def prediction():
#recalling data
data = forclassification()
#recalling trained model
model = training()
a = model.predict(data)
print(a.shape)
#reshaping the prediction or classification result to original image size
clmap = np.reshape(a, (img2.shape[1], img2.shape[2]))
#converting array into TIF map
output = "landslide_detection_map.tif"
raster.export(clmap, ds, filename=output, dtype="int")
return (clmap, clmap.shape)
def exportTifChip(self,
labelType,
featureWindow,
labelWindow=None,
featureFile='x.tif',
labelFile='y.tif',
ds=None):
if labelType == 'label':
raster.export(featureWindow, ds, filename=featureFile, bands='all')
if labelType == 'mask':
raster.export(featureWindow, ds, filename=featureFile, bands='all')
raster.export(labelWindow, ds, filename=labelFile)
#predicted=np.argmax(predicted, axis=1)
#print("distribution de predicted")
#unique, counts = np.unique(predicted, return_counts=True)
#print(np.asarray((unique, counts/predicted.size)).T)
#print("distribution de predicted filter")
#unique, counts = np.unique(predicted_filter, return_counts=True)
#print(np.asarray((unique, counts/predicted_filter.size)).T)
#Export raster
#prediction = np.reshape(predicted, (ds1.RasterYSize, ds1.RasterXSize))
outFile_1 = 'data_predicted_v24_randomforest_nofilter_Seredou_2017.tif'
raster.export(prediction_1,ds1, filename=outFile_1, dtype='float')
#â—„prediction_filter = np.reshape(predicted_filter, (ds1.RasterYSize, ds1.RasterXSize-1))
outFile_2 = 'data_predicted_v24_randomforest_nofilter_Seredou_2015.tif'
raster.export(prediction_2, ds1, filename=outFile_2, dtype='float')
#enregistrement modele
from joblib import dump,load
dump(model, "randomforest_v24_from_seredou2015&2017.sav")
#-----------------------
## test sur DIECKE 2017
#from joblib import dump,load
#model=load("random_forest_median_filter_v22_from_seredou2015&2017.sav")
ds2, tempArr2 = raster.read(RasterFile2)
nBands1, rows1, cols1 = ds1.RasterCount, ds1.RasterXSize, ds1.RasterYSize
features1 = np.empty((nBands1, cols1, rows1))
features2 = np.empty((nBands1, cols1, rows1))
features1[:, :, :] = tempArr1
features2[:, :, :] = tempArr2
features_result1 = np.empty((cols1, rows1))
features_result2 = np.empty((cols1, rows1))
for i in range(0, cols1):
for j in range(0, rows1):
list1 = []
list2 = []
for k in range(0, nBands1):
list1.append(features1[k, i, j])
list2.append(features2[k, i, j])
r, p_value = pearsonr(list1, list2)
features_result1[i, j] = r
features_result2[i, j] = p_value
outFile1 = 'd:/aa.tif'
outFile2 = 'd:/bb.tif'
raster.export(features_result1,
ds1,
filename=outFile1,
dtype='float',
bands='all')
raster.export(features_result2,
ds1,
filename=outFile2,
dtype='float',
bands='all')
features = np.empty((rows * cols, kSize, kSize, nBands))
n = 0
for row in range(margin, rows + margin):
for col in range(margin, cols + margin):
feat = mxBands[:, row - margin:row + margin + 1,
col - margin:col + margin + 1]
b1, b2, b3, b4, b5, b6 = feat
feat = np.dstack((b1, b2, b3, b4, b5, b6))
features[n, :, :, :] = feat
n += 1
return (features)
# Call the function to generate features tensor
new_features = CNNdataGenerator(featuresHyderabad, kSize=11)
print('Shape of the new features', new_features.shape)
# Predict new data and export the probability raster
newPredicted = model.predict(new_features)
newPredicted = newPredicted[:, 1]
prediction = np.reshape(newPredicted, (ds.RasterYSize, ds.RasterXSize))
raster.export(prediction, ds, filename=outFile, dtype='float')
plt.imshow(prediction)
plt.show()