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Small ImageItem-related improvements (#1501) 

* Initial asv configuration

* makeARGB benchmarks are working

* Fix array type checking and allow making QImage in greyscale mode

* Performance improvements

* benchmark minor update

* Add CLI args to video speed test for easier / automated benchmarking

* udpate asv conf

* use a buffer-qimage so we can avoid allocing so much

this should improve performance under windows

* Add CLI args to video speed test for easier / automated benchmarking

* use a buffer-qimage so we can avoid allocing so much

this should improve performance under windows

* playing with numba

* oh, mins/maxes in the other order

* maybe put the cupy in here and see what happens

* pre-alloc for gpu and cpu

* handle possibility of not having cupy

* no numba in this branch

* organize imports

* name them after their use, not their expected device

* cupy.take does not support clip mode, so do it explicitly

* add CUDA option to the VideoSpeedTest

* rename private attr xp to _xp

* handle resizes at the last moment

* cupy is less accepting of lists as args

* or somehow range isn't allowed? what histogram is this?

* construct the array with python objects

* get the python value right away

* put LUT into cupy if needed

* docstring about cuda toolkit version

* better handling and display of missing cuda lib

* lint

* import need

* handle switching between cupy and numpy in a single ImageItem

* only use xp when necessary

we can now depend on numpy >= 1.17, which means __array_function__-implementing cupy can
seamlessly pass into numpy functions. the remaining uses of xp are for our functions which
need to allocate new data structures, an operation that has to be substrate-specific.

remove empty_cupy; just check if the import succeeded, instead.

* use an option to control use of cupy

* convert cupy.ceil array to int for easier mathing

* RawImageWidget gets to use the getCupy function now, too

* raise error to calm linters; rename for clarity

* Add Generated Template Files

* document things better

* cruft removal

* warnings to communicate when cupy is expected but somehow broken

* Merge branch 'cupy-rebase' into 'l/imageitem-performance'

Abundant conflicts; accept theirs in nearly every case.

* playing with settings to suss out timeout

* playing with more stuff to suss out timeout

* replace with empty list

* skip test_ExampleApp on linux+pyside2 only

* clean out some bits that no longer make sense; linty

* ignore airspeed velocity dir

* lint

* tidy up for merge

* lint

* lint, avoid shadowing

* specific import; run-as-script setup

Co-authored-by: Luke Campagnola <luke.campagnola@gmail.com>
Co-authored-by: Ogi Moore <ognyan.moore@gmail.com>
This commit is contained in:
Martin Chase 2021-01-19 23:19:03 -08:00 committed by GitHub
parent 510626c15f
commit 1654cb62ac
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6 changed files with 251 additions and 27 deletions
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1
.gitignore vendored
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@ -107,3 +107,4 @@ rtr.cvs
# ctags
.tags*
.asv/

140
asv.conf.json Normal file
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@ -0,0 +1,140 @@
{
// The version of the config file format. Do not change, unless
// you know what you are doing.
"version": 1,
// The name of the project being benchmarked
"project": "pyqtgraph",
// The project's homepage
"project_url": "http://pyqtgraph.org/",
// The URL or local path of the source code repository for the
// project being benchmarked
"repo": ".",
// List of branches to benchmark. If not provided, defaults to "master"
// (for git) or "default" (for mercurial).
"branches": ["master"], // for git
// "branches": ["default"], // for mercurial
// The DVCS being used. If not set, it will be automatically
// determined from "repo" by looking at the protocol in the URL
// (if remote), or by looking for special directories, such as
// ".git" (if local).
// "dvcs": "git",
// The tool to use to create environments. May be "conda",
// "virtualenv" or other value depending on the plugins in use.
// If missing or the empty string, the tool will be automatically
// determined by looking for tools on the PATH environment
// variable.
"environment_type": "conda",
// timeout in seconds for installing any dependencies in environment
// defaults to 10 min
//"install_timeout": 600,
// the base URL to show a commit for the project.
"show_commit_url": "http://github.com/pyqtgraph/pyqtgraph/commit/",
// The Pythons you'd like to test against. If not provided, defaults
// to the current version of Python used to run `asv`.
"pythons": ["2.7", "3.8"],
// The matrix of dependencies to test. Each key is the name of a
// package (in PyPI) and the values are version numbers. An empty
// list or empty string indicates to just test against the default
// (latest) version. null indicates that the package is to not be
// installed. If the package to be tested is only available from
// PyPi, and the 'environment_type' is conda, then you can preface
// the package name by 'pip+', and the package will be installed via
// pip (with all the conda available packages installed first,
// followed by the pip installed packages).
//
"matrix": {
"numpy": [],
"numba": [],
"pyqt": ["4", "5"],
},
// Combinations of libraries/python versions can be excluded/included
// from the set to test. Each entry is a dictionary containing additional
// key-value pairs to include/exclude.
//
// An exclude entry excludes entries where all values match. The
// values are regexps that should match the whole string.
//
// An include entry adds an environment. Only the packages listed
// are installed. The 'python' key is required. The exclude rules
// do not apply to includes.
//
// In addition to package names, the following keys are available:
//
// - python
// Python version, as in the *pythons* variable above.
// - environment_type
// Environment type, as above.
// - sys_platform
// Platform, as in sys.platform. Possible values for the common
// cases: 'linux2', 'win32', 'cygwin', 'darwin'.
//
"exclude": [
{"python": "3.8", "pyqt": "4"},
],
//
// "include": [
// // additional env for python2.7
// {"python": "2.7", "numpy": "1.8"},
// // additional env if run on windows+conda
// {"platform": "win32", "environment_type": "conda", "python": "2.7", "libpython": ""},
// ],
// The directory (relative to the current directory) that benchmarks are
// stored in. If not provided, defaults to "benchmarks"
"benchmark_dir": "benchmarks",
// The directory (relative to the current directory) to cache the Python
// environments in. If not provided, defaults to "env"
"env_dir": ".asv/env",
// The directory (relative to the current directory) that raw benchmark
// results are stored in. If not provided, defaults to "results".
"results_dir": ".asv/results",
// The directory (relative to the current directory) that the html tree
// should be written to. If not provided, defaults to "html".
"html_dir": ".asv/html",
// The number of characters to retain in the commit hashes.
// "hash_length": 8,
// `asv` will cache wheels of the recent builds in each
// environment, making them faster to install next time. This is
// number of builds to keep, per environment.
"build_cache_size": 5
// The commits after which the regression search in `asv publish`
// should start looking for regressions. Dictionary whose keys are
// regexps matching to benchmark names, and values corresponding to
// the commit (exclusive) after which to start looking for
// regressions. The default is to start from the first commit
// with results. If the commit is `null`, regression detection is
// skipped for the matching benchmark.
//
// "regressions_first_commits": {
// "some_benchmark": "352cdf", // Consider regressions only after this commit
// "another_benchmark": null, // Skip regression detection altogether
// }
// The thresholds for relative change in results, after which `asv
// publish` starts reporting regressions. Dictionary of the same
// form as in ``regressions_first_commits``, with values
// indicating the thresholds. If multiple entries match, the
// maximum is taken. If no entry matches, the default is 5%.
//
// "regressions_thresholds": {
// "some_benchmark": 0.01, // Threshold of 1%
// "another_benchmark": 0.5, // Threshold of 50%
// }
}

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benchmarks/__init__.py Normal file
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@ -0,0 +1 @@

72
benchmarks/makeARGB.py Normal file
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@ -0,0 +1,72 @@
import numpy as np
from pyqtgraph.functions import makeARGB
class TimeSuite(object):
def __init__(self):
self.c_map = None
self.float_data = None
self.uint8_data = None
self.uint8_lut = None
self.uint16_data = None
self.uint16_lut = None
def setup(self):
size = (500, 500)
self.float_data = {
'data': np.random.normal(size=size),
'levels': [-4., 4.],
}
self.uint16_data = {
'data': np.random.randint(100, 4500, size=size).astype('uint16'),
'levels': [250, 3000],
}
self.uint8_data = {
'data': np.random.randint(0, 255, size=size).astype('ubyte'),
'levels': [20, 220],
}
self.c_map = np.array([
[-500., 255.],
[-255., 255.],
[0., 500.],
])
self.uint8_lut = np.zeros((256, 4), dtype='ubyte')
for i in range(3):
self.uint8_lut[:, i] = np.clip(np.linspace(self.c_map[i][0], self.c_map[i][1], 256), 0, 255)
self.uint8_lut[:, 3] = 255
self.uint16_lut = np.zeros((2 ** 16, 4), dtype='ubyte')
for i in range(3):
self.uint16_lut[:, i] = np.clip(np.linspace(self.c_map[i][0], self.c_map[i][1], 2 ** 16), 0, 255)
self.uint16_lut[:, 3] = 255
def make_test(dtype, use_levels, lut_name, func_name):
def time_test(self):
data = getattr(self, dtype + '_data')
makeARGB(
data['data'],
lut=getattr(self, lut_name + '_lut', None),
levels=use_levels and data['levels'],
)
time_test.__name__ = func_name
return time_test
for dt in ['float', 'uint16', 'uint8']:
for levels in [True, False]:
for ln in [None, 'uint8', 'uint16']:
name = f'time_makeARGB_{dt}_{"" if levels else "no"}levels_{ln or "no"}lut'
setattr(TimeSuite, name, make_test(dt, levels, ln, name))
if __name__ == "__main__":
ts = TimeSuite()
ts.setup()

63
pyqtgraph/functions.py
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@ -1000,7 +1000,7 @@ def makeRGBA(*args, **kwds):
def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False, output=None):
"""
"""
Convert an array of values into an ARGB array suitable for building QImages,
OpenGL textures, etc.
@ -1091,7 +1091,6 @@ def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False, output=None
dtype = xp.min_scalar_type(lut.shape[0]-1)
# awkward, but fastest numpy native nan evaluation
#
nanMask = None
if data.dtype.kind == 'f' and xp.isnan(data.min()):
nanMask = xp.isnan(data)
@ -1128,7 +1127,7 @@ def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False, output=None
if lut is not None:
data = applyLookupTable(data, lut)
else:
if data.dtype is not xp.ubyte:
if data.dtype != xp.ubyte:
data = xp.clip(data, 0, 255).astype(xp.ubyte)
profile('apply lut')
@ -1189,10 +1188,14 @@ def makeQImage(imgData, alpha=None, copy=True, transpose=True):
============== ===================================================================
**Arguments:**
imgData Array of data to convert. Must have shape (width, height, 3 or 4)
and dtype=ubyte. The order of values in the 3rd axis must be
(b, g, r, a).
alpha If True, the QImage returned will have format ARGB32. If False,
imgData Array of data to convert. Must have shape (height, width),
(height, width, 3), or (height, width, 4). If transpose is
True, then the first two axes are swapped. The array dtype
must be ubyte. For 2D arrays, the value is interpreted as
greyscale. For 3D arrays, the order of values in the 3rd
axis must be (b, g, r, a).
alpha If the input array is 3D and *alpha* is True, the QImage
returned will have format ARGB32. If False,
the format will be RGB32. By default, _alpha_ is True if
array.shape[2] == 4.
copy If True, the data is copied before converting to QImage.
@ -1208,30 +1211,35 @@ def makeQImage(imgData, alpha=None, copy=True, transpose=True):
## create QImage from buffer
profile = debug.Profiler()
## If we didn't explicitly specify alpha, check the array shape.
if alpha is None:
alpha = (imgData.shape[2] == 4)
copied = False
if imgData.shape[2] == 3: ## need to make alpha channel (even if alpha==False; QImage requires 32 bpp)
if copy is True:
d2 = np.empty(imgData.shape[:2] + (4,), dtype=imgData.dtype)
d2[:,:,:3] = imgData
d2[:,:,3] = 255
imgData = d2
copied = True
if imgData.ndim == 2:
imgFormat = QtGui.QImage.Format_Grayscale8
elif imgData.ndim == 3:
# If we didn't explicitly specify alpha, check the array shape.
if alpha is None:
alpha = (imgData.shape[2] == 4)
if imgData.shape[2] == 3: # need to make alpha channel (even if alpha==False; QImage requires 32 bpp)
if copy is True:
d2 = np.empty(imgData.shape[:2] + (4,), dtype=imgData.dtype)
d2[:,:,:3] = imgData
d2[:,:,3] = 255
imgData = d2
copied = True
else:
raise Exception('Array has only 3 channels; cannot make QImage without copying.')
profile("add alpha channel")
if alpha:
imgFormat = QtGui.QImage.Format_ARGB32
else:
raise Exception('Array has only 3 channels; cannot make QImage without copying.')
if alpha:
imgFormat = QtGui.QImage.Format_ARGB32
imgFormat = QtGui.QImage.Format_RGB32
else:
imgFormat = QtGui.QImage.Format_RGB32
raise TypeError("Image array must have ndim = 2 or 3.")
if transpose:
imgData = imgData.transpose((1, 0, 2)) ## QImage expects the row/column order to be opposite
profile()
imgData = imgData.transpose((1, 0, 2)) # QImage expects row-major order
if not imgData.flags['C_CONTIGUOUS']:
if copy is False:
@ -1240,9 +1248,12 @@ def makeQImage(imgData, alpha=None, copy=True, transpose=True):
imgData = np.ascontiguousarray(imgData)
copied = True
profile("ascontiguousarray")
if copy is True and copied is False:
imgData = imgData.copy()
profile("copy")
if QT_LIB == 'PySide':
ch = ctypes.c_char.from_buffer(imgData, 0)
img = QtGui.QImage(ch, imgData.shape[1], imgData.shape[0], imgFormat)

1
pyqtgraph/graphicsItems/ImageItem.py
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@ -50,7 +50,6 @@ class ImageItem(GraphicsObject):
self.qimage = None ## rendered image for display
self.paintMode = None
self.levels = None ## [min, max] or [[redMin, redMax], ...]
self.lut = None
self.autoDownsample = False