118 lines
3.9 KiB
Python
118 lines
3.9 KiB
Python
'''
|
||
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()
|