Follow Python's file object semantics. User code is responsible for
closing resources (usually through a context manager) in a deterministic
way.
To achieve this, remove __del__ functions. These functions used to
closed open file handlers in an attempt to silence Python
ResourceWarnings. However, using __del__ has the following drawbacks:
- __del__ isn't called until the object's reference count reaches 0.
Therefore, resource handlers remain open or in use longer than
necessary.
- The __del__ method isn't guaranteed to execute on system exit. See the
Python documentation:
https://docs.python.org/3/reference/datamodel.html#object.__del__
> It is not guaranteed that __del__() methods are called for objects
> that still exist when the interpreter exits.
- Exceptions that occur inside __del__ are ignored instead of raised.
This has the potential of hiding bugs. This is also in the Python
documentation:
> Warning: Due to the precarious circumstances under which __del__()
> methods are invoked, exceptions that occur during their execution
> are ignored, and a warning is printed to sys.stderr instead.
Instead, always close resource handlers when they are no longer in use.
This will close the file handler at a specified point in the user's code
and not wait until the interpreter chooses to. It is always guaranteed
to run. And, if an exception occurs while closing the file handler, the
bug will not be ignored.
Now, when code receives a ResourceWarning, it will highlight an area
that is mishandling resources. It should not simply be silenced, but
fixed by closing resources with a context manager.
All warnings that were emitted during tests have been cleaned up. To
enable warnings, I passed the `-Wa` CLI option to Python. This exposed
some mishandling of resources in ImageFile.__init__() and
SpiderImagePlugin.loadImageSeries(), they too were fixed.
This better follows PEP 8 style guide:
https://www.python.org/dev/peps/pep-0008/#imports
> Imports are always put at the top of the file, just after any module
> comments and docstrings, and before module globals and constants.
This also avoids duplicate import code within the same file.
Similar to the recent adoption of Black. isort is a Python utility to
sort imports alphabetically and automatically separate into sections. By
using isort, contributors can quickly and automatically conform to the
projects style without thinking. Just let the tool do it.
Uses the configuration recommended by the Black to avoid conflicts of
style.
Rewrite TestImageQt.test_deprecated to no rely on import order.
With the introduction and use of pytest, it is simple and easy to
execute specific tests in isolation through documented command line
arguments. Either by specifying the module path or through the `-k
EXPRESSION` argument. There is no longer any need to provide the
boilerplate:
if __name__ == '__main__':
unittest.main()
To every test file. It is simply noise.
The pattern remains in test files that aren't named with `test_*` as
those files are not discovered and executed by pytest by default.
The previous test configuration made it difficult to run a single test
with the pytest CLI. There were two major issues:
- The Tests directory was not a package. It now includes a __init__.py
file and imports from other tests modules are done with relative
imports.
- setup.cfg always specified the Tests directory. So even if a specific
test were specified as a CLI arg, this configuration would also always
include all tests. This configuration has been removed to allow
specifying a single test on the command line.
Contributors can now run specific tests with a single command such as:
$ tox -e py37 -- Tests/test_file_pdf.py::TestFilePdf.test_rgb
This makes it easy and faster to iterate on a single test failure and is
very familiar to those that have previously used tox and pytest.
When running tox or pytest with no arguments, they still discover and
runs all tests in the Tests directory.
Instead, allow exceptions to bubble up to the unittest exception
handler.
Prevents replacing the exception trace with a less informative
message. As the exceptions are always unexpected, should not need to
catch them explicitly in tests.
This is a quick solution that will allow to save tiff stacks from
separate images, e.g. from Numpy arrays.
Previously, tiff stacks could be saved only from multiframe images.
This behavior is similar to what is possible now with GIFs.
Note however, that for correct results, all the appended images should
have the same encoder{info,config} properties.
Example:
import numpy as np
from PIL import Image
a = np.ones((100,100,100), dtype=np.uint8)
imlist = []
for m in a:
imlist.append(Image.fromarray(m))
imlist[0].save("test.tif", compression="tiff_deflate", save_all=True,
append_images=imlist[1:])
(Should result in a 100-frame, 100x100 tiff stack.)
Signed-off-by: Leonid Bloch <leonid.bloch@esrf.fr>
In the TIFF code saving icc_profile is conditional on tag_v2 being set which doesn't make sense to me. I believe this is merely an indentation typo.
I've been trying to save TIFFs with im.info['icc_profile'] set and compression=raw, but unfortunately this results in TIFFs without ICC color profiles.
With the attached patch TIFFs with said conditions will be saved with the profile set in im.info['icc_profile'].
Note: There are a number of different conditions that need to be met for code to succeed in saving with the profile since it branches between using libtiff and ImageFile._save(..), and the libtiff code does not currently save the ICC color profile.
For instance setting compression=tiff_lzw will result in using libtiff and no profile will be saved.
