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sat_processor.py
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sat_processor.py
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import rasterio as rio
import numpy as np
from rio_color import operations, utils
from PIL import Image
import PIL
from rich import print as rprint
from felicette.utils.color import color
from felicette.utils.gdal_pansharpen import gdal_pansharpen
from felicette.utils.file_manager import file_paths_wrt_id
from felicette.utils.image_processing_utils import process_sat_image
from felicette.utils.sys_utils import display_file
# increase PIL image processing pixels count limit
PIL.Image.MAX_IMAGE_PIXELS = 933120000
def process_landsat_vegetation(id, bands):
# get paths of files related to this id
paths = file_paths_wrt_id(id)
# stack NIR, R, G bands
# open files from the paths, and save it as stack
b5 = rio.open(paths["b5"])
b4 = rio.open(paths["b4"])
b3 = rio.open(paths["b3"])
# read as numpy ndarrays
nir = b5.read(1)
r = b4.read(1)
g = b3.read(1)
with rio.open(
paths["stack"],
"w",
driver="Gtiff",
width=b4.width,
height=b4.height,
count=3,
crs=b4.crs,
transform=b4.transform,
dtype=b4.dtypes[0],
photometric="RGB",
) as rgb:
rgb.write(nir, 1)
rgb.write(r, 2)
rgb.write(g, 3)
rgb.close()
source_path_for_rio_color = paths["stack"]
rprint("Let's make our 🌍 imagery a bit more colorful for a human eye!")
# apply rio-color correction
ops_string = "sigmoidal rgb 20 0.2"
# refer to felicette.utils.color.py to see the parameters of this function
# Bug: number of jobs if greater than 1, fails the job
color(
1,
"uint16",
source_path_for_rio_color,
paths["vegetation_path"],
ops_string.split(","),
{"photometric": "RGB"},
)
# resize and save as jpeg image
print("Generated 🌍 images!🎉")
rprint("[yellow]Please wait while I resize and crop the image :) [/yellow]")
process_sat_image(paths["vegetation_path"], paths["vegetation_path_jpeg"])
rprint("[blue]GeoTIFF saved at:[/blue]")
print(paths["vegetation_path"])
rprint("[blue]JPEG image saved at:[/blue]")
print(paths["vegetation_path_jpeg"])
# display generated image
display_file(paths["vegetation_path_jpeg"])
def process_landsat_rgb(id, bands):
# get paths of files related to this id
paths = file_paths_wrt_id(id)
# stack R,G,B bands
# open files from the paths, and save it as stack
b4 = rio.open(paths["b4"])
b3 = rio.open(paths["b3"])
b2 = rio.open(paths["b2"])
# read as numpy ndarrays
r = b4.read(1)
g = b3.read(1)
b = b2.read(1)
with rio.open(
paths["stack"],
"w",
driver="Gtiff",
width=b4.width,
height=b4.height,
count=3,
crs=b4.crs,
transform=b4.transform,
dtype=b4.dtypes[0],
photometric="RGB",
) as rgb:
rgb.write(r, 1)
rgb.write(g, 2)
rgb.write(b, 3)
rgb.close()
source_path_for_rio_color = paths["stack"]
# check if band 8, i.e panchromatic band has to be processed
if 8 in bands:
# pansharpen the image
rprint(
"Pansharpening image, get ready for some serious resolution enhancement! ✨"
)
gdal_pansharpen(["", paths["b8"], paths["stack"], paths["pan_sharpened"]])
# set color operation's path to the pansharpened-image's path
source_path_for_rio_color = paths["pan_sharpened"]
rprint("Let's make our 🌍 imagery a bit more colorful for a human eye!")
# apply rio-color correction
ops_string = "sigmoidal rgb 20 0.2"
# refer to felicette.utils.color.py to see the parameters of this function
# Bug: number of jobs if greater than 1, fails the job
color(
1,
"uint16",
source_path_for_rio_color,
paths["output_path"],
ops_string.split(","),
{"photometric": "RGB"},
)
# resize and save as jpeg image
print("Generated 🌍 images!🎉")
rprint("[yellow]Please wait while I resize and crop the image :) [/yellow]")
process_sat_image(paths["output_path"], paths["output_path_jpeg"])
rprint("[blue]GeoTIFF saved at:[/blue]")
print(paths["output_path"])
rprint("[blue]JPEG image saved at:[/blue]")
print(paths["output_path_jpeg"])
# display generated image
display_file(paths["output_path_jpeg"])
def process_landsat_data(id, bands):
if bands == [2, 3, 4] or bands == [2, 3, 4, 8]:
process_landsat_rgb(id, bands)
elif bands == [3, 4, 5]:
process_landsat_vegetation(id, bands)