commit 2df86e0ef01ae571c80a86c1e1966b1b976adf59 Author: kaiza_hikaru Date: Thu May 29 23:35:07 2025 +0800 first commit diff --git a/main.py b/main.py new file mode 100644 index 0000000..d230110 --- /dev/null +++ b/main.py @@ -0,0 +1,110 @@ +import numpy as np +import pandas as pd +from pathlib import Path +from scipy.interpolate import interp1d, CubicSpline, UnivariateSpline, PchipInterpolator, Akima1DInterpolator +from statsmodels.tsa.stattools import acf +from scipy.signal import find_peaks +import matplotlib.pyplot as plt + +# 1. 读取 CSV 文件并提取指定列 +def read_csv_column(file_path, column_name): + if isinstance(file_path, str): + file_path = Path(file_path) + df = pd.read_csv(file_path) + if column_name not in df.columns: + raise ValueError(f"Column '{column_name}' not found in CSV file.") + return df[column_name].values + +# 2. 计算自相关函数（ACF） +def compute_acf(data): + n = len(data) + nlags = n // 2 + acf_values = acf(data, nlags=nlags, fft=True) + return acf_values[:nlags] # 截断为原长度的一半 + +# 3. 插值函数 +""" +- 'linear' : 线性插值 +- 'cubic' : 三次样条插值 (CubicSpline) +- 'spline' : 样条插值 (UnivariateSpline) +- 'pchip' : 分段三次 Hermite 插值 (PchipInterpolator) +- 'akima' : Akima 插值 (Akima1DInterpolator) +- 'quadratic' : 二次插值 +""" +def interpolate_data(data, method='cubic', num_points=100, **kwargs): + x = np.arange(len(data)) + + if method == 'linear': + interp_func = interp1d(x, data, kind='linear', fill_value="extrapolate", **kwargs) + elif method == 'cubic': + interp_func = CubicSpline(x, data, bc_type='natural', **kwargs) + elif method == 'spline': + # UnivariateSpline 默认使用三次样条，但可以指定 degree=s + interp_func = UnivariateSpline(x, data, s=kwargs.get('s', None), k=kwargs.get('k', 3), **kwargs) + elif method == 'pchip': + interp_func = PchipInterpolator(x, data, **kwargs) + elif method == 'akima': + interp_func = Akima1DInterpolator(x, data, **kwargs) + elif method == 'quadratic': + interp_func = interp1d(x, data, kind='quadratic', fill_value="extrapolate", **kwargs) + else: + raise ValueError(f"Unsupported interpolation method: {method}. " + f"Supported methods: linear, cubic, spline, pchip, akima, quadratic, quintic") + + x_new = np.linspace(0, len(data) - 1, num_points) + y_new = interp_func(x_new) + return y_new + +# 4. 查找极大值点 +def find_maxima(data): + peaks, _ = find_peaks(data, height=np.mean(data)) + return peaks.tolist(), data[peaks].tolist() + +# 5. 主流程 +if __name__ == "__main__": + # 示例文件路径和列名 + file_path = Path("尺寸标定板/ver-1.csv") # 假设当前目录下有 data.csv + column_name = "Gray_Value" + + # Step 1: 读取 CSV 文件 + data = read_csv_column(file_path, column_name) + print(len(data)) + + + # Step 2: 计算自相关函数 + acf_data = compute_acf(data) + print(len(acf_data)) + + # Step 3: 插值自相关函数 + interpolated_acf = interpolate_data(acf_data, method='cubic', num_points=len(acf_data) * 1000) + print(len(interpolated_acf)) + + # Step 4: 查找极大值 + peaks_indices, peaks_values = find_maxima(interpolated_acf) + + # Step 5: 可视化 + plt.figure(figsize=(12, 10)) + + # 原始数据 + plt.subplot(3, 1, 1) + plt.plot(data, label='Original Data') + plt.title('Original Data') + plt.grid(True) + + # 自相关函数 + plt.subplot(3, 1, 2) + plt.plot(acf_data, label='ACF', marker='o') + plt.title('Autocorrelation Function (Truncated)') + plt.grid(True) + + # 插值后的自相关函数及极大值 + plt.subplot(3, 1, 3) + x_interpolated = np.linspace(0, len(acf_data) - 1, len(interpolated_acf)) + print(x_interpolated[peaks_indices]) + plt.plot(x_interpolated, interpolated_acf, label='Interpolated ACF', color='tab:orange') + plt.plot(x_interpolated[peaks_indices], peaks_values, 'rx', markersize=10, label='Maxima') + plt.title('Interpolated ACF with Maxima') + plt.grid(True) + plt.tight_layout() + plt.legend() + plt.show() \ No newline at end of file diff --git a/尺寸标定板/hor-1.csv b/尺寸标定板/hor-1.csv new file mode 100644 index 0000000..0012163 --- /dev/null +++ b/尺寸标定板/hor-1.csv @@ -0,0 +1,770 @@ +Distance_(pixels),Gray_Value +0,0 +1,0 +2,0 +3,0 +4,0 +5,0 +6,0 +7,0 +8,0 +9,0 +10,0 +11,0 +12,0 +13,255 +14,255 +15,255 +16,255 +17,255 +18,255 +19,255 +20,255 +21,255 +22,255 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