convert matlab code to python code online

Compile C ++ code in Matlab with OpenCV support Provides a function named as "Mexico _opencv (src )" The code Function mex_opencv (src) ARC = 'x64 '; BIN_PATH = ['d: libscv.4.10', ARC, 'c12. 0in']; INC_OPT = '-ID: libscv.4.101_deopencv'; INC_OPT = [INC_OPT, '-ID: libscv.4.10include']; LIB_PATH = ['d: libscv.4.10 ', ARC, 'c12. 0lib ']; LIBS = ''; LIBS_ORG = d

EMD and BEMD algorithm implementations (MATLAB code) Today combined with their own on the Internet to find some implementation code, slightly modified to test, no more experiments, may be in some of the problem of processing or relatively coarse.Emd function IMF = EMD (x)% empiricial Mode decomposition (Hilbert-huang Transform)% EMD decomposition or HHT transfor

any of the pattern samples as the first cluster center, such as Z1 = x1 Step Two: Select the distance z1 the farthest sample as the second cluster center. by Calculation, | | X6-Z1 | | Max, so z2 = x6 Step three: Calculate the distance between each pattern sample {XI, i =,..., N} and {z1, z2}, i.e. di1= | | XI-Z1 | | di2= | | XI–Z2 | | and select the Minimum distance min (Di1, Di2), i =,..., N Fourth step: Select the maximum distance in the minimum value of all pattern samples, if the maximum v

MATLAB implementation of image Error-cutting source code% wrong cutIm= (Imread (' robot.jpg '));Im1=rgb2gray (IM);Figure,imshow (IM1);[Row,col]=size (IM1); % gets the number of rows and columns % image g=zeros (row,col); a=pi/6;% horizontal error 30 degrees b=tan (a); for m=1:row for N=1:col G (round (m+b*n), N) =im1 (m,n ); end end figure,imshow (Uint8 (G)); % vertical error tangent of

(2:3: Hs*vs*dim) = Reshape (Yuvimg (:,:,2)', 1,hs*vs); %%uYuvimout (3:3: Hs*vs*dim) = Reshape (Yuvimg (:,:,3)', 1,hs*vs); %%vFid= fopen ('IMGA_720P.YUV','WB'); %can be quickly output to file fwrite (Fid,yuvimout,'uint8'); fclose (FID); FID= fopen ('Imga_720p.txt','W'); fprintf (FID,'%02x\n', yuvimout); fclose (FID); Yuvimg=Yuvimb;yuvimout= Zeros (1, hs*vs*Dim); Yuvimout (1:3: Hs*vs*dim) = Reshape (Yuvimg (:,:,1)', 1,hs*vs);Yuvimout (2:3: Hs*vs*dim) = Reshape (Yuvimg (:,:,2)', 1,hs*vs);Yuvimout

imageBW = IM2BW (IOBRCBR, Graythresh (IOBRCBR));Subplot (122); Imshow (BW),Title (' BW ')The background pixel is black, but ideally, we don't want the background marker to be too close to the edge of our target object. We subdivide by ' skeletal ', divide the distance of the two value image, and then look for the boundary of the watershed.%step 11:D = Bwdist (BW);% calculated distanceDL = watershed (D);% Watershed TransformationBGM = DL = = 0;% to find the split boundaryFigure Imshow (BGM),% sh

2 B->C 3 7 1 a->c Fully available: 1, when the disc number and the number of disks, both perform a->c operation; 2, the implementation of 2 discs from the a->b,a->c,b->c and other operations are the same; Inference: When there are n disks, it can be decomposed into n-1,n-2,n-3 ... 2 discs are executed to form recursion, and n-1 pillars are executed each time with the help of C to achieve a to B, then a to C, and finally the implementation of the B-C process with a. When there are

, signal is not sharp, which seems to be a lot of filtering methods of a common problem. So one of the things that can be excited is how to keep the edges better while filtering, especially in filtering.In matlab, call y = Medfilt1 (x,n) to perform a one-dimensional signal median filter operation (x for input signal, N-value window width)Call B = Medfilt2 (A, [M n]) to complete the median filter operation of the two-dimensional signal (A is the image

less than 1, then the result is less than the base, dark place gray to smaller If I (x, y) is brighter where the gray level is 192,γ is the square root 0.5, is less than 1, the base is less than 1, then the result is greater than the base. The brightness of the local gray scale becomes larger The result is that the dark areas become darker, the brighter places become brighter, and obviously not. The index of gamma should be m-i (x, y), so the formula in the paper is to deduce it yourself ~ ~

Today saw the spectral Clustering algorithm review (mainly recently looked at the paper seems to be biased in the cluster division here), the specific look at an algorithm, the realization is also very simple, a little late, omitting the principle part (tomorrow), paste matlab code. %%% a little overview (every time you turn to PDF 、、、) NJW algorithm The function u=njw (data,k)%%NJW algorithm selects the ei

Work needs to calculate the camera and the position of the object relationship, by colleagues introduced DLT, feel good on the study. DLT Full name Direct Linear transformation Specific algorithm description (English) in: http://www.kwon3d.com/theory/dlt/dlt.html See the above DLT algorithm introduced, is very foggy. This algorithm has 2 kinds:A. A matrix of 11 times 1 needs to be computed without calculating the distortion coefficient (the given data is distorted). B. The calculation

cigar diagram of the rotation. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%rotated cigar x from [-5 5]% x=-7:0.01:7;% [X,y]=meshgrid (x);% for I=1:size (x,1)% for J=1:size (y,1)% p=[x (i,j), Y (i,j)] ';% x (i,j) =m (1,:) *p;% y (i,j) =m (2,:) *p;% end% end% z=x.^2+ (10^4) *y.^2;% axis ([ -7,7 , -7,7]);% MESHC (x, y, z);%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% drawing ellipse. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%ellipse x from [ -5-5]% x=-5:0.01:5;% [X,y]=meshgrid (x);% z=x.^2+10.^4* (y.^2);% Axis ([ -5,

Matlab section: xMAP = Repmat (Linspace (-REGIONW/2, REGIONW/2, regionw), Regionh, 1);%linspace [x1,x2,n] arithmetic progression ymap = Repmat (li Nspace (-REGIONH/2, REGIONH/2, Regionh) ', 1, regionw); % transpose%compute the angle of the vector p1-->p2vecp1p2 = labeldata (2,:)-labeldata (1,:); angle =-atan2 (VECP1P2 (2), VECP1P2 (1)); The% angle calculates the four-quadrant inverse tangent widthoftherealregion = norm (vecp1p2) + offset; % seek p1p

The code is as follows:function M_main () Clearclcmax_gen = 100;% perform algebra pop_size = 100;% population size Chromsome = 10;% chromosome length pc = 0.9;% crossover probability pm = 0.25;% mutation Probability Gen = 0;% Statistical algebra% Initialization init = 40*rand (pop_size, chromsome) -20;pop = Init;fit = obj_fitness (POP); [Max_fit, Index_max] = max (FIT); maxfit = Max_fit; [Min_fit, Index_min] = min (fit), Best_indiv = Pop (Index_max,:)

In the use of two-dimensional optimization algorithm, it is often necessary to call one-dimensional search algorithm to find the step, while one-dimensional search algorithm needs to determine the search interval. The method can not only be used as a one-dimensional search algorithm, but also can be used to determine the one-dimensional searching interval. The principle of the search interval determined by the Retreat method is: Suppose the one-dimensional objective function is a single-peak fun

Second, Photoshop image Filter--Sketch algorithm Sketch algorithm, there are many network, but the effect is not particularly ideal. Friends who are familiar with Photoshop know that the effect of drawing color pictures is only a few steps: 1, de-color, 2, copy the color layer, and inverse color, 3, the inverse color image gaussian blur, 4, the blurred image overlay mode to select the color Dodge effect. The color of the image is relatively simple, not much to explain. Assuming that the origi

PHP Packaging Program (online decompression and online packaging download source code) This program supports file download decompression and online compression, so we look at the example of online decompression and online packagin

Previously, a svm-based Ordinal regression model, a special multi-classification model, was developed to show the effect of the model classification in a visual way, with different color representations for each category area. However, also read a lot of code, but the basic is to show two classification, when expanded into a multi-classification will appear problems, so my thesis finally had to draw the boundary of the figure. Today in the study of ra

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