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#!/usr/bin/env python3 | |
# -*- coding: utf8 -*- | |
#https://stackoverflow.com/questions/29731726/how-to-calculate-a-gaussian-kernel-matrix-efficiently-in-numpy | |
import numpy as np | |
def gauss_kernel(size=5, sigma=None): | |
if sigma is None: | |
sigma = size/5 | |
xs = np.arange(-size//2 + 1, size//2 + 1) | |
grid_x, grid_y = np.meshgrid(xs, xs) |
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#!/usr/bin/env python3 | |
# -*- coding: utf8 -*- | |
#https://stackoverflow.com/questions/29731726/how-to-calculate-a-gaussian-kernel-matrix-efficiently-in-numpy | |
import numpy as np | |
from scipy.ndimage import convolve | |
def gauss_kernel(size=5, sigma=None): | |
if sigma is None: | |
sigma = size/5 | |
xs = np.arange(-size//2 + 1, size//2 + 1) |
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/* | |
* Adapt from this example: http://docs.opencv.org/3.0-beta/modules/core/doc/command_line_parser.html | |
*/ | |
#include "opencv2/core.hpp" | |
#include "opencv2/core/cvstd.hpp" // cv::String | |
#include <iostream> | |
using namespace cv; | |
using std::cout; | |
using std::cerr; |
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# Package Information for pkg-config | |
prefix=/Applications/Julia-0.6.app/Contents/Resources/julia | |
exec_prefix=${prefix} | |
libdir=${exec_prefix}/lib | |
includedir=${prefix}/include/julia | |
Name: Julia | |
Description: A fresh approach to technical computing | |
Version: 0.6 |
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python | |
*.pb | |
*.pbtxt |
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* | |
!*.ipynb | |
!.gitignore | |
!*.py | |
*_pb2.py | |
!requirements.txt |
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* | |
!.gitignore | |
!*.ipynb | |
!*.py | |
!*.jpg | |
!synset.txt | |
!README.md |
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* | |
!.gitignore | |
!*.ipynb | |
!*.py |
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# -*- coding:utf-8 -*- | |
from __future__ import print_function | |
from functools import wraps | |
def log_deco(func): | |
@wraps(func) | |
def wrapped(*args, **kwargs): | |
print("calling {}".format(func.__name__)) | |
return func(*args, **kwargs) |
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#!/usr/bin/env python3 | |
# -*- coding:utf8 -*- | |
def main(): | |
a = 5 | |
def foo(): | |
nonlocal a; | |
a += 1 | |
print(a) | |
print(foo()) |