Initial commit

new file:   Copy_of_Exz_with_hamamatsu.m
	new file:   Exz_mod.m
	new file:   Exz_with_hamamatsu.m
	new file:   GS.m
	new file:   MRAF.m
	new file:   MRAF_8bit.bmp
	new file:   PSF of spherical aberration/Exz.m
	new file:   PSF of spherical aberration/Thumbs.db
	new file:   PSF of spherical aberration/josaa-12-10-2136.pdf
	new file:   PSF of spherical aberration/josaa-12-2-325.pdf
	new file:   PSF of spherical aberration/main.m
	new file:   "PSF of spherical aberration/\351\207\215\350\246\201psf\346\226\207\347\214\256.pdf"
	new file:   Spherical_aberration_SiminCao.m
	new file:   gen_m.m
	new file:   gen_rectangle.m
	new file:   hamamatsu.m
	new file:   m.tif
	new file:   m2.tif
	new file:   photo/50-30.BMP
	new file:   photo/8bit_50-30.BMP
	new file:   photo/8bit_ellipse.BMP
	new file:   photo/convert_8bit.exe
	new file:   photo/ellipse.BMP
	new file:   rect_MRAF_SiminCao.m
	new file:   rectangle.tif
	new file:   size/.vscode/settings.json
	new file:   size/black_c_20THSize_4f_1.064lamda.bmp
	new file:   size/black_c_30THSize_4f_61.064lamda.bmp
	new file:   size/black_output.bmp
	new file:   size/black_rect_30THSize_4f_1.064lamda.bmp
	new file:   size/black_rect_30THSize_4f_6_1.064lamda.bmp
	new file:   size/c_20THSize_4f_1.064lamda.bmp
	new file:   size/c_20THSize_4f_1.064lamda_resize.bmp
	new file:   size/c_30THSize_4f_61.064lamda.bmp
	new file:   size/c_30THSize_4f_61.064lamda_resize.bmp
	new file:   size/noisy_output.bmp
	new file:   size/output.bmp
	new file:   size/rect_30THSize_4f_1.064lamda.bmp
	new file:   size/rect_30THSize_4f_1.064lamda_resize.bmp
	new file:   size/rect_30THSize_4f_6_1.064lamda.bmp
	new file:   size/rect_30THSize_4f_6_1.064lamda_resize.bmp
	new file:   size/resize_4.7z
	new file:   size/resize_black.7z
	new file:   size/size copy.py
	new file:   size/size.py
	new file:   size/wave.7z
	new file:   sp.m
	new file:   to8bit.m
	new file:   trans_8bit.zip
	new file:   wavef/A.bmp
	new file:   wavef/B_linear.bmp
	new file:   wavef/PHA SID230828-2003.csv
	new file:   wavef/PHA_bilinear_1280_1024.csv
	new file:   wavef/PHA_bilinear_reversal.csv
	new file:   wavef/PHA_output_1280_1024.csv
	new file:   wavef/Untitled-1.py
	new file:   wavef/filled.bmp
	new file:   wavef/from PIL import Image.py
	new file:   wavef/matrix_filled.csv
	new file:   wavef/output.bmp
	new file:   wavef/output.csv
	new file:   wavef/output2.bmp
	new file:   wavef/pha_wavef copy.py
	new file:   wavef/pha_wavef.py
	new file:   wavef/pha_wavef_step.py
	new file:   wavef/wavef.zip
	new file:   wavef/xy_values.csv
	new file:   "wavef/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/matrix.csv"
	new file:   "wavef/\346\226\260\345\273\272\346\226\207\344\273\266\345\244\271/matrix_filled.csv"

wavef/PHA SID230828-2003.csv

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wavef/Untitled-1.py

@@ -0,0 +1,8 @@
+
+u = 18
+v = 58
+
+x = int(0.211 * u - 4.538 * v + 894.754)
+y = int(-4.594 * u - 0.1044 * v + 800.7384)
+
+print("uvxy=", u, v, x, y)

wavef/from PIL import Image.py

@@ -0,0 +1,20 @@
+from PIL import Image
+import numpy as np
+
+# 创建一个1280x1024的矩阵，所有元素都填充为217
+matrix = np.full((1280, 1024), 217)
+
+# 计算T形状的起始和结束位置
+t_start_row = 390  # T形状的起始行位置
+t_end_row = t_start_row + 500  # T形状的结束行位置
+t_start_col = 262  # T形状的起始列位置
+t_end_col = t_start_col + 500  # T形状的结束列位置
+
+# 在指定位置生成T形状
+matrix[t_start_row:t_end_row, t_start_col:t_end_col] = 0  # 将T形状区域的元素值设为0
+
+# 创建PIL图像对象
+image = Image.fromarray(matrix.astype(np.uint8), 'L')
+
+# 保存图像文件
+image.save('t_shape.png')

wavef/pha_wavef copy.py

@@ -0,0 +1,55 @@
+import numpy as np
+import pandas as pd
+from scipy.spatial import KDTree
+from PIL import Image
+
+# 读取CSV文件
+data = np.genfromtxt('PHA SID230828-2003.csv', delimiter=',')
+
+# 创建矩阵A
+A = np.zeros((120, 120))
+A[:data.shape[0], :data.shape[1]] = data
+
+# 创建矩阵B
+B = np.zeros((1280, 1024))
+
+# 进行映射和填充已知点
+for u in range(120):
+    for v in range(120):
+        x = 0.211 * u - 4.538 * v + 894.754
+        y = -4.594 * u - 0.1044 * v + 800.7384
+
+        # 判断坐标是否在矩阵B的范围内
+        if 0 <= int(x) < 1280 and 0 <= int(y) < 1024:
+            B[int(x), int(y)] = A[u, v]
+
+# 寻找未知点的最近邻并进行填充
+known_points = np.argwhere(B != 0)  # 获取已知点的坐标
+tree = KDTree(known_points)  # 构建KD树
+
+unknown_points = np.argwhere(B == 0)  # 获取未知点的坐标
+for u, v in unknown_points:
+    x = 0.211 * u - 4.538 * v + 894.754
+    y = -4.594 * u - 0.1044 * v + 800.7384
+
+    # 寻找最近邻点的索引
+    _, idx = tree.query([u, v])
+    nearest_point = known_points[idx]
+    nearest_u, nearest_v = nearest_point[0], nearest_point[1]
+
+    # 用最近邻点的值填充未知点
+    B[u, v] = B[nearest_u, nearest_v]
+
+# 将矩阵B保存为CSV文件
+df = pd.DataFrame(B)
+df.to_csv('output.csv', index=False, header=False)
+
+matrix_B = B.copy()
+# 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('output2.bmp')

wavef/pha_wavef.py

@@ -0,0 +1,86 @@
+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')

wavef/pha_wavef_step.py

@@ -0,0 +1,91 @@
+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)
+
+
+# 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
+print(matrix.shape)
+# 生成网格点坐标
+grid_x, grid_y = np.mgrid[0:matrix_size[0], 0:matrix_size[1]]
+
+# 使用双线性插值填充未知点
+points = np.transpose(np.nonzero(matrix == 0))
+filled_values = griddata(known_points, known_values, points, method='linear')
+
+# 将填充后的值放入矩阵
+for i in range(len(points)):
+    x, y = points[i]
+    value = filled_values[i]
+    matrix[x, y] = value
+
+# 将矩阵中的未知位置填充为0
+matrix = np.nan_to_num(matrix, nan=0)
+
+# 将矩阵保存为CSV文件
+df = pd.DataFrame(matrix)
+df.to_csv('matrix_filled.csv', index=False, header=False)
+
+# # 将矩阵保存为CSV文件
+# df = pd.DataFrame(matrix)
+# df.to_csv('matrix.csv', index=False, header=False)
+
+matrix_B = matrix.copy()
+print(matrix_B.shape)
+matrix_c = np.where(matrix_B != 0, matrix_B * 217, matrix_B)
+
+image_filled = Image.fromarray(matrix_c.astype(np.uint8))
+# 保存为BMP图像
+image_filled.save('filled.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')