from rasterio import features
from rasterio.plot import show
from rasterio.windows import Window, transform

Vector to Raster mask

convert vectors into raster masks where each pixel is a numerical representation of a label/category

generate_mask[source]

generate_mask(tiff_file, shape_file, output_file, labels_column, labels_dict:Dict[str, int], plot=False)

Generates a segmentation mask for one TIFF image. Returns: image (np.array): A binary mask as a numpy array

Type Default Details
tiff_file Path to reference TIFF file
shape_file Path to shapefile
output_file Path to output file
labels_column Feature in the shapefile that contains labels/categories
labels_dict typing.Dict[str, int] Dictionary of desired labels and assigned values for the mask
plot bool False Plots vector, reference TIF image, and raster mask output

Raster to Dataframe

Reads the bands for each image in the list and returns a dataframe where each band is one column with the image name as a suffix for column name.

read_bands[source]

read_bands(image_list:None, mask:str)

Reads the bands for each image in the list and returns a dataframe where each band is one column with the image name as a suffix for column name.

Test data

Generating a raster mask 

tiff_file = "../data/vector_to_raster_mask_sample/cabanglasan.tif"
shape_file = "../data/vector_to_raster_mask_sample/labels_20220816.gpkg"
target_file = shape_file.replace("gpkg", "tiff")

Given a raster image of a certain area that will be masked to use as a reference and a shape file that contains that area. Note that the shape file must include a column that contains labels/categories.

gpd.read_file(shape_file).head(3)
year label uid ADM3_EN ADM3_PCODE ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE geometry
0 2017.0 mining 72_2017_mining Sofronio Española PH175324000 Palawan PH175300000 Region IV-B PH170000000 MULTIPOLYGON Z (((117.95961 9.03303 0.00000, 1...
1 2017.0 mining 71_2017_mining Sofronio Española PH175324000 Palawan PH175300000 Region IV-B PH170000000 MULTIPOLYGON Z (((117.95507 9.03809 0.00000, 1...
2 2017.0 mining 70_2017_mining Sofronio Española PH175324000 Palawan PH175300000 Region IV-B PH170000000 MULTIPOLYGON Z (((117.95663 9.03869 0.00000, 1...

And a dictionary with your desired labels and assigned values in creating a mask

labels = {
    "mining": 1,
    "neg": 2,
    "agriculture": 3,
    "product_extraction": 4,
    "kaingin": 5,
    "biophysical": 6,
}

Input them in the generate_mask function to create a raster mask of the same dimension as the reference raster image

masks, grids, values = generate_mask(
    tiff_file=tiff_file,
    shape_file=shape_file,
    output_file=target_file,
    labels_column="label",
    labels_dict=labels,
    plot=True,
)

/tmp/ipykernel_23338/3832611824.py:14: FutureWarning: Currently, index_parts defaults to True, but in the future, it will default to False to be consistent with Pandas. Use `index_parts=True` to keep the current behavior and True/False to silence the warning.
  gs = gdf.explode()
No description has been provided for this image 
masks

array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
grids

array([[0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       ...,
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0],
       [0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
values

{'mining': 1,
 'neg': 2,
 'agriculture': 3,
 'product_extraction': 4,
 'kaingin': 5,
 'biophysical': 6}

Choose raster images to convert into a dataframe and use the generated raster mask to add the labels the converted dataframe.

tiff_files = ["../data/vector_to_raster_mask_sample/cabanglasan.tif"]
mask_file = "../data/vector_to_raster_mask_sample/labels_20220816.tiff"

data = read_bands(tiff_files, mask_file)

data
B1_0 B2_0 B3_0 B4_0 B5_0 B6_0 B7_0 B8_0 B9_0 B10_0 B11_0 B12_0 label
0 0.1198 0.09635 0.09330 0.0698 0.10665 0.20250 0.2490 0.23525 0.28125 0.0377 0.19925 0.1002 0
1 0.1198 0.09580 0.09245 0.0708 0.10665 0.20250 0.2490 0.23925 0.28125 0.0377 0.19925 0.1002 0
2 0.1148 0.09420 0.09460 0.0707 0.10380 0.20395 0.2478 0.23150 0.27165 0.0385 0.18240 0.0902 0
3 0.1148 0.09190 0.08850 0.0631 0.10380 0.20395 0.2478 0.23300 0.27165 0.0385 0.18240 0.0902 0
4 0.1148 0.09350 0.09080 0.0643 0.10565 0.20830 0.2466 0.24205 0.26990 0.0385 0.18050 0.0894 0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
775824 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775825 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775826 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775827 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0
775828 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.00000 0.0000 0.00000 0.0000 0

775829 rows × 13 columns