Using PCL, I'm trying to detect and localize a rectangular cut from a large steel frame(img below):
Now I'm using the Concave hull class, and I do get the outlines from the rectangle. However, the outer borders of the camera view also follow.
I used a passthrough filter to get rid of the borders, however that only works in specific cases.
What I'm asking is, do you happen to know any methods that could give a better result?
For the holes, they are not always at the same height or location. But they are of a standard size(+/- 1 cm). A size criteria can eliminate false detections.
This is a gazebo simulated model, and the point cloud captured from a simulated kinect using ROS.
Using PCL, I used SAC planar segmentation, then extract a concave hull. As seen on the image, the edges of the camera view are also considered as a concave.
pcl::SACSegmentation<pcl::PointXYZ> segmentation;
segmentation.setOptimizeCoefficients (true);
segmentation.setModelType(pcl::SACMODEL_PLANE);
segmentation.setMethodType(pcl::SAC_RANSAC);
segmentation.setMaxIterations(1000);
segmentation.setDistanceThreshold(0.01);
segmentation.setInputCloud(cloud_ptr);//breytti
segmentation.segment(*inliers, *coefficients);
pcl::ConcaveHull<pcl::PointXYZ> chull;
chull.setInputCloud (cloud_projected);
chull.setAlpha (0.1);
chull.reconstruct (*cloud_hull, hullPolygons);
Eigen::Vector4f centroid;//new object for centroid calculation
pcl::PointXYZ minpt, maxpt;//min max boundary of new cloud
pcl::compute3DCentroid(*cloud_hull, centroid);
pcl::getMinMax3D(*cloud_hull,minpt,maxpt);
To sum it up, looking for a robust method or ideas to detect a rectangular cut from the frame.
Thanks
An alternative tool could be to use CloudCompare. In version 2.12 alpha, Tools> Sand box (research) > find biggest inner rectangle 2D it can be used to detect rectangular holes.
Related
I am currently working on a robotic project: a robot must grab an cube using a Kinect camera that process cube detection and calculate coordinates.
I am new in computer vision. I first worked on static image of square in order to get a basic understanding. Using C++ and openCV, I managed to get the corners (and their x y pixel coordinates) of the square using smoothing (remove noise), edge detection (canny function), lines detection (Hough transform) and lines intersection (mathematical calculation) on an simplified picture (uniform background).
By adjusting some threshold I can achieve corners detection assuming that I have only one square and no line feature in the background.
Now is my question: do you have any direction/recommendation/advice/literature about cube recognition algorithm ?
What I have found so far involves shape detection combined with texture detection and/or learning sequence. Moreover, in their applications, they often use GPU/parallellisation computing, which I don't have...
The Kinect also provided a depth camera which gives distance of the pixel from the camera. Maybe I can use this to bypass "complicated" image processing ?
Thanks in advance.
OpenCV 3.0 with contrib includes surface_matching module.
Cameras and similar devices with the capability of sensation of 3D
structure are becoming more common. Thus, using depth and intensity
information for matching 3D objects (or parts) are of crucial
importance for computer vision. Applications range from industrial
control to guiding everyday actions for visually impaired people. The
task in recognition and pose estimation in range images aims to
identify and localize a queried 3D free-form object by matching it to
the acquired database.
http://docs.opencv.org/3.0.0/d9/d25/group__surface__matching.html
I am trying to detect a ball in an filtered image.
In this image I've already removed the stuff that can't be part of the object.
Of course I tried the HoughCircle function, but I did not get the expected output.
Either it didn't find the ball or there were too many circles detected.
The problem is that the ball isn't completly round.
Screenshots:
I had the idea that it could work, if I identify single objects, calculate their center and check whether the radius is about the same in different directions.
But it would be nice if it detect the ball also if he isn't completely visible.
And with that method I can't detect semi-circles or something like that.
EDIT: These images are from a video stream (real time).
What other method could I try?
Looks like you've used difference imaging or something similar to obtain the images you have..? Instead of looking for circles, look for a more generic loop. Suggestions:
Separate all connected components.
For every connected component -
Walk around the contour and collect all contour pixels in a list
Suggestion 1: Use least squares to fit an ellipse to the contour points
Suggestion 2: Study the curvature of every contour pixel and check if it fits a circle or ellipse. This check may be done by computing a histogram of edge orientations for the contour pixels, or by checking the gradients of orienations from contour pixel to contour pixel. In the second case, for a circle or ellipse, the gradients should be almost uniform (ask me if this isn't very clear).
Apply constraints on perimeter, area, lengths of major and minor axes, etc. of the ellipse or loop. Collect these properties as features.
You can either use hard-coded heuristics/thresholds to classify a set of features as ball/non-ball, or use a machine learning algorithm. I would first keep it simple and simply use thresholds obtained after studying some images.
Hope this helps.
I am attempting to detect coloured tennis balls on a similar coloured background. I am using OpenCV and C++
This is the test image I am working with:
http://i.stack.imgur.com/yXmO4.jpg
I have tried using multiple edge detectors; sobel, laplace and canny. All three detect the white line, but when the threshold is at a value where it can detect the edge of the tennis ball, there is too much noise in the output.
I have also tried the Hough Circle transform but as it is based on canny, it isn't effective.
I cannot use background subtraction because the background can move. I also cannot modify the threshold values as lighting conditions may create gradients within the tennis ball.
I feel my only option is too template match or detect the white line, however I would like to avoid this if possible.
Do you have any suggestions ?
I had to tilt my screen to spot the tennisball myself. It's a hard image.
That said, the default OpenCV implementation of the Hough transform uses the Canny edge detector, but it's not the only possible implementation. For these harder cases, you might need to reimplement it yourself.
You can certainly run the Hough algorithm repeatedly with different settings for the edge detection, to generate multiple candidates. Besides comparing candidates directly, you can also check that each candidate has a dominant texture (after local shading corrections) and possibly a stripe. But that might be very tricky if those tennisballs are actually captured in flight, i.e. moving.
What are you doing to the color image BEFORE the edge detection? Simply converting it to gray?
In my experience colorful balls pop out best when you use the HSV color space. Then you would have to decide which channel gives the best results.
Perhaps transform the image to a different feature space might be better then relying on color. Maybe try LBP which responds to texture. Then do PCA on the result to reduce the feature space to 1 single channel image and try Hough Transform on that.
I have written an algorithm to process a camera capture and extract a binary image of two features I'm interested in. I'm trying to find the best (fastest) way of detecting when the two features intersect and where the lowest (y coordinate is greatest) point is (this will be the intersection).
I do not want to use a findContours() based method as this is too slow and, in my opinion, unnecessary. I also think blob detection libraries are too bloated for this.
I have two sample images (sorry for low quality):
(not touching: http://i.imgur.com/7bQ9qMo.jpg)
(touching: http://i.imgur.com/tuSmKw7.jpg)
Due to the way these images are created, there is often noise in the top right corner which looks like pixelated lines but methods such as dilation and erosion lose resolution around the features I'm trying to find.
My initial thought would be to use direct pixel access to form a width filter and a height filter. The lowest point in the image is therefore the intersection.
I have no idea how to detect when they touch... logically I can see that a triangle is formed when they intersect and otherwise there is no enclosed black area. Can I fill the image starting from the corner with say, red, and then calculate how much of the image is still black?
Does anyone have any suggestions?
Thanks
Your suggestion is a way more slow than finding contours. For binary images, finding contour is very easy and quick because you just need to find a black pixel followed by a white pixel or vice versa.
Anyway, if you don't want to use it, you can use the vertical projection or vertical profile you will see it the objects intersect or not.
For example, in the following image check the the letter "n" which is little similar to non-intersecting object, and the letter "o" which is similar to intersecting objects :
By analyzing the histograms you can recognize which one is intersecting or not.
I'm trying to detect a shape (a cross) in my input video stream with the help of OpenCV. Currently I'm thresholding to get a binary image of my cross which works pretty good. Unfortunately my algorithm to decide whether the extracted blob is a cross or not doesn't perform very good. As you can see in the image below, not all corners are detected under certain perspectives.
I'm using findContours() and approxPolyDP() to get an approximation of my contour. If I'm detecting 12 corners / vertices in this approximated curve, the blob is assumed to be a cross.
Is there any better way to solve this problem? I thought about SIFT, but the algorithm has to perform in real-time and I read that SIFT is not really suitable for real-time.
I have a couple of suggestions that might provide some interesting results although I am not certain about either.
If the cross is always near the center of your image and always lies on a planar surface you could try to find a homography between the camera and the plane upon which the cross lies. This would enable you to transform a sample image of the cross (at a selection of different in plane rotations) to the coordinate system of the visualized cross. You could then generate templates which you could match to the image. You could do some simple pixel agreement tests to determine if you have a match.
Alternatively you could try to train a Haar-based classifier to recognize the cross. This type of classifier is often used in face detection and detects oriented edges in images, classifying faces by the relative positions of several oriented edges. It has good classification accuracy on faces and is extremely fast. Although I cannot vouch for its accuracy in this particular situation it might provide some good results for simple shapes such as a cross.
Computing the convex hull and then taking advantage of the convexity defects might work.
All crosses should have four convexity defects, making up four sets of two points, or four vectors. Furthermore, if your shape was a cross then these four vectors will have two pairs of supplementary angles.