Merge pull request #47 from iamtekson/dev

advance deep learning related docs update

docs/source/pages/advance.md

@@ -7,15 +7,15 @@ The main motivation of this library is to create the easy pipeline to handle the
 The library is able to create the labels or masks from the vector data. The vector data can be in any format such as geojson, shapefile, etc. The library is able to create the masks of the vector data in the raster format. The below is the example of creating the mask from the shapefile,
 
 ```python
-from geotile.geotile import GeoTile
+from geotile import GeoTile
 gt = GeoTile('/path/to/raster/file.tif')
 gt.rasterization('/path/to/vector/file.shp', output_folder='/path/to/output/file.tif')
 ```
 
 If you didn't pass the `value_col` parameter, the library will create the mask of the vector data with binary values, i.e. 0 and 1. If you want to create the mask with the specific values, you can pass the `value_col` parameter as below,
 
 ```python
-from geotile.geotile import GeoTile
+from geotile import GeoTile
 gt = GeoTile('/path/to/raster/file.tif')
 gt.rasterization('/path/to/vector/file.shp', out_path='/path/to/output/file.tif', value_col='class')
 ```
@@ -29,7 +29,7 @@ Using the rasterization function, you don't need to worry about the metadata and
 The library is able to create the tiles from both images and masks. The below is the example of creating the tiles from the image,
 
 ```python
-from geotile.geotile import GeoTile
+from geotile import GeoTile
 
 # create the tiles of the raster imagery
 gt_img = GeoTile('/path/to/raster/file.tif')
@@ -44,7 +44,7 @@ If you don't want to save the tiles, you have to pass the `save_tiles=False` par
 The below is the example of creating the tiles without saving them,
 
 ```python
-from geotile.geotile import GeoTile
+from geotile import GeoTile
 
 # create the tiles of the raster imagery
 gt_img = GeoTile('/path/to/raster/file.tif')
@@ -61,15 +61,15 @@ If you want to preprocess (such as normalization, removing nan, etc.) the tiles
 
 ```python
 # generate the tiles
-gt.generate_tiles(save_tiles=False)
-gt.normalize_tiles()
-gt.convert_nan_to_zero()
+gt_img.generate_tiles(save_tiles=False)
+gt_img.convert_nan_to_zero()
+gt_img.normalize_tiles()
 
 # save the tiles in numpy format; shape of the array will be (num_tiles, tile_x, tile_y, bands)
-gt.save_numpy(output_folder=r'/path/to/output/folder')
+gt_img.save_numpy(output_folder=r'/path/to/output/folder')
 
 # save the tiles in tif format
-gt.save_tiles(output_folder=r'/path/to/output/folder', prefix='tile_')
+gt_img.save_tiles(output_folder=r'/path/to/output/folder', prefix='tile_')
 ```
 
 If your main goal is to train the deep learning model, you can only save the tiles as a numpy array and ignore saving tiles as a tif file. The numpy array will be more efficient and faster to load in the deep learning model.
@@ -79,7 +79,7 @@ If your main goal is to train the deep learning model, you can only save the til
 You can now use your deep learning model to predict the tiles. The below is the example of predicting the tiles,
 
 ```python
-from geotile.geotile import GeoTile
+from geotile import GeoTile
 from geotile import mosaic
 
 # create the tiles of the raster imagery