Update app.py
Browse files
app.py
CHANGED
@@ -64,19 +64,30 @@ def predict(lat, lon):
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timer('unzip data',start_time_unzip)
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start_time_processing = timer('processing data',None)
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name,cld_prob,days_ago = select_best_cloud_coverage_tile()
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bandes_path_10,bandes_path_20,bandes_path_60,tile_path,path_cld_20,path_cld_60 =paths(name)
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# create image dataset
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images_10 = extract_sub_image(bandes_path_10,tile_path,cord)
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# bandes with 20m resolution
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#path_cld_20
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images_20 = extract_sub_image(bandes_path_20,tile_path,cord,20,1)
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# bandes with 60m resolution
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#path_cld_60
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images_60 = extract_sub_image(bandes_path_60,tile_path,cord,60)
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#
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feature = images_10.tolist()+images_20.tolist()+images_60.tolist()
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bands = ['B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B11', 'B12','B01','B09']
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X = pd.DataFrame([feature],columns = bands)
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@@ -88,11 +99,15 @@ def predict(lat, lon):
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# make prediction
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biomass = loaded_model.predict(X)[0]
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carbon = 0.55*biomass
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# NDVI
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ndvi_index = ndvi(cord,name)
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timer('processing data',start_time_processing)
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-
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# deleted download files
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delete_tiles()
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timer('unzip data',start_time_unzip)
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start_time_processing = timer('processing data',None)
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start_time_select_best_tile = timer('select best tile',None)
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name,cld_prob,days_ago = select_best_cloud_coverage_tile()
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bandes_path_10,bandes_path_20,bandes_path_60,tile_path,path_cld_20,path_cld_60 =paths(name)
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timer('select best tile',start_time_select_best_tile)
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start_time_10m = timer('create 10m image',None)
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# create image dataset
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images_10 = extract_sub_image(bandes_path_10,tile_path,cord)
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timer('create 10m image',start_time_10m)
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start_time_20m = timer('create 20m image',None)
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# bandes with 20m resolution
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#path_cld_20
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images_20 = extract_sub_image(bandes_path_20,tile_path,cord,20,1)
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start_time_20m = timer('create 20m image',start_time_20m)
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start_time_60m = timer('create 60m image',None)
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# bandes with 60m resolution
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#path_cld_60
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images_60 = extract_sub_image(bandes_path_60,tile_path,cord,60)
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start_time_60m = timer('create 60m image',start_time_60m)
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#
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start_time_make_prediction = timer('make prediction',None)
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feature = images_10.tolist()+images_20.tolist()+images_60.tolist()
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bands = ['B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B11', 'B12','B01','B09']
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X = pd.DataFrame([feature],columns = bands)
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# make prediction
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biomass = loaded_model.predict(X)[0]
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carbon = 0.55*biomass
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timer('make prediction',start_time_make_prediction)
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# NDVI
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start_time_make_ndvi = timer('NDVI calculation',None)
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ndvi_index = ndvi(cord,name)
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timer('NDVI calculation',start_time_make_ndvi)
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timer('processing data',start_time_processing)
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# deleted download files
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delete_tiles()
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