zensvi 0.11.0

This package handles downloading, cleaning, analyzing street view imagery in a one-stop and zen manner.

ZenSVI

This package is a one-stop solution for downloading, cleaning, analyzing street view imagery. Detailed documentation can be found here.

Installation of zensvi

$ pip install zensvi

Installation of pytorch and torchvision

Since zensvi uses pytorch and torchvision, you may need to install them separately. Please refer to the official website for installation instructions.

Usage

Downloading Street View Imagery

Mapillary

For downloading images from Mapillary, utilize the MLYDownloader. Ensure you have a Mapillary client ID:

from zensvi.download import MLYDownloader

mly_api_key = "YOUR_OWN_MLY_API_KEY"  # Please register your own Mapillary API key
downloader = MLYDownloader(mly_api_key=mly_api_key)
# with lat and lon:
downloader.download_svi("path/to/output_directory", lat=1.290270, lon=103.851959)
# with a csv file with lat and lon:
downloader.download_svi("path/to/output_directory", input_csv_file="path/to/csv_file.csv")
# with a shapefile:
downloader.download_svi("path/to/output_directory", input_shp_file="path/to/shapefile.shp")
# with a place name that works on OpenStreetMap:
downloader.download_svi("path/to/output_directory", input_place_name="Singapore")

Running Segmentation

To perform image segmentation, use the Segmenter:

from zensvi.cv import Segmenter

segmenter = Segmenter(dataset="cityscapes", # or "mapillary"
                      task="semantic" # or "panoptic"
                      )
segmenter.segment("path/to/input_directory", 
                  dir_image_output = "path/to/image_output_directory",
                  dir_summary_output = "path/to/segmentation_summary_output"
                  )

Running Places365

To perform scene classification, use the ClassifierPlaces365:

# initialize the classifier
classifier = ClassifierPlaces365(
    device="cpu",  # device to use (either "cpu" or "gpu")
)

# set arguments
classifier = ClassifierPlaces365()
classifier.classify(
    "path/to/input_directory",
    dir_image_output="path/to/image_output_directory",
    dir_summary_output="path/to/classification_summary_output"
)

Running Low-Level Feature Extraction

To extract low-level features, use the get_low_level_features:

from zensvi.cv import get_low_level_features

get_low_level_features(
    "path/to/input_directory",
    dir_image_output="path/to/image_output_directory",
    dir_summary_output="path/to/low_level_feature_summary_output"
)

Transforming Images

Transform images from panoramic to perspective or fisheye views using the ImageTransformer:

from zensvi.transform import ImageTransformer

dir_input = "path/to/input"
dir_output = "path/to/output"
image_transformer = ImageTransformer(
    dir_input="path/to/input", 
    dir_output="path/to/output"
)
image_transformer.transform_images(
    style_list="perspective equidistant_fisheye orthographic_fisheye stereographic_fisheye equisolid_fisheye",  # list of projection styles in the form of a string separated by a space
    FOV=90,  # field of view
    theta=120,  # angle of view (horizontal)
    phi=0,  # angle of view (vertical)
    aspects=(9, 16),  # aspect ratio
    show_size=100,  # size of the image to show (i.e. scale factor)
)

Visualizing Results

To visualize the results, use the plot_map and plot_image functions:

from zensvi.visualization import plot_map, plot_image

# Plotting a map
plot_map(
    "path/to/pid_file.csv",  # path to the file containing latitudes and longitudes
    variable_name="vegetation", 
    plot_type="point"  # this can be either "point", "line", or "hexagon"
)

# Plotting images in a grid
plot_image(
    "path/to/image_directory", 
    4,  # number of rows
    5  # number of columns
)

Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

License

zensvi was created by Koichi Ito. It is licensed under the terms of the CC BY-SA 4.0.

Please cite the following paper if you use zensvi in a scientific publication: (place holder for the paper citation)

@article{ito2024zensvi,
  title={ZenSVI: One-Stop Python Package for Integrated Analysis of Street View Imagery},
  author={Ito, Koichi, XXX, XXX, XXX, ...},
  journal={XXX},
  volume={XXX},
  pages={XXX},
  year={2024}
}

Credits

zensvi was created with cookiecutter and the py-pkgs-cookiecutter template.