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import numpy as np
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import pandas as pd
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import calc_way
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from scipy import stats
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import calc_slope_line
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import matplotlib.pyplot as plt
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import model
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import os
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# 数据截断线
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model = model.Model()
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limit_slope = model.limit_slope
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limit_intercept = model.limit_intercept
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def grid_downsample(points, cell_size=15):
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"""网格化降采样，保持空间结构"""
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df = pd.DataFrame(points, columns=['x', 'y'])
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df['x_grid'] = (df['x'] // cell_size) * cell_size
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df['y_grid'] = (df['y'] // cell_size) * cell_size
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sampled = df.groupby(['x_grid', 'y_grid']).first().reset_index()
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return sampled[['x', 'y']].values
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"""
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读取yolo网络识别路沿的坐标数据,筛选出目标区域的数据点，并将路沿上下侧数据分离
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参数：保存数据的txt文件路径
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返回值：在目标区域内的下侧数据点坐标x_bot、y_bot，上侧数据点坐标x_top,y_top
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"""
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def get_data(txt_name):
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# 加载数据
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data = np.loadtxt(txt_name)
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int_data = data.astype(int)
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# 网格化降采样
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grid_sampled = grid_downsample(int_data, cell_size=20)
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# 数据截断
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x = []
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y = []
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for i in range(grid_sampled.shape[0]):
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grid_sampled[i][1] = 960 - int(grid_sampled[i][1])
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if limit_slope * int(grid_sampled[i][0]) + limit_intercept - int(grid_sampled[i][1]) < 0:
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continue
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x.append(int(grid_sampled[i][0]))
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y.append(int(grid_sampled[i][1]))
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x = np.array(x)
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y = np.array(y)
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# 原始数据粗分类
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slope, intercept, r_2 = calc_slope_line.linear_regression(x, y)
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y_pred = slope * x + intercept
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x_bot = []
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y_bot = []
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x_top = []
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y_top = []
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for i in range(len(x)):
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if x[i] * slope + intercept - y[i] > 0:
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x_bot.append(x[i])
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y_bot.append(y[i])
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else:
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x_top.append(x[i])
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y_top.append(y[i])
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x_bot = np.array(x_bot)
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y_bot = np.array(y_bot)
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x_top = np.array(x_top)
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y_top = np.array(y_top)
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slope_bot, intercept_bot, r2_bot = calc_slope_line.linear_regression(x_bot, y_bot)
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slope_top, intercept_top, r2_top = calc_slope_line.linear_regression(x_top, y_top)
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print(f"未清洗数据拟合上下沿：r2_bot = {r2_bot},r2_top = {r2_top}")
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# 第一次数据清洗，消除误识别点
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# 计算残差
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residuals = y - y_pred
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# 计算残差的标准差 (MSE的平方根)
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residual_std = np.sqrt(np.sum(residuals ** 2) / (len(x) - 2))
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standardized_residuals = residuals / residual_std
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# 设置阈值 (常用 2.5-3.0 个标准差)
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threshold = 2.0
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# 标记异常点
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outlier_mask = np.abs(standardized_residuals) > threshold
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outliers_x = x[outlier_mask]
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outliers_y = y[outlier_mask]
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print(f"第一次数据清洗发现 {np.sum(outlier_mask)} 个异常点:")
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for i, (x_val, y_val) in enumerate(zip(outliers_x, outliers_y)):
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print(f"点 {i + 1}: x={x_val}, y={y_val}, 残差={residuals[outlier_mask][i]:.2f}")
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# 剔除异常点
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clean_x = x[~outlier_mask]
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clean_y = y[~outlier_mask]
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clean_slope, clean_intercept, clean_r_2 = calc_slope_line.linear_regression(clean_x, clean_y)
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print(f"清洗数据后整体拟合参数r_2 = {r_2}")
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# 第一次数据清洗后的数据再分类
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x_bot_clean = []
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y_bot_clean = []
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x_top_clean = []
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y_top_clean = []
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for i in range(len(clean_x)):
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if clean_x[i] * clean_slope + clean_intercept - clean_y[i] > 0:
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x_bot_clean.append(clean_x[i])
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y_bot_clean.append(clean_y[i])
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else:
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x_top_clean.append(clean_x[i])
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y_top_clean.append(clean_y[i])
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x_bot_clean = np.array(x_bot_clean)
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y_bot_clean = np.array(y_bot_clean)
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x_top_clean = np.array(x_top_clean)
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y_top_clean = np.array(y_top_clean)
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# 第二次数据清洗，消除误分类点
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clean_slope_bot, clean_intercept_bot, clean_r2_bot = calc_slope_line.linear_regression(x_bot_clean, y_bot_clean)
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clean_slope_top, clean_intercept_top, clean_r2_top = calc_slope_line.linear_regression(x_top_clean, y_top_clean)
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print(f"清洗数据后上下沿拟合参数clean_r2_bot = {clean_r2_bot},clean_r2_top = {clean_r2_top}")
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# 绘制拟合线
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y_bot_pred = clean_slope_bot * x_bot_clean + clean_intercept_bot
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y_top_pred = clean_slope_top * x_top_clean + clean_intercept_top
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# 计算残差
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residuals_bot = y_bot_clean - y_bot_pred
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residuals_top = y_top_clean - y_top_pred
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# 计算残差的标准差 (MSE的平方根)
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residual_std_bot = np.sqrt(np.sum(residuals_bot ** 2) / (len(x_bot_clean) - 2))
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residual_std_top = np.sqrt(np.sum(residuals_top ** 2) / (len(x_top_clean) - 2))
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# 计算标准化残差 (Z-score)
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standardized_residuals_bot = residuals_bot / residual_std_bot
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standardized_residuals_top = residuals_top / residual_std_top
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# 设置阈值 (常用 2.5-3.0 个标准差)
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threshold = 1.5
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# 标记异常点
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outlier_mask_bot = np.abs(standardized_residuals_bot) > threshold
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outlier_mask_top = np.abs(standardized_residuals_top) > threshold
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outliers_x_bot = x_bot_clean[outlier_mask_bot]
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outliers_y_bot = y_bot_clean[outlier_mask_bot]
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outliers_x_top = x_top_clean[outlier_mask_top]
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outliers_y_top = y_top_clean[outlier_mask_top]
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print(f"第二次数据清洗下沿发现 {np.sum(outlier_mask_bot)} 个异常点:")
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# for i, (x_val, y_val) in enumerate(zip(outliers_x_bot, outliers_y_bot)):
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# print(f"点 {i + 1}: x={x_val}, y={y_val}, 残差={residuals_bot[outlier_mask_bot][i]:.2f}")
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print(f"第二次数据清洗上沿发现 {np.sum(outlier_mask_top)} 个异常点:")
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# for i, (x_val, y_val) in enumerate(zip(outliers_x_top, outliers_y_top)):
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# print(f"点 {i + 1}: x={x_val}, y={y_val}, 残差={residuals_top[outlier_mask_top][i]:.2f}")
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# 剔除异常点
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x_bot_clean = x_bot_clean[~outlier_mask_bot]
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y_bot_clean = y_bot_clean[~outlier_mask_bot]
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x_top_clean = x_top_clean[~outlier_mask_top]
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y_top_clean = y_top_clean[~outlier_mask_top]
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# 判断数据的有效性
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clean_slope_bot, clean_intercept_bot, clean_r2_bot = calc_slope_line.linear_regression(x_bot_clean, y_bot_clean)
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clean_slope_top, clean_intercept_top, clean_r2_top = calc_slope_line.linear_regression(x_top_clean, y_top_clean)
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print(f"清洗数据后上下沿拟合参数clean_r2_bot = {clean_r2_bot},clean_r2_top = {clean_r2_top}")
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if ((1-clean_r2_bot) > (1-0.98)) or ((1-clean_r2_top) > (1-0.98)):
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print("无效数据")
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return 0, None, None, None, None
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return 1, x_bot_clean, y_bot_clean, x_top_clean, y_top_clean
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def test3_get_data(txt_name):
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# 加载数据
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data = np.loadtxt(txt_name)
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int_data = data.astype(int)
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grid_sampled = grid_downsample(int_data, cell_size=20)
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x = []
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y = []
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for i in range(grid_sampled.shape[0]):
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grid_sampled[i][1] = 960 - int(grid_sampled[i][1])
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if limit_slope * int(grid_sampled[i][0]) + limit_intercept - int(grid_sampled[i][1]) < 0:
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continue
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x.append(int(grid_sampled[i][0]))
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y.append(int(grid_sampled[i][1]))
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x = np.array(x)
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y = np.array(y)
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# with open(txt_name, 'r', encoding='utf-8') as f:
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# lines = f.readlines()
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# data = []
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# for i, line in enumerate(lines, 1):
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# data.append(line.split())
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# print(data)
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# if not data:
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# return 0, None, None, None, None
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# x = []
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# y = []
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#
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# for i in range(len(data)):
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# data[i][1] = 960 - int(data[i][1])
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# if limit_slope * int(data[i][0]) + limit_intercept - int(data[i][1]) < 0:
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# continue
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# x.append(int(data[i][0]))
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# y.append(int(data[i][1]))
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# x = np.array(x)
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# y = np.array(y)
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slope, intercept, r_2 = calc_slope_line.linear_regression(x, y)
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print(f"原始数据拟合参数r_2 = {r_2}" )
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fig1, axes1 = plt.subplots(nrows=4, ncols=3, figsize=(10, 8))
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fig1.tight_layout()
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fig1.suptitle(f"{txt_name}")
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axes1[0, 0].set_title("original data")
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axes1[0, 0].scatter(x,y, color='blue', label='orgin')
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# 绘制拟合线
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y_pred = slope * x + intercept
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axes1[0, 0].plot(x, y_pred, color='red', label='fix')
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# for i in range(len(x)):
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x_bot = []
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y_bot = []
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x_top = []
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y_top = []
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for i in range(len(x)):
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if x[i] * slope + intercept - y[i] > 0:
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x_bot.append(x[i])
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y_bot.append(y[i])
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else:
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x_top.append(x[i])
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y_top.append(y[i])
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x_bot = np.array(x_bot)
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y_bot = np.array(y_bot)
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x_top = np.array(x_top)
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y_top = np.array(y_top)
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slope_bot, intercept_bot, r2_bot = calc_slope_line.linear_regression(x_bot, y_bot)
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slope_top, intercept_top, r2_top = calc_slope_line.linear_regression(x_top, y_top)
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print(f"未清洗数据拟合上下沿：r2_bot = {r2_bot},r2_top = {r2_top}")
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axes1[0, 1].set_title("original bot data")
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axes1[0, 1].scatter(x_bot, y_bot, color='blue', label='orgin')
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# 绘制拟合线
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y_bot_pred = slope_bot * x_bot + intercept_bot
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axes1[0, 1].plot(x_bot, y_bot_pred, color='red', label='fix')
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axes1[0, 2].set_title("original top data")
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axes1[0, 2].scatter(x_top, y_top, color='blue', label='orgin')
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# 绘制拟合线
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y_top_pred = slope_top * x_top + intercept_top
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axes1[0, 2].plot(x_top, y_top_pred, color='red', label='fix')
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# 计算残差
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residuals = y - y_pred
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# print(f"residuals = {residuals}")
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# 计算残差的标准差 (MSE的平方根)
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residual_std = np.sqrt(np.sum(residuals ** 2) / (len(x) - 2))
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print(f"residual_std = {residual_std}")
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# 计算标准化残差 (Z-score)
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standardized_residuals = residuals / residual_std
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for i in range(len(standardized_residuals)):
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print(f"第{i+1}个点的坐标为：{x[i],y[i]},标准化残差为{standardized_residuals[i]}\n")
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# print(f"standardized_residuals = {standardized_residuals}")
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# 设置阈值 (常用 2.5-3.0 个标准差)
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threshold = 2.0
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# 标记异常点
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outlier_mask = np.abs(standardized_residuals) > threshold
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# print(f"outlier_mask = {outlier_mask}")
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outliers_x = x[outlier_mask]
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outliers_y = y[outlier_mask]
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axes1[2, 0].set_title("abnormal data")
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axes1[2, 0].scatter(outliers_x, outliers_y, color='blue', label='orgin')
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print(f"发现 {np.sum(outlier_mask)} 个异常点:")
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# for i, (x_val, y_val) in enumerate(zip(outliers_x, outliers_y)):
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# print(f"点 {i + 1}: x={x_val}, y={y_val}, 残差={residuals[outlier_mask][i]:.2f}")
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# 剔除异常点
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clean_x = x[~outlier_mask]
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clean_y = y[~outlier_mask]
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clean_slope, clean_intercept, clean_r_2 = calc_slope_line.linear_regression(clean_x, clean_y)
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print(f"清洗数据后整体拟合参数r_2 = {r_2}")
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axes1[1, 0].set_title("clean data")
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axes1[1, 0].scatter(clean_x,clean_y , color='blue', label='orgin')
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# 绘制拟合线
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y_pred = clean_slope * clean_x + clean_intercept
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axes1[1, 0].plot(clean_x, y_pred, color='red', label='fix')
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x_bot_clean = []
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y_bot_clean = []
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x_top_clean = []
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y_top_clean = []
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for i in range(len(clean_x)):
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if clean_x[i] * clean_slope + clean_intercept - clean_y[i] > 0:
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x_bot_clean.append(clean_x[i])
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y_bot_clean.append(clean_y[i])
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else:
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x_top_clean.append(clean_x[i])
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y_top_clean.append(clean_y[i])
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x_bot_clean = np.array(x_bot_clean)
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y_bot_clean = np.array(y_bot_clean)
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x_top_clean = np.array(x_top_clean)
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y_top_clean = np.array(y_top_clean)
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clean_slope_bot, clean_intercept_bot, clean_r2_bot = calc_slope_line.linear_regression(x_bot_clean, y_bot_clean)
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clean_slope_top, clean_intercept_top, clean_r2_top = calc_slope_line.linear_regression(x_top_clean, y_top_clean)
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print(f"清洗数据后上下沿拟合参数clean_r2_bot = {clean_r2_bot},clean_r2_top = {clean_r2_top}")
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axes1[1, 1].set_title("clean bot data")
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axes1[1, 1].scatter(x_bot_clean, y_bot_clean, color='blue', label='orgin')
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# 绘制拟合线
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y_bot_pred = clean_slope_bot * x_bot_clean + clean_intercept_bot
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axes1[1, 1].plot(x_bot_clean, y_bot_pred, color='red', label='fix')
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axes1[1, 2].set_title("clean top data")
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axes1[1, 2].scatter(x_top_clean, y_top_clean, color='blue', label='orgin')
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# 绘制拟合线
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y_top_pred = clean_slope_top * x_top_clean + clean_intercept_top
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axes1[1, 2].plot(x_top_clean, y_top_pred, color='red', label='fix')
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residuals_bot = y_bot_clean - y_bot_pred
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residuals_top = y_top_clean - y_top_pred
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residual_std_bot = np.sqrt(np.sum(residuals_bot ** 2) / (len(x_bot_clean) - 2))
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residual_std_top = np.sqrt(np.sum(residuals_top ** 2) / (len(x_top_clean) - 2))
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print(f"residual_std_bot = {residual_std_bot}")
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print(f"residual_std_top = {residual_std_top}")
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# 计算标准化残差 (Z-score)
|
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|
|
standardized_residuals_bot = residuals_bot / residual_std_bot
|
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standardized_residuals_top = residuals_top / residual_std_top
|
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|
# print(f"standardized_residuals_bot = {standardized_residuals_bot}")
|
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# print(f"standardized_residuals_top = {standardized_residuals_top}")
|
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|
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|
|
# 设置阈值 (常用 2.5-3.0 个标准差)
|
|
|
|
|
|
threshold = 2.0
|
|
|
|
|
|
|
|
|
|
|
|
# 标记异常点
|
|
|
|
|
|
outlier_mask_bot = np.abs(standardized_residuals_bot) > threshold
|
|
|
|
|
|
outlier_mask_top = np.abs(standardized_residuals_top) > threshold
|
|
|
|
|
|
# print(f"outlier_mask = {outlier_mask}")
|
|
|
|
|
|
outliers_x_bot = x_bot_clean[outlier_mask_bot]
|
|
|
|
|
|
outliers_y_bot = y_bot_clean[outlier_mask_bot]
|
|
|
|
|
|
outliers_x_top = x_top_clean[outlier_mask_top]
|
|
|
|
|
|
outliers_y_top = y_top_clean[outlier_mask_top]
|
|
|
|
|
|
axes1[2, 1].set_title("re clean abnormal bot data")
|
|
|
|
|
|
axes1[2, 1].scatter(outliers_x_bot, outliers_y_bot, color='blue', label='delet_bot')
|
|
|
|
|
|
axes1[2, 2].set_title("re clean abnormal top data")
|
|
|
|
|
|
axes1[2, 2].scatter(outliers_x_top, outliers_y_top, color='blue', label='delet_top')
|
|
|
|
|
|
print(f"发现 {np.sum(outlier_mask_bot)} 个异常点:")
|
|
|
|
|
|
# for i, (x_val, y_val) in enumerate(zip(outliers_x_bot, outliers_y_bot)):
|
|
|
|
|
|
# print(f"点 {i + 1}: x={x_val}, y={y_val}, 残差={residuals_bot[outlier_mask_bot][i]:.2f}")
|
|
|
|
|
|
print(f"发现 {np.sum(outlier_mask_top)} 个异常点:")
|
|
|
|
|
|
# for i, (x_val, y_val) in enumerate(zip(outliers_x_top, outliers_y_top)):
|
|
|
|
|
|
# print(f"点 {i + 1}: x={x_val}, y={y_val}, 残差={residuals_top[outlier_mask_top][i]:.2f}")
|
|
|
|
|
|
|
|
|
|
|
|
# 剔除异常点
|
|
|
|
|
|
x_bot_clean = x_bot_clean[~outlier_mask_bot]
|
|
|
|
|
|
y_bot_clean = y_bot_clean[~outlier_mask_bot]
|
|
|
|
|
|
x_top_clean = x_top_clean[~outlier_mask_top]
|
|
|
|
|
|
y_top_clean = y_top_clean[~outlier_mask_top]
|
|
|
|
|
|
|
|
|
|
|
|
clean_slope_bot, clean_intercept_bot, clean_r2_bot = calc_slope_line.linear_regression(x_bot_clean, y_bot_clean)
|
|
|
|
|
|
clean_slope_top, clean_intercept_top, clean_r2_top = calc_slope_line.linear_regression(x_top_clean, y_top_clean)
|
|
|
|
|
|
print(f"清洗数据后上下沿拟合参数clean_r2_bot = {clean_r2_bot},clean_r2_top = {clean_r2_top}")
|
|
|
|
|
|
axes1[3, 1].set_title("re clean bot data")
|
|
|
|
|
|
axes1[3, 1].scatter(x_bot_clean, y_bot_clean, color='blue', label='orgin')
|
|
|
|
|
|
# 绘制拟合线
|
|
|
|
|
|
y_bot_pred = clean_slope_bot * x_bot_clean + clean_intercept_bot
|
|
|
|
|
|
axes1[3, 1].plot(x_bot_clean, y_bot_pred, color='red', label='fix')
|
|
|
|
|
|
axes1[3, 2].set_title("re clean top data")
|
|
|
|
|
|
axes1[3, 2].scatter(x_top_clean, y_top_clean, color='blue', label='orgin')
|
|
|
|
|
|
# 绘制拟合线
|
|
|
|
|
|
y_top_pred = clean_slope_top * x_top_clean + clean_intercept_top
|
|
|
|
|
|
axes1[3, 2].plot(x_top_clean, y_top_pred, color='red', label='fix')
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
plt.show()
|
|
|
|
|
|
# plt.savefig("my_plot.png")
|
|
|
|
|
|
# file_base = os.path.splitext(txt_name)[0] # 去掉扩展名
|
|
|
|
|
|
# output_file = f"{file_base}_plot.png"
|
|
|
|
|
|
# plt.savefig(output_file)
|
|
|
|
|
|
if ((1-clean_r2_bot) > 1e-3) or ((1-clean_r2_top) > 1e-3):
|
|
|
|
|
|
print("无效数据")
|
|
|
|
|
|
return 0, None, None, None, None
|
|
|
|
|
|
return 1, x_bot_clean, y_bot_clean, x_top_clean, y_top_clean
|
