utils/eval_baselines.py

"""
Runs several baseline compression algorithms and stores results for each FITS file in a csv.
This code is written functionality-only and cleaning it up is a TODO.
"""


import os
import re
from pathlib import Path
import argparse
import os.path
from astropy.io import fits
import numpy as np
from time import time
import pandas as pd
from tqdm import tqdm

from astropy.io.fits import CompImageHDU
from imagecodecs import (
 jpeg2k_encode, 
 jpeg2k_decode, 
 jpegls_encode, 
 jpegls_decode, 
 jpegxl_encode,
 jpegxl_decode,
 rcomp_encode,
 rcomp_decode,
)

# Functions that require some preset parameters. All others default to lossless.

jpegxl_encode_max_effort_preset = lambda x: jpegxl_encode(x, lossless=True, effort=9)
jpegxl_encode_preset = lambda x: jpegxl_encode(x, lossless=True)

def find_matching_files():
 """
 Returns list of test set file paths.
 """
 df = pd.read_json("./splits/full_test.jsonl", lines=True)
 return list(df['image'])

def benchmark_imagecodecs_compression_algos(arr, compression_type):

 encoder, decoder = ALL_CODECS[compression_type]

 write_start_time = time()
 encoded = encoder(arr)
 write_time = time() - write_start_time

 read_start_time = time()
 if compression_type == "RICE":
 decoded = decoder(encoded, shape=arr.shape, dtype=np.uint16)
 else:
 decoded = decoder(encoded)
 read_time = time() - read_start_time

 assert np.array_equal(arr, decoded)

 buflength = len(encoded)

 return {compression_type + "_BPD": buflength / arr.size,
 compression_type + "_WRITE_RUNTIME": write_time,
 compression_type + "_READ_RUNTIME": read_time,
 #compression_type + "_TILE_DIVISOR": np.nan,
 }

def main(dim):

 save_path = f"baseline_results_{dim}.csv"

 file_paths = find_matching_files()
 
 df = pd.DataFrame(columns=columns, index=[str(p) for p in file_paths])
 
 print(f"Number of files to be tested: {len(file_paths)}")
 
 ct = 0

 for path in tqdm(file_paths):
 with fits.open(path) as hdul:
 if dim == '2d': # run on just 2d arrays of the first timestep frame
 arrs = [hdul[1].data[0][0]]
 elif dim == '2d_diffs' and len(hdul[1].data[0]) > 1: # run on ALL residual frame images
 arrs = [hdul[1].data[0][i + 1] - hdul[1].data[0][i] for i in range(len(hdul[1].data[0]) - 1)]
 elif dim == '3dt' and len(hdul[1].data[0]) > 2: # compress the first 3 timestep frames as a 3D tensor
 arrs = [hdul[1].data[0][0:3]]
 else:
 continue

 ct += 1
 if ct % 10 == 0:
 print(df.mean())
 df.to_csv(save_path)
 for group, arr in enumerate(arrs):
 for algo in ALL_CODECS.keys():
 try:
 if algo == "JPEG_2K" and dim == '3dt':
 test_results = benchmark_imagecodecs_compression_algos(arr.transpose(1, 2, 0), algo)
 else:
 test_results = benchmark_imagecodecs_compression_algos(arr, algo)

 for column, value in test_results.items():
 if column in df.columns:
 df.at[path + f"_{group}", column] = value

 except Exception as e:
 print(f"Failed at {path} under exception {e}.")


if __name__ == "__main__":
 parser = argparse.ArgumentParser(description="Process some 2D or 3D data.")
 parser.add_argument(
 "dimension", 
 choices=['2d', '2d_diffs', '3dt'],
 help="Specify whether the data is 2d, 2d_diffs (compressing residuals between second and first exposures), or 3dt (3d time dimension)."
 )
 args = parser.parse_args()
 dim = args.dimension.lower()
 
 # RICE REQUIRES UNIQUE INPUT OF ARR SHAPE AND DTYPE INTO DECODER
 
 if dim == '2d' or dim == '2d_diffs':
 ALL_CODECS = {
 "JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
 "JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
 "JPEG_2K": [jpeg2k_encode, jpeg2k_decode], 
 "JPEG_LS": [jpegls_encode, jpegls_decode],
 "RICE": [rcomp_encode, rcomp_decode],
 }
 else:
 ALL_CODECS = {
 "JPEG_XL_MAX_EFFORT": [jpegxl_encode_max_effort_preset, jpegxl_decode],
 "JPEG_XL": [jpegxl_encode_preset, jpegxl_decode],
 "JPEG_2K": [jpeg2k_encode, jpeg2k_decode],
 }

 columns = []
 for algo in ALL_CODECS.keys():
 columns.append(algo + "_BPD")
 columns.append(algo + "_WRITE_RUNTIME")
 columns.append(algo + "_READ_RUNTIME")
 #columns.append(algo + "_TILE_DIVISOR")
 
 main(dim)