第一次提交

1、hpi的可用代码；
2、修复了多次点击曝光后，福亮度数据错误的问题；
3、定标方式为大的蓝菲积分球的标准能量曲线，而不是基于asd的能量曲线；

0.1remove dark noise.py

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+'''
+1、读取影像
+2、bin
+3、去除暗电流 + 转反射率
+4、保存光谱
+'''
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+from osgeo import gdal   #读写影像数据
+
+class GRID:
+
+    #读图像文件
+    @classmethod
+    def read_img(cls, filename):
+        try:
+            dataset = gdal.Open(filename)  # 打开文件
+            im_width = dataset.RasterXSize  # 栅格矩阵的列数
+            im_height = dataset.RasterYSize  # 栅格矩阵的行数
+            num_bands = dataset.RasterCount  # 栅格矩阵的波段数
+            im_geotrans = dataset.GetGeoTransform()  # 仿射矩阵
+            im_proj = dataset.GetProjection()  # 地图投影信息
+            im_data = dataset.ReadAsArray(0, 0, im_width, im_height)  # 将数据写成数组，对应栅格矩阵
+            del dataset
+            return im_proj, im_geotrans, im_data
+        except:
+            sys.exit()
+
+    #写文件，以写成tif为例
+    @classmethod
+    def write_img(cls, dst_filename, data):
+        format = "ENVI"
+        driver = gdal.GetDriverByName(format)
+        RasterXSize = data.shape[2]  # 遥感影像的sample（列数）
+        RasterYSize = data.shape[1]  # 遥感影像的line（行数）
+        band = data.shape[0]
+        dst_ds = driver.Create(dst_filename, RasterXSize, RasterYSize,
+                               band,
+                               gdal.GDT_Float32)  # driver.Create()函数中RasterXSize代表影像的sample（列数），RasterYSize代表影像的line（行数）
+        for i in range(band):
+            dst_ds.GetRasterBand(i + 1).WriteArray(data[i, :, :])  # gdal的band从1开始，所以dst_ds.GetRasterBand(i+1)
+        dst_ds = None
+
+    # bin
+    @classmethod
+    def bin(cls, img, nBin):
+        if nBin == 1:
+            return img
+        image_bin = np.empty((int(img.shape[0] / nBin), img.shape[1], img.shape[2]))
+        k = np.arange(img.shape[0])[0::nBin]
+        for i in range(image_bin.shape[0]):
+            for j in range(nBin):
+                image_bin[i] += img[k[i] + j]
+        return image_bin
+
+# 文件名
+image = r'D:\py_program\corning410\test_spectral\lib_spectral2_20帧\delete\leaf'
+dark_image = r'D:\py_program\corning410\test_spectral\lib_spectral2_20帧\delete\dark'
+baiban_image = r'D:\py_program\corning410\test_spectral\lib_spectral2_20帧\delete\baiban'
+
+# 读取影像
+print('读取影像')
+im_proj, im_geotrans, im_data = GRID.read_img(image)
+d1, d2, dark_noise = GRID.read_img(dark_image)
+b1, b2, baiban = GRID.read_img(baiban_image)
+
+
+
+n_bin = 1
+im_data = GRID.bin(im_data, n_bin)
+dark_noise = GRID.bin(dark_noise, n_bin)
+baiban = GRID.bin(baiban, n_bin)
+
+
+# （1）去除暗电流；（2）转反射率
+dark_noise_mean = dark_noise.mean(axis=1)
+baiban_mean = baiban.mean(axis=1)
+im_data = im_data.astype(np.float)
+for i in range(im_data.shape[1]):
+    im_data[:, i, :] = (im_data[:, i, :] - dark_noise_mean).astype(np.float) / baiban_mean.astype(np.float)
+
+
+print('将影像写入到文件')
+GRID.write_img(image + '_reflectivity', im_data)
+
+
+# 计算波长
+def calculate_wavelength(x):
+    wavelength = x * 1.999564 - 279.893
+    return wavelength
+wavelength_tmp = np.empty(300)
+for i in range(340, 640):
+    wavelength_tmp[i - 340] = calculate_wavelength(i)
+wavelength = np.empty(im_data.shape[0])
+k = np.arange(300)[0::n_bin]
+for i in range(wavelength.shape[0]):
+    tmp = 0
+    for j in range(n_bin):
+        tmp += wavelength_tmp[k[i] + j]
+    wavelength[i] = tmp / n_bin
+
+
+
+
+# 保存光谱为txt文件
+spectralNumber = 1
+spectral_container = np.empty((im_data.shape[0], spectralNumber)).astype(np.float)
+spectral_container = np.insert(spectral_container, 0, wavelength, axis=1)
+
+
+spectral = im_data.mean(1).mean(1)
+spectral_container = np.insert(spectral_container, 1, spectral, axis=1)
+np.savetxt(r'D:\py_program\corning410\test_spectral\lib_spectral2_20帧\delete\spectral.txt', spectral_container[:, [0, 1]], fmt='%f')
+
+plt.plot(wavelength, spectral)
+plt.show()