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"

size/.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+    "[python]": {
+        "editor.defaultFormatter": "ms-python.autopep8"
+    },
+    "python.formatting.provider": "none"
+}

size/size copy.py

@@ -0,0 +1,56 @@
+from PIL import Image
+import numpy as np
+
+# 读取原始图像
+path = "output.bmp"
+original_image = Image.open(path)
+original_width, original_height = original_image.size
+
+
+# 定义裁剪后的新高度
+new_height = original_height - 400  # 上下各裁剪200像素
+
+# 裁剪图像，上下各裁剪200像素
+# 左上角坐标为(0, 200)，右下角坐标为(width, height-200)
+cropped_image = original_image.crop(
+    (0, 200, original_width, original_height-200))
+cropped_image.show()
+
+# 创建目标图像（1280x1024像素，灰度模式）
+target_width, target_height = 1280, 1024
+target_image = Image.new("L", (target_width, target_height), color=0)
+
+# 计算图像放置位置
+x_offset = (target_width - original_width) // 2
+y_offset = (target_height - new_height) // 2
+
+# 将原始图像放置在目标图像中央
+target_image.paste(cropped_image, (x_offset, y_offset))
+
+# 将目标图像转换为NumPy数组
+target_array = np.array(target_image)
+
+# # 在周围填充随机噪声
+# noise_level = 256  # 随机噪声的灰度级别
+# noise = np.random.randint(
+#     0, noise_level, (target_height, target_width)).astype(np.uint8)
+# target_array = np.where(target_array == 0, noise, target_array)
+
+# # 创建包含噪声的新图像
+# noisy_image = Image.fromarray(target_array.astype(np.uint8))
+
+# # 保存为位图（BMP）图像
+# noisy_image.save("black_"+path)
+
+# 创建黑色填充
+black_color = 0  # 黑色像素值为0
+target_array[:y_offset, :] = black_color  # 上方填充
+target_array[y_offset + original_height:, :] = black_color  # 下方填充
+target_array[:, :x_offset] = black_color  # 左侧填充
+target_array[:, x_offset + original_width:] = black_color  # 右侧填充
+
+# 创建包含黑色填充的新图像
+padded_image = Image.fromarray(target_array)
+
+# 保存为位图（BMP）图像
+padded_image.save("black_"+path)

size/size.py

@@ -0,0 +1,46 @@
+from PIL import Image
+import numpy as np
+
+# 读取原始图像
+path = "c_30THSize_4f_61.064lamda.bmp"
+original_image = Image.open(path)
+original_width, original_height = original_image.size
+
+# 创建目标图像（1280x1024像素，灰度模式）
+target_width, target_height = 1280, 1024
+target_image = Image.new("L", (target_width, target_height), color=0)
+
+# 计算图像放置位置
+x_offset = (target_width - original_width) // 2
+y_offset = (target_height - original_height) // 2
+
+# 将原始图像放置在目标图像中央
+target_image.paste(original_image, (x_offset, y_offset))
+
+# 将目标图像转换为NumPy数组
+target_array = np.array(target_image)
+
+# # 在周围填充随机噪声
+# noise_level = 256  # 随机噪声的灰度级别
+# noise = np.random.randint(
+#     0, noise_level, (target_height, target_width)).astype(np.uint8)
+# target_array = np.where(target_array == 0, noise, target_array)
+
+# # 创建包含噪声的新图像
+# noisy_image = Image.fromarray(target_array.astype(np.uint8))
+
+# # 保存为位图（BMP）图像
+# noisy_image.save("black_"+path)
+
+# 创建黑色填充
+black_color = 0  # 黑色像素值为0
+target_array[:y_offset, :] = black_color  # 上方填充
+target_array[y_offset + original_height:, :] = black_color  # 下方填充
+target_array[:, :x_offset] = black_color  # 左侧填充
+target_array[:, x_offset + original_width:] = black_color  # 右侧填充
+
+# 创建包含黑色填充的新图像
+padded_image = Image.fromarray(target_array)
+
+# 保存为位图（BMP）图像
+padded_image.save("black_"+path)