Detect if uploaded file is saved to the temporary folder using Django

When uploading a file to the Django server, using HTML form and POST method, it is not always clear what is the used upload handler and if the file is saved to the temporary folder or just kept in the memory.

Here is one way to detect if the file is stored to the temporary folder or not.

def upload(request):
    uploaded_file = request.FILES['file']
    if isinstance(uploaded_file, TemporaryUploadedFile):
        # file has exceeded the value FILE_UPLOAD_MAX_MEMORY_SIZE
        # and it has been saved to the temporary folder
        print uploaded_tile.temporary_file_path()
        # closing the file will delete the file
        uploaded_file.close()
        return "file too large"
    else:
        # file is instance of InMemoryUploadedFile
    # handle file
    return "ok"

Other comparison function that can be used is:

    if hasattr(uploaded_file, 'temporary_file_path'):

Links: 
https://docs.djangoproject.com/en/dev/topics/http/file-uploads/
http://stackoverflow.com/questions/11835274/django-control-time-of-temporaryuploadedfile-life

How to start using South database migration tool in your Django project

If you are working with Django application using database, at some point you may need change your database schema. In my projects, when working with extreme style, the database changes are frequent.

The 'syncdb' handles new tables, but it does not update changes to the existing tables. One solution is to start using migration tool 'South'.

A lot of documentation for the South exists, but for some reason I was not able to get it up and running as easily as I thought I would. So I wrote the steps you have to take when starting to use South. I hope it helps.

How to start using South in your Django project

1. start using south by setting it to
    - add 'south' to INSTALLED_APPS in your settings.py
2. create needed databases, if you are starting from the scratch
    - python manage.py syncdb
3. create initial migration files
    - python manage.py schemamigration APPNAME --initial
4. make the first migration, use parameter --fake to avoid "table already exists error"
    - python manage.py migrate APPNAME --fake
5. create migration file for fixtures (if you have any)
    - manage.py datamigration APPNAME load_fixtures
6. edit the fixture file "000x_load_fixtures.py", search the file from "migrations" folder
    - edit the function forwards() to include json fixture of your own
        def forwards(self, orm):
       from django.core.management import call_command
       call_command("loaddata", "my_initial_data.json")
7. run the migration to import the fixtures
    - python manage.py migrate APPNAME



   
Links:
http://south.readthedocs.org/en/latest/index.html
http://south.readthedocs.org/en/latest/installation.html#installation
http://stackoverflow.com/questions/5687627/django-south-error-with-initial-migration
http://south.readthedocs.org/en/latest/fixtures.html

Eclipse autocomplete not working - solution

Suddenly the autocompletion in my Eclipse PHP-project  was not working anymore. I tried to restart Eclipse, clean and rebuild my projects but nothing worked. Until I found out the brute force solution: remove the index files autocomplete uses!

1. Just delete folder in from your workspace: /.metadata/.plugins/org.eclipse.dltk.core.index.sql.h2
2. Restart Eclipse

Note: If the autocomplete has never worked in your setup, perhaps you have not enabled it? To check and enable autocomplete, go to Eclipse menu: Window > Preferences > PHP > Editor > Content Assist and select "Enable auto activation".

Multiple static directories with Nginx and Django

I had troubles setting up Nginx configuration to serve static files from two different Django applications. Finally, after searching hints for the solution from Nginx error logs, I managed to got the location configurations correctly.

The following solution may not be optimal, but at least it works. If you know how to achieve similar functionality more elegantly, give me a comment, thanks! :)

... location /static/tools/ {     alias /var/www/sites/mysite/tools/static/tools/; } location /static/editor/ {     alias /var/www/sites/mysite/editor/static/editor/; } ...

Image segmentation with K-means algorithm - Java implementation

K-means algorithm is a well known clustering algorithm and it is described in many papers and online texts. But even after reading many of these said documents, I was confused and still had many questions in my mind.

I was wondering questions like:

• What are the K cluster centers in digital image case?
• What is the minimal distance function when comparing clusters?

I left these questions in my mind for few days, and finally on one morning I realized what the K-means algorithm means in a context of digital image segmentation. Segmentation in this case means "finding the continuous areas where the color and intensity is similar to adjacent pixels".

I will try to explain the questions above in this post.

K-means algorithm for digital images in a nut shell

1. create k clusters, k being the cluster count and the "center" being a color value
2. for each pixel, find the cluster which has minimal distance to the pixel
3. if the pixel was already in other cluster, remove the pixel from other cluster and add it to a new cluster
4. when pixel is added to a cluster, adjust the cluster center color value by adding the pixel colors to it, remove the pixel color values from the old cluster
5. loop back to 2. until there are no pixels that are changing clusters

The algorithm works like magic! By adding and removing the pixels from clusters, the clusters will transform automatically close to the optimal solution (but the result may not be the optional solution).

K-means cluster centers in digital images 

In K-means algorithm you have to decide how many clusters you will have in your image. The simplest case is that the cluster count means the color count and the cluster centers are the color values. In other words, if you have an RGB image with millions of colors, after K-means clustering with value 20, you will have the image converted to a version which only has 20 colors.

Take a look at the histograms below. Now, choosing the value 20 and running the K-means algorithm on the image, the colors are reduced to 20 colors. The histogram after running the algorithm is on below.

Histogram from original image

Histogram after K-means algorithm

In my algorithm implementation, I am choosing the cluster centers always in the same way, so the result for the same original image is always the same. But if you choose the cluster centers randomly, you will end up having slightly different results.

Distance function when comparing clusters

If you chose to continue with simplest case where cluster centers are simply color values, the compare function is also simple. Just calculate how far, or what is the distance, between the pixel you are working with and the cluster center color is.

When working with gray level images the distance calculation is easy, but for color images you need to split the RGB values to individual red, green and blue values. Then calculate the difference between each pixel color and cluster color and finally average the result.

My implementation

I have implemented two different approaches for adding pixels to the clusters. I have named them "continuous" and "iterative" clustering.

In "continuous clustering" I am adding and removing pixels to and from clusters for each pixel if necessary, and I am also counting the cluster center again after each added or removed pixel. The algorithm finds solution faster than the iterative clustering.

In "iterative clustering" I am adding all the pixels to clusters first, and after that I am calculating the new cluster centers.

There are small differences between the results of these two methods. I _guess_ the differences are coming from integer rounding.

Sample images

The original test image

Result of continuous clustering (took 6 loops, 816 milliseconds)

Result of iterative clustering (took 62 loops, 9158 milliseconds)

Java implementation

package popscan;

import java.awt.image.BufferedImage;

import java.io.File;

import java.util.Arrays;

import javax.imageio.ImageIO;

public class KMeans {

BufferedImage original;

BufferedImage result;

Cluster[] clusters;

public static final int MODE_CONTINUOUS = 1;

public static final int MODE_ITERATIVE = 2;

public static void main(String[] args) {

if (args.length!=4) {

System.out.println("Usage: java popscan.KMeans"

+ " [source image filename]"

+ " [destination image filename]"

+ " [clustercount 0-255]"

+ " [mode -i (ITERATIVE)|-c (CONTINUOS)]");

return;

}

// parse arguments

String src = args[0];

String dst = args[1];

int k = Integer.parseInt(args[2]);

String m = args[3];

int mode = 1;

if (m.equals("-i")) {

mode = MODE_ITERATIVE;

} else if (m.equals("-c")) {

mode = MODE_CONTINUOUS;

}

// create new KMeans object

KMeans kmeans = new KMeans();

// call the function to actually start the clustering

BufferedImage dstImage = kmeans.calculate(loadImage(src),

k,mode);

// save the resulting image

saveImage(dst, dstImage);

}

public KMeans() {    }

public BufferedImage calculate(BufferedImage image,

int k, int mode) {

long start = System.currentTimeMillis();

int w = image.getWidth();

int h = image.getHeight();

// create clusters

clusters = createClusters(image,k);

// create cluster lookup table

int[] lut = new int[w*h];

Arrays.fill(lut, -1);

// at first loop all pixels will move their clusters

boolean pixelChangedCluster = true;

// loop until all clusters are stable!

int loops = 0;

while (pixelChangedCluster) {

pixelChangedCluster = false;

loops++;

for (int y=0;y<h;y++) {

for (int x=0;x<w;x++) {

int pixel = image.getRGB(x, y);

Cluster cluster = findMinimalCluster(pixel);

if (lut[w*y+x]!=cluster.getId()) {

// cluster changed

if (mode==MODE_CONTINUOUS) {

if (lut[w*y+x]!=-1) {

// remove from possible previous

// cluster

clusters[lut[w*y+x]].removePixel(

pixel);

}

// add pixel to cluster

cluster.addPixel(pixel);

}

// continue looping

pixelChangedCluster = true;

// update lut

lut[w*y+x] = cluster.getId();

}

}

}

if (mode==MODE_ITERATIVE) {

// update clusters

for (int i=0;i<clusters.length;i++) {

clusters[i].clear();

}

for (int y=0;y<h;y++) {

for (int x=0;x<w;x++) {

int clusterId = lut[w*y+x];

// add pixels to cluster

clusters[clusterId].addPixel(

image.getRGB(x, y));

}

}

}

}

// create result image

BufferedImage result = new BufferedImage(w, h,

BufferedImage.TYPE_INT_RGB);

for (int y=0;y<h;y++) {

for (int x=0;x<w;x++) {

int clusterId = lut[w*y+x];

result.setRGB(x, y, clusters[clusterId].getRGB());

}

}

long end = System.currentTimeMillis();

System.out.println("Clustered to "+k

+ " clusters in "+loops

+" loops in "+(end-start)+" ms.");

return result;

}

public Cluster[] createClusters(BufferedImage image, int k) {

// Here the clusters are taken with specific steps,

// so the result looks always same with same image.

// You can randomize the cluster centers, if you like.

Cluster[] result = new Cluster[k];

int x = 0; int y = 0;

int dx = image.getWidth()/k;

int dy = image.getHeight()/k;

for (int i=0;i<k;i++) {

result[i] = new Cluster(i,image.getRGB(x, y));

x+=dx; y+=dy;

}

return result;

}

public Cluster findMinimalCluster(int rgb) {

Cluster cluster = null;

int min = Integer.MAX_VALUE;

for (int i=0;i<clusters.length;i++) {

int distance = clusters[i].distance(rgb);

if (distance<min) {

min = distance;

cluster = clusters[i];

}

}

return cluster;

}

public static void saveImage(String filename,

BufferedImage image) {

File file = new File(filename);

try {

ImageIO.write(image, "png", file);

} catch (Exception e) {

System.out.println(e.toString()+" Image '"+filename

+"' saving failed.");

}

}

public static BufferedImage loadImage(String filename) {

BufferedImage result = null;

try {

result = ImageIO.read(new File(filename));

} catch (Exception e) {

System.out.println(e.toString()+" Image '"

+filename+"' not found.");

}

return result;

}

class Cluster {

int id;

int pixelCount;

int red;

int green;

int blue;

int reds;

int greens;

int blues;

public Cluster(int id, int rgb) {

int r = rgb>>16&0x000000FF;

int g = rgb>> 8&0x000000FF;

int b = rgb>> 0&0x000000FF;

red = r;

green = g;

blue = b;

this.id = id;

addPixel(rgb);

}

public void clear() {

red = 0;

green = 0;

blue = 0;

reds = 0;

greens = 0;

blues = 0;

pixelCount = 0;

}

int getId() {

return id;

}

int getRGB() {

int r = reds / pixelCount;

int g = greens / pixelCount;

int b = blues / pixelCount;

return 0xff000000|r<<16|g<<8|b;

}

void addPixel(int color) {

int r = color>>16&0x000000FF;

int g = color>> 8&0x000000FF;

int b = color>> 0&0x000000FF;

reds+=r;

greens+=g;

blues+=b;

pixelCount++;

red   = reds/pixelCount;

green = greens/pixelCount;

blue  = blues/pixelCount;

}

void removePixel(int color) {

int r = color>>16&0x000000FF;

int g = color>> 8&0x000000FF;

int b = color>> 0&0x000000FF;

reds-=r;

greens-=g;

blues-=b;

pixelCount--;

red   = reds/pixelCount;

green = greens/pixelCount;

blue  = blues/pixelCount;

}

int distance(int color) {

int r = color>>16&0x000000FF;

int g = color>> 8&0x000000FF;

int b = color>> 0&0x000000FF;

int rx = Math.abs(red-r);

int gx = Math.abs(green-g);

int bx = Math.abs(blue-b);

int d = (rx+gx+bx) / 3;

return d;

}

}

}

Check out my blogpost about watershed segmentation: http://popscan.blogspot.fi/2014/04/watershed-image-segmentation-algorithm.html

Color image segmentation by thresholding with Java

You may need the image segmentation tool for various image analysis tasks. Different tasks may also need specialized segmentation algorithm, for example the segmentation process may discard all green pixels and keep yellow and red, or if you know the object you are trying to detect contains only blue.

It may be difficult to adjust one general segmentation algorithm for special cases, so you may need to tweak the segmentation code itself, not just the parameters.

I will present here one variation from the simplest segmentation algorithm, segmentation by thresholding, implemented in pure Java.

More sophisticated methods exist, and you should use them if your problem requires so.

In my opinion, this thresholding function gives you a nice starting point when starting to make experiments with segmentation. From the command line, you can only adjust the threshold level, int value 0 - 255, but from the source you can change the connectivity testing and balance the color thresholding if needed.

Example images:

The original photo

Segmented with threshold value 20

Segmented with threshold value 40

Segmented with threshold value 60

Segmented with threshold value 80

Segmented with threshold value 100

Segmented with threshold value 120

Feel free to copy the code and adapt it to your own source and frameworks.

Improvements:
I will make an version from the algorithm where the segment color is averaged from the pixels in the segment. In this version, the color is the color which is the "seed" pixel color.

The algorithm in pseudo code:

for each pixel in image
    if pixel is not in segment
        create new segment
        add the pixel as new "seed" point to list of candidates
        while segment has candidate points
            remove first point from the list of candidates
            if the first candidate point is within threshold limit 
                add the first candidate point to the segment
                add neighbor pixel above to the candidate list
                add neighbor pixel below to the candidate list
                add neighbor pixel right to the candidate list
                add neighbor pixel left  to the candidate list

This kind of algorithm must use the candidate list and while loop instead of recursion, because in the worst case, the whole image (its pixels) belong to one segment. In that case, when using recursion, you will most likely see "StackOverflowError" with big images.

The Java implementation:

package popscan;

import java.awt.image.BufferedImage;

import java.io.File;

import java.util.Arrays;

import java.util.Vector;

import javax.imageio.ImageIO;

public class Segmentize {

// value for visited pixel

int VISITED = 0x00FF0000;

// value for not visited pixel

int NOT_VISITED = 0x00000000;

// source image filename

String _srcFilename;

// destination image filename

String _dstFilename;

// the source image

BufferedImage _srcImage;

// the destination image

BufferedImage _dstImage;

// threshold value 0 - 255

int _threshold;

// image width

int _width;

// image height

int _height;

// "seed" color / segment color

int _color;

// red value from seed

int _red;

// green value from seed

int _green;

// blue value from seed

int _blue;

// pixels from source image

int[] _pixels;

// table for keeping track or visits

int[] _visited;

// keeping for candidate points

Vector<SPoint> _points;

class SPoint {

int x;

int y;

public SPoint(int x, int y) {

this.x = x;

this.y = y;

}

}

public static void main(String[] args) {

if (args.length!=3) {

System.out.println("Usage: java Segmentize"

+ " [source image filename]"

+ " [destination image filename]"

+ " [threshold 0-255]");

return;

}

// parse arguments

String src = args[0];

String dst = args[1];

int threshold = Integer.parseInt(args[2]);

// create new Segmentize object

Segmentize s = new Segmentize(loadImage(src),threshold);

// call the function to actually start the segmentation

BufferedImage dstImage = s.segmentize();

// save the resulting image

saveImage(dst, dstImage);

}

public Segmentize(BufferedImage _srcImage, int threshold) {

_threshold = threshold;

_width       = _srcImage.getWidth();

_height       = _srcImage.getHeight();

// extract pixels from source image

_pixels       = _srcImage.getRGB(0, 0, _width, _height,

null, 0, _width);

// create empty destination image

_dstImage  = new BufferedImage(_width,

_height,

BufferedImage.TYPE_INT_RGB);

_visited   = new int[_pixels.length];

_points       = new Vector<SPoint>();

}

private BufferedImage segmentize() {

// initialize points

_points.clear();

// clear table with NOT_VISITED value

Arrays.fill(_visited, NOT_VISITED);

// loop through all pixels

for (int x=0;x<_width;x++) {

for (int y=0;y<_height;y++) {

// if not visited, start new segment

if (_visited[_width*y+x]==NOT_VISITED) {

// extract segment color info from pixel

_color = _pixels[_width*y+x];

_red   = _color>>16&0xff;

_green = _color>>8&0xff;

_blue  = _color&0xff;

// add "seed"

_points.add(new SPoint(x, y));

// start finding neighboring pixels

flood();

}

}

}

// save the result image

_dstImage.setRGB(0, 0, _width, _height, _pixels, 0, _width);

return _dstImage;

}

public void flood() {

// while there are candidates in points vector

while (_points.size()>0) {

// remove the first candidate

SPoint current = _points.remove(0);

int x = current.x;

int y = current.y;

if ((x>=0)&&(x<_width)&&(y>=0)&&(y<_height)) {

// check if the candidate is NOT_VISITED yet

if (_visited[_width*y+x]==NOT_VISITED) {

// extract color info from candidate pixel

int _c = _pixels[_width*y+x];

int red   = _c>>16&0xff;

int green = _c>>8&0xff;

int blue  = _c>>0&0xff;

// calculate difference between

// seed's and candidate's

// red, green and blue values

int rx = Math.abs(red - _red);

int gx = Math.abs(green - _green);

int bx = Math.abs(blue - _blue);

// if all colors are under threshold

if (rx<=_threshold

&&gx<=_threshold

&&bx<=_threshold) {

// add the candidate to the segment (image)

_pixels[_width*y+x] = _color;

// mark the candidate as visited

_visited[_width*y+x] = VISITED;

// add neighboring pixels as candidate

// (8-connected here)

_points.add(new SPoint(x-1,y-1));

_points.add(new SPoint(x  ,y-1));

_points.add(new SPoint(x+1,y-1));

_points.add(new SPoint(x-1,y));

_points.add(new SPoint(x+1,y));

_points.add(new SPoint(x-1,y+1));

_points.add(new SPoint(x  ,y+1));

_points.add(new SPoint(x+1,y+1));

}

}

}

}

}

public static void saveImage(String filename,

BufferedImage image) {

File file = new File(filename);

try {

ImageIO.write(image, "png", file);

} catch (Exception e) {

System.out.println(e.toString()+" Image '"+filename

+"' saving failed.");

}

}

public static BufferedImage loadImage(String filename) {

BufferedImage result = null;

try {

result = ImageIO.read(new File(filename));

} catch (Exception e) {

System.out.println(e.toString()+" Image '"

+filename+"' not found.");

}

return result;

}

}