Nicely Out of Focus

Before a script can reliably identify the three areas at the center of the image, you need to make some preparations. Figure 2 clearly shows how much the graph fluctuates, and this is obviously going to make it difficult to identify the somewhat flatter areas. Thus, the cardfind detection script (Listing 2) needs to run a blur filter that uses the "Gaussian Blur" method with a radius of 10 to defocus the image (lines 15ff.).

In an out-of-focus image (see Figure 3), the color transitions between individual pixels are less abrupt. Instead of jumping directly from a white to black pixel, an out-of-focus image will show a transition with several gray scale values. The graph shown in Figure 4, which represents the pixel values on the same horizontal line, is far smoother as a result of this, and also simplifies the task of identifying the three areas to be identified.

Figure 3: Setting the blur filter to "Gaussian Blur" with a radius of 10 pixels defocuses the image and smoothes the pixel values.

Figure 4: The image, defocused with the blur filter, has smoother graphs, making it easier to identify the flat spots caused by the three cards.

Back to School?

In these card areas, the curve is fairly flat over a length of hundreds of pixels. If you remember your math from school, you might recall that the first derivative of a graph like this at flat spots is constant and about zero, whereas the values will be far higher and fluctuate significantly everywhere else.

Figure 5 shows the first derivative of intensity values, which are calculated by adding the pixel values for the red, green, and blue channels. The recorded values are indicative of the fluctuation of the original graph and drop to zero over quite considerable distances.

Figure 5: The first derivative of the intensity graph tends toward zero for homogeneous sections of the image.

The cards, with their homogeneous gray scales, occupy these positions in the original image. Thus, the script just needs to follow this graph, create a ring buffer of about 50 investigated values, and alert when the buffer average drops to a value close to zero. When it does so, it has located a card.

Return to Search

When the buffer values start to fluctuate again, the script has left the card area and returns to the state "search for the next homogeneous location." The script should be able to find all three regions you are looking for and return the RGB values it finds there in YAML format. This lets the picfix script I discussed in last month's Perl column adjust the white balance of other images with the same light conditions.