【水果识别】基于计算机视觉实现橙子数量识别含Matlab源码

1 简介

在现实生活中成人识别水果是十分简易的但对于幼儿来说在没有实物之前是无法识别水果的,因此本文设计了一个简易水果识别系统为幼儿在电子设备上识别水果提供可能.本文通过matlab GUI设计了一个水果识别系统界面并通过对水果图像进行二值化处理,边缘处理最后实现了橙子数量识别.​

2 部分代码

function [t,em] = otsuthresh(counts) %#codegen%OTSUTHRESH Global histogram threshold using Otsu's method - M-to-C codegen.%   Copyright 2015 The MathWorks, Inc.%   Syntax%   ------%%       [t,em] = otsuthresh(counts)%%   Input Specs%   -----------%%      counts:%        numeric%        vector%        real%        finite%        non-sparse%        non-negative%%   Output Specs%   ------------%%     t:%       scalar%       double%       in [0,1]%%     em:%       scalar%       double%       in [0,1]%% Validate countsvalidateattributes(counts,{'numeric'}, ...    {'vector','real','finite','nonsparse','nonnegative'},mfilename,'COUNTS');% Number of binsnum_bins = numel(counts);% Number of elementsnum_elems = 0;for k = 1:num_bins    num_elems = num_elems + double(counts(k));end% CDF of the histogramomega = coder.nullcopy(zeros(num_bins,1));omega(1) = double(counts(1))/num_elems;​mu = coder.nullcopy(zeros(num_bins,1));mu(1) = omega(1);​for k = 2:num_bins    % PDF    p = double(counts(k))/num_elems;    % CDF    omega(k) = omega(k-1) + p;    % "weighted" CDF    mu(k) = mu(k-1) + p*k;end​mu_t = mu(end);% Equation 18 in the papersigma_b_squared = coder.nullcopy(zeros(num_bins,1));maxval = -coder.internal.inf;for k = 1:num_bins    sigma_b_squared(k) = (mu_t*omega(k) - mu(k))^2 / (omega(k)*(1-omega(k)));    maxval = max(maxval,sigma_b_squared(k));end% Find the location of the maximum value of sigma_b_squared.% If maxval is NaN, meaning that sigma_b_squared% is all NaN, then return 0.isfinite_maxval = isfinite(maxval);if isfinite_maxval    % The maximum may extend over several bins,    % so average together the locations.    idx = double(0);    num_maxval = double(0);    for k = 1:num_bins        idx = idx + k * double(sigma_b_squared(k) == maxval);        num_maxval = num_maxval + 1 * double(sigma_b_squared(k) == maxval);    end    idx = idx / num_maxval;        % Normalize the threshold to the range [0,1]    t = (idx - 1) / (num_bins - 1);else    t = 0;end% Compute the effectiveness metricif nargout > 1    if isfinite_maxval        d = 0;        for k = 1:num_bins            d = d + double(counts(k))/num_elems * k^2;        end        em = maxval/(d - mu_t^2);    else        em = 0;    endend​

3 仿真结果

4 参考文献

[1]阳江平. 基于计算机视觉的果蔬识别方法研究[D]. 大连理工大学.

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