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utils.py
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import os
import folium
import confuse
import numpy as np
from math import isnan
import geopandas as gpd
from shapely.geometry import Point
from PIL import Image
from tqdm import tqdm
# Initialzie custom basemaps for folium
basemaps = {
'Google Maps': folium.TileLayer(
tiles = 'https://mt1.google.com/vt/lyrs=m&x={x}&y={y}&z={z}',
attr = 'Google',
name = 'Google Maps',
overlay = True,
control = True
),
'Google Satellite': folium.TileLayer(
tiles = 'https://mt1.google.com/vt/lyrs=s&x={x}&y={y}&z={z}',
attr = 'Google',
name = 'Google Satellite',
overlay = True,
control = True
),
'Google Terrain': folium.TileLayer(
tiles = 'https://mt1.google.com/vt/lyrs=p&x={x}&y={y}&z={z}',
attr = 'Google',
name = 'Google Terrain',
overlay = True,
control = True
),
'Google Satellite Hybrid': folium.TileLayer(
tiles = 'https://mt1.google.com/vt/lyrs=y&x={x}&y={y}&z={z}',
attr = 'Google',
name = 'Google Satellite',
overlay = True,
control = True
),
'Esri Satellite': folium.TileLayer(
tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{z}/{y}/{x}',
attr = 'Esri',
name = 'Esri Satellite',
overlay = True,
control = True
),
'openstreetmap': folium.TileLayer('openstreetmap'),
'cartodbdark_matter': folium.TileLayer('cartodbdark_matter')
}
# Dictionary of JavaScript files (More Readable)
scripts_dir = './scripts/'
scripts_files = [f for f in os.listdir(scripts_dir) if f.endswith('.js')]
Scripts = {}
for f in scripts_files:
key = f.split('.')[0].upper()
with open(scripts_dir + f) as f:
Scripts[key] = f.read()
def calculate_bbox(df, field):
'''
Calculate the bounding box of a specfic field ID in a given data frame
'''
field = int(field)
bbox = df.loc[df['Field_Id'] == field].bounds
r = bbox.iloc[0]
return [r.minx, r.miny, r.maxx, r.maxy]
def tiff_to_geodataframe(im, metric, date, crs):
'''
Convert a tiff image to a geodataframe
'''
x_cords = im.coords['x'].values
y_cords = im.coords['y'].values
vals = im.values
dims = vals.shape
points = []
v_s = []
for lat in range(dims[1]):
y = y_cords[lat]
for lon in range(dims[2]):
x = x_cords[lon]
v = vals[:,lat,lon]
if isnan(v[0]):
continue
points.append(Point(x,y))
v_s.append(v.item())
d = {f'{metric}_{date}': v_s, 'geometry': points}
df = gpd.GeoDataFrame(d, crs = crs)
return df
def get_bearer_token_headers(bearer_token):
'''
Get the bearer token headers to be used in the request to the SentinelHub API
'''
headers = {
'Content-Type': 'application/json',
'Authorization': 'Bearer '+ bearer_token,
}
return headers
def get_downloaded_location_img_path(clientName, metric, date, field, extension='tiff'):
'''
Get the path of the downloaded image in TIFF based on the:
'''
date_dir = f'./{clientName}/raw/{metric}/{date}/field_{field}/'
print(f'True Color Date Dir: {date_dir}')
os.makedirs(date_dir, exist_ok=True)
intermediate_dirs = os.listdir(date_dir)
print(f'Intermediate Dirs: {intermediate_dirs}')
if len(intermediate_dirs) == 0:
return None
imagePath = f'{date_dir}{os.listdir(date_dir)[0]}/response.{extension}'
print(f'Image Path: {imagePath}')
if not os.path.exists(imagePath):
return None
print(f'Image Path: {imagePath}')
return imagePath
def get_masked_location_img_path(clientName, metric, date, field):
'''
Get the path of the downloaded image after applying the mask in TIFF based on the:
'''
date_dir = f'./{clientName}/processed/{metric}/{date}/field_{field}/'
imagePath = date_dir + 'masked.tiff'
return imagePath
def get_curated_location_img_path(clientName, metric, date, field):
'''
Get the path of the downloaded image after applying the mask and converting it to geojson formay based on the:
'''
date_dir = f'./{clientName}/curated/{metric}/{date}/field_{field}/'
imagePath = date_dir + 'masked.geojson'
if os.path.exists(imagePath):
return imagePath
else:
return None
def parse_app_config(path=r'config-fgm-dev.yaml'):
config = confuse.Configuration('CropHealth', __name__)
config.set_file(path)
return config
def fix_image(img):
def normalize(band):
band_min, band_max = (band.min(), band.max())
return ((band-band_min)/((band_max - band_min)))
def brighten(band):
alpha=3
beta=0
return np.clip(alpha*band+beta, 0,255)
def gammacorr(band):
gamma=0.9
return np.power(band, 1/gamma)
red = img[:, :, 0]
green = img[:, :, 1]
blue = img[:, :, 2]
red_b=brighten(red)
blue_b=brighten(blue)
green_b=brighten(green)
red_bg=gammacorr(red_b)
blue_bg=gammacorr(blue_b)
green_bg=gammacorr(green_b)
red_bgn = normalize(red_bg)
green_bgn = normalize(green_bg)
blue_bgn = normalize(blue_bg)
rgb_composite_bgn= np.dstack((red_b, green_b, blue_b))
return rgb_composite_bgn
def creat_gif(dataset, gif_name, duration=50):
'''
Create a gif from a list of images
'''
imgs = [Image.fromarray((255*img).astype(np.uint8)) for img in dataset]
# duration is the number of milliseconds between frames; this is 40 frames per second
imgs[0].save(gif_name, save_all=True, append_images=imgs[1:], duration=duration, loop=1)
def add_lat_lon_to_gdf_from_geometry(gdf):
gdf['Lat'] = gdf['geometry'].apply(lambda p: p.x)
gdf['Lon'] = gdf['geometry'].apply(lambda p: p.y)
return gdf
def gdf_column_to_one_band_array(gdf, column_name):
gdf = gdf.sort_values(by=['Lat', 'Lon'])
gdf = gdf.reset_index(drop=True)
unique_lats_count = gdf['Lat'].nunique()
unique_lons_count = gdf['Lon'].nunique()
rows_arr = [[] for i in range(unique_lats_count)]
column_values = gdf[column_name].values
for i in tqdm(range(len(column_values))):
row_index = i // unique_lons_count
rows_arr[row_index].append(column_values[i])
max_row_length = max([len(row) for row in rows_arr])
for row in rows_arr:
while len(row) < max_row_length:
row.append(0)
rows_arr = np.array(rows_arr)
return rows_arr