File size: 4,783 Bytes
a462809 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
"""
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) |