TLDR; Of the various compression algorithms available in python gzip, bz2, lzma, etc, which has the best decompression performance?
Full discussion:
Python 3 has various modules for compressing/decompressing data
including gzip, bz2 and lzma. gzip and bz2 additionally have different compression levels you can set.
If my goal is to balance file size (/compression ratio) and decompression speed (compression speed is not a concern), which is going to be the best choice? Decompression speed is more important than file size, but as the uncompressed files in question would be around 600-800MB each (32-bit RGB .png image files), and I have a dozen of them, I do want some compression.
My use case is that I am loading a dozen images from disk, doing some processing on them (as a numpy array) and then using the processed array data in my program.
pickle) rather than loading the raw, unprocessed, images every time. Initial tests were promising - loading the raw/uncompressed pickled data took less than a second, vs 3 or 4 seconds to load and process the original image - but as mentioned resulted in file sizes of around 600-800MB, while the original png images were only around 5MB. So I'm hoping I can strike a balance between loading time and file size by storing the picked data in a compressed format.UPDATE: The situation is actually a bit more complicated than I represented above. My application uses PySide2, so I have access to the Qt libraries.
pillow (PIL.Image), I actually don't have to do any processing, but the total time to read the image into the array is around 4 seconds.QImage to read the image, I then have to do some processing on the result to make it usable for the rest of my program due to the endian-ness of how QImage loads the data - basically I have to swap the bit order and then rotate each "pixel" so that the alpha channel (which is apparently added by QImage) comes last rather than first. This whole process takes about 3.8 seconds, so marginally faster than just using PIL.numpy array uncompressed, then I can load them back in in .8 seconds, so by far the fastest, but with large file size.┌────────────┬────────────────────────┬───────────────┬─────────────┐
│ Python Ver │ Library/Method │ Read/unpack + │ Compression │
│ │ │ Decompress (s)│ Ratio │
├────────────┼────────────────────────┼───────────────┼─────────────┤
│ 3.7.2 │ pillow (PIL.Image) │ 4.0 │ ~0.006 │
│ 3.7.2 │ Qt (QImage) │ 3.8 │ ~0.006 │
│ 3.7.2 │ numpy (uncompressed) │ 0.8 │ 1.0 │
│ 3.7.2 │ gzip (compresslevel=9) │ ? │ ? │
│ 3.7.2 │ gzip (compresslevel=?) │ ? │ ? │
│ 3.7.2 │ bz2 (compresslevel=9) │ ? │ ? │
│ 3.7.2 │ bz2 (compresslevel=?) │ ? │ ? │
│ 3.7.2 │ lzma │ ? │ ? │
├────────────┼────────────────────────┼───────────────┼─────────────┤
│ 3.7.3 │ ? │ ? │ ? │
├────────────┼────────────────────────┼───────────────┼─────────────┤
│ 3.8beta1 │ ? │ ? │ ? │
├────────────┼────────────────────────┼───────────────┼─────────────┤
│ 3.8.0final │ ? │ ? │ ? │
├────────────┼────────────────────────┼───────────────┼─────────────┤
│ 3.5.7 │ ? │ ? │ ? │
├────────────┼────────────────────────┼───────────────┼─────────────┤
│ 3.6.10 │ ? │ ? │ ? │
└────────────┴────────────────────────┴───────────────┴─────────────┘
Sample .png image: As an example, take this 5.0Mb png image, a fairly high resolution image of the coastline of Alaska.
Code for the png/PIL case (load into a numpy array):
from PIL import Image
import time
import numpy
start = time.time()
FILE = '/path/to/file/AlaskaCoast.png'
Image.MAX_IMAGE_PIXELS = None
img = Image.open(FILE)
arr = numpy.array(img)
print("Loaded in", time.time()-start)
this load takes around 4.2s on my machine with Python 3.7.2.
Alternatively, I can instead load the uncompressed pickle file generated by picking the array created above.
Code for the uncompressed pickle load case:
import pickle
import time
start = time.time()
with open('/tmp/test_file.pickle','rb') as picklefile:
arr = pickle.load(picklefile)
print("Loaded in", time.time()-start)
Loading from this uncompressed pickle file takes ~0.8s on my machine.
It is very fast and for small arrays (<2GB) also quite easy to use. On easily compressable data like your example, it is often faster to compress the data for IO operations. (SATA-SSD: about 500 MB/s, PCIe- SSD: up to 3500MB/s) In the decompression step the array allocation is the most costly part. If your images are of similar shape you can avoid repeated memory allocation.
Example
A contigous array is assumed for the following example.
import blosc
import pickle
def compress(arr,Path):
#c = blosc.compress_ptr(arr.__array_interface__['data'][0], arr.size, arr.dtype.itemsize, clevel=3,cname='lz4',shuffle=blosc.SHUFFLE)
c = blosc.compress_ptr(arr.__array_interface__['data'][0], arr.size, arr.dtype.itemsize, clevel=3,cname='zstd',shuffle=blosc.SHUFFLE)
f=open(Path,"wb")
pickle.dump((arr.shape, arr.dtype),f)
f.write(c)
f.close()
return c,arr.shape, arr.dtype
def decompress(Path):
f=open(Path,"rb")
shape,dtype=pickle.load(f)
c=f.read()
#array allocation takes most of the time
arr=np.empty(shape,dtype)
blosc.decompress_ptr(c, arr.__array_interface__['data'][0])
return arr
#Pass a preallocated array if you have many similar images
def decompress_pre(Path,arr):
f=open(Path,"rb")
shape,dtype=pickle.load(f)
c=f.read()
#array allocation takes most of the time
blosc.decompress_ptr(c, arr.__array_interface__['data'][0])
return arr
Benchmarks
#blosc.SHUFFLE, cname='zstd' -> 4728KB,
%timeit compress(arr,"Test.dat")
1.03 s ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
#611 MB/s
%timeit decompress("Test.dat")
146 ms ± 481 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#4310 MB/s
%timeit decompress_pre("Test.dat",arr)
50.9 ms ± 438 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#12362 MB/s
#blosc.SHUFFLE, cname='lz4' -> 9118KB,
%timeit compress(arr,"Test.dat")
32.1 ms ± 437 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#19602 MB/s
%timeit decompress("Test.dat")
146 ms ± 332 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#4310 MB/s
%timeit decompress_pre("Test.dat",arr)
53.6 ms ± 82.9 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
#11740 MB/s
Edit
This version is more for general use. It does handle f-contiguous, c-contiguous and non-contiguous arrays and arrays >2GB. Also have a look at bloscpack.
import blosc
import pickle
def compress(file, arr,clevel=3,cname='lz4',shuffle=1):
"""
file path to file
arr numpy nd-array
clevel 0..9
cname blosclz,lz4,lz4hc,snappy,zlib
shuffle 0-> no shuffle, 1->shuffle,2->bitshuffle
"""
max_blk_size=100_000_000 #100 MB
shape=arr.shape
#dtype np.object is not implemented
if arr.dtype==np.object:
raise(TypeError("dtype np.object is not implemented"))
#Handling of fortran ordered arrays (avoid copy)
is_f_contiguous=False
if arr.flags['F_CONTIGUOUS']==True:
is_f_contiguous=True
arr=arr.T.reshape(-1)
else:
arr=np.ascontiguousarray(arr.reshape(-1))
#Writing
max_num=max_blk_size//arr.dtype.itemsize
num_chunks=arr.size//max_num
if arr.size%max_num!=0:
num_chunks+=1
f=open(file,"wb")
pickle.dump((shape,arr.size,arr.dtype,is_f_contiguous,num_chunks,max_num),f)
size=np.empty(1,np.uint32)
num_write=max_num
for i in range(num_chunks):
if max_num*(i+1)>arr.size:
num_write=arr.size-max_num*i
c = blosc.compress_ptr(arr[max_num*i:].__array_interface__['data'][0], num_write,
arr.dtype.itemsize, clevel=clevel,cname=cname,shuffle=shuffle)
size[0]=len(c)
size.tofile(f)
f.write(c)
f.close()
def decompress(file,prealloc_arr=None):
f=open(file,"rb")
shape,arr_size,dtype,is_f_contiguous,num_chunks,max_num=pickle.load(f)
if prealloc_arr is None:
if prealloc_arr.flags['F_CONTIGUOUS']==True
prealloc_arr=prealloc_arr.T
if prealloc_arr.flags['C_CONTIGUOUS']!=True
raise(TypeError("Contiguous array is needed"))
arr=np.empty(arr_size,dtype)
else:
arr=np.frombuffer(prealloc_arr.data, dtype=dtype, count=arr_size)
for i in range(num_chunks):
size=np.fromfile(f,np.uint32,count=1)
c=f.read(size[0])
blosc.decompress_ptr(c, arr[max_num*i:].__array_interface__['data'][0])
f.close()
#reshape
if is_f_contiguous:
arr=arr.reshape(shape[::-1]).T
else:
arr=arr.reshape(shape)
return arr