I'm trying to calculate the motion of dust particles, and I can't seem to find a package that can identify the dimensions of a non-standard shape (i.e. approximately the radius if we assume the dust particle to be circular.
Say if I had the following image, and feed the package the location of the 'centre' of one of these particles, it would return some dimensions (shape, major-axis radius etc).
![dust] (https://scontent-lht6-1.xx.fbcdn.net/v/t1.15752-9/47071656_339293956870649_3030152264015675392_n.png?_nc_cat=106&_nc_ht=scontent-lht6-1.xx&oh=b72ca130a01659d870f085ae8a5a0e87&oe=5CB1C1FF
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I'm working on a project which locates the Machine Readable Zone on ID cards.
For this I need to do some pre processing to extract the ID card from a scanned image which typically are randomly disposed on a white page. I'm able to locate the majority of the cards by using a Histogram equalization with CLAHE before a contour detection. But in some cases the border around the MRZ is totally invisible (white on white) as shown on the attached image.
I'd like to detect rectangle of a predefined shape as I know the shape of the ID card will be always the same but so far I wasn't able to find a way do do something like this with OpenCV.
Basically what I need is to find two rectangle of a fixed ratio that best match the 2 cards on the scan.
I'm wondering if I need to try OpenCV matchers or if there is a simpler way to accomplish this kind of detection.
The solution to you problem is likely going to be matrix transformations. The concept is to pinpoint 4 coordinates on the card that can be easily detected using opencv, such as the the rectangle colored in blue & cyan.
Have coordinates of the card with the predefined shape stored in an array, where a corner of the card is at the 0, 0. Also store the coordinates of the blue * cyan rectangle in an array. With the two arrays you can find the perspective transform of the two arrays using the cv2.getPerspectiveTransform method.
Using the perspective transform found, you can detect the coordinates of the whole card every time you detect the coordinates of the blue & cyan rectangle.
I am trying to measure the precision of my marker tracking algorithm via post-processing a video.
My algorithm is: Find a printed planar marker in a Videostream and place a virtual marker at that position. I am working with AR.
Here are two frames of such a video:
Virtual Marker on top of detected marker
Virtual Marker with offset to actual marker
I want to calculate the Intersecion over Union / Jaccard Index of the actual marker and virtual marker. For the first picture it would give me ~98% and the second ~1/5th %. This will give me the quality for my algorithm, how precise and well it works.
I want to get the position and rotation of both markers in each frame with OpenCV and calculate the Jaccard Index. As you can see though, if I directly place a virtual marker on top of the paper marker, I will make it difficult for myself (with OpenCV) to detect them.
My idea is to not place a white marker on top of the actual marker, but place an easily detectable "thing" with a specific color or shape with an offset to the marker, let's say 10cm to the right maybe. Then I subtract the offset. So now, at the best case scenario, the position and rotation of the actual marker and the "thing" with the offset subtracted will be the same.
But what should I use as the easily detectable "thing"? I don't have enough experience with OpenCV to know what (colored?) shape I should use. The augmentation can go in front, behind, left, right... of the actual marker anytime during the video and it should do two things:
Not hinder the detection of the actual marker, like currently shown in the pictures
Be easily detectable itself
Help would be much appreciated!
Assuming you have enough white background around the visual marker:
You could use colored circles, for example in red, green, blue and black.
Use opencv blob detection [1] to detect all blobs and filter for circular ones:
Look-up average color values for detected blobs and filter for the colors of the circles.
Alternatively you could filter the whole image for each color and do blob detection on the filtered images. But this is slower.
Find the centroids (~ center point) of each blob using moments of the blob contours. [2] "Center of multiple blobs in an Image".
Now you have the four pixel positions of your circles. If you know the world coordinates of your light projected circles you can use solvePnP to get a pose from this.
Knowing the correct world coordinates is tricky in your case because you project the circle with light on a surface. This involves some 3D geometry. You need to know the transformation from camera coordinate system to pattern projector coordinate system and the projection parameters of your projector.
I guess you send the projected pattern as an image to the projector. I think you can then model the projector as a camera with a certain camera matrix (basically field of view & center point). Naturally you know the pixel coordinates of the projected circles. From this you can compute rays in 3D space (in projector coordinate system). As a starting point see [3]. Intersecting [4] them with the correct surface plane (in projector coordinate system) gives you the 3D coordinates of
the projected circle pattern in projector coordinate system. Transform these to camera coordinate system using your known transformation. Now use opencv solvePnP to determine pose of projected light marker.
How to get surface plane?
If your setup is static you could use visual marker detection of all recorded images and use mean oder median of marker pose as surface plane. Not sure what this implies for your evaluation though..
[1] https://www.learnopencv.com/blob-detection-using-opencv-python-c/
[2] https://www.learnopencv.com/find-center-of-blob-centroid-using-opencv-cpp-python/
[3] https://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html
[4] https://www.cs.princeton.edu/courses/archive/fall00/cs426/lectures/raycast/sld017.htm
I'm working with OpenCV for a task on measuring the solar angle in a photo (without any camera parameter). In the photo there is a straight stick with the height of 3 meters standing in the middle of the field. The shadow it casts, however, lies obliquely on the ground (not in the same projection plane as the stick). I can obtain the pixel length of the stick and shadow, but I don't know if the ratio should be directly calculated with the two numbers, since only lines within the same projection plane have the same scale.
This is more like a graphic issue rather than algorithm. Can anyone shed some light on me about how to determine the height-shadow ratio?
What is Distance Transform?What is the theory behind it?if I have 2 similar images but in different positions, how does distance transform help in overlapping them?The results that distance transform function produce are like divided in the middle-is it to find the center of one image so that the other is overlapped just half way?I have looked into the documentation of opencv but it's still not clear.
Look at the picture below (you may want to increase you monitor brightness to see it better). The pictures shows the distance from the red contour depicted with pixel intensities, so in the middle of the image where the distance is maximum the intensities are highest. This is a manifestation of the distance transform. Here is an immediate application - a green shape is a so-called active contour or snake that moves according to the gradient of distances from the contour (and also follows some other constraints) curls around the red outline. Thus one application of distance transform is shape processing.
Another application is text recognition - one of the powerful cues for text is a stable width of a stroke. The distance transform run on segmented text can confirm this. A corresponding method is called stroke width transform (SWT)
As for aligning two rotated shapes, I am not sure how you can use DT. You can find a center of a shape to rotate the shape but you can also rotate it about any point as well. The difference will be just in translation which is irrelevant if you run matchTemplate to match them in correct orientation.
Perhaps if you upload your images it will be more clear what to do. In general you can match them as a whole or by features (which is more robust to various deformations or perspective distortions) or even using outlines/silhouettes if they there are only a few features. Finally you can figure out the orientation of your object (if it has a dominant orientation) by running PCA or fitting an ellipse (as rotated rectangle).
cv::RotatedRect rect = cv::fitEllipse(points2D);
float angle_to_rotate = rect.angle;
The distance transform is an operation that works on a single binary image that fundamentally seeks to measure a value from every empty point (zero pixel) to the nearest boundary point (non-zero pixel).
An example is provided here and here.
The measurement can be based on various definitions, calculated discretely or precisely: e.g. Euclidean, Manhattan, or Chessboard. Indeed, the parameters in the OpenCV implementation allow some of these, and control their accuracy via the mask size.
The function can return the output measurement image (floating point) - as well as a labelled connected components image (a Voronoi diagram). There is an example of it in operation here.
I see from another question you have asked recently you are looking to register two images together. I don't think the distance transform is really what you are looking for here. If you are looking to align a set of points I would instead suggest you look at techniques like Procrustes, Iterative Closest Point, or Ransac.
I need to find the size or coordinates of a rectangle that is displayed as a quadrilateral in a 3D image. The quadrilateral is on a plane that lines up with 3d world vanishing points. To clarify, the quadrilateral IS a rectangle in the 3D world, and that's the rectangle I want the size of.
I do not need to get all the textures and make a new image. I also do not know the coordinates of the target rectangle as required by the homography (perspective transformation) solutions I've seen, because I don't know the aspect ratio it's supposed to have.
I've read through this thread: proportions of a perspective-deformed rectangle and the guy seemed to find an algorithm that works. However I've read other research papers that claim to calculate a homography yet they don't say how they did it. Also it seems such a basic function there would be something in the existing openCV library.
Thanks.