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opencvchartscolorsdetectionsquare

Detect missing squares in color calibration chart opencv python

I am am currently working on a method to extract colors from a macbeth color chart. So far I have had moderate success by using thresholding and then extracting square contours. Sadly through, colors that are too close to each other either mix together or do no get detected.

The code in it's current form:

<script src="https://pastebin.com/embed_js/mNi0TcDE"></script>

The image before any processing

After thresholding, you can see that there are areas where lines are incomplete due to too small differences in color. I have tried to use dilation to midigate these issues and it does work to a degree. But not enough to detect all squares.

Image after thresholding

This results in the following contours being detected

Detected contours

I have tried using:

  1. Hough lines, sadly no lines here detected.
  2. Centroids of contours, but I was unable to find a way to use centroids to draw lines and detect the centers of the missing contours
  3. Corner detection, corners where found. But I was unsuccessful in finding a real way to put them to use.

Can anyone point me in the right direction?

Thaks in advance, Emil

Solution

  • Hum, if your goal is color calibration, you really do not need to detect the squares in their entirety. A 10x10 sample near the center of the image of each physical square will give you 100 color samples, which is plenty for any reasonable calibration procedure.

    There are many ways to approach this problem. If you can guarantee that the chart will cover the image, you could even just do k-means clustering, since you know in advance the exact number of clusters you seek.

    If you insist on using geometry, I'd do template matching in scale+angle space - it is reasonable to assume that the chart will be mostly facing, and only slightly rotated, so you only need to estimate scale and a small rotation about the axis orthogonal to the chart.