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Author: zhoukaisheng

.gitattributes

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+*[[:space:]].npy filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text

README.md

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+# Landslide-Susceptibility
+
+
+
+SlopeCardMake.py 输入tif格式的影响因子图层与以fid为灰度值的斜坡单元图层,输出fid.npy的斜坡单元卡片与1D因子。
+SlopeCardResize.py 将斜坡单元卡片rezise为统一大小并得到一个npy文件。
+data_read.py 数据读取与增强。
+nni_trial 借助nni实现模型的超参数优化，并进行滑坡易发性评价。
+
+
+requirement:keras/nni/pandas/numpy/matplotlib

SlopeCardMake.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Oct 30 13:13:10 2018
+
+@author: 75129
+"""
+
+import scipy.io as sio
+from scipy import misc
+import numpy as np
+import matplotlib as plt
+from numpy.matlib import repmat
+from sklearn.decomposition import PCA
+from scipy import stats
+import pandas as pd
+
+#import arcpy
+#totaldata读取
+yinzi_name=['altitude','aspect','fault','ndvi','plan','profile','rainfall','river','road','slope','spi','sti','twi','soil_envi','landuse_envi','lithology_envi']
+yinzi_type_ori=['float','float','float','float','float','float','float','float','float','float','float','float','float','str','str','str']
+#load_fn = 'ysxp_lable.mat'
+#load_data = sio.loadmat(load_fn)
+#lable = load_data['lable']
+#load_fn = 'img_yx.mat'
+#load_data = sio.loadmat(load_fn)
+#lable = load_data['lable'] #假设文件中存有字符变量是matrix，例如matlab中save(load_fn, 'matrix');当然可以保存多个save(load_fn, 'matrix_x', 'matrix_y', ...);
+lable = misc.imread('C:/Users/75129/Desktop/mypy/demo_all/SUyushan.tif')
+row=len(lable)
+col=len(lable[0])
+nums_factors=len(yinzi_name)
+img=np.zeros((row,col,nums_factors))
+i=0
+yinzi_type=[]
+for i in range(nums_factors):
+    name=yinzi_name[i]
+    PATH='C:/Users/75129/Desktop/mypy/demo_all/tif_yushan/'
+    img[:,:,i]=misc.imread(PATH+name+'.tif')
+#    if yinzi_type_ori[i]=='str':
+#        plt.pyplot.figure()
+#        plt.pyplot.imshow(img[:,:,i])
+    ttt=img[:,:,i]
+    ttt[ttt==-32768]=0
+#    ttt[ttt==-3.402823e+038]=0
+    img[:,:,i]=ttt
+#    img_i=load_data[name]
+#    ma=np.max(img_i)
+#    mi=np.min(img_i)
+#    img[:,:,i]=(img_i-mi)/(ma-mi)
+    i=i+1
+#pca = PCA(n_components=3)
+#img1=np.reshape(img,(2636*2206,16))
+#img_pca=pca.fit_transform(img1)
+#img_pca=np.reshape(img_pca,(2636,2206,3))
+#img=img_pca
+#plt.pyplot.imshow(img_pca[:,:,0])
+xfanwei=[]
+yfanwei=[]
+print('开始裁剪 。')
+empty_index=[]
+max_id=np.max(lable[lable!=65536])
+yinzi_1d=np.zeros([max_id+1,nums_factors])
+for it in range(max_id,max_id+1):#4890  
+    fanwei=(lable==it)
+    fanwei=fanwei+np.zeros((row,col))
+    img_it=np.zeros((row,col,nums_factors))
+    for i in range(nums_factors):
+        img_it[:,:,i]=fanwei*img[:,:,i]
+    
+    index_fanwei=np.argwhere(lable==it)
+    if len(index_fanwei)!=0:
+        x_min=min(index_fanwei[:,0])
+        x_max=max(index_fanwei[:,0])
+        y_min=min(index_fanwei[:,1])
+        y_max=max(index_fanwei[:,1])
+        img_it_resize=img_it[x_min:(x_max+1),y_min:(y_max+1),:]
+#        for i in range(nums_factors):
+#            plt.pyplot.figure()
+#            plt.pyplot.imshow(img_it_resize[:,:,i])
+        filename='C:/Users/75129/Desktop/mypy/demo_all/yushan_all_OriCard/'+str(it)
+        np.save(filename,img_it_resize)
+        xfanwei.append(x_max-x_min+1)
+        yfanwei.append(y_max-y_min+1)
+        
+        for f in range(nums_factors):
+            x,y,z=img_it_resize.shape
+            values=np.reshape(img_it_resize,[x*y,z])
+            values_f=values[:,f]
+            values_f=values_f[values_f!=0]
+            if yinzi_type_ori[f]=='str':
+                if values_f.size!=0:
+                    yinzi_1d[it,f]=stats.mode(values_f)[0][0]
+            else: 
+                yinzi_1d[it,f]=np.mean(values_f)
+        print(str(it)+' is done'+' x:'+str(x_max-x_min+1)+' y:'+str(y_max-y_min+1))
+        print(yinzi_1d[it,15])
+    else:
+        empty_index.append(it)
+        filename='C:/Users/75129/Desktop/mypy/demo_all/yushan_all_OriCard/'+str(it)
+        np.save(filename,np.zeros([1,1,nums_factors]))
+        print(str(it)+' is done,but it is empty')
+
+df = pd.DataFrame(yinzi_1d,columns=yinzi_name)
+df.to_csv('C:/Users/75129/Desktop/mypy/demo_all/yushan_all_OriCard/yinzi1d.csv')
+        
+
+#    plt.pyplot.imshow(img_it_resize[:,:,0])
+print('裁剪结束')
+
+#band=len(totaldata[0][0])
+#totaldata=np.reshape(totaldata,(row*col,band))
+
+
+#假如这是pro,显示最后的概率分布图
+#pro=totaldata[:,4]
+#pro_map=np.reshape(pro,(row,col))
+#plt.pyplot.imshow(pro_map,cmap='bone')
+
+#如果要输入到arcgis当中，就把pro_map保存为tif

SlopeCardResize.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov  2 16:03:36 2018
+
+@author: 75129
+"""
+
+import scipy.io as sio
+import numpy as np
+import matplotlib as plt
+import scipy.interpolate  as siInter
+
+def resample(img,x_size,y_size,method):
+#    plt.pyplot.figure()
+#    plt.pyplot.imshow(img)
+    (xOldSize,yOldSize)=img.shape
+    newImg=np.zeros((x_size,y_size))
+    pointsNew=np.where(newImg>-999999)
+    pointsNew=np.array(pointsNew,dtype=float)
+    pointsNew=pointsNew.T
+    valueImg=img.reshape((xOldSize*yOldSize,1))
+    pointsOld=np.where(img>-999999)
+    pointsOld=np.array(pointsOld,dtype=float)
+    pointsOld=pointsOld.T
+    pointsOld[:,0]=pointsOld[:,0]/(xOldSize-1)*(x_size-1)
+    pointsOld[:,1]=pointsOld[:,1]/(yOldSize-1)*(y_size-1)
+    valueNewImg=siInter.griddata(pointsOld,valueImg,pointsNew,method)
+    NewImg=np.reshape(valueNewImg,(x_size,y_size))
+#    plt.pyplot.figure()
+#    plt.pyplot.imshow(NewImg)
+    return NewImg
+    
+    
+    
+
+
+#methoddict={0:'cubic',1:'nearest',2:'cubic',3:'nearest',4:'nearest',5:'cubic',6:'cubic',7:'cubic',8:'cubic',9:'cubic',10:'cubic',11:'cubic',12:'nearest',13:'cubic',14:'cubic',15:'cubic'}
+#{'altitude','aspect','fault','landuse','lithology','ndvi','plan','profile','rainfall','river','road','slope','soil','spi','sti','twi'}
+size_x=40
+size_y=40
+num_factos=16
+num_samples=7598
+img_new=np.zeros((num_samples,size_x,size_y,num_factos))
+
+for it in range(num_samples):
+    filename='C:/Users/75129/Desktop/mypy/demo_all/yushan_all_OriCard/'+str(it)
+    img_resize=np.zeros((size_x,size_y,num_factos))
+    img=np.load(filename+'.npy')
+    (row,col)=img[:,:,0].shape
+    if row==0 :
+        img_resize=np.zeros(size_x,size_y,num_factos)
+        print('row col ==  0')
+    
+    elif row==1 or col==1:
+        img_resize=np.ones([size_x,size_y,num_factos])
+        for i in range(num_factos):
+            img_resize[:,:,i]=img_resize[:,:,i]*np.double(img[0,0,i])
+        print('row col ==  1')
+    else:        
+        for i in range(num_factos):
+            img_resize[:,:,i]=resample(img[:,:,i],size_x,size_y,'nearest')
+            
+#    newfilename='C:/Users/75129/Desktop/mypy/xp_kp_newsize8080/'+str(it)
+#    np.save(newfilename,img_resize)
+#    plt.pyplot.imshow(img_resize[:,:,0])
+    
+#    newfilename='C:/Users/75129/Desktop/mypy/xp_kp_newsize8080/'+str(it)
+#    img_it=np.load(newfilename+'.npy')
+    img_new[it,:,:,:]=img_resize
+    print(str(it)+' is done')
+#    print(it)
+newfilename='C:/Users/75129/Desktop/mypy/demo_all/yushan_all_ResizeCard/all_yushan'
+np.save(newfilename,img_new)
+
+
+    
+        
+    
+