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;
This commit is contained in:
tangchao0503
2022-09-06 22:54:14 +08:00
commit 98cf134cca
106 changed files with 39400 additions and 0 deletions

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0.1remove dark noise.py Normal file
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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()