import numpy as np
import pandas as pd
from scipy.interpolate import griddata
from PIL import Image


# 读取CSV文件并填充矩阵A
data = pd.read_csv('PHA SID230828-2003.csv', header=None)
matrix_A = data.values
# matrix_A = np.flipud(matrix_A)

print(matrix_A[27,15])

# 获取已知点和对应的值
known_points = []
known_values = []
for u in range(120):
    for v in range(120):
        if matrix_A[u, v] != 0:
            x = int(0.211 * u - 4.538 * v + 894.754)
            y = int(-4.594 * u - 0.1044 * v + 800.7384)
            if u == 27 and v == 15:
                print(x, y)
            known_points.append([x, y])
            known_values.append(matrix_A[u, v])

known_points = np.array(known_points)
known_values = np.array(known_values)

# 创建一个1280x1024的矩阵
matrix_size = (1280, 1024)
matrix = np.zeros(matrix_size)

# 根据已知点和对应的值填充矩阵
for i in range(len(known_points)):
    x, y = known_points[i]
    value = known_values[i]
    matrix[x, y] = value

# 将矩阵保存为CSV文件
df = pd.DataFrame(matrix)
df.to_csv('matrix.csv', index=False, header=False)

# 创建矩阵B的网格坐标
x = np.linspace(0, 1279, 1280)
y = np.linspace(0, 1023, 1024)
xx, yy = np.meshgrid(x, y)

# 保存 x 和 y 的值到 CSV 文件
xy_values = np.column_stack((xx.flatten(), yy.flatten()))
df_xy = pd.DataFrame(xy_values, columns=['x', 'y'])
df_xy.to_csv('xy_values.csv', index=False)

# 使用双线性插值进行插值
matrix_B = griddata(known_points, known_values, (xx, yy), method='linear')

# 将矩阵B中的未知位置填充为0
matrix_B = np.nan_to_num(matrix_B, nan=0)

# # 保存中间图像
# matrix_B_gray = matrix_B * (255 / np.max(matrix_B))
# matrix_B_gray = matrix_B_gray.astype(np.uint8)
# image = Image.fromarray(matrix_B_gray, mode='L')
# image.save('B_linear.bmp')

# 将矩阵B中的非零值取反
matrix_B = np.where(matrix_B != 0, -matrix_B, matrix_B)

# 对负数进行+1操作
matrix_B = np.where(matrix_B < 0, matrix_B + 1, matrix_B)

# 再次对负数进行+1操作
matrix_B = np.where(matrix_B < 0, matrix_B + 1, matrix_B)

# 将矩阵B保存为CSV文件
df = pd.DataFrame(matrix_B)
df.to_csv('PHA_bilinear_reversal.csv', header=False, index=False)

# 将矩阵B中的非零值映射到灰度值范围
matrix_B = np.where(matrix_B != 0, matrix_B * 217, matrix_B)

# 将矩阵B转换为8位灰度图像
image = Image.fromarray(matrix_B.astype(np.uint8))

# 保存为BMP图像
image.save('output.bmp')