Measuring an object from a picture using a known object size

Question

So what I need to do is measuring a foot length from an image taken by an ordinary user. That image will contain a foot with a black sock wearing, a coin (or other known size o

Answer 1

You don't have to calibrate the camera if you have a known-size object in your image. Well... at least if your camera doesn't distort too much and if you're not expecting high quality measurements.

A simple approach would be to detect a white (perspective-distorted) rectangle, mapping the corners to an undistorted rectangle (using e.g. cv::warpPerspective()) and use the known size of that rectangle to determine the size of other objects in the picture. But this only works for objects in the same plane as the paper, preferably not too far away from it.

Answer 2

I am not sure if you need to build this yourself, but if you just need to do it, and not code it. You can use KLONK Image Measurement for this. There is a free and payable versions.

Answer 3

First, some image acquisition things:

1. Can you count on the black sock and white background? The colors don't matter as much as the high contrast between the sock and background.
2. Can you standardize the viewing angle? Looking directly down at the foot will reduce perspective distortion.
3. Can you standardize the lighting of the scene? That will ease a lot of the processing discussed below.
4. Lastly, you'll get a better estimate if you zoom (or position the camera closer) so that the foot fills more of the image frame.

Analysis. (Note this discussion will directed to your question of identifying the axes of the foot. Identifying and analyzing the coin would use a similar process, but some differences would arise.)

1. The next task is to isolate the region of interest (ROI). If your camera is looking down at the foot, then the ROI can be limited to the white rectangle. My answer to this Stack Overflow post is a good start to square/rectangle identification: What is the simplest *correct* method to detect rectangles in an image?
2. If the foot lies completely in the white rectangle, you can clip the image to the rect found in step #1. This will limit the image analysis to region inside the white paper.
3. "Binarize" the image using a threshold function: http://opencv.willowgarage.com/documentation/cpp/miscellaneous_image_transformations.html#cv-threshold. If you choose the threshold parameters well, you should be able to reduce the image to a black region (sock pixels) and white regions (non-sock pixel).
4. Now the fun begins: you might try matching contours, but if this were my problem, I would use bounding boxes for a quick solution or moments for a more interesting (and possibly robust) solution.
5. Use cvFindContours to find the contours of the black (sock) region: http://opencv.willowgarage.com/documentation/structural_analysis_and_shape_descriptors.html#findcontours
6. Use cvApproxPoly to convert the contour to a polygonal shape http://opencv.willowgarage.com/documentation/structural_analysis_and_shape_descriptors.html#approxpoly
7. For the simple solution, use cvMinRect2 to find an arbitrarily oriented bounding box for the sock shape. The short axis of the box should correspond to the line in largura.jpg and the long axis of the box should correspond to the line in comprimento.jpg. http://opencv.willowgarage.com/documentation/structural_analysis_and_shape_descriptors.html#minarearect2
8. If you want more (possible) accuracy, you might try cvMoments to compute the moments of the shape. http://opencv.willowgarage.com/documentation/structural_analysis_and_shape_descriptors.html#moments
9. Use cvGetSpatialMoment to determine the axes of the foot. More information on the spatial moment may be found here: http://en.wikipedia.org/wiki/Image_moments#Examples_2 and here http://opencv.willowgarage.com/documentation/structural_analysis_and_shape_descriptors.html#getspatialmoment
10. With the axes known, you can then rotate the image so that the long axis is axis-aligned (i.e. vertical). Then, you can simply count pixels horizontally and vertically to obtains the lengths of the lines. Note that there are several assumptions in this moment-oriented process. It's a fun solution, but it may not provide any more accuracy - especially since the accuracy of your size measurements is largely dependent on the camera positioning issues discussed above.

Lastly, I've provided links to the older C interface. You might take a look at the new C++ interface (I simply have not gotten around to migrating my code to 2.4)

Answer 4

Antonio Criminisi likely wrote the last word on this subject years ago. See his "Single View Metrology" paper , and his PhD thesis if you have time.