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A couple of days ago, Cameron, a PyImageSearch reader emailed in and asked about methods to find the distance from a camera to an object/marker in an image. He had spent some time researching, but hadn’t found an implementation. I knew exactly how Cameron felt. Years ago I was working on a small project to analyze the movement of a baseball as it left the pitcher’s hand and headed for home plate.
Using motion analysis and trajectory-based tracking I was able to find/estimate the ball location in the frame of the video. And since a baseball has a known size, I was also able to estimate the distance to home plate. It was an interesting project to work on, although the system was not as accurate as I wanted it to be — the “motion blur” of the ball moving so fast made it hard to obtain highly accurate estimates. My project was definitely an “outlier” situation, but in general, determining the distance from a camera to a marker is actually a very well studied problem in the computer vision/image processing space. You can find techniques that are very straightforward and succinct like the triangle similarity. And you can find methods that are complex (albeit, more accurate) using the intrinsic parameters of the camera model. In this blog post I’ll show you how Cameron and I came up with a solution to compute the distance from our camera to a known object or marker.
Aug 13, 2012 - library; claimed: only dependency is OpenCV; up to 1024 distinct markers.[8]. For the corner finding function is that the chessboard dimensions should be given in the order (height, width), as the. Module applies a Canny Edge filter in the form of OpenCV's Canny function, which finds edges in the. ○Stationary chess board. ○Allow pre-calibration of chessboard. ○Clean / plain. Divide the canny chess board image into 8 x 8 chess square images and apply threshold to detect whether a chess square is occupied. ○C++, OpenCV, OpenGL for Vision part. ○Java socket programming for interface with game engine.
Definitely give this post a read — you won’t want to miss it! Looking for the source code to this post? OpenCV and Python versions: This example will run on Python 2.7/Python 3.4+ and OpenCV 2.4.X. Triangle Similarity for Object/Marker to Camera Distance In order to determine the distance from our camera to a known object or marker, we are going to utilize triangle similarity. The triangle similarity goes something like this: Let’s say we have a marker or object with a known width W. We then place this marker some distance D from our camera. We take a picture of our object using our camera and then measure the apparent width in pixels P.
This allows us to derive the perceived focal length F of our camera: F = (P x D) / W For example, let’s say I place a standard piece of 8.5 x 11in piece of paper (horizontally; W = 11) D = 24 inches in front of my camera and take a photo. When I measure the width of the piece of paper in the image, I notice that the perceived width of the paper is P = 248 pixels. My focal length F is then: F = (248px x 24in) / 11in = 543.45 As I continue to move my camera both closer and farther away from the object/marker, I can apply the triangle similarity to determine the distance of the object to the camera: D’ = (W x F) / P Again, to make this more concrete, let’s say I move my camera 3 ft (or 36 inches) away from my marker and take a photo of the same piece of paper. Through automatic image processing I am able to determine that the perceived width of the piece of paper is now 170 pixels. Plugging this into the equation we now get: D’ = (11in x 543.45) / 170 = 35in Or roughly 36 inches, which is 3 feet.
Note: When I captured the photos for this example my tape measure had a bit of slack in it and thus the results are off by roughly 1 inch. Furthermore, I also captured the photos hastily and not 100% on top of the feet markers on the tape measure, which added to the 1 inch error. Samsung Instructions For T700.
That all said, the triangle similarity still holds and you can use this method to compute the distance from an object or marker to your camera quite easily. Make sense now? Let’s move into some code to see how finding the distance from your camera to an object or marker is done using Python, OpenCV, and image processing and computer vision techniques. Finding the distance from your camera to object/marker using Python and OpenCV Let’s go ahead and get this project started. Open up a new file, name it distance_to_camera.