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