Simple object recognition

Question

===SOLVED===

Thanks for your suggestions and comments. By working on the flood_fill algorithm given in Beginning Python Visualization book (Chapter 9 - Image Processing) I have implemented what I have wanted. I can count the objects, get enclosing rectangles for each object (therefore height and widths), and lastly can construct NumPy arrays or matrices for each of them.

Although it is not an optimized approach it does what I want. The source code (lab2.py) and the png file (lab2-particles.png) that I use have been put under http://code.google.com/p/ccnworks/source/browse/#svn/trunk/AtSc450.

You need NumPy and PIL installed, and matplotlib to see the histogram. Core of the code lies within the objfind function where the main recursive object search action occurs.

One further update:

SciPy's ndimage.label() does exactly what I want, too.

Cheers for David-Warde Farley and Zachary Pincus from the NumPy and SciPy mailing-lists for pointing this right into my eyes :)

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Hello,

I have an image that contains the shadows of ice particles measured by a particle spectrometer. I want to be able to identify each object, so that I can later classify and use them further in my calculations.

In essence, what I am willing to do is to simply implement a fuzzy selection tool where I can simply select each entity.

How could I easily solve this problem? (Preferably using Python)

Thanks.

NOTE: In my question I am referring to each specific connected pixels as objects or entities. My intention to extract them and create NumPy array representations as shown below. (Here I am using the top-left object; if a pixel exist use 1's if not use 0's. This object's shape is 3 by 3 which correspondingly 3 pixel height by 3 pixel width. These are projections of real ice-particles onto 2D domain, under the assumption of their being spherical and equivalent radius is (height+width)/2, and later some scalings --from pixels to actual sizes and volume calculations will follow)

import numpy as np

np.array([[1,1,1], [1,1,1], [0,0,1]])

array([[1, 1, 1],
       [1, 1, 1],
       [0, 0, 1]])

Here is a section from the image which I am going to use.

screenshot http://img43.imageshack.us/img43/2327/particles.png

Answer 1

1. Scan every square (e.g. from the top-left, left-to-right, top-to-bottom)

2. When you hit a blue square then:

   a. Record this square as a location of a new object

   b. Find all the other contiguous blue squares (e.g. by looking at the neighbours of this square, and the neighbours of those neighbours, etc.) and mark them as being part of the same object

3. Continue to scan

4. When you find another blue square, test to see whether it's part of a known object before going to step 2; alternatively in step 2b, erase any square after you've associated it with an object