rgbcube.m下载

function rgbcube(vx,vy,vz) vertices_matrix = [0 0 0;0 0 1;0 1 0;0 1 1;1 0 0;1 0 1;1 1 0;1 1 1]; faces_matrix = [1 5 6 2;1 3 7 5;1 2 4 3;2 4 8 6;3 7 8 4;5 6 8 7]; colors = vertices_matrix; patch('Vertices',vertices_matrix,'Faces',faces_matrix,'FaceVertexCData',colors,'FaceColor','interp','EdgeAlpha',0); %set up viewing point

imnoise3.m

function [r,R,S] = imnoise3(M, N, C, A, B) K =size(C, 1); if nargin < 4 A = ones(1,K); end if nargin < 5 B = zeros(K, 2); end R = zeros(M, N);

reader.py下载

# -*- coding: utf-8 -*- """ Created on Mon May 7 19:43:04 2018 @author: Administrator """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import tensorflow as tf import os import numpy as np def _read_words(filename): with tf.gfile.GFile&#40;filename, "r"&#41; as f: return f.read().replace("\n", "<eos>").split() def _build_vocab(filename): data = _read_words(filename) counter = collections.Counter(data) count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0])) words, _ = list(zip(*count_pairs)) word_to_id = dict(zip(words, range(len(words)))) return word_to_id

深度学习lenet5学习框架

深度学习lenet5学习框架，包括训练，测试和验证三个文件

statmoments.m

function [v,unv]=statmoments(p,n) % STATMOMENTS Computes statistical central moments of image histogram. % [v,unv]=statmoments(p,n) computes up to the Nth statistical % central moment of a histogram whose components are in vector % P. The length of must equal 256 or 65536. % ****from ggbondg**** % The program outputs a V with V(1)=mean, V(2) = variance. % V(3) = 3rd moment,...V(N)=Nth central moment. The random % variable values are normalized to the range [0,1], so all % moments also are in this range. % ****from ggbondg**** % The program also outputs a Vector UNV containing the same moments % as V,but using un-normalized random variable values (e.g., 0 to % 255 if length(P)=2^8). For example, if length(P)=256 and V(1) % = 0.5, then UNV(1) would have the value UNV(1)=127.5 (half of % the [0 255] range). %****from ggbondg****

imnoise2.m

function R = imnoise2(type, varargin) %IMNOISE2 Generates an array of random numbers with specified PDF. % R = IMNOISE2(TYPE, M, N, A, B) generates an array, R, of size % M-by-N, whose elements are random numbers of the specified TYPE % with parameters A and B. If only TYPE is included in the % input argument list, a single random number of the specified % TYPE and default parameters shown below is generated. If only % TYPE, M, and N are provided, the default parameters shown below % are used. If M = N = 1, IMNOISE2 generates a single random % number of the specified TYPE and parameters A and B. % % Valid values for TYPE and parameters A and B are: % % 'uniform' Uniform random numbers in the interval (A, B). % The default values are (0, 1). % 'gaussian' Gaussian random numbers with mean A and standard % deviation B. The default values are A = 0, B = 1. % 'salt & pepper' Salt and pepper numbers of amplitude 0 with % probability Pa = A, and amplitude 1 with % probability Pb = B. The default values are Pa = % Pb = A = B = 0.05. Note that the noise has % values 0 (with probability Pa = A) and 1 (with % probability Pb = B), so scaling is necessary if % values other than 0 and 1 are required. The noise % matrix R is assigned three values. If R(x, y) = % 0, the noise at (x, y) is pepper (black). If % R(x, y) = 1, the noise at (x, y) is salt % (white). If R(x, y) = 0.5, there is no noise % assigned to coordinates (x, y). % 'lognormal' Lognormal numbers with offset A and shape % parameter B. The defaults are A = 1 and B = % 0.25. % 'rayleigh' Rayleigh noise with parameters A and B. The % default values are A = 0 and B = 1. % 'exponential' Exponential random numbers with parameter A. The % default is A = 1. % 'erlang' Erlang (gamma) random numbers with parameters A % and B. B must be a positive integer. The % defaults are A = 2 and B = 5. Erlang random % numbers are approximated as the sum of B % exponential random numbers. % Copyright 2002-2004 R. C. Gonzalez, R. E. Woods, & S. L. Eddins % Digital Image Processing Using MATLAB, Prentice-Hall, 2004 % $Revision: 1.5 $ $Date: 2003/10/12 23:37:29 $ % Set default values.

dftfilt.m下载

function g = dftfilt(f,H) % 此函数可接受输入图像和一个滤波函数，可处理所有的 % 滤波细节并输出经滤波和剪切后的图像 % 将此.m文件保存在一个文件夹 % file->set path->add with subfolder % 将你函数所在文件夹添加到搜索路径 % save就可以将其添加到你的函数库了

adpmedian.m

function f = adpmedian(g, Smax) if(Smax <=1) || (Smax/2 ==round(Smax/2)) || (Smax ~= round(Smax)) error('SMAX must be an odd integer >1.') end % Initial setup f = g; f(:) = 0; alreadyProcessed = false(size(g)); % Begin filtering.