HOW TO READ MULTIPLE IMAGES IN GUI

I need to detect text in grocey items . I already get the result in MATLAB . In matlab i run the image one by one in another folder. The problem now is i need to do gui . In gui i need to run all the images in one folder . How can i read the images one by one in one coding?

    function varargout = PLISJADIGUI(varargin)
    % PLISJADIGUI MATLAB code for PLISJADIGUI.fig
    %      PLISJADIGUI, by itself, creates a new PLISJADIGUI or raises the existing
    %      singleton*.
    %
    %      H = PLISJADIGUI returns the handle to a new PLISJADIGUI or the handle to
    %      the existing singleton*.
    %
    %      PLISJADIGUI('CALLBACK',hObject,eventData,handles,...) calls the local
    %      function named CALLBACK in PLISJADIGUI.M with the given input arguments.
    %
    %      PLISJADIGUI('Property','Value',...) creates a new PLISJADIGUI or raises the
    %      existing singleton*.  Starting from the left, property value pairs are
    %      applied to the GUI before PLISJADIGUI_OpeningFcn gets called.  An
    %      unrecognized property name or invalid value makes property application
    %      stop.  All inputs are passed to PLISJADIGUI_OpeningFcn via varargin.
    %
    %      *See GUI Options on GUIDE's Tools menu.  Choose "GUI allows only one
    %      instance to run (singleton)".
    %
    % See also: GUIDE, GUIDATA, GUIHANDLES

    % Edit the above text to modify the response to help PLISJADIGUI

    % Last Modified by GUIDE v2.5 27-Apr-2017 09:18:09

    % Begin initialization code - DO NOT EDIT
    gui_Singleton = 1;
    gui_State = struct('gui_Name',       mfilename, ...
                       'gui_Singleton',  gui_Singleton, ...
                       'gui_OpeningFcn', @PLISJADIGUI_OpeningFcn, ...
                       'gui_OutputFcn',  @PLISJADIGUI_OutputFcn, ...
                       'gui_LayoutFcn',  [] , ...
                       'gui_Callback',   []);
    if nargin && ischar(varargin{1})
        gui_State.gui_Callback = str2func(varargin{1});
    end

    if nargout
        [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});
    else
        gui_mainfcn(gui_State, varargin{:});
    end
    % End initialization code - DO NOT EDIT

    % --- Executes just before PLISJADIGUI is made visible.
    function PLISJADIGUI_OpeningFcn(hObject, eventdata, handles, varargin)
    % This function has no output args, see OutputFcn.
    % hObject    handle to figure
    % eventdata  reserved - to be defined in a future version of MATLAB
    % handles    structure with handles and user data (see GUIDATA)
    % varargin   command line arguments to PLISJADIGUI (see VARARGIN)

    % Choose default command line output for PLISJADIGUI
    handles.output = hObject;

    % Update handles structure
    guidata(hObject, handles);

    % UIWAIT makes PLISJADIGUI wait for user response (see UIRESUME)
    % uiwait(handles.figure1);

    % --- Outputs from this function are returned to the command line.
    function varargout = PLISJADIGUI_OutputFcn(hObject, eventdata, handles) 
    % varargout  cell array for returning output args (see VARARGOUT);
    % hObject    handle to figure
    % eventdata  reserved - to be defined in a future version of MATLAB
    % handles    structure with handles and user data (see GUIDATA)

    % Get default command line output from handles structure
    varargout{1} = handles.output;

    % --- Executes on button press in pushbutton1.
    function pushbutton1_Callback(hObject, eventdata, handles)
    % hObject    handle to pushbutton1 (see GCBO)
    % eventdata  reserved - to be defined in a future version of MATLAB
    % handles    structure with handles and user data (see GUIDATA)
    %here we load image

    global im im2
    [path,user_cance]=imgetfile();

    im=imread(path);
    im2=im;
    axes(handles.axes1);
    imshow(im);

    % --- Executes on button press in pushbutton2.
    function pushbutton2_Callback(hObject, eventdata, handles)
    % hObject    handle to pushbutton2 (see GCBO)
    % eventdata  reserved - to be defined in a future version of MATLAB
    % handles    structure with handles and user data (see GUIDATA)
    %here we reset the image to original

    global im2
    axes(handles.axes1);
    imshow(im2);

    % --- Executes on button press in pushbutton3.
    function pushbutton3_Callback(hObject, eventdata, handles)
    % hObject    handle to pushbutton3 (see GCBO)
    % eventdata  reserved - to be defined in a future version of MATLAB
    % handles    structure with handles and user data (see GUIDATA)

    global im
     colorImage = imread('cadbury.jpg');
    I = rgb2gray(colorImage);

    % Detect MSER regions.
    [mserRegions, mserConnComp] = detectMSERFeatures(I, ...
        'RegionAreaRange',[200 8000],'ThresholdDelta',4);

    figure
    axes(handles.axes1);
    imshow(I)
    hold on
    plot(mserRegions, 'showPixelList', true,'showEllipses',false)
    title('MSER regions')
    hold off
    global im
    mserStats = regionprops(mserConnComp, 'BoundingBox', 'Eccentricity', ...
        'Solidity', 'Extent', 'Euler', 'Image');

    % Compute the aspect ratio using bounding box data.
    bbox = vertcat(mserStats.BoundingBox);
    w = bbox(:,3);
    h = bbox(:,4);
    aspectRatio = w./h;

    % Threshold the data to determine which regions to remove. These thresholds
    % may need to be tuned for other images.
    filterIdx = aspectRatio' > 3;
    filterIdx = filterIdx | [mserStats.Eccentricity] > .995 ;
    filterIdx = filterIdx | [mserStats.Solidity] < .3;
    filterIdx = filterIdx | [mserStats.Extent] < 0.2 | [mserStats.Extent] > 0.9;
    filterIdx = filterIdx | [mserStats.EulerNumber] < -4;

    % Remove regions
    mserStats(filterIdx) = [];
    mserRegions(filterIdx) = [];

    % Show remaining regions
    figure
    axes(handles.axes1);
    imshow(I)
    hold on
    plot(mserRegions, 'showPixelList', true,'showEllipses',false)
    title('After Removing Non-Text Regions Based On Geometric Properties')
    hold off

    global im
    % Get a binary image of the a region, and pad it to avoid boundary effects
    % during the stroke width computation.
    regionImage = mserStats(6).Image;
    regionImage = padarray(regionImage, [1 1]);

    % Compute the stroke width image.
    distanceImage = bwdist(~regionImage);
    skeletonImage = bwmorph(regionImage, 'thin', inf);

    strokeWidthImage = distanceImage;
    strokeWidthImage(~skeletonImage) = 0;

    % Show the region image alongside the stroke width image.
    figure
    subplot(1,2,1)
    imagesc(regionImage)
    title('Region Image')

    subplot(1,2,2)
    imagesc(strokeWidthImage)
    title('Stroke Width Image')

    % Compute the stroke width variation metric
    strokeWidthValues = distanceImage(skeletonImage);
    strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);

    % Threshold the stroke width variation metric
    strokeWidthThreshold = 0.4;
    strokeWidthFilterIdx = strokeWidthMetric > strokeWidthThreshold;

    % Process the remaining regions
    for j = 1:numel(mserStats)

        regionImage = mserStats(j).Image;
        regionImage = padarray(regionImage, [1 1], 0);

        distanceImage = bwdist(~regionImage);
        skeletonImage = bwmorph(regionImage, 'thin', inf);

        strokeWidthValues = distanceImage(skeletonImage);

        strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);

        strokeWidthFilterIdx(j) = strokeWidthMetric > strokeWidthThreshold;

    end

    % Remove regions based on the stroke width variation
    mserRegions(strokeWidthFilterIdx) = [];
    mserStats(strokeWidthFilterIdx) = [];

    % Show remaining regions
    figure
    axes(handles.axes1);
    imshow(I)
    hold on
    plot(mserRegions, 'showPixelList', true,'showEllipses',false)
    title('After Removing Non-Text Regions Based On Stroke Width Variation')
    hold off

    global im
    % Get bounding boxes for all the regions
    bboxes = vertcat(mserStats.BoundingBox);

    % Convert from the [x y width height] bounding box format to the [xmin ymin
    % xmax ymax] format for convenience.
    xmin = bboxes(:,1);
    ymin = bboxes(:,2);
    xmax = xmin + bboxes(:,3) - 1;
    ymax = ymin + bboxes(:,4) - 1;

    % Expand the bounding boxes by a small amount.
    expansionAmount = 0.02;
    xmin = (1-expansionAmount) * xmin;
    ymin = (1-expansionAmount) * ymin;
    xmax = (1+expansionAmount) * xmax;
    ymax = (1+expansionAmount) * ymax;

    % Clip the bounding boxes to be within the image bounds
    xmin = max(xmin, 1);
    ymin = max(ymin, 1);
    xmax = min(xmax, size(I,2));
    ymax = min(ymax, size(I,1));

    % Show the expanded bounding boxes
    expandedBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
    IExpandedBBoxes = insertShape(colorImage,'Rectangle',expandedBBoxes,'LineWidth',3);

    figure
    imshow(IExpandedBBoxes)
    title('Expanded Bounding Boxes Text')

    % Compute the overlap ratio
    overlapRatio = bboxOverlapRatio(expandedBBoxes, expandedBBoxes);

    % Set the overlap ratio between a bounding box and itself to zero to
    % simplify the graph representation.
    n = size(overlapRatio,1);
    overlapRatio(1:n+1:n^2) = 0;

    % Create the graph
    g = graph(overlapRatio);

    % Find the connected text regions within the graph
    componentIndices = conncomp(g);

    % Merge the boxes based on the minimum and maximum dimensions.
    xmin = accumarray(componentIndices', xmin, [], @min);
    ymin = accumarray(componentIndices', ymin, [], @min);
    xmax = accumarray(componentIndices', xmax, [], @max);
    ymax = accumarray(componentIndices', ymax, [], @max);

    % Compose the merged bounding boxes using the [x y width height] format.
    textBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];

    % Remove bounding boxes that only contain one text region
    numRegionsInGroup = histcounts(componentIndices);
    textBBoxes(numRegionsInGroup == 1, :) = [];

    % Show the final text detection result.
    ITextRegion = insertShape(colorImage, 'Rectangle', textBBoxes,'LineWidth',3);

    figure
    axes(handles.axes1);
    imshow(ITextRegion)
    title('Detected Text')

   ocrtxt = ocr(I, textBBoxes);
   [ocrtxt.Text]

i just can load the image cadbury only . at pushbutton 3 how can i set to read all images process that i have for example which is i want to put image of cadbury,shampoo,toiletries and so on too. how can i do the same process in different image in one coding .

[Jan]

Your code is not readable. Please use the "{} Code" button. It would increase the chance to get an anwer if you post the relevant part of the code only.

do not include the code for the computations inside the GUI. Keeping them separated is a big advantage.

[Sara]

sory sory thanks you . i already fix it

Answer 1

[Jan]

I'm not sure what exactly is your question. I see this part of the code:

    global im im2
    [path,user_cance]=imgetfile();
    im=imread(path);
    im2=im;
    axes(handles.axes1);
    imshow(im);

This contains some problems:

1. Using globals is a bad idea in general. Better store the data in the figure's UserData or ApplicationData. Search in this forum for "share data between callbacks".
2. Do not use "path" as name of a variable. Because this is an important Matlab function, you might get very strange effects during debugging. This is called "shadowing" and should be avoided.
3. If you move the code for the actual computations in an extra function, you can call it from Matlab in a batch process or from a loop over all files found in a folder. Currently the code is contained inside a callback and running it for a bunch of files would need strange and indirect constructions. It is not clear how you want the outputs to appear for a set of files.

[Sara]

thanks jan simon for your feedback .i use the code below from youtube ,it is for to get the image from file . but its not compatible. the main problem is i want to call all the images that i have and run the process . for examples i have 10 images in file. means like ,after i finishes process the image 1 ,i want to proceed with images 2 with the same process(same coding) that i use in the images 1 and repeated the same process until image 10 . how can i do the process ? and how can I implement to my coding in gui .

global im im2
      [path,user_cance]=imgetfile();
      im=imread(path);
      im2=im;
      axes(handles.axes1);
      imshow(im);

this is my real coding. this coding is for detect text in images

colorImage = imread('cadbury.jpg');
        I = rgb2gray(colorImage);
        % Detect MSER regions.
        [mserRegions, mserConnComp] = detectMSERFeatures(I, ...
            'RegionAreaRange',[200 8000],'ThresholdDelta',4);
        figure
        axes(handles.axes1);
        imshow(I)
        hold on
        plot(mserRegions, 'showPixelList', true,'showEllipses',false)
        title('MSER regions')
        hold off

          mserStats = regionprops(mserConnComp, 'BoundingBox', 'Eccentricity', ...
              'Solidity', 'Extent', 'Euler', 'Image');
          % Compute the aspect ratio using bounding box data.
          bbox = vertcat(mserStats.BoundingBox);
          w = bbox(:,3);
          h = bbox(:,4);
          aspectRatio = w./h;
          % Threshold the data to determine which regions to remove. These thresholds
          % may need to be tuned for other images.
          filterIdx = aspectRatio' > 3;
          filterIdx = filterIdx | [mserStats.Eccentricity] > .995 ;
          filterIdx = filterIdx | [mserStats.Solidity] < .3;
          filterIdx = filterIdx | [mserStats.Extent] < 0.2 | [mserStats.Extent] > 0.9;
          filterIdx = filterIdx | [mserStats.EulerNumber] < -4;
          % Remove regions
          mserStats(filterIdx) = [];
          mserRegions(filterIdx) = [];
          % Show remaining regions
          figure
          axes(handles.axes1);
          imshow(I)
          hold on
          plot(mserRegions, 'showPixelList', true,'showEllipses',false)
          title('After Removing Non-Text Regions Based On Geometric Properties')
          hold off

          % Get a binary image of the a region, and pad it to avoid boundary effects
          % during the stroke width computation.
          regionImage = mserStats(6).Image;
          regionImage = padarray(regionImage, [1 1]);
          % Compute the stroke width image.
          distanceImage = bwdist(~regionImage);
          skeletonImage = bwmorph(regionImage, 'thin', inf);
          strokeWidthImage = distanceImage;
          strokeWidthImage(~skeletonImage) = 0;
          % Show the region image alongside the stroke width image.
          figure
          subplot(1,2,1)
          imagesc(regionImage)
          title('Region Image')
          subplot(1,2,2)
          imagesc(strokeWidthImage)
          title('Stroke Width Image')
          % Compute the stroke width variation metric
          strokeWidthValues = distanceImage(skeletonImage);
          strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);
          % Threshold the stroke width variation metric
          strokeWidthThreshold = 0.4;
          strokeWidthFilterIdx = strokeWidthMetric > strokeWidthThreshold;
          % Process the remaining regions
          for j = 1:numel(mserStats)
              regionImage = mserStats(j).Image;
              regionImage = padarray(regionImage, [1 1], 0);
              distanceImage = bwdist(~regionImage);
              skeletonImage = bwmorph(regionImage, 'thin', inf);
              strokeWidthValues = distanceImage(skeletonImage);
              strokeWidthMetric = std(strokeWidthValues)/mean(strokeWidthValues);
              strokeWidthFilterIdx(j) = strokeWidthMetric > strokeWidthThreshold;
          end
          % Remove regions based on the stroke width variation
          mserRegions(strokeWidthFilterIdx) = [];
          mserStats(strokeWidthFilterIdx) = [];
          % Show remaining regions
          figure
          axes(handles.axes1);
          imshow(I)
          hold on
          plot(mserRegions, 'showPixelList', true,'showEllipses',false)
          title('After Removing Non-Text Regions Based On Stroke Width Variation')
          hold off

          % Get bounding boxes for all the regions
          bboxes = vertcat(mserStats.BoundingBox);
          % Convert from the [x y width height] bounding box format to the [xmin ymin
          % xmax ymax] format for convenience.
          xmin = bboxes(:,1);
          ymin = bboxes(:,2);
          xmax = xmin + bboxes(:,3) - 1;
          ymax = ymin + bboxes(:,4) - 1;
          % Expand the bounding boxes by a small amount.
          expansionAmount = 0.02;
          xmin = (1-expansionAmount) * xmin;
          ymin = (1-expansionAmount) * ymin;
          xmax = (1+expansionAmount) * xmax;
          ymax = (1+expansionAmount) * ymax;
          % Clip the bounding boxes to be within the image bounds
          xmin = max(xmin, 1);
          ymin = max(ymin, 1);
          xmax = min(xmax, size(I,2));
          ymax = min(ymax, size(I,1));
          % Show the expanded bounding boxes
          expandedBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
          IExpandedBBoxes = insertShape(colorImage,'Rectangle',expandedBBoxes,'LineWidth',3);
          figure
          imshow(IExpandedBBoxes)
          title('Expanded Bounding Boxes Text')
          % Compute the overlap ratio
          overlapRatio = bboxOverlapRatio(expandedBBoxes, expandedBBoxes);
          % Set the overlap ratio between a bounding box and itself to zero to
          % simplify the graph representation.
          n = size(overlapRatio,1);
          overlapRatio(1:n+1:n^2) = 0;
          % Create the graph
          g = graph(overlapRatio);
          % Find the connected text regions within the graph
          componentIndices = conncomp(g);
          % Merge the boxes based on the minimum and maximum dimensions.
          xmin = accumarray(componentIndices', xmin, [], @min);
          ymin = accumarray(componentIndices', ymin, [], @min);
          xmax = accumarray(componentIndices', xmax, [], @max);
          ymax = accumarray(componentIndices', ymax, [], @max);
          % Compose the merged bounding boxes using the [x y width height] format.
          textBBoxes = [xmin ymin xmax-xmin+1 ymax-ymin+1];
          % Remove bounding boxes that only contain one text region
          numRegionsInGroup = histcounts(componentIndices);
          textBBoxes(numRegionsInGroup == 1, :) = [];
          % Show the final text detection result.
          ITextRegion = insertShape(colorImage, 'Rectangle', textBBoxes,'LineWidth',3);
          figure
          axes(handles.axes1);
          imshow(ITextRegion)
          title('Detected Text')
         ocrtxt = ocr(I, textBBoxes);
         [ocrtxt.Text]

[h z]

file_path = 'C:\Users\zh\Desktop\MSERDetect\Images\';

img_path_list = dir(strcat(file_path,'*.jpg'));

img_num = length(img_path_list);

if img_num > 0

        for j = 1:img_num

             image_name = img_path_list(j).name;

             colorImage = imread(strcat(file_path,image_name));

             grayImage = rgb2gray(colorImage);

             mserRegions = detectMSERFeatures(grayImage,'ThresholdDelta',4);

             figure ;imshow(grayImage); hold on;  plot(mserRegions);

    end
end