Counting objects on a grid with OpenCV

I'm relatively new to 开发者_Go百科OpenCV, and I'm working on a project where I need to count the number of objects on a grid. the grid is the background of the image, and there's either an object in each space or there isn't; I need to count the number present, and I don't really know where to start. I've searched here and other places, but can't seem to find what I'm looking for. I will need to be tracking the space numbers of the grid in the future, so I will also eventually need to know whether each grid space is occupied or empty. I'm not going so far as to ask for a coded example, but does anybody know of any source or tutorials to accomplish this task or one similar to it? Thanks for your help!

Further Details: images will come from a stable-mounted camera, objects are of relatively uniform shape, but varying size and color.

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I would first answer a few questions:

1. Will an object be completely enclosed in a grid cell? Or can it be placed on top of a grid line? (In other words, will the object hide a line from the camera?)
2. Will more than one object be in one cell?
3. Can an object occupy more than one cell? (closely related to question 1)

Given reasonable answers to those questions, I believe the problem can be broken into two parts: first, identify the centers of each grid space. To count objects, you can then sample that region to see if anything "not background" is there.

You can then assume that a grid space is defined by four strong, regularly-placed, corner features. (For the sake of discussion, I'll assume you've performed the initial image preparation as needed: histogram equalization, gaussian blur for noise reduction, etc.) From there, you might try some of OpenCV's methods for finding corners (Harris corner detector, cvGoodFeaturesToTrack, etc). It's likely that you can borrow some of the techniques found in OpenCV's square finding example (samples/c/square.c). For this task, it's probably sufficient to assume that the grid center is just the centroid of each set of "adjacent" (or sufficiently near) corners.

Alternatively, you might use the Hough transform to identify the principal horizontal and vertical lines in the image. You can then determine the intersection points to identify the extents of each grid cell. This implementation might be more challenging since inferring structure (or adjacency) from "nearby" vertices in order to find a grid center seems more difficult.