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feat: flood detection algorithm #103

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3 changes: 2 additions & 1 deletion requirements.txt
Original file line number Diff line number Diff line change
Expand Up @@ -5,4 +5,5 @@ postgis==1.0.4
uvicorn==0.30.1
boto3
pyyaml
gunicorn
gunicorn
scikit-image
4 changes: 3 additions & 1 deletion src/cogserver/algorithms/__init__.py
Original file line number Diff line number Diff line change
@@ -1,8 +1,10 @@
from titiler.core.algorithm import Algorithms, algorithms as default_algorithms
from .rca import RapidChangeAssessment
from .flood_detection import DetectFlood

algorithms: Algorithms = default_algorithms.register(
{
"rca": RapidChangeAssessment
"rca": RapidChangeAssessment,
"flood_detection": DetectFlood,
}
)
89 changes: 89 additions & 0 deletions src/cogserver/algorithms/flood_detection.py
Original file line number Diff line number Diff line change
@@ -0,0 +1,89 @@
# Credit: Sashka Warner (https://github.com/sashkaw/flood-data-api)
"""
MIT License

Copyright (c) 2023 Sashka Warner

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

from typing import List, Sequence

import numpy as np
from titiler.core.algorithm import BaseAlgorithm
from rio_tiler.models import ImageData
from skimage.filters import threshold_otsu


class DetectFlood(BaseAlgorithm):
title: str = "Flood detection "
description: str = "Algorithm to calculate Modified Normalized Difference Water Index (MNDWI), and apply Otsu thresholding algorithm to identify surface water"

"""
Desc: Algorithm to calculate Modified Normalized Difference Water Index (MNDWI),
and apply Otsu thresholding algorithm to identify surface water.
"""

input_bands: List = [
{'title': 'Green band', 'description': 'The green band with the wavelength between 0.53µm - 0.59µm',
'required': True,
'keywords': ['green', 'b3']},
{'title': 'Short wave infrared band', 'description': 'The SWIR band with wavelength between 0.9μ – 1.7μm',
'required': True,
'keywords': ['swir', 'b6']},
]
input_description: str = "The bands that will be used to make this calculation"

# Metadata
input_nbands: int = 2
output_nbands: int = 1
output_min: Sequence[int] = [-1]
output_max: Sequence[int] = [1]
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output_colormap_name: str = 'viridis'
output_description: str = "The output is a binary image where 1 represents water and 0 represents non-water"

def __call__(self, img: ImageData, *args, **kwargs):
# Extract bands of interest
green_band = img.data[0].astype("float32")
swir_band = img.data[1].astype("float32")

# Calculate Modified Normalized Difference Water Index (MNDWI)
numerator = (green_band - swir_band)
denominator = (green_band + swir_band)
# Use np.divide to avoid divide by zero errors
mndwi_arr = np.divide(numerator, denominator, np.zeros_like(numerator), where=denominator != 0)

# Apply Otsu thresholding method
otsu_threshold = threshold_otsu(mndwi_arr)

# Use Otsu threshold to classify the computed MNDWI
classified_arr = mndwi_arr >= otsu_threshold

# Reshape data -> ImageData only accepts image in form of (count, height, width)
# classified_arr = np.around(classified_arr).astype(int)
# classified_arr = np.expand_dims(classified_arr, axis=0).astype(self.output_dtype)
classified_arr = np.expand_dims(classified_arr, axis=0).astype(int)

return ImageData(
classified_arr,
img.mask,
assets=img.assets,
crs=img.crs,
bounds=img.bounds,
)
11 changes: 6 additions & 5 deletions src/cogserver/dependencies.py
Original file line number Diff line number Diff line change
Expand Up @@ -3,17 +3,17 @@
from typing import List
import base64

def parse_signed_url(url:str=None):

def parse_signed_url(url: str = None):
if '?' in url:
furl, b64token = url.split('?')
try:
decoded_token = base64.b64decode(b64token).decode()
except Exception:
decoded_token = b64token
decoded_url = f'{furl}?{decoded_token}'
decoded_url = f'{furl}?{decoded_token}'
else:
decoded_url = f'{url}'
decoded_url = f'{url}'
return decoded_url


Expand Down Expand Up @@ -42,6 +42,7 @@ def SignedDatasetPath(url: Annotated[str, Query(description="Unsigned/signed dat
"""
return parse_signed_url(url=url)


def SignedDatasetPaths(url: Annotated[List[str], Query(description="Unsigned/signed dataset URLs")]) -> str:
"""
FastAPI dependency function that enables
Expand All @@ -61,8 +62,8 @@ def SignedDatasetPaths(url: Annotated[List[str], Query(description="Unsigned/sig
The returned value is a str representing a RAM stored GDAL VRT file
which Titiler will use to resolve the request

Obviously the rasters need to spatially overlap. Additionaly, the VRT can be created with various params
(spatial align, resolution, resmapling) that, to some extent can influence the performace of the server
Obviously the rasters need to spatially overlap. Additionally, the VRT can be created with various params
(spatial align, resolution, resampling) that, to some extent can influence the performance of the server

"""
decoded_urls = list()
Expand Down