image focus calculation
I'm trying to develop an image focusing algorithm for some test automation work. I've chosen to use AForge.net, since it seems like a nice mature .net friendly system.
Unfortunately, I can't seem to find information on building autofocus algorithms from scratch, so I've given it my best try:
take image. apply sobel edge detection filter, which generates a greyscale edge outline. generate a histogram and save the standard dev. move camera one step closer to subject and take another picture. if the standard dev is smaller than previous one, we're getting more in focus. otherwise, we've past the optimal distance to be taking pictures.
is there a better way?
update: HUGE flaw in this, by the way. as I get past the optimal focus point, my "image in focus" value continues growing. you'd expect a parabolic-ish function looking at distance/focus-value, but in reality you get something that's more logarithmic
update 2: okay, so I went back to this and the current method we're exploring is given a few known edges (okay, so I know exactly what the objects in the picture are), I do a manual pixel intensity comparison. as the resulting graph gets steeper, I get more in focus. I'll post code once the core algorithm gets ported from matlab into c# (yeah, matlab.. :S)
update 3: yay final update. came back to this again. the final code looks like this:
step 1: get image from the list of images (I took a hundred photos through the focused point)
step 2: find an edge for the object I'm focusing (In my case its a rectangular object that's always in the same place, so I crop a HIGH and NARROW rectangle of one edge)
step 3: get the HorizontalIntensityStatistics (Aforge.net class) for that cropped image.
step 4: get the Histogram (gray, in my case)
step 5: find the derivative of the values of the histogram
step 6: when your slope is the largest, is when you're in the most focused point.
Answer
You can have a look at the technique used in the NASA Curiosity Mars Rover.
The technique is described in this article
which is available as a PDF here.
Quoting from the article:
7.2.2 Autofocus
Autofocus is anticipated to be the primary method by which MAHLI is focused on Mars. The autofocus command instructs the camera to move to a specified starting motor count position and collect an image, move a specified number of steps and collect another image, and keep doing so until reaching a commanded total number of images, each separated by a specified motor count increment. Each of these images is JPEG compressed (Joint Photographic Experts Group; see CCITT (1993)) with the same compression quality factor applied. The file size of each compressed image is a measure of scene detail, which is in turn a function of focus (an in-focus image shows more detail than a blurry, out of focus view of the same scene). As illustrated in Fig. 23, the camera determines the relationship between JPEG file size and motor count and fits a parabola to the three neighboring maximum file sizes. The vertex of the parabola provides an estimate of the best focus motor count position. Having made this determination, MAHLI moves the lens focus group to the best motor position and acquires an image; this image is stored, the earlier images used to determine the autofocus position are not saved.
Autofocus can be performed over the entire MAHLI field of view, or it can be performed on a sub-frame that corresponds to the portion of the scene that includes the object(s) to be studied. Depending on the nature of the subject and knowledge of the uncertainties in robotic arm positioning of MAHLI, users might elect to acquire a centered autofocus sub-frame or they might select an off-center autofocus sub-frame if positioning knowledge is sufficient to determine where the sub-frame should be located. Use of sub-frames to perform autofocus is highly recommended because this usually results in the subject being in better focus than is the case when autofocus is applied to the full CCD; further, the resulting motor count position from autofocus using a sub-frame usually results in a more accurate determination of working distance from pixel scale.
The following is Figure 23:
This idea was suggested also in this answer: https://stackoverflow.com/a/2173259/15485