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；

0.2stretch.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
+from PIL import Image
+import cv2
+
+
+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
+
+    # 计算波长
+    @classmethod
+    def calculate_wavelength(cls, x):
+        wavelength = x * 1.999564 - 279.893
+        return wavelength
+
+wavelength = np.empty(639 - 339)
+for i in range(339, 639):
+    wavelength[i - 339] = GRID.calculate_wavelength(i)
+
+
+# 等效于ENVI拉伸：No stretch
+def stretch(img, minimum=0, maximum=255):
+    if len(img.shape) == 2:
+        img_new = (img - minimum) / (maximum - minimum)
+        img_new[img_new < 0] = 0
+        img_new[img_new > 1] = 1
+        return img_new
+    else:
+        img_new = np.empty(img.shape)
+        for i in range(img.shape[2]):
+            img_new[:, :, i] = (img[:, :, i] - minimum) / (maximum - minimum)
+            img_new[:, :, i][img_new[:, :, i] < 0] = 0
+            img_new[:, :, i][img_new[:, :, i] > 1] = 1
+            return img_new
+
+
+# 当lowPercentile=0, highPercentile=100时，等效于Min-Max Stretching
+# lowPercentile=2, highPercentile=98时，等效于ENVI拉伸：Linear 2%
+# https://blog.csdn.net/LEILEI18A/article/details/80180483
+def percentile_stretching(img, lowPercentile=0, highPercentile=100, minout=0, maxout=255):
+    if len(img.shape) == 2:
+        low = np.percentile(img, lowPercentile)
+        up = np.percentile(img, highPercentile)
+
+        img_new = ((img - low) / (up - low)) * (maxout - minout) + minout
+        img_new[img_new < minout] = minout
+        img_new[img_new > maxout] = maxout
+        img_out = np.uint8(img_new)
+        return img_out
+    else:  # 对于彩色照片，需要先单独对每个波段拉伸
+        img_new = np.empty(img.shape)
+        for i in range(img.shape[2]):
+            low = np.percentile(img[:, :, i], lowPercentile)
+            up = np.percentile(img[:, :, i], highPercentile)
+
+            img_new[:, :, i] = minout + ((img[:, :, i] - low) / (up - low)) * (maxout - minout)
+            img_new[:, :, i][img_new[:, :, i] < minout] = minout
+            img_new[:, :, i][img_new[:, :, i] > maxout] = maxout
+        img_out = np.uint8(img_new)
+        return img_out
+
+
+# 画出图像直方图
+# https://blog.csdn.net/fly_wt/article/details/83904207
+def image_hist(image):  # 画三通道图像的直方图
+    color = ("blue", "green", "red")  # 画笔颜色的值可以为大写或小写或只写首字母或大小写混合
+    for i, color in enumerate(color):
+        hist = cv2.calcHist([image], [i], None, [256], [0, 256])
+        plt.plot(hist, color=color)
+        plt.xlim([0, 256])
+    plt.show()
+
+
+print("读取影像")
+#image = r'D:\py_program\corning410\2%拉伸显示问题 + 漏帧\x270\dn值\corning410_test10'
+image = r'D:\corning410_test10'
+im_proj, im_geotrans, im_data = GRID.read_img(image)
+
+
+print("挑取波段用于真彩色显示")
+rgb_raw = np.dstack((im_data[121], im_data[76], im_data[36]))
+rgb = rgb_raw.astype(np.uint8)
+
+
+x1 = stretch(rgb_raw)  #等效于ENVI拉伸：No stretch
+x2 = percentile_stretching(rgb_raw)  # Min-Max Stretching
+x3 = percentile_stretching(rgb_raw, 2, 98)  # 2% Stretching
+
+print("画出影像")
+plt.imshow(x3)
+plt.show()