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import numpy as np
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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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# 车胎横向分界线
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k = 0.62849534
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b = 21.8122503
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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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# 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(line.split())
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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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# k_num = 0
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# k_num2 = 0
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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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# for i in range(len(data)):
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# if k * int(data[i][0]) + b - int(data[i][1]) < 0:
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# k_num2 = i
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# continue
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# if i > 1:
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# if (int(data[i][0]) - int(data[i-1][0]))*(int(data[i+2][0]) - int(data[i][0])) < 0:
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# k_num = i
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# # print(f"k_num:{k_num},data:")
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# break
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# for i in range(len(data)):
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# if k * int(data[i][0]) + b - int(data[i][1]) < 0:
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# continue
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# if i < k_num:
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# x_bot.append(int(data[i][0]))
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# y_bot.append(int(data[i][1]))
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# else:
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# x_top.append(int(data[i][0]))
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# y_top.append(int(data[i][1]))
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# # print(x_bot, y_bot, x_top, y_top)
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# return x_bot, y_bot, x_top, y_top, k_num2
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def get_data(txt_name):
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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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if not data:
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return None, None, None, None
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for i, line in enumerate(lines, 1):
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data.append(line.split())
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x = []
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y = []
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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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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(r_2)
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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"r2_bot = {r2_bot},r2_top = {r2_top}")
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if ((1-r2_bot) > 1e-2) or ((1-r2_bot) > 1e-2):
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return None, None, None, None
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delet = []
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for i in range(len(x_bot)):
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if abs(y_bot[i] - slope_bot * x_bot[i] - intercept_bot) > 10:
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delet.append(i)
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# print(f"len(x_bot): {len(x_bot)},delet: {delet})")
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x_bot = np.delete(x_bot, delet)
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y_bot = np.delete(y_bot, delet)
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# y_pred = slope_bot * x_bot + intercept_bot
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delet = []
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for i in range(len(x_top)):
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if abs(y_top[i] - slope_top * x_top[i] - intercept_top) > 10:
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delet.append(i)
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x_top = np.delete(x_top, delet)
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y_top = np.delete(y_top, delet)
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# y_pred = slope_top * x_top + intercept_top
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return x_bot, y_bot, x_top, y_top
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txt_name = "C:\\Users\\Administrator\\Desktop\\BYD\\20250520\\frame_7800_2.jpg_zuobiao.txt"
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test_name = r'C:\Users\Administrator\Desktop\BYD\error\20250620\20250620\INPUT_MUST_NOT_BE_EMPTY.txt'
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def test_get_data(txt_name):
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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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x = []
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y = []
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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 k * int(data[i][0]) + b - 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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fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
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axes[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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axes[0, 0].plot(x, y_pred, color='red', label='fix')
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print(r_2)
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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"r2_bot = {r2_bot},r2_top = {r2_top}")
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axes[1, 0].scatter(x_bot,y_bot, color='blue', label='orgin')
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# 绘制拟合线
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y_pred = slope_bot * x_bot + intercept_bot
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axes[1, 0].plot(x_bot, y_pred, color='red', label='fix')
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axes[1, 1].scatter(x_top,y_top, color='blue', label='orgin')
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# 绘制拟合线
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y_pred = slope_top * x_top + intercept_top
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axes[1, 1].plot(x_top, y_pred, color='red', label='fix')
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plt.show()
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return x_bot, y_bot, x_top, y_top
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