202 lines
7.2 KiB
Python
202 lines
7.2 KiB
Python
#
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# 样本处理的相关的库
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#
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from tool.algorithm.image.ImageHandle import ImageHandler
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import math
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import numpy as np
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import random
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import scipy
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# 最小二乘求解非线性方程组
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from scipy.optimize import leastsq,fsolve,root
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from osgeo import gdal,gdalconst
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import pandas as pds
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from scipy import interpolate
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from multiprocessing import pool
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# 常量声明区域
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imageHandler=ImageHandler()
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# python 的函数类
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def read_sample_csv(csv_path):
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""" 读取样本的csv
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Args:
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csv_path (string): 样本csv的地址，绝对路径
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return:
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[
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['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数"],
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['日期','样方编号','经度','纬度','叶面积指数',"后向散射系数"],......
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]
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"""
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lai_csv=pds.read_csv(csv_path)# 代码测试区域
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lai_csv=lai_csv.loc[:,['id','lon','lat','leaf',"cal"]]
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result=[]
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for i in range(len(lai_csv)):
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result.append([
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0,
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lai_csv.loc[i,'id'],
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lai_csv.loc[i,'lon'], # lon,x
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lai_csv.loc[i,'lat'], # lat,y
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lai_csv.loc[i,'leaf'],
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10**(float(lai_csv.loc[i,'cal'])/10),
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])
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return result
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def read_tiff(tiff_path):
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""" 从文件中读取影像
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Args:
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tiff_path (string): 文件影像路径
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"""
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im_proj, im_geotrans, im_arr=imageHandler.read_img(tiff_path)
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return {
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'proj':im_proj,
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'geotrans':im_geotrans,
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'data':im_arr
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}
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def ReprojectImages2(in_tiff_path,ref_tiff_path,out_tiff_path,resampleAlg=gdalconst.GRA_Bilinear):
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""" 将输入影像重采样到参考影像的范围内
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Args:
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in_tiff_path (string): 输入影像
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ref_tiff_path (string): 参考影像
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out_tiff_path (string): 输出地址
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resampleAlg (gadlconst): 插值方法
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"""
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# 若采用gdal.Warp()方法进行重采样
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# 获取输出影像信息
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inputrasfile = gdal.Open(in_tiff_path, gdal.GA_ReadOnly)
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inputProj = inputrasfile.GetProjection()
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# 获取参考影像信息
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referencefile = gdal.Open(ref_tiff_path, gdal.GA_ReadOnly)
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referencefileProj = referencefile.GetProjection()
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referencefileTrans = referencefile.GetGeoTransform()
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bandreferencefile = referencefile.GetRasterBand(1)
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x = referencefile.RasterXSize
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y = referencefile.RasterYSize
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nbands = referencefile.RasterCount
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# 创建重采样输出文件（设置投影及六参数）
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driver = gdal.GetDriverByName('GTiff')
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output = driver.Create(out_tiff_path, x, y, nbands, bandreferencefile.DataType)
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output.SetGeoTransform(referencefileTrans)
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output.SetProjection(referencefileProj)
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options = gdal.WarpOptions(srcSRS=inputProj, dstSRS=referencefileProj, resampleAlg=gdalconst.GRA_Bilinear)
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gdal.Warp(output, in_tiff_path, options=options)
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def combine_sample_attr(sample_list,attr_tiff):
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""" 构建样本
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Args:
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sample_list (list): 原样本
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attr_tiff (string): 添加的属性数据
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Returns:
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list:[sample,new_attr]
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"""
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result=[]
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# 因为soil_tiff 的影像的 影像分辨率较低
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inv_gt=gdal.InvGeoTransform(attr_tiff['geotrans'])
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for sample_item in sample_list:
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sample_lon=sample_item[2]
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sample_lat=sample_item[3]
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sample_in_tiff_x=inv_gt[0]+inv_gt[1]*sample_lon+inv_gt[2]*sample_lat # x
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sample_in_tiff_y=inv_gt[3]+inv_gt[4]*sample_lon+inv_gt[5]*sample_lat # y
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x_min=int(np.floor(sample_in_tiff_x))
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x_max=int(np.ceil(sample_in_tiff_x))
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y_min=int(np.floor(sample_in_tiff_y))
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y_max=int(np.ceil(sample_in_tiff_y))
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if x_min<0 or y_min<0 or x_max>=attr_tiff['data'].shape[1] or y_max>=attr_tiff['data'].shape[0]:
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continue
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#
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"""
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f = interpolate.interp2d([0,0,1,1], [0,1,1,0],
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[attr_tiff['data'][y_min,x_min],
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attr_tiff['data'][y_max,x_min],
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attr_tiff['data'][y_max,x_max],
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attr_tiff['data'][y_min,x_min]
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], kind='linear')
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interp_value=f(sample_in_tiff_x-x_min,sample_in_tiff_y-y_min)
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sample_item.append(interp_value[0])
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"""
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# 9x9
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x_min=x_min-4 if x_min-9>=0 else 0
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y_min=y_min-4 if y_min-9>=0 else 0
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x_max=x_max+4 if x_max+4<attr_tiff['data'].shape[1] else attr_tiff['data'].shape[1]
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y_max=y_max+4 if y_max+4<attr_tiff['data'].shape[0] else attr_tiff['data'].shape[0]
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interp_value=np.mean(attr_tiff['data'][y_min:y_max,x_min:x_max])
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sample_item.append(interp_value)
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result.append(sample_item)
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return result
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def check_sample(sample_list):
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""" 检查样本值
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Args:
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sample_list (list): 样本值：[ ['日期', '样方编号', '经度', '纬度', 'LAI','土壤含水量','入射角','后向散射系数'] ]
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Returns:
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list : 处理之后的样本值
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"""
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result=[]
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for item in sample_list:
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if len(item)==10:
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sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma,sample_NDVI=item
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else:
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sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma=item
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if sample_sigma<=0:
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continue
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if (sample_inc*180/np.pi)>90:
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continue
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if sample_soil<=0 or sample_soil>=1:
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continue
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if sample_lai<=0 or sample_lai>=20:
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continue
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result.append(item)
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# 绘制分布图
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# lai=[]
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# sigma=[]
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# csv_sigmas=[]
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# text_label=[]
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# for item in result:
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# if len(item)==10:
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# sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma,sample_NDVI=item
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# else:
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# sample_time,sample_code,sample_lon,sample_lat,sample_lai,csv_sigma,sample_soil,sample_inc,sample_sigma=item
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# text_label.append(sample_code)
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# lai.append(sample_lai)
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# sigma.append(sample_sigma)
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# csv_sigmas.append(csv_sigma)
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# from matplotlib import pyplot as plt
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# plt.scatter(np.array(lai),np.array(sigma),label="lai-tiff_sigma")
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# for i in range(len(sigma)):
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# plt.annotate(text_label[i], xy = (lai[i], sigma[i])) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
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#
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# plt.scatter(np.array(lai),np.array(csv_sigmas),label="lai-csv_sigmas")
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# for i in range(len(csv_sigmas)):
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# plt.annotate(text_label[i], xy = (lai[i],csv_sigmas[i])) # 这里xy是需要标记的坐标，xytext是对应的标签坐标
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# plt.legend()
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# plt.show()
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return result
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def split_sample_list(sample_list,train_ratio):
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""" 切分样本比值
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Args:
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sample_list (list): 样本列表
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train_ratio (double): 训练样本的比重
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Returns:
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list: [sample_train,sample_test]
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"""
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sample_train=[]
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sample_test=[]
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n=len(sample_list)
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for i in range(n):
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if random.random()<=train_ratio:
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sample_train.append(sample_list[i])
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else:
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sample_test.append(sample_list[i])
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return [sample_train,sample_test] |