import numpy as np

import calc_way
from scipy import stats
import calc_slope_line
import matplotlib.pyplot as plt
# 车胎横向分界线
k = 0.62849534
b = 21.8122503

"""
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
参数：保存数据的txt文件路径
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
"""
# def get_data(txt_name):
#     with open(txt_name, 'r', encoding='utf-8') as f:
#         lines = f.readlines()
#     data = []
#     for i, line in enumerate(lines, 1):
#         data.append(line.split())
#         # print(line.split())
#     x_bot = []
#     y_bot = []
#     x_top = []
#     y_top = []
#     k_num = 0
#     k_num2 = 0
#     for i in range(len(data)):
#         data[i][1] = 960 - int(data[i][1])
#     for i in range(len(data)):
#         if k * int(data[i][0]) + b - int(data[i][1]) < 0:
#             k_num2 = i
#             continue
#         if i > 1:
#             if (int(data[i][0]) - int(data[i-1][0]))*(int(data[i+2][0]) - int(data[i][0])) < 0:
#                 k_num = i
#                 # print(f"k_num:{k_num},data:")
#                 break
#     for i in range(len(data)):
#         if k * int(data[i][0]) + b - int(data[i][1]) < 0:
#             continue
#         if i < k_num:
#             x_bot.append(int(data[i][0]))
#             y_bot.append(int(data[i][1]))
#         else:
#             x_top.append(int(data[i][0]))
#             y_top.append(int(data[i][1]))
#     # print(x_bot, y_bot, x_top, y_top)
#     return x_bot, y_bot, x_top, y_top, k_num2


def get_data(txt_name):
    with open(txt_name, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    data = []
    if not data:
        return None, None, None, None
    for i, line in enumerate(lines, 1):
        data.append(line.split())
    x = []
    y = []
    for i in range(len(data)):
        data[i][1] = 960 - int(data[i][1])
        x.append(int(data[i][0]))
        y.append(int(data[i][1]))
    x = np.array(x)
    y = np.array(y)
    slope, intercept, r_2 = calc_slope_line.linear_regression(x, y)
    # print(r_2)
    x_bot = []
    y_bot = []
    x_top = []
    y_top = []
    for i in range(len(x)):
        if x[i] * slope + intercept - y[i] > 0:
            x_bot.append(x[i])
            y_bot.append(y[i])
        else:
            x_top.append(x[i])
            y_top.append(y[i])
    x_bot = np.array(x_bot)
    y_bot = np.array(y_bot)
    x_top = np.array(x_top)
    y_top = np.array(y_top)
    slope_bot, intercept_bot, r2_bot = calc_slope_line.linear_regression(x_bot, y_bot)
    slope_top, intercept_top, r2_top = calc_slope_line.linear_regression(x_top, y_top)
    # print(f"r2_bot = {r2_bot},r2_top = {r2_top}")
    if ((1-r2_bot) > 1e-2) or ((1-r2_bot) > 1e-2):
        return None, None, None, None

    delet = []
    for i in range(len(x_bot)):
        if abs(y_bot[i] - slope_bot * x_bot[i] - intercept_bot) > 10:
            delet.append(i)
    # print(f"len(x_bot): {len(x_bot)},delet: {delet})")
    x_bot = np.delete(x_bot, delet)
    y_bot = np.delete(y_bot, delet)
    # y_pred = slope_bot * x_bot + intercept_bot
    delet = []
    for i in range(len(x_top)):
        if abs(y_top[i] - slope_top * x_top[i] - intercept_top) > 10:
            delet.append(i)
    x_top = np.delete(x_top, delet)
    y_top = np.delete(y_top, delet)
    # y_pred = slope_top * x_top + intercept_top
    return x_bot, y_bot, x_top, y_top

txt_name = "C:\\Users\\Administrator\\Desktop\\BYD\\20250520\\frame_7800_2.jpg_zuobiao.txt"
test_name = r'C:\Users\Administrator\Desktop\BYD\error\20250620\20250620\INPUT_MUST_NOT_BE_EMPTY.txt'
def test_get_data(txt_name):
    with open(txt_name, 'r', encoding='utf-8') as f:
        lines = f.readlines()
    data = []
    for i, line in enumerate(lines, 1):
        data.append(line.split())
    x = []
    y = []
    for i in range(len(data)):
        data[i][1] = 960 - int(data[i][1])
        # if k * int(data[i][0]) + b - int(data[i][1]) < 0:
        #     continue
        x.append(int(data[i][0]))
        y.append(int(data[i][1]))
    x = np.array(x)
    y = np.array(y)
    slope,intercept,r_2 = calc_slope_line.linear_regression(x, y)
    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 8))
    axes[0, 0].scatter(x,y, color='blue', label='orgin')
    # 绘制拟合线
    y_pred = slope * x + intercept
    axes[0, 0].plot(x, y_pred, color='red', label='fix')



    print(r_2)

    x_bot = []
    y_bot = []
    x_top = []
    y_top = []
    for i in range(len(x)):
        if x[i] * slope + intercept - y[i] > 0:
            x_bot.append(x[i])
            y_bot.append(y[i])
        else:
            x_top.append(x[i])
            y_top.append(y[i])
    x_bot = np.array(x_bot)
    y_bot = np.array(y_bot)
    x_top = np.array(x_top)
    y_top = np.array(y_top)
    slope_bot, intercept_bot, r2_bot = calc_slope_line.linear_regression(x_bot, y_bot)
    slope_top, intercept_top, r2_top = calc_slope_line.linear_regression(x_top, y_top)
    print(f"r2_bot = {r2_bot},r2_top = {r2_top}")
    axes[1, 0].scatter(x_bot,y_bot, color='blue', label='orgin')
    # 绘制拟合线
    y_pred = slope_bot * x_bot + intercept_bot
    axes[1, 0].plot(x_bot, y_pred, color='red', label='fix')


    axes[1, 1].scatter(x_top,y_top, color='blue', label='orgin')
    # 绘制拟合线
    y_pred = slope_top * x_top + intercept_top
    axes[1, 1].plot(x_top, y_pred, color='red', label='fix')

    plt.show()
    return x_bot, y_bot, x_top, y_top