@homm could you clarify how RGBX rawmode read helps with performance in jpeg decoder? python-pillow/Pillow#1989
Isn't it end up shuffling same way to RGB? RGB->RGB and RGBX->RGB unpackers are same, so how speedup is achieved?
Hi. I don't quite understand your question. However, I will attempt to answer.
To read an entire file in Python -
with open('in.txt', 'r') as f:
data = f.read()
To write that data back to a file -
with open('out.txt', 'w') as f:
f.write(data)
If you are having trouble because your data is bytes, not a string -
with open('out.txt', 'wb') as f:
f.write(data)
Also, if your question relates to Pillow in some way, please explain further, because I don't see it.
@zakishaheen Sorry, I don't understend what do you want. setup.cfg is automaticaly generated, I even never open it and don't know what it contains )
is there a way one can map one image over other by uv map?
that is, I have a uv map, and I transform a given image according to it
When you say UV map, you’re talking about Blender, yes? Are you sure this is a Pillow question, and not a Blender question?
A bit of Googling, and https://blender.stackexchange.com/questions/7855/how-can-i-set-the-image-to-use-the-uv-map-using-the-python-api seems interesting.
currently getting this ^, I can post code if needed, but I
.show() each frame and none of them show this blend of frames.
But my save looks like:
snaps[0].save('build/sprite.gif', transparency=0, save_all=True, append_images=snaps[1:], loop=0)
as for the code, I'm just iterating over a list of quadrants, cropping them, then saving them. I'll get a snippet going here in a sec...
snaps = list()
for q in quadrants:
crop = image.crop(q)
alpha = crop.getchannel('A')
mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0)
crop.paste(0, mask)
crop.info['transparency'] = 255
snaps.append(crop)
snaps[0].save('build/sprite.gif', transparency=0, save_all=True, append_images=snaps[1:], loop=0)
there we go (more or less), I've been experimenting with a couple different ways, none of which have worked
from PIL import Image
image = Image.open('walking-sprite.png')
snaps = []
for i in range(0, 8):
crop = image.crop((i * 28, 111-28, (i+1)*28, 111))
alpha = crop.getchannel('A')
mask = Image.eval(alpha, lambda a: 255 if a <= 128 else 0)
crop.paste(0, mask)
crop.info['transparency'] = 255
snaps.append(crop)
snaps[0].save('sprite.gif', transparency=0, save_all=True, append_images=snaps[1:], loop=0, disposal=2)
I need the ability to save bitonal files with a Photometric Interpretation (TIffTag 262) of 0, which is white-is-min. This is a requirement for the HathiTrust book preservation project. This is my first time trying to assist/work on someone else's project so I'm not sure exactly how to do that part of things
In the TiffImagePlugin.py, I added this option and don't know if it would be of use to others
Hi. The images that you’ve attached aren’t working, so I can’t comment on that. You could also create an issue at https://github.com/python-pillow/Pillow/issues and upload the images there if you want to talk about it further. Or you could try and create a pull request with your suggested code changes at https://github.com/python-pillow/Pillow/pulls
From your description, it sounds like you’re trying to save custom tiff tags. What version of Pillow are you using? You may find that updating to the latest Pillow gives you the feature you’re after - https://pillow.readthedocs.io/en/stable/releasenotes/5.4.0.html#new-custom-tiff-tags
should hopefully work
I'm on the newest version of Pillow. The problem is that Photometric Interpretation seems to be hard-coded when you're saving the image, but I'm trying out using the a custom tag
I'd just tried using tiffinfo = {262: 0} before, but I think that wasn't the correct value to pass as it doesn't work with the code I linked either. I think it needs to be (0, ) and NOT 0 tiffinfo = {262:(0,)} like my linked code needs (just replacing ifd directly while saving)
I'd just tried using tiffinfo = {262: 0} before, but I think that wasn't the correct value to pass as it doesn't work with the code I linked either. I think it needs to be (0, ) and NOT 0 tiffinfo = {262:(0,)} like my linked code needs (just replacing ifd directly while saving)
I'll upload this as an issue to GitHub
Hi. I'm using PIL to serve images using the IIIF protocol and have a performance question. Has there been any work done, or interest shown, in partial decoding of JPEG2000-images? There are two things that speed up the decompression of an image a lot when using opj_decompress: (1) reducing the size by a power of two and (2) cropping. This is useful when creating tiles and/or thumbnails on the fly from large image collections, i.e when you don't want to create and save a huge number of pre-scaled/cropped tiles and sizes. As far as I can tell you can set Jpeg2KImageFile.reduce to decrease the final size which will result in faster decode time, but there is no way to decode only the cropped region. Am I correct, or his there any way to tell PIL to tell OpenJPEG to do partial decoding?
I wonder if there are any attempts to adopt your lib for accessing from the side of Autodesk's Python SDK/for an integration purpose
looks like your lib is itself is a replacement for the relict one, which it is still referencing
plus I wonder about the language version - products' versions for 2014 relly on Python 2.x, not 3.x
...who is coming to the PyCon Ukraine 2019?
Pillow currently works with both Python 2 and 3. However, at the start of 2020, Pillow 7.0.0 will drop support for Python 2. See https://pythonclock.org/ and https://python3statement.org/
Pillow currently works with both Python 2 and 3. However, at the start of 2020, Pillow 7.0.0 will drop support for Python 2. See https://pythonclock.org/ and https://python3statement.org/
It does not seam that Autodesk switched their Python SDK to Python 3.x. At least - not for their current releases of their products
Is there a build artifact for windows for 6.1.0.x ? I am up against bug #3772, in a Windows environment. On a separate machine under OSX, I was easily able to build from source, but I would like it to work on this desktop PC (compute machine w/ GPU).