口、只写 PNG；`--model` 指定其它 ONNX 文件。
-使用onnxruntime在c++上进行推理
+
 ## Python API
 ```python
 import mouse_control as mc
 # 加载模型：优先 ./mouse.onnx，否则回退到包内打包的预训练模型
 session = mc.load_model()
 # 推理：输入 (dx, dy)，输出 (10, 2) 轨迹点
 pts = mc.predict(session, 120, -80)
 # -> ndarray, shape (10, 2), dtype float32
 # 直接使用模型类（用于自训练 / 微调）
 from mouse_control import SimpleNet
 ```
 ## ONNX 推理（其它语言）
 ### Python (onnxruntime)
 ```python
 import numpy as np
 import onnxruntime as ort
 session = ort.InferenceSession("mouse.onnx")
 inp = np.array([[[100.0, 200.0]]], dtype=np.float32)  # dx=100, dy=200
 output = session.run(None, {"input": inp})[0]          # shape (1, 10, 2)
 ```
 ### C++ (OpenCV DNN)
 ```cpp
 cv::dnn::Net net = cv::dnn::readNetFromONNX("mouse.onnx");
 cv::Mat blob(1, 2, CV_32F, cv::Scalar(100, 100));  // 输入 dx=100, dy=100
 net.setInput(blob, "input");
 cv::Mat output = net.forward("output");            // 输出 1*10*2
 std::cout << output.at<float>(0, 8, 0) << std::endl;
 ```
 ## 打包发布
 ```bash
 uv build
 # -> dist/mouse_control-0.1.0-py3-none-any.whl
 # -> dist/mouse_control-0.1.0.tar.gz
 ```
 wheel 内含 `assets/mouse.onnx` 预训练模型，安装后直接 `mc.load_model()` 即可使用。
--- a/collect_mouse_data.py
+++ b/collect_mouse_data.py
@@ -1,130 +0,0 @@
 import math
 import random
 import tkinter as tk
 import matplotlib.pyplot as plt
 import csv
 from tkinter import Label
 # 创建窗口
 root = tk.Tk()
 root.attributes('-fullscreen', True)  # 全屏显示
 label_n = Label(root, text="n: 0", font=("Helvetica", 16))
 label_n.pack()
 csv_file_path = "mouse_data.csv"
 screen_width = root.winfo_screenwidth()
 screen_height = root.winfo_screenheight()
 # 设置小球的初始位置
 ball1_pos = (screen_width/2, screen_height/2)
 ball2_pos = (ball1_pos[0] + random.randint(-200, 200),ball1_pos[1] + random.randint(-200, 200))
 # 设置小球的半径
 ball_radius = 20
 # 设置鼠标记录状态
 recording = False
 mouse_path = []
 n=0
 # 鼠标移动事件处理函数
 def motion(event):
    global recording, mouse_path, n
    if recording:
        mouse_path.append((event.x, event.y))
 # 鼠标点击事件处理函数
 def mouse_click(event):
    global recording, mouse_path, ball2_pos, n
    if event.x >= ball1_pos[0] - ball_radius and event.x <= ball1_pos[0] + ball_radius and event.y >= ball1_pos[1] - ball_radius and event.y <= ball1_pos[1] + ball_radius:
        recording = True
        mouse_path = [(event.x, event.y)]
    elif event.x >= ball2_pos[0] - ball_radius and event.x <= ball2_pos[0] + ball_radius and event.y >= ball2_pos[1] - ball_radius and event.y <= ball2_pos[1] + ball_radius:
        recording = False
        canvas.delete("ball2")
        #visualize_path(mouse_path)  # 可视化鼠标轨迹
        save_to_csv(mouse_path)
        n = n+1
        if n == 100:
            root.destroy()
        label_n.config(text=f"n: {n}")
        mouse_path = []
        # 重新生成第二个小球的位置
        ball2_pos = (ball1_pos[0] + random.randint(-200, 200), ball1_pos[1] + random.randint(-200, 200))
        # 绘制新的第二个小球
        canvas.create_oval(ball2_pos[0]-ball_radius, ball2_pos[1]-ball_radius, ball2_pos[0]+ball_radius, ball2_pos[1]+ball_radius, fill="blue", tags="ball2")
 # 键盘事件处理函数
 def key(event):
    if event.keysym == "Escape":
        root.destroy()
 def save_to_csv(path):
    # 将路径坐标转换为相对于起点的坐标
    x_rel = [px - path[0][0] for px, py in path]
    y_rel = [-(py - path[0][1]) for px, py in path]
    # 计算每个点相对于起点的距离，用于z轴表示
    distances = [math.sqrt((x_rel[0] - px)**2 + (y_rel[0] - py)**2) for px, py in zip(x_rel, y_rel)]
    # 选择10个关键点
    key_points_indices = [int(i) for i in range(0, len(path), max(1, len(path)//10))]
    key_points_x = [x_rel[i] for i in key_points_indices]
    key_points_y = [y_rel[i] for i in key_points_indices]
    key_points_distances = [distances[i] for i in key_points_indices]
    # 打开 CSV 文件进行写操作
    with open(csv_file_path, mode='a', newline='') as csv_file:
        csv_writer = csv.writer(csv_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
        # 写入一行数据
        csv_writer.writerow([f"{key_points_x[-1]},{key_points_y[-1]}"] + [f"{key_points_x[i]},{key_points_y[i]}" for i in range(0,10)])
 def visualize_path(path):
    # 将路径坐标转换为相对于起点的坐标
    x_rel = [px - path[0][0] for px, py in path]
    y_rel = [-(py - path[0][1]) for px, py in path]
    # 计算每个点相对于起点的距离，用于z轴表示
    distances = [math.sqrt((x_rel[0] - px)**2 + (y_rel[0] - py)**2) for px, py in zip(x_rel, y_rel)]
    # 选择10个关键点
    key_points_indices = [int(i) for i in range(0, len(path), max(1, len(path)//10))]
    key_points_x = [x_rel[i] for i in key_points_indices]
    key_points_y = [y_rel[i] for i in key_points_indices]
    key_points_distances = [distances[i] for i in key_points_indices]
    # 使用z轴信息，通过颜色表示距离的远近
    plt.scatter(key_points_x, key_points_y, c=key_points_distances, cmap='viridis', marker='o', s=50)
    # 在关键点位置添加文本标签，显示终点到起点的距离
    plt.text(key_points_x[-1], key_points_y[-1], f'Distance to Origin: {key_points_distances[-1]:.2f}', ha='right', va='bottom', bbox=dict(facecolor='white', alpha=0.5))
    # 添加颜色条，表示z轴信息
    plt.colorbar(label='Distance to Endpoint')
    plt.show()
 # 绘制小球
 canvas = tk.Canvas(root, width=root.winfo_screenwidth(), height=root.winfo_screenheight())
 canvas.pack()
 canvas.create_oval(ball1_pos[0]-ball_radius, ball1_pos[1]-ball_radius, ball1_pos[0]+ball_radius, ball1_pos[1]+ball_radius, fill="red")
 canvas.create_oval(ball2_pos[0]-ball_radius, ball2_pos[1]-ball_radius, ball2_pos[0]+ball_radius, ball2_pos[1]+ball_radius, fill="blue", tags="ball2")
 # 绑定鼠标事件
 canvas.bind('<Motion>', motion)
 canvas.bind('<Button-1>', mouse_click)
 # 绑定键盘事件
 root.bind('<Key>', key)
 # 运行窗口
 root.mainloop()
--- a/data/mouse_data.csv
+++ b/data/mouse_data.csv
@@ -0,0 +1,380 @@
 "189,-42","0,0","7,-1","58,-25","136,-49","188,-57","219,-60","216,-54","211,-51","205,-47","196,-43"
 "117,102","0,0","12,7","36,24","72,45","96,60","108,71","110,77","112,83","114,89","116,96"
 "-83,87","0,0","-4,2","-11,10","-26,28","-40,44","-56,60","-63,69","-70,75","-76,79","-80,83"
 "-55,-84","0,0","-6,-4","-12,-15","-22,-32","-33,-46","-41,-57","-45,-64","-46,-69","-49,-73","-51,-78"
 "-18,-150","0,0","-1,-5","-5,-19","-12,-49","-16,-97","-23,-117","-24,-129","-23,-138","-21,-146","-18,-154"
 "-172,-121","0,0","-9,-10","-40,-34","-80,-61","-112,-82","-132,-93","-138,-98","-146,-108","-159,-119","-165,-120"
 "69,-37","0,0","2,-2","6,-6","12,-11","19,-16","31,-22","46,-29","60,-32","64,-34","66,-36"
 "-166,-62","0,0","-8,-4","-29,-16","-75,-42","-118,-61","-128,-63","-134,-62","-144,-61","-154,-61","-161,-62"
 "99,-15","0,0","6,0","20,-3","57,-6","83,-7","113,-12","134,-14","130,-14","117,-14","105,-15"
 "65,106","0,0","-4,-1","2,5","11,18","28,34","47,54","55,70","59,83","63,96","65,106"
 "48,-83","0,0","4,-10","9,-21","12,-31","17,-44","24,-59","29,-70","37,-79","42,-85","46,-86"
 "-146,38","0,0","-11,0","-39,3","-80,11","-125,18","-149,19","-159,21","-164,23","-168,26","-157,32"
 "-149,-9","0,0","-13,-5","-40,-12","-76,-19","-104,-24","-128,-24","-143,-23","-144,-18","-145,-13","-149,-9"
 "-9,5","0,0","6,-5","15,-8","20,-9","25,-9","28,-7","25,-5","18,-2","11,0","6,1"
 "200,-109","0,0","2,-4","8,-11","30,-36","59,-60","85,-73","120,-87","152,-98","171,-104","184,-106"
 "-15,184","0,0","2,19","2,50","-3,83","-17,112","-20,132","-16,151","-16,164","-17,170","-18,176"
 "109,-132","0,0","-7,0","-2,-10","23,-38","63,-81","90,-109","107,-124","121,-136","126,-138","118,-136"
 "-127,-91","0,0","-8,-19","-34,-44","-66,-58","-83,-64","-90,-68","-103,-74","-114,-77","-120,-82","-124,-86"
 "-186,185","0,0","-10,13","-58,72","-106,118","-118,141","-133,158","-143,166","-152,171","-163,179","-181,190"
 "38,92","0,0","4,3","11,13","21,25","31,37","38,51","39,62","39,70","38,75","38,79"
 "52,154","0,0","1,7","15,37","35,80","57,108","64,121","64,129","65,137","64,145","59,152"
 "149,-106","0,0","4,-6","18,-21","44,-43","68,-61","89,-73","110,-87","120,-94","125,-96","132,-100"
 "-45,-81","0,0","-1,-16","-4,-45","-11,-60","-16,-73","-20,-82","-26,-87","-33,-89","-39,-91","-43,-88"
 "-18,44","0,0","0,2","-1,7","-4,20","-6,24","-7,29","-9,33","-11,36","-12,38","-13,39"
 "68,-144","0,0","11,-26","22,-70","41,-116","51,-137","83,-165","93,-172","86,-166","77,-159","73,-154"
 "-124,-125","0,0","-8,-5","-26,-20","-54,-41","-79,-68","-100,-95","-115,-114","-124,-122","-128,-125","-126,-128"
 "-109,62","0,0","-10,8","-22,21","-34,33","-56,48","-79,60","-86,63","-92,64","-98,64","-104,63"
 "-171,-36","0,0","-13,-1","-51,-14","-100,-33","-127,-45","-139,-47","-145,-44","-154,-42","-163,-42","-167,-40"
 "-129,-163","0,0","-5,-11","-22,-44","-47,-86","-79,-129","-87,-135","-93,-140","-100,-143","-106,-146","-117,-155"
 "92,21","0,0","10,-1","37,-3","77,4","114,14","128,20","120,21","112,22","106,22","99,22"
 "-182,-85","0,0","-13,-6","-38,-23","-72,-44","-106,-62","-139,-72","-153,-77","-158,-79","-165,-80","-171,-82"
 "-17,-72","0,0","0,-8","0,-14","0,-22","-3,-32","-7,-42","-11,-51","-14,-58","-17,-64","-18,-67"
 "-114,72","0,0","3,-3","4,-7","6,-9","1,-6","-11,5","-32,21","-56,39","-75,53","-96,64"
 "64,-108","0,0","1,-4","2,-12","6,-24","11,-44","24,-68","40,-89","44,-91","47,-94","51,-96"
 "-75,179","0,0","-3,12","-16,52","-39,98","-57,127","-65,143","-69,151","-71,158","-72,165","-74,172"
 "-133,-26","0,0","-9,-7","-28,-15","-51,-23","-78,-26","-98,-27","-106,-26","-113,-26","-119,-26","-127,-26"
 "143,126","0,0","3,3","25,20","59,46","102,75","140,95","142,102","142,108","142,115","143,120"
 "-71,-90","0,0","-3,-9","-16,-28","-34,-54","-45,-73","-49,-77","-52,-82","-54,-86","-56,-90","-62,-90"
 "2,-135","0,0","0,-5","-1,-15","-3,-30","-5,-50","-5,-71","-4,-93","-2,-112","-2,-125","-1,-131"
 "-102,152","0,0","-5,17","-38,85","-54,104","-64,113","-73,124","-79,130","-84,137","-90,144","-96,149"
 "174,-92","0,0","10,-8","25,-19","53,-37","88,-55","112,-65","130,-72","135,-74","140,-76","155,-84"
 "-76,155","0,0","-2,17","-9,48","-23,78","-38,99","-48,115","-53,125","-55,131","-59,135","-65,144"
 "116,81","0,0","10,7","24,17","61,41","74,59","79,63","83,69","94,74","101,77","109,79"
 "19,169","0,0","7,12","15,43","23,78","26,105","22,126","20,141","19,149","18,155","18,161"
 "74,-92","0,0","3,-8","14,-27","34,-51","42,-58","45,-62","53,-71","59,-79","67,-88","74,-92"
 "-114,198","0,0","-8,32","-28,80","-34,100","-47,121","-57,136","-71,157","-82,171","-92,176","-102,186"
 "116,110","0,0","16,12","44,35","78,65","90,77","95,91","105,108","119,121","127,127","122,119"
 "-155,-189","0,0","-1,-4","-12,-29","-34,-69","-69,-107","-103,-149","-123,-173","-132,-180","-137,-186","-144,-191"
 "60,76","0,0","-5,-2","1,2","17,16","39,44","64,65","71,72","75,78","79,86","75,84"
 "155,65","0,0","10,1","40,6","88,13","135,21","151,27","153,34","154,40","156,45","156,51"
 "74,-107","0,0","2,-8","20,-32","40,-60","49,-76","60,-93","68,-108","69,-114","72,-119","74,-113"
 "191,65","0,0","8,1","51,4","124,22","161,37","166,44","170,52","175,58","181,62","191,65"
 "41,110","0,0","-6,-1","-2,2","11,13","20,28","32,55","38,77","39,84","41,95","42,103"
 "114,56","0,0","7,1","30,3","63,11","86,20","95,25","98,30","106,35","112,41","116,48"
 "-138,105","0,0","-18,12","-48,36","-65,56","-85,70","-102,79","-110,83","-118,87","-125,92","-130,99"
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 "111,9","0,0","4,0","11,-2","19,-2","28,-3","43,-3","59,-2","74,-1","85,2","96,4"
 "166,183","0,0","15,12","49,39","73,67","89,108","114,143","123,160","130,171","137,173","145,175"
 "149,7","0,0","2,-1","10,-1","23,-1","43,2","66,6","82,8","100,8","125,7","142,7"
 "-49,-28","0,0","-3,-1","-10,-6","-21,-15","-27,-20","-30,-22","-33,-23","-37,-23","-41,-24","-44,-25"
 "173,-21","0,0","3,-1","10,-4","47,-10","70,-10","91,-10","111,-11","124,-11","130,-12","137,-16"
 "-75,130","0,0","1,5","1,11","-1,23","-6,39","-17,68","-39,99","-62,120","-70,125","-73,127"
 "-153,46","0,0","-4,0","-11,0","-24,3","-44,9","-69,17","-92,26","-115,34","-135,42","-148,46"
 "-119,6","0,0","-4,0","-10,1","-19,1","-30,2","-47,3","-65,4","-83,4","-101,5","-119,6"
 "131,-9","0,0","8,-1","21,-5","46,-10","75,-13","106,-14","129,-13","139,-9","142,-7","136,-8"
 "-82,-134","0,0","-3,-32","-21,-74","-40,-101","-49,-109","-54,-120","-57,-134","-60,-141","-65,-144","-75,-141"
 "-141,51","0,0","-6,6","-24,15","-57,29","-84,37","-99,43","-106,48","-114,52","-125,53","-131,53"
 "189,-189","0,0","9,-15","27,-41","55,-74","96,-115","132,-144","161,-167","176,-179","179,-181","183,-184"
--- a/data/mouse_data_test.csv
+++ b/data/mouse_data_test.csv
@@ -0,0 +1,20 @@
 "-122,-17","0,0","-3,-3","-9,-7","-19,-11","-32,-16","-51,-19","-68,-20","-84,-20","-102,-19","-114,-17"
 "-115,-153","0,0","-10,-13","-34,-62","-68,-105","-74,-128","-78,-139","-82,-147","-89,-152","-98,-155","-107,-156"
 "-77,193","0,0","-2,9","-12,31","-26,68","-41,106","-54,136","-63,156","-67,170","-71,178","-75,183"
 "-85,-86","0,0","-5,-5","-20,-19","-47,-36","-69,-50","-80,-57","-85,-61","-85,-66","-85,-71","-85,-77"
 "29,-175","0,0","9,-10","25,-51","32,-90","30,-119","30,-138","30,-146","29,-152","33,-165","36,-175"
 "-201,197","0,0","-12,20","-64,77","-113,129","-130,160","-139,175","-152,178","-171,181","-184,183","-191,187"
 "-124,-16","0,0","-6,-1","-20,-4","-42,-8","-67,-13","-90,-16","-109,-19","-116,-21","-119,-20","-122,-18"
 "-123,53","0,0","-16,7","-47,17","-73,22","-92,24","-106,29","-109,32","-112,38","-115,45","-118,49"
 "-146,-125","0,0","0,-12","-12,-40","-33,-58","-64,-84","-95,-115","-116,-123","-126,-125","-134,-122","-141,-122"
 "132,49","0,0","9,5","32,11","56,18","81,30","95,36","102,39","108,41","114,44","123,48"
 "157,164","0,0","9,10","36,35","72,68","107,97","132,121","146,137","149,145","151,152","154,158"
 "-52,48","0,0","-5,5","-14,11","-20,18","-26,24","-29,30","-30,36","-31,41","-32,47","-40,48"
 "-135,-106","0,0","-13,-15","-34,-37","-56,-61","-75,-78","-86,-90","-93,-98","-99,-104","-104,-105","-116,-107"
 "197,99","0,0","3,6","23,21","62,33","111,48","157,63","177,74","181,82","184,86","192,91"
 "129,-194","0,0","7,-7","19,-26","45,-55","77,-85","104,-117","117,-139","118,-149","119,-162","123,-180"
 "167,-56","0,0","14,-6","42,-15","79,-23","115,-31","138,-39","149,-45","152,-48","155,-50","159,-51"
 "132,143","0,0","28,4","84,24","113,57","116,77","118,88","120,98","120,107","122,124","126,135"
 "-113,89","0,0","-11,8","-38,23","-71,41","-81,48","-86,56","-89,62","-92,67","-97,73","-103,79"
 "-46,-8","0,0","-2,1","-4,1","-7,1","-11,0","-16,-2","-24,-4","-30,-5","-35,-7","-39,-8"
 "-40,181","0,0","0,37","-11,92","-12,120","-12,133","-12,148","-14,168","-21,178","-25,184","-32,184"
--- a/mouse_data.csv
+++ b/mouse_data.csv
@@ -1 +0,0 @@
--- a/mouse_data_test.csv
+++ b/mouse_data_test.csv
@@ -1 +0,0 @@
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -0,0 +1,51 @@
 [build-system]
 requires = ["hatchling"]
 build-backend = "hatchling.build"
 [project]
 name = "mouse-control"
 version = "0.1.0"
 description = "Neural-network mouse trajectory generation that mimics human motion."
 readme = "README.md"
 requires-python = ">=3.12"
 keywords = ["mouse", "trajectory", "neural-network", "onnx", "anti-detection"]
 classifiers = [
    "Programming Language :: Python :: 3",
    "Programming Language :: Python :: 3.12",
    "Operating System :: Microsoft :: Windows",
    "Topic :: Scientific/Engineering :: Artificial Intelligence",
 ]
 dependencies = [
    "matplotlib>=3.10",
    "numpy>=2.0",
    "onnxruntime>=1.20",
    "onnxscript>=0.7",
    "pandas>=2.0",
    "scipy>=1.14",
    "torch>=2.5",
 ]
 [project.scripts]
 mouse-collect = "mouse_control.collect:main"
 mouse-train = "mouse_control.train:main"
 mouse-visualize = "mouse_control.visualize:main"
 [dependency-groups]
 dev = ["pytest>=8.0"]
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 addopts = "-ra"
 [tool.hatch.build.targets.wheel]
 packages = ["src/mouse_control"]
 artifacts = ["src/mouse_control/assets/*.onnx"]
 [tool.hatch.build.targets.sdist]
 include = [
    "src/",
    "data/",
    "imgs/",
    "README.md",
    "pyproject.toml",
 ]
--- a/show.py
+++ b/show.py
@@ -1,25 +0,0 @@
 import matplotlib.pyplot as plt
 import torch
 # 您的tensor数据
 points = torch.tensor([
    [   0.4762,    0.9082],
        [ -31.5831,  -39.9315],
        [ -52.1844,  -71.0071],
        [ -60.4211,  -83.4873],
        [ -69.3365, -100.6035],
        [ -80.3101, -110.0435],
        [ -86.7765, -120.0472],
        [ -93.2574, -127.8565],
        [ -98.5992, -135.3300]
 ])
 # 提取x和y坐标
 x = points[:, 0].tolist()
 y = points[:, 1].tolist()
 # 绘制散点图
 plt.scatter(x, y)
 # 显示图形
 plt.show()
--- a/src/mouse_control/init.py
+++ b/src/mouse_control/init.py
@@ -0,0 +1,7 @@
 """Neural-network mouse trajectory generation that mimics human motion."""
 from mouse_control.inference import load_model, predict
 from mouse_control.model import SimpleNet
 __version__ = "0.1.0"
 __all__ = ["SimpleNet", "load_model", "predict", "__version__"]
--- a/src/mouse_control/assets/init.py
+++ b/src/mouse_control/assets/init.py
--- a/src/mouse_control/assets/mouse.onnx
+++ b/src/mouse_control/assets/mouse.onnx
--- a/src/mouse_control/collect.py
+++ b/src/mouse_control/collect.py
@@ -0,0 +1,133 @@
 import argparse
 import csv
 import random
 import tkinter as tk
 from pathlib import Path
 BG_COLOR = "#2b2b2b"
 BALL_RADIUS = 20
 TARGET_OFFSET_RANGE = 200
 N_KEYPOINTS = 10
 class TrajectoryCollector:
    def __init__(self, csv_path: Path, n_trajectories: int = 100) -> None:
        self.csv_path = Path(csv_path)
        self.n_target = n_trajectories
        self.root = tk.Tk()
        self.root.attributes("-fullscreen", True)
        self.root.configure(bg=BG_COLOR)
        screen_w = self.root.winfo_screenwidth()
        screen_h = self.root.winfo_screenheight()
        self.ball1_pos = (screen_w / 2, screen_h / 2)
        self.ball2_pos = self._new_target()
        self.label = tk.Label(
            self.root, text="n: 0", font=("Helvetica", 16),
            bg=BG_COLOR, fg="white",
        )
        self.label.pack()
        self.canvas = tk.Canvas(
            self.root, width=screen_w, height=screen_h,
            bg=BG_COLOR, highlightthickness=0,
        )
        self.canvas.pack()
        self._draw_ball(self.ball1_pos, "red")
        self._draw_ball(self.ball2_pos, "blue", tags="ball2")
        self.recording = False
        self.mouse_path: list[tuple[int, int]] = []
        self.n = 0
        self.canvas.bind("<Motion>", self._on_motion)
        self.canvas.bind("<Button-1>", self._on_click)
        self.root.bind("<Key>", self._on_key)
    def _new_target(self) -> tuple[float, float]:
        cx, cy = self.ball1_pos
        return (
            cx + random.randint(-TARGET_OFFSET_RANGE, TARGET_OFFSET_RANGE),
            cy + random.randint(-TARGET_OFFSET_RANGE, TARGET_OFFSET_RANGE),
        )
    def _draw_ball(self, pos: tuple[float, float], color: str, tags: str = "") -> None:
        x, y = pos
        self.canvas.create_oval(
            x - BALL_RADIUS, y - BALL_RADIUS,
            x + BALL_RADIUS, y + BALL_RADIUS,
            fill=color, tags=tags,
        )
    @staticmethod
    def _hit(event: tk.Event, pos: tuple[float, float]) -> bool:
        return (
            abs(event.x - pos[0]) <= BALL_RADIUS
            and abs(event.y - pos[1]) <= BALL_RADIUS
        )
    def _on_motion(self, event: tk.Event) -> None:
        if self.recording:
            self.mouse_path.append((event.x, event.y))
    def _on_click(self, event: tk.Event) -> None:
        if self._hit(event, self.ball1_pos):
            self.recording = True
            self.mouse_path = [(event.x, event.y)]
        elif self._hit(event, self.ball2_pos):
            if not self.recording or len(self.mouse_path) < N_KEYPOINTS:
                return
            self.recording = False
            self.canvas.delete("ball2")
            self._save_path(self.mouse_path)
            self.n += 1
            if self.n >= self.n_target:
                self.root.destroy()
                return
            self.label.config(text=f"n: {self.n}")
            self.mouse_path = []
            self.ball2_pos = self._new_target()
            self._draw_ball(self.ball2_pos, "blue", tags="ball2")
    def _on_key(self, event: tk.Event) -> None:
        if event.keysym == "Escape":
            self.root.destroy()
    def _save_path(self, path: list[tuple[int, int]]) -> None:
        origin_x, origin_y = path[0]
        x_rel = [px - origin_x for px, _ in path]
        y_rel = [-(py - origin_y) for _, py in path]
        step = max(1, len(path) // N_KEYPOINTS)
        idx = list(range(0, len(path), step))
        kx = [x_rel[i] for i in idx]
        ky = [y_rel[i] for i in idx]
        with open(self.csv_path, "a", newline="") as f:
            writer = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
            row = [f"{kx[-1]},{ky[-1]}"] + [
                f"{kx[i]},{ky[i]}" for i in range(N_KEYPOINTS)
            ]
            writer.writerow(row)
    def run(self) -> None:
        self.root.mainloop()
 def main() -> None:
    parser = argparse.ArgumentParser(
        description="Collect human mouse trajectories via a fullscreen Tk GUI.",
    )
    parser.add_argument(
        "--output", "-o", type=Path, default=Path("mouse_data.csv"),
        help="CSV file to append trajectories to (default: mouse_data.csv).",
    )
    parser.add_argument(
        "--count", "-n", type=int, default=100,
        help="Number of trajectories to collect before exiting (default: 100).",
    )
    args = parser.parse_args()
    TrajectoryCollector(args.output, args.count).run()
 if __name__ == "__main__":
    main()
--- a/src/mouse_control/inference.py
+++ b/src/mouse_control/inference.py
@@ -0,0 +1,28 @@
 from importlib.resources import files
 from pathlib import Path
 import numpy as np
 import onnxruntime as ort
 def load_model(model_path: str | Path | None = None) -> ort.InferenceSession:
    """Load the ONNX mouse-trajectory model.
    Resolution order when ``model_path`` is None:
    1. ``./mouse.onnx`` in the current working directory (freshly trained).
    2. The model bundled inside the installed package.
    """
    if model_path is not None:
        return ort.InferenceSession(str(model_path))
    cwd_model = Path("mouse.onnx")
    if cwd_model.is_file():
        return ort.InferenceSession(str(cwd_model))
    data = files("mouse_control.assets").joinpath("mouse.onnx").read_bytes()
    return ort.InferenceSession(data)
 def predict(session: ort.InferenceSession, dx: float, dy: float) -> np.ndarray:
    """Return a (10, 2) array of trajectory points for the given displacement."""
    inp = np.array([[[float(dx), float(dy)]]], dtype=np.float32)
    input_name = session.get_inputs()[0].name
    return session.run(None, {input_name: inp})[0][0]
--- a/src/mouse_control/model.py
+++ b/src/mouse_control/model.py
@@ -0,0 +1,17 @@
 import torch
 import torch.nn as nn
 class SimpleNet(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.fc1 = nn.Linear(2, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 20)
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = torch.flatten(x, start_dim=1)
        x = nn.functional.relu(self.fc1(x))
        x = nn.functional.relu(self.fc2(x))
        x = self.fc3(x)
        return x.view(-1, 10, 2)
--- a/src/mouse_control/train.py
+++ b/src/mouse_control/train.py
@@ -0,0 +1,113 @@
 import argparse
 from pathlib import Path
 import pandas as pd
 import torch
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import DataLoader, Dataset
 from mouse_control.model import SimpleNet
 class TrajectoryDataset(Dataset):
    def __init__(self, dx_dy: torch.Tensor, labels: torch.Tensor) -> None:
        self.dx_dy = dx_dy
        self.labels = labels
    def __len__(self) -> int:
        return len(self.dx_dy)
    def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        return self.dx_dy[idx], self.labels[idx]
 def _load_csv(csv_path: Path) -> tuple[torch.Tensor, torch.Tensor]:
    df = pd.read_csv(csv_path, header=None)
    inputs = df.iloc[:, 0].apply(lambda x: list(map(int, x.split(","))))
    labels = df.apply(
        lambda row: [list(map(int, row[i].split(","))) for i in range(1, 11)],
        axis=1,
    )
    inputs_t = torch.Tensor(inputs.tolist()).unsqueeze(1)
    labels_t = torch.Tensor(labels.tolist())
    return inputs_t, labels_t
 def train(
    train_csv: Path,
    test_csv: Path,
    output: Path,
    epochs: int = 1000,
    batch_size: int = 64,
    lr: float = 1e-3,
 ) -> None:
    train_x, train_y = _load_csv(train_csv)
    test_x, test_y = _load_csv(test_csv)
    train_loader = DataLoader(
        TrajectoryDataset(train_x, train_y), batch_size=batch_size, shuffle=True,
    )
    test_loader = DataLoader(
        TrajectoryDataset(test_x, test_y), batch_size=batch_size, shuffle=False,
    )
    model = SimpleNet()
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=lr)
    scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.9)
    model.train()
    for epoch in range(epochs):
        epoch_loss = 0.0
        for batch_x, batch_y in train_loader:
            optimizer.zero_grad()
            output_t = model(batch_x)
            loss = criterion(output_t, batch_y)
            loss.backward()
            optimizer.step()
            scheduler.step()
            epoch_loss += loss.item()
        if (epoch + 1) % 50 == 0 or epoch == 0:
            print(f"epoch {epoch + 1:>4}/{epochs}  train_loss={epoch_loss / len(train_loader):.4f}")
    model.eval()
    with torch.no_grad():
        total = 0.0
        last_batch = None
        for batch_x, batch_y in test_loader:
            pred = model(batch_x)
            total += criterion(pred, batch_y).item()
            last_batch = (batch_x, pred)
        print(f"test_loss={total / len(test_loader):.4f}")
        if last_batch is not None:
            sample_x, sample_pred = last_batch
            print("sample input :", sample_x[0].tolist())
            print("sample output:", sample_pred[0].tolist())
    dummy = torch.randn(1, 1, 2)
    torch.onnx.export(
        model, dummy, str(output),
        input_names=["input"], output_names=["output"],
        external_data=False,
    )
    print(f"exported -> {output}")
 def main() -> None:
    parser = argparse.ArgumentParser(description="Train the mouse trajectory model.")
    parser.add_argument("--train-csv", type=Path, default=Path("data/mouse_data.csv"))
    parser.add_argument("--test-csv", type=Path, default=Path("data/mouse_data_test.csv"))
    parser.add_argument("--output", "-o", type=Path, default=Path("mouse.onnx"))
    parser.add_argument("--epochs", type=int, default=1000)
    parser.add_argument("--batch-size", type=int, default=64)
    parser.add_argument("--lr", type=float, default=1e-3)
    args = parser.parse_args()
    train(
        args.train_csv, args.test_csv, args.output,
        epochs=args.epochs, batch_size=args.batch_size, lr=args.lr,
    )
 if __name__ == "__main__":
    main()
--- a/src/mouse_control/visualize.py
+++ b/src/mouse_control/visualize.py
@@ -0,0 +1,104 @@
 import argparse
 from pathlib import Path
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.interpolate import CubicSpline
 from mouse_control.inference import load_model, predict
 def _interpolate(xs: np.ndarray, ys: np.ndarray, n_points: int) -> tuple[np.ndarray, np.ndarray]:
    chord = np.cumsum(np.sqrt(np.diff(xs) ** 2 + np.diff(ys) ** 2))
    t = np.concatenate([[0.0], chord])
    if t[-1] == 0:
        return xs, ys
    cs_x = CubicSpline(t, xs)
    cs_y = CubicSpline(t, ys)
    t_dense = np.linspace(0, t[-1], n_points)
    return cs_x(t_dense), cs_y(t_dense)
 def visualize(
    model_path: Path | None = None,
    n_trajectories: int = 10,
    n_interp: int = 80,
    seed: int | None = None,
    save_path: Path | None = Path("trajectories.png"),
    show: bool = True,
 ) -> None:
    rng = np.random.default_rng(seed)
    targets = rng.integers(low=-200, high=201, size=(n_trajectories, 2))
    session = load_model(model_path)
    cmap = plt.get_cmap("tab10")
    fig, ax = plt.subplots(figsize=(10, 8))
    for i, (dx, dy) in enumerate(targets):
        pred = predict(session, dx, dy)  # (10, 2)
        xs = np.concatenate([[0.0], pred[:, 0]])
        ys = np.concatenate([[0.0], pred[:, 1]])
        xs_dense, ys_dense = _interpolate(xs, ys, n_interp)
        color = cmap(i % cmap.N)
        ax.plot(xs_dense, ys_dense, "-", color=color, alpha=0.85, linewidth=1.6)
        ax.scatter(xs_dense, ys_dense, color=color, s=6, alpha=0.5, zorder=2)
        ax.scatter(
            pred[:, 0], pred[:, 1], color=color, s=35, zorder=3,
            edgecolors="white", linewidths=0.6,
        )
        ax.scatter(
            [dx], [dy], color=color, marker="x", s=140, linewidths=2.5,
            zorder=4, label=f"target ({dx}, {dy})",
        )
    ax.scatter([0], [0], color="black", s=90, zorder=5, label="start (0, 0)")
    ax.axhline(0, color="gray", linewidth=0.5, alpha=0.5)
    ax.axvline(0, color="gray", linewidth=0.5, alpha=0.5)
    ax.set_aspect("equal")
    ax.set_title(
        f"Mouse trajectories — 10 model points + cubic-spline interpolation ({n_interp} pts)",
    )
    ax.set_xlabel("dx")
    ax.set_ylabel("dy")
    ax.legend(loc="best", fontsize=8, ncol=2)
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    if save_path is not None:
        plt.savefig(save_path, dpi=120)
        print(f"saved -> {save_path}")
    if show:
        plt.show()
    plt.close(fig)
 def main() -> None:
    parser = argparse.ArgumentParser(
        description="Render predicted mouse trajectories for random targets.",
    )
    parser.add_argument(
        "--model", type=Path, default=None,
        help="Path to ONNX model (defaults to ./mouse.onnx or bundled model).",
    )
    parser.add_argument("--n-trajectories", "-n", type=int, default=10)
    parser.add_argument("--n-interp", type=int, default=80)
    parser.add_argument("--seed", type=int, default=None)
    parser.add_argument("--save", type=Path, default=Path("trajectories.png"))
    parser.add_argument(
        "--no-show", action="store_true",
        help="Skip opening the matplotlib window (still writes --save).",
    )
    args = parser.parse_args()
    visualize(
        model_path=args.model,
        n_trajectories=args.n_trajectories,
        n_interp=args.n_interp,
        seed=args.seed,
        save_path=args.save,
        show=not args.no_show,
    )
 if __name__ == "__main__":
    main()
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -0,0 +1,32 @@
 from __future__ import annotations
 from pathlib import Path
 import pytest
 REPO_ROOT = Path(__file__).resolve().parent.parent
 DATA_DIR = REPO_ROOT / "data"
@pytest.fixture(scope="session")
 def train_csv() -> Path:
    path = DATA_DIR / "mouse_data.csv"
    if not path.is_file():
        pytest.skip(f"missing training csv: {path}")
    return path
@pytest.fixture(scope="session")
 def test_csv() -> Path:
    path = DATA_DIR / "mouse_data_test.csv"
    if not path.is_file():
        pytest.skip(f"missing test csv: {path}")
    return path
@pytest.fixture(scope="session")
 def bundled_session():
    """Shared inference session for the bundled model (loading is slow)."""
    from mouse_control import load_model
    return load_model()
--- a/tests/test_cli.py
+++ b/tests/test_cli.py
@@ -0,0 +1,49 @@
 from __future__ import annotations
 import subprocess
 import sys
 from pathlib import Path
 import pytest
 CLI_COMMANDS = ["mouse-collect", "mouse-train", "mouse-visualize"]
@pytest.mark.parametrize("command", CLI_COMMANDS)
 def test_cli_help(command: str) -> None:
    """Every CLI must respond to --help with exit 0 and an argparse usage line."""
    result = subprocess.run(
        [command, "--help"], capture_output=True, text=True, check=False,
    )
    assert result.returncode == 0, result.stderr
    assert "usage:" in result.stdout
 def test_visualize_no_show_produces_png(tmp_path: Path) -> None:
    save_path = tmp_path / "out.png"
    result = subprocess.run(
        [
            "mouse-visualize", "--no-show", "--seed", "1",
            "--n-trajectories", "3", "--n-interp", "20",
            "--save", str(save_path),
        ],
        capture_output=True, text=True, check=False,
    )
    assert result.returncode == 0, result.stderr
    assert save_path.exists()
    assert save_path.stat().st_size > 1000
 def test_visualize_module_invocation(tmp_path: Path) -> None:
    """The module is also runnable via `python -m mouse_control.visualize`."""
    save_path = tmp_path / "out.png"
    result = subprocess.run(
        [
            sys.executable, "-m", "mouse_control.visualize",
            "--no-show", "--seed", "2", "--n-trajectories", "2",
            "--save", str(save_path),
        ],
        capture_output=True, text=True, check=False,
    )
    assert result.returncode == 0, result.stderr
    assert save_path.exists()
--- a/tests/test_inference.py
+++ b/tests/test_inference.py
@@ -0,0 +1,95 @@
 from __future__ import annotations
 import shutil
 from importlib.resources import as_file, files
 from pathlib import Path
 import numpy as np
 import onnxruntime as ort
 import pytest
 from mouse_control import load_model, predict
 def _bundled_path() -> Path:
    """Resolve the bundled mouse.onnx to a stable filesystem path for tests."""
    with as_file(files("mouse_control.assets").joinpath("mouse.onnx")) as p:
        return Path(p)
 def test_load_model_returns_inference_session(bundled_session) -> None:
    assert isinstance(bundled_session, ort.InferenceSession)
 def test_bundled_input_output_names(bundled_session) -> None:
    assert bundled_session.get_inputs()[0].name == "input"
    assert bundled_session.get_outputs()[0].name == "output"
 def test_load_model_explicit_path() -> None:
    session = load_model(_bundled_path())
    assert isinstance(session, ort.InferenceSession)
 def test_load_model_missing_path_raises() -> None:
    with pytest.raises(Exception):
        load_model("/no/such/path.onnx")
 def test_load_model_cwd_override(tmp_path, monkeypatch) -> None:
    """When ./mouse.onnx exists, it should take precedence over the bundle."""
    monkeypatch.chdir(tmp_path)
    shutil.copy(_bundled_path(), tmp_path / "mouse.onnx")
    session = load_model()  # implicit
    assert isinstance(session, ort.InferenceSession)
    # Must produce the same output as the bundle since the file is identical.
    pkg_session = load_model(_bundled_path())
    a = predict(session, 75, -40)
    b = predict(pkg_session, 75, -40)
    np.testing.assert_array_equal(a, b)
 def test_load_model_falls_back_to_bundled(tmp_path, monkeypatch) -> None:
    """With no ./mouse.onnx, load_model() must succeed via the bundle."""
    monkeypatch.chdir(tmp_path)
    assert not (tmp_path / "mouse.onnx").exists()
    session = load_model()
    assert isinstance(session, ort.InferenceSession)
 def test_predict_shape_and_dtype(bundled_session) -> None:
    out = predict(bundled_session, 100.0, 200.0)
    assert out.shape == (10, 2)
    assert out.dtype == np.float32
 def test_predict_is_deterministic(bundled_session) -> None:
    a = predict(bundled_session, 100, 100)
    b = predict(bundled_session, 100, 100)
    np.testing.assert_array_equal(a, b)
 def test_predict_int_and_float_args_equivalent(bundled_session) -> None:
    a = predict(bundled_session, 50, 50)
    b = predict(bundled_session, 50.0, 50.0)
    np.testing.assert_array_equal(a, b)
@pytest.mark.parametrize(
    "dx,dy",
    [
        (150, 0), (-150, 0), (0, 150), (0, -150),
        (100, 100), (-100, 100), (100, -100), (-100, -100),
    ],
 )
 def test_predict_endpoint_near_target(bundled_session, dx: int, dy: int) -> None:
    """Trained model's endpoint should fall within ~50px of the target."""
    end = predict(bundled_session, dx, dy)[-1]
    err = float(np.linalg.norm(end - np.array([dx, dy])))
    assert err < 50, f"target ({dx},{dy}) endpoint err={err:.1f}"
 def test_predict_zero_displacement_stays_near_origin(bundled_session) -> None:
    end = predict(bundled_session, 0, 0)[-1]
    assert abs(end[0]) < 20
    assert abs(end[1]) < 20
--- a/tests/test_model.py
+++ b/tests/test_model.py
@@ -0,0 +1,74 @@
 from __future__ import annotations
 import pytest
 import torch
 from mouse_control import SimpleNet
@pytest.fixture
 def model() -> SimpleNet:
    return SimpleNet()
@pytest.mark.parametrize("batch_size", [1, 4, 16, 64])
 def test_forward_with_unsqueezed_input(model: SimpleNet, batch_size: int) -> None:
    out = model(torch.randn(batch_size, 1, 2))
    assert out.shape == (batch_size, 10, 2)
@pytest.mark.parametrize("batch_size", [1, 8, 32])
 def test_forward_with_flat_input(model: SimpleNet, batch_size: int) -> None:
    """SimpleNet's first layer flattens, so (N, 2) is also valid."""
    out = model(torch.randn(batch_size, 2))
    assert out.shape == (batch_size, 10, 2)
 def test_layers_have_expected_dimensions(model: SimpleNet) -> None:
    assert model.fc1.in_features == 2
    assert model.fc1.out_features == 64
    assert model.fc2.in_features == 64
    assert model.fc2.out_features == 32
    assert model.fc3.in_features == 32
    assert model.fc3.out_features == 20
 def test_model_has_trainable_parameters(model: SimpleNet) -> None:
    params = list(model.parameters())
    assert params, "SimpleNet should expose parameters"
    assert all(p.requires_grad for p in params)
 def test_forward_is_differentiable(model: SimpleNet) -> None:
    inp = torch.randn(2, 1, 2, requires_grad=True)
    out = model(inp)
    out.sum().backward()
    assert inp.grad is not None
    assert inp.grad.shape == inp.shape
 def test_onnx_export_round_trips(tmp_path, model: SimpleNet) -> None:
    """Trained or not, the model architecture must round-trip via ONNX."""
    import numpy as np
    import onnxruntime as ort
    onnx_path = tmp_path / "round_trip.onnx"
    dummy = torch.randn(1, 1, 2)
    model.eval()
    torch.onnx.export(
        model, dummy, str(onnx_path),
        input_names=["input"], output_names=["output"],
        external_data=False,
    )
    assert onnx_path.exists()
    assert not onnx_path.with_suffix(onnx_path.suffix + ".data").exists(), \
        "external_data=False must produce a single-file ONNX"
    session = ort.InferenceSession(str(onnx_path))
    inp = np.array([[[42.0, -17.0]]], dtype=np.float32)
    out = session.run(None, {"input": inp})[0]
    assert out.shape == (1, 10, 2)
    with torch.no_grad():
        torch_out = model(torch.from_numpy(inp)).numpy()
    np.testing.assert_allclose(out, torch_out, atol=1e-4)
--- a/tests/test_train.py
+++ b/tests/test_train.py
@@ -0,0 +1,103 @@
 from __future__ import annotations
 import csv
 from pathlib import Path
 import numpy as np
 import onnxruntime as ort
 import pytest
 import torch
 from mouse_control.train import TrajectoryDataset, _load_csv, train
 def _write_csv(path: Path, rows: list[list[tuple[int, int]]]) -> None:
    """Write rows in the project's quirky single-quoted-pair format."""
    with open(path, "w", newline="") as f:
        writer = csv.writer(f, quoting=csv.QUOTE_MINIMAL)
        for row in rows:
            # First column is the target (last keypoint), then 10 keypoints.
            target_x, target_y = row[-1]
            cells = [f"{target_x},{target_y}"] + [f"{x},{y}" for x, y in row]
            writer.writerow(cells)
@pytest.fixture
 def synthetic_csv(tmp_path: Path) -> Path:
    """Tiny linear trajectories from origin -> target."""
    path = tmp_path / "synth.csv"
    rows = []
    rng = np.random.default_rng(0)
    for _ in range(20):
        tx, ty = int(rng.integers(-150, 151)), int(rng.integers(-150, 151))
        keypoints = [
            (int(round(tx * i / 9)), int(round(ty * i / 9))) for i in range(10)
        ]
        rows.append(keypoints)
    _write_csv(path, rows)
    return path
 def test_load_csv_shapes(synthetic_csv: Path) -> None:
    inputs, labels = _load_csv(synthetic_csv)
    assert inputs.shape == (20, 1, 2)
    assert labels.shape == (20, 10, 2)
    assert inputs.dtype == torch.float32
    assert labels.dtype == torch.float32
 def test_load_csv_round_trip(synthetic_csv: Path) -> None:
    """The target column (input) must equal the last keypoint of the labels."""
    inputs, labels = _load_csv(synthetic_csv)
    last_keypoint = labels[:, -1, :]
    np.testing.assert_array_equal(inputs.squeeze(1).numpy(), last_keypoint.numpy())
 def test_dataset_indexing(synthetic_csv: Path) -> None:
    inputs, labels = _load_csv(synthetic_csv)
    ds = TrajectoryDataset(inputs, labels)
    assert len(ds) == 20
    x, y = ds[0]
    assert x.shape == (1, 2)
    assert y.shape == (10, 2)
 def test_load_csv_missing_file_raises(tmp_path: Path) -> None:
    with pytest.raises(FileNotFoundError):
        _load_csv(tmp_path / "missing.csv")
 def test_train_end_to_end_produces_single_file_onnx(
    synthetic_csv: Path, tmp_path: Path,
 ) -> None:
    """A quick training run must export a valid, self-contained ONNX model."""
    output = tmp_path / "trained.onnx"
    train(
        train_csv=synthetic_csv,
        test_csv=synthetic_csv,
        output=output,
        epochs=3,
        batch_size=8,
    )
    assert output.exists()
    assert output.stat().st_size > 1000
    # external_data=False -> no .data sidecar
    assert not output.with_suffix(output.suffix + ".data").exists()
    session = ort.InferenceSession(str(output))
    inp = np.array([[[100.0, 50.0]]], dtype=np.float32)
    out = session.run(None, {"input": inp})[0]
    assert out.shape == (1, 10, 2)
 def test_train_with_real_data(train_csv: Path, test_csv: Path, tmp_path: Path) -> None:
    """Smoke test against the project's actual data."""
    output = tmp_path / "real.onnx"
    train(
        train_csv=train_csv,
        test_csv=test_csv,
        output=output,
        epochs=2,
        batch_size=32,
    )
    assert output.exists()
--- a/train.py
+++ b/train.py
@@ -1,97 +0,0 @@
 import numpy as np
 import pandas as pd
 import torch
 from torch.utils.data import Dataset, DataLoader
 import torch.nn as nn
 import torch.optim as optim
 import onnxruntime
 import onnx
 from onnxsim import simplify
 # 读取CSV文件
 train_csv_path = 'mouse_data.csv'  # 替换为你的CSV文件路径
 train_csv = pd.read_csv(train_csv_path, header=None)
 test_csv_path = 'mouse_data_test.csv'  # 替换为你的CSV文件路径
 test_csv = pd.read_csv(test_csv_path, header=None)
 # 提取dx, dy和标签
 dx_dy_train = train_csv.iloc[:, 0].apply(lambda x: list(map(int, x.split(','))))
 dx_dy_labels_train = train_csv.apply(lambda row: [list(map(int, row[i].split(','))) for i in range(1,10)], axis=1)
 dx_dy_test = test_csv.iloc[:, 0].apply(lambda x: list(map(int, x.split(','))))
 dx_dy_labels_test = test_csv.apply(lambda row: [list(map(int, row[i].split(','))) for i in range(1,10)], axis=1)
 # 转换为PyTorch Tensor
 dx_dy_train_tensor = torch.Tensor(dx_dy_train.tolist())
 dx_dy_train_tensor = dx_dy_train_tensor.unsqueeze(1)
 labels_train_tensor = torch.Tensor(dx_dy_labels_train.tolist())
 dx_dy_test_tensor = torch.Tensor(dx_dy_test.tolist())
 dx_dy_test_tensor = dx_dy_test_tensor.unsqueeze(1)
 labels_test_tensor = torch.Tensor(dx_dy_labels_test.tolist())
 # 创建自定义Dataset
 class CustomDataset(Dataset):
    def __init__(self, dx_dy, labels):
        self.dx_dy = dx_dy
        self.labels = labels
    def __len__(self):
        return len(self.dx_dy)
    def __getitem__(self, idx):
        return self.dx_dy[idx], self.labels[idx]
 # 创建Dataset和DataLoader
 train_dataset = CustomDataset(dx_dy_train_tensor, labels_train_tensor)
 test_dataset = CustomDataset(dx_dy_test_tensor, labels_test_tensor)
 batch_size = 64
 train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 test_dataloader = DataLoader(test_dataset, batch_size=batch_size, shuffle=True)
 # 定义神经网络模型
 class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(2, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 18)
    def forward(self, input_data):
        x = torch.flatten(input_data, start_dim=1)
        x = self.fc1(x)
        x = nn.ReLU()(x)
        x = self.fc2(x)
        x = nn.ReLU()(x)
        x = self.fc3(x)
        return x.view(-1, 9, 2)
 # 初始化模型、损失函数和优化器
 model = SimpleNet()
 criterion = nn.MSELoss()
 optimizer = optim.Adam(model.parameters(), lr=0.001)
 scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=50, gamma=0.9)
 # 训练模型
 epochs = 1000
 for epoch in range(epochs):
    for batch_dx_dy, batch_labels in train_dataloader:
        optimizer.zero_grad()
        output = model(batch_dx_dy)
        loss = criterion(output, batch_labels)
        print(loss)
        loss.backward()
        optimizer.step()
        scheduler.step()
 print('test')
 for idx, data in enumerate(test_dataloader):
        batch_dx_dy, batch_labels = data
        output = model(batch_dx_dy)
        loss = criterion(output, batch_labels)
        print(loss)
        if idx == len(test_dataloader)-1:
            print(batch_dx_dy[0])
            print(output[0])
 model.eval()
 onnx_name = 'mouse.onnx'
 dummy = torch.randn(1, 1, 2)
 torch.onnx.export(model, dummy, onnx_name,verbose=True, input_names=['input'], output_names=['output'])
--- a/uv.lock
+++ b/uv.lock
Author	SHA1	Message	Date
Huang Qi	c8fa5db7d3	Add pytest suite with 40 unit and integration tests Coverage: - test_model: SimpleNet forward (parametrized over batch sizes and both unsqueezed and flat input shapes), layer dimensions, differentiability, and ONNX round-trip - test_inference: load_model resolution order (bundled, cwd override, explicit path, missing path), and predict shape/dtype/determinism plus endpoint sanity across 8 cardinal/diagonal targets - test_train: _load_csv parsing, TrajectoryDataset indexing, full train() pipeline producing a single-file ONNX, plus a smoke test against the real data shipped under data/ - test_cli: --help for the three console scripts and a real run of mouse-visualize via both the entry point and python -m Wire up pytest via dependency-groups and tool.pytest.ini_options.	2026-05-12 00:33:15 +08:00
Huang Qi	de602365e9	Refactor into standard Python package with uv - Move all code into src/mouse_control/ following the src-layout convention, with model/collect/train/visualize/inference as separate modules and SimpleNet extracted into model.py - Add hatchling-based pyproject.toml with three CLI entry points (mouse-collect, mouse-train, mouse-visualize) and bundle the trained ONNX model as a package resource under assets/ - Move training data to data/, delete superseded show.py, remove dead imports (numpy, onnxruntime, onnx, onnxsim) and the unused visualize_path() helper - Fix crashes in the collector: guard against destroyed-widget access after the n==100 destroy(), and skip save_to_csv when recording is off or the path is too short to derive 10 keypoints - Switch ONNX export to external_data=False so the model ships as a single self-contained 19KB file - Bytes-load the bundled model via importlib.resources so packaging remains correct under zip-safe distributions - Rewrite README around the new layout, commands, and public API	2026-05-12 00:30:08 +08:00
suixin1424	2c482150d4	Update README.md	2025-01-21 14:17:27 +08:00
suixin1424	d62373e810	Update README.md	2025-01-19 18:49:23 +08:00
suixin1424	1c8dbe1ccd	Update README.md	2025-01-19 18:28:14 +08:00
suixin1424	5043f8c674	Update README.md	2024-02-10 15:31:18 +08:00
suixin1424	7ddb7969cc	Update README.md	2024-02-02 17:43:44 +08:00
suixin1424	a1a6c36502	fix bug	2024-02-02 17:43:09 +08:00
suixin1424	902b2a9386	Update README.md	2024-01-28 20:44:17 +08:00
suixin1424	e0bc413150	Update README.md	2024-01-28 20:43:44 +08:00