python

I created a tiny python script that executes some python code, that executes some C code: # contents of dbg_broadcast.py import numpy print list(numpy.broadcast([[1,2]],[[3],[4]])) Running in console: > gdb python GNU gdb (Ubuntu 7.7.1-0ubuntu5~14.04.2) 7.7.1 Copyright (C) 2014 Free Software Foundation, Inc. License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html> This is free software: you are free to change and redistribute it. .................. bla bla bla .................................................. # Adding breakpoint to function "arraymultiter_new" (example): (gdb) break arraymultiter_new # There is name completition by Tab....

Building from source root using command pypy_src$ rpython/bin/rpython -Ojit pypy/goal/targetpypystandalone.py produces structure with obsolete pypy-c and libpypy-c.so in /tmp/usession-release-4.0.1-XXXX/build/pypy-nightly/bin/ Probably pypy compiler places there files integrated in the src distribution. To get fresh versions I had to use pypy-c and libpypy-c.so from sources root. UPDATE: Probably I was completely wrong. pypy/tool/release/package.py has an option for (not) stripping resulting binary file: “–nostrip”. By default it is enabled. Looks like it removed something unused from binaries....

Implemented using python https://github.com/serge-m/object_detection_ir_video

Python has a great built-in logging library. It allows to avoid a lot of boilerplate code and provides great features. Here are some examples. Logging allows to set up a unified formatting for logs: 2023-10-03 12:57:22,377|classifier.py| 62|522130|MainThread|INFO|Classifying messages from ./messages.ndjson (images ./save_path)... 2023-10-03 12:57:22,378|classifier.py| 44|522130|MainThread|INFO|Start reading input messages from ./messages.ndjson 2023-10-03 12:57:22,378|classifier.py| 48|522130|MainThread|INFO|Loaded event {'id': 104162, 'date': '2023-09-27T07:12:08+00:00', 'entities': []} 2023-10-03 12:57:22,378|classifier.py| 48|522130|MainThread|INFO|Loaded event {'id': 58934, 'date': '2023-09-27T07:12:09+00:00', 'entities': [{'_': 'MessageEntityBold', 'offset': 92, 'length': 8}]} 2023-10-03 12:57:22,378|classifier....

install Anaconda download Theano sources from git (install it using setup.py) Setup NVIDIA GPU Toolkit. I have installed version 6.5 Setup Visual Studio Community Edition 2013 Create config file .theanorc in c:\Users\X: [global] floatX = float32 device = gpu [nvcc] fastmath = True compiler_bindir=C:\Program Files (x86)\Microsoft Visual Studio 12.0\VC\bin\cl.exe Without the line with path to cl.exe I got: nvcc fatal : Cannot find compiler 'cl.exe' in PATH ['nvcc', '-shared', '-O3', '-use_fast_math', '-Xlinker', '/DEBUG', '-D HAVE_ROUND', '-m64', '-Xcompiler', '-DCUDA_NDARRAY_CUH=f411a53ee0a470fbaad3b5c4a681ef64,-D NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION,/Zi,/MD', '-Ic:\\anaconda\\theano\\theano\\sandbox\\cuda', '-Ic:\\anaconda\\lib\\site-packages\\numpy\\core\\include', '-Ic:\\anaconda\\include', '-o', 'C:\\Users\\X\\AppData\\Local\\Theano\\compiledir_Windows-7-6....

Today, my imaginary readers, we improve our optical flow dramatically. Lets see, what our algorithm produces for images that have more than 1-pixel shifts. I0 I1 OF results: <img alt="OF results" src="data:image/png;base64,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" /> </td></tr> You can see, that the algorithm cannot deal with too large displacements. Even with multiple warps. Lets apply multiscale scheme now. We need to construct image pyramid: #from flow_huber_py import construct_image_pyramid def construct_image_pyramid(I, pyrlevels, pyrfactor): factor = 2....

After fixing some errors, it seems my OF is working. [](https://www.blogger.com/blogger.g?blogID=636453477220885924" imageanchor=“1” style=“clear: right; float: right; margin-bottom: 1em; margin-left: 1em;) [](https://www.blogger.com/blogger.g?blogID=636453477220885924" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;)Image I0Image I1 [](https://www.blogger.com/blogger.g?blogID=636453477220885924" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;)first iteration OF 1st iteration of warped I1 Second Iteration OF Second iteration of warped I1 3 iteration Of 3rd iteration of warped I1 Last iteration Some strange results for another image pair:...

First of all we need a couple of test images: # import numpy from StringIO import StringIO I0 = numpy.loadtxt(StringIO(""" 0 0 0 0 0 0 0 0.5000 0 0 0 0 1.0000 0 0 0 0 0.5000 0 0 0 0 0 0 0""") ) I1 = numpy.loadtxt(StringIO(""" 0 0 0 0 0 0 0.5 0 0 0 0 1.0 0 0 0 0 0.5 0 0 0 0 0 0 0 0 "...

I find default visualization settings for images in matplotlib inconvenient. Usually I prefer to see images in grayscale color map and without smoothing. Thus I see actual data without distortions. Grayscale is easier to understand. 1 2 3 import matplotlib.pyplot as plt plt.rcParams['image.cmap'] = 'gray' plt.rcParams['image.interpolation'] = 'nearest' Compare default colors and interpolation: and after applying the settings:

Okay children, today we learn how to convert text to numpy matrix. Source is [here](http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html" target="_blank). # loading modules import numpy from StringIO import StringIO # Using StringIO as a file-like wrapper over text I0 = numpy.loadtxt(StringIO(""" 0 0 0 0 0 0 0 0.5000 0 0 0 0 1.0000 0 0 0 0 0.5000 0 0 0 1.0000 0 0 0 0 0.5000 0 0 0 0 0.5000 1.0000 0 0 0 0 0 0 0"...