Databases
StackOverflow2013
Postsdbo
PO
 

Note that there are some explanatory texts on larger screens.

plurals
  1. PO
    primarykey
    data
    text
    <p>Some old-fashioned image processing approach...<br/> The idea is based on the <strong>assumption that images depict lighted trees on typically darker and smoother backgrounds</strong> (or foregrounds in some cases). The <strong>lighted tree area is more "energetic" and has higher intensity</strong>. <br/> The process is as follows:</p> <ol> <li>Convert to graylevel</li> <li>Apply LoG filtering to get the most "active" areas</li> <li>Apply an intentisy thresholding to get the most bright areas</li> <li>Combine the previous 2 to get a preliminary mask</li> <li>Apply a morphological dilation to enlarge areas and connect neighboring components</li> <li>Eliminate small candidate areas according to their area size</li> </ol> <p>What you get is a binary mask and a bounding box for each image.</p> <p><strong>Here are the results using this naive technique:</strong> <img src="https://i.stack.imgur.com/C402E.jpg" alt="enter image description here"></p> <p><strong>Code on MATLAB follows:</strong> The code runs on a folder with JPG images. Loads all images and returns detected results.</p> <pre><code>% clear everything clear; pack; close all; close all hidden; drawnow; clc; % initialization ims=dir('./*.jpg'); imgs={}; images={}; blur_images={}; log_image={}; dilated_image={}; int_image={}; bin_image={}; measurements={}; box={}; num=length(ims); thres_div = 3; for i=1:num, % load original image imgs{end+1}=imread(ims(i).name); % convert to grayscale images{end+1}=rgb2gray(imgs{i}); % apply laplacian filtering and heuristic hard thresholding val_thres = (max(max(images{i}))/thres_div); log_image{end+1} = imfilter( images{i},fspecial('log')) &gt; val_thres; % get the most bright regions of the image int_thres = 0.26*max(max( images{i})); int_image{end+1} = images{i} &gt; int_thres; % compute the final binary image by combining % high 'activity' with high intensity bin_image{end+1} = log_image{i} .* int_image{i}; % apply morphological dilation to connect distonnected components strel_size = round(0.01*max(size(imgs{i}))); % structuring element for morphological dilation dilated_image{end+1} = imdilate( bin_image{i}, strel('disk',strel_size)); % do some measurements to eliminate small objects measurements{i} = regionprops( logical( dilated_image{i}),'Area','BoundingBox'); for m=1:length(measurements{i}) if measurements{i}(m).Area &lt; 0.05*numel( dilated_image{i}) dilated_image{i}( round(measurements{i}(m).BoundingBox(2):measurements{i}(m).BoundingBox(4)+measurements{i}(m).BoundingBox(2)),... round(measurements{i}(m).BoundingBox(1):measurements{i}(m).BoundingBox(3)+measurements{i}(m).BoundingBox(1))) = 0; end end % make sure the dilated image is the same size with the original dilated_image{i} = dilated_image{i}(1:size(imgs{i},1),1:size(imgs{i},2)); % compute the bounding box [y,x] = find( dilated_image{i}); if isempty( y) box{end+1}=[]; else box{end+1} = [ min(x) min(y) max(x)-min(x)+1 max(y)-min(y)+1]; end end %%% additional code to display things for i=1:num, figure; subplot(121); colormap gray; imshow( imgs{i}); if ~isempty(box{i}) hold on; rr = rectangle( 'position', box{i}); set( rr, 'EdgeColor', 'r'); hold off; end subplot(122); imshow( imgs{i}.*uint8(repmat(dilated_image{i},[1 1 3]))); end </code></pre>
    singulars
    1. This table or related slice is empty.
    1. POHow to detect a Christmas Tree?
      singulars
      1. PTQuestion
    1. PTAnswer
    1. USsepdek
    1. USsepdek
    plurals
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. This table or related slice is empty.
    1. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    2. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    3. VO
      singulars
      1. PO
      1. This table or related slice is empty.
      1. VTUpMod
    1. CODon't forget to upload the resulting images, like Faust did.
      singulars
      1. PO
      1. USkarlphillip
    2. COI am a noob here so I cannot upload images. Please see results on the provided links in my description.
      singulars
      1. PO
      1. USsepdek
    3. COOk, but you still have to use the images shared on the question like everybody else is doing. Once you process them, upload it somewhere and edit your answer to add the links. Later I'll edit your answer and place the images inside it for you.
      singulars
      1. PO
      1. USkarlphillip
 

Querying!

 
Guidance
A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.

SQuiL has stopped working due to an internal error.

If you are curious you may find further information in the browser console, which is accessible through the devtools (F12).

Reload