import os
import numpy as np
from polsartools.utils.proc_utils import process_chunks_parallel
from polsartools.utils.utils import conv2d,time_it
from .dxp_infiles import dxpc2files
[docs]
@time_it
def rvidp(infolder, window_size=1, outType="tif",
cog_flag=False, cog_overviews = [2, 4, 8, 16],
write_flag=True, max_workers=None,block_size=(512, 512),
progress_callback=None, # for QGIS plugin
):
"""
Computes Radar vegetation index from the input dual-polarization (dual-pol) C2 matrix data, and writes
the output in various formats (GeoTIFF or binary). The computation is performed in parallel for efficiency.
Example:
--------
>>> rvidp("path_to_C2_folder", window_size=5, outType="tif", cog_flag=True)
This will compute Radar vegetation index from the C2 matrix in the specified folder,
generating output in Geotiff format with Cloud Optimized GeoTIFF settings enabled.
Parameters:
-----------
infolder : str
Path to the input folder containing C2 matrix data.
window_size : int, optional
Size of the processing window (default is 1).
outType : str, optional
Output format of the files; can be "tif" (GeoTIFF) or "bin" (binary) (default is "tif").
cog_flag : bool, optional
If True, outputs Cloud Optimized GeoTIFF (COG) (default is False).
cog_overviews : list of int, optional
List of overview levels to be used for COGs (default is [2, 4, 8, 16]).
write_flag : bool, optional
Whether to write the computed output files (default is True).
max_workers : int, optional
Number of parallel workers for processing (default is None, which uses one less than the number of available CPU cores).
block_size : tuple of int, optional
Size of each processing block (default is (512, 512)), defining the spatial chunk dimensions used in parallel computation.
Returns:
--------
None
The function writes the computed Radar vegetation index to the specified output format.
Output Files:
-------------
- "rvidp.tif" or "rvidp.bin"
"""
input_filepaths = dxpc2files(infolder)
output_filepaths = []
if outType == "bin":
output_filepaths.append(os.path.join(infolder, "rvidp.bin"))
else:
output_filepaths.append(os.path.join(infolder, "rvidp.tif"))
process_chunks_parallel(input_filepaths, list(output_filepaths), window_size=window_size, write_flag=write_flag,
processing_func=process_chunk_rvidp,block_size=block_size, max_workers=max_workers, num_outputs=1,
cog_flag=cog_flag,
cog_overviews=cog_overviews,
progress_callback=progress_callback
)
def process_chunk_rvidp(chunks, window_size,*args):
kernel = np.ones((window_size,window_size),np.float32)/(window_size*window_size)
c11_T1 = np.array(chunks[0])
c12_T1 = np.array(chunks[1])+1j*np.array(chunks[2])
c21_T1 = np.conj(c12_T1)
c22_T1 = np.array(chunks[3])
if window_size>1:
c11_T1r = conv2d(np.real(c11_T1),kernel)
c11_T1i = conv2d(np.imag(c11_T1),kernel)
c11s = c11_T1r+1j*c11_T1i
c12_T1r = conv2d(np.real(c12_T1),kernel)
c12_T1i = conv2d(np.imag(c12_T1),kernel)
c12s = c12_T1r+1j*c12_T1i
c21_T1r = conv2d(np.real(c21_T1),kernel)
c21_T1i = conv2d(np.imag(c21_T1),kernel)
c21s = c21_T1r+1j*c21_T1i
c22_T1r = conv2d(np.real(c22_T1),kernel)
c22_T1i = conv2d(np.imag(c22_T1),kernel)
c22s = c22_T1r+1j*c22_T1i
c2_det = (c11s*c22s-c12s*c21s)
c2_trace = c11s+c22s
rvidp = np.real(4*c22s/c2_trace)
else:
c2_det = (c11_T1*c22_T1-c12_T1*c21_T1)
c2_trace = c11_T1+c22_T1
rvidp = np.real(4*c22_T1/c2_trace)
return rvidp.astype(np.float32)
