I have an image I want to extract lines from (a vascular network), using the Hough line algorithm. First I preprocess the image, then use Canny edge detection to generate the binary image.
I want to get a polygon/an array of joined line segments representing the shape of the vascular network. However applying the Hough line transform directly on this image yields mediocre results, partly because edge detection means each vessel is represented by two lines on each side, instead of a single line.
I'm new to OpenCV and image processing in general, so I'm probably going about this the wrong way. Any suggestions, or any recommended literature?
I suggest not using Canny edge detection.
Instead, first use a binary threshold to get a binary image of the vascular network (see http://docs.opencv.org/3.1.0/d7/d4d/tutorial_py_thresholding.html#gsc.tab=0 for applying a binary threshold). Then, pixels that are "on" should be points inside the network and those that are "off" should be outside.
Then use the findContours method:
http://opencvexamples.blogspot.com/2013/09/find-contour.html
This method gives you an array of contours, each of which is a list of points. A list of points will represent the list of line segments you are looking for (it will represent a contour, and if you are lucky it might be a polygon!).
Hough may not be the best tool for this job. Hough will give you straight lines or other geometric shapes. It is not designed to follow a detailed pattern like this.
Given the image, I would read research papers which already solve this. Here are a few examples from a search on Google Scholar. If they don't work for you, look up the citations as they should lead you down other paths.
https://scholar.google.com/scholar?hl=en&q=retina+computer+vision+vascular
http://ijesat.org/Volumes/2012_Vol_02_Iss_04/IJESAT_2012_02_04_25.pdf
http://www.vision.cs.rpiscrews.us/publications/pdfs/shen_itbm_submitted.pdf
Related
I ran findCountours on the following Image:
And got the following contour image (I'm showing only "parent" contours according to the hierarchy):
As you can see, there are many different contours around each object (each one in a different color). Now, I want to unify the contours around the person to obtain one enclosing contour, so I could segment her our from the image.
I'm not sure that it can be done, but I thought I should ask here.
Is there any method to intelligently unify the contours in the image so I could segment different objects out?
Thanks,
Gil.
First, do you want to achieve the result only on this image or any other image where different people will present in different pose and different dresses?
If you want to segment only this image, then with some color thresholding or with some morphology operations you can achieve it. But to make it work for any image with different persons probably you may need to pursue a PhD in computer vision.
But if your task is segmentation only then I would suggest a Semi-Automatic Segmentation technique like Grab Cut or graph cut. These are very popular segmentation algorithms which are readily available in opencv or matlab. They work very well on all kind of images. Here is the result of grab cut algorithm on your image.
There is lots of work on Contour based segmentation in the literature out there.
The Ultrametric contour map produces a hierarchy of contours which are segmentations of objects in an input image.
Pub: Contour Detection and Hierarchical Image SegmentationPablo Arbelaez, Michael Maire, Charless Fowlkes, Jitendra Malik
I am doing something similar to this problem:
Matching a curve pattern to the edges of an image
Basically, I have the same curve in two images, but with some affine transform between the two. Here is an example of two images:
Image1
Image2
So in order to get to Image2, you can apply some translation, rotation, scale, etc. to Image1.
Does anyone know how to solve for this transform?
Phase correlation doesn't work because it's not a translation only. Optical flow doesn't work since there's not enough detail to resolve translation, rotation, scale (It's pretty much a binary image). I'm not sure if Hough Transforms will give me good data.
I think some sort of keypoint matching algorithm like sift or surf would work with this kind of data as well.
The basic idea would be to find a limited number of "interesting" keypoints in each image, then match these keypoints pairwise.
Here is a quick test of your image with an online ASIFT demo:
http://demo.ipol.im/demo/my_affine_sift/result?key=BF9F4E4E006AB5168497709836C39C74#
It is probably more suited for normal greyscale images, but nevertheless it seems to work for this data. It looks like the lines connect roughly the same points around both of the curves; plugging all these pairs into something like the FindHomography function in OpenCv, the small discrepancies should even themselves out and you get the affine transformation matrix between the two images.
For your particular data you might be able to come up with better keypoint descriptors; perhaps something to detect the line ends, line crossings and sharp corners.
Or how about this: It is a little more work, but if you can vectorize your paths into a bezier or b-spline, you can get some natural keypoints from the spline descriptors.
I do not know any vectorisation library, but Inkscape has a basic implementation with which you could test the approach.
Once you have a small set of descriptors instead of a large 2d bitmap, you only need to match these descriptors between the two images, as per FindHomography.
answer to comment:
The points of interest are merely small areas that have certain properties. So the center of those areas might be black or white; the algorithm does not specifically look for white pixels or large-scale shapes such as the curve. What matter is that the lines connect roughly the same points on both curves, at least at first glance.
How to get an array of coordinates of a (drawn) line in image? Coordinates should be relative to image borders. Input: *.img . Output array of coordinates (with fixed step). Any 3rd party software to do this? For example there is high contrast difference - white background and color black line; or red and green etc.
Example:
Oh, you mean non-straight lines. You need to define a "line". Intuitively, you might mean a connected area of the image with a high aspect ratio between the length of its medial axis and the distance between medial axis and edges (ie relatively long and narrow, even if it winds around). Possible approach:
Threshold or select by color. Perhaps select by color based on a histogram of colors, or posterize as described here: Adobe Photoshop-style posterization and OpenCV, then call scipy.ndimage.measurements.label()
For each area above, skeletonize. Helpful tutorial: "Skeletonization using OpenCV-Python". However, you will likely need the distance to the edges as well, so use skimage.morphology.medial_axis(..., return_distance=True)
Do some kind of cleanup/filtering on the skeleton to remove short branches, etc. Thinking about your particular use, and assuming your lines don't loop around, you can just find the longest single path in the skeleton. This is where you can also decide if a shape is a "line" or not, based on how long the longest path in its skeleton is, relative to distance to the edges. Not sure how to best do that in opencv, but "Analyze Skeleton" in Fiji/ImageJ will let you filter by branch length.
What is left is the most elongated medial axis of the original "line" shape. You can resample that to some step that you prefer, or fit it with a spline, etc.
Due to the nature of what you want to do, it is hard to come up with a sample code that will work on a range of images. This is likely to require some careful tuning. I recommend using a small set of images (corpus), running any version of your algo on them and checking the results manually until it is pretty good, then trying it on a large corpus.
EDIT: Original answer, only works for straight lines:
You probably want to use the Hough transform (OpenCV tutorial).
Python sample code: Horizontal Line detection with OpenCV
EDIT: Related question with sample code to skeletonize: How can I get a full medial-axis line with its perpendicular lines crossing it?
I am currently looking for a way to fit a simple shape (e.g. a T or an L shape) to a 2D point cloud. What I need as a result is the position and orientation of the shape.
I have been looking at a couple of approaches but most seem very complicated and involve building and learning a sample database first. As I am dealing with very simple shapes I was hoping that there might be a simpler approach.
By saying you don't want to do any training I am guessing that you mean you don't want to do any feature matching; feature matching is used to make good guesses about the pose (location and orientation) of the object in the image, and would be applicable along with RANSAC to your problem for guessing and verifying good hypotheses about object pose.
The simplest approach is template matching, but this may be too computationally complex (it depends on your use case). In template matching you simply loop over the possible locations of the object and its possible orientations and possible scales and check how well the template (a cloud that looks like an L or a T at that location and orientation and scale) matches (or you sample possible locations orientations and scales randomly). The checking of the template could be made fairly fast if your points are organised (or you organise them by e.g. converting them into pixels).
If this is too slow there are many methods for making template matching faster and I would recommend to you the Generalised Hough Transform.
Here, before starting the search for templates you loop over the boundary of the shape you are looking for (T or L) and for each point on its boundary you look at the gradient direction and then the angle at that point between the gradient direction and the origin of the object template, and the distance to the origin. You add that to a table (Let us call it Table A) for each boundary point and you end up with a table that maps from gradient direction to the set of possible locations of the origin of the object. Now you set up a 2D voting space, which is really just a 2D array (let us call it Table B) where each pixel contains a number representing the number of votes for the object in that location. Then for each point in the target image (point cloud) you check the gradient and find the set of possible object locations as found in Table A corresponding to that gradient, and then add one vote for all the corresponding object locations in Table B (the Hough space).
This is a very terse explanation but knowing to look for Template Matching and Generalised Hough transform you will be able to find better explanations on the web. E.g. Look at the Wikipedia pages for Template Matching and Hough Transform.
You may need to :
1- extract some features from the image inside which you are looking for the object.
2- extract another set of features in the image of the object
3- match the features (it is possible using methods like SIFT)
4- when you find a match apply RANSAC algorithm. it provides you with transformation matrix (including translation, rotation information).
for using SIFT start from here. it is actually one of the best source-codes written for SIFT. It includes RANSAC algorithm and you do not need to implement it by yourself.
you can read about RANSAC here.
Two common ways for detecting the shapes (L, T, ...) in your 2D pointcloud data would be using OpenCV or Point Cloud Library. I'll explain steps you may take for detecting those shapes in OpenCV. In order to do that, you can use the following 3 methods and the selection of the right method depends on the shape (Size, Area of the shape, ...):
Hough Line Transformation
Template Matching
Finding Contours
The first step would be converting your point to a grayscale Mat object, by doing that you basically make an image of your 2D pointcloud data and so you can use other OpenCV functions. Then you may smooth the image in order to reduce the noises and the result would be somehow a blurry image which contains real edges, if your application does not need real-time processing, you can use bilateralFilter. You can find more information about smoothing here.
The next step would be choosing the method. If the shape is just some sort of orthogonal lines (such as L or T) you can use Hough Line Transformation in order to detect the lines and after detection, you can loop over the lines and calculate the dot product of the lines (since they are orthogonal the result should be 0). You can find more information about Hough Line Transformation here.
Another way would be detecting your shape using Template Matching. Basically, you should make a template of your shape (L or T) and use it in matchTemplate function. You should consider that the size of the template you want to use should be in the order of your image, otherwise you may resize your image. More information about the algorithm can be found here.
If the shapes include areas you can find contours of the shape using findContours, it will give you the number of polygons which are around your shape you want to detect. For instance, if your shape is L, it would have polygon which has roughly 6 lines. Also, you can use some other filters along with findContours such as calculating the area of the shape.
Having a match-3 game screenshot (for example http://www.gameplay3.com/images/games/jewel-quest-ii-01S.jpg), what would be the correct way to find the bound box for the grid (table with tiles)? The board doesn't have to be a perfect rectangle (as can be seen in the screenshot), but each cell is completely square.
I've tried several games, and found that there are some per-game image transformations that can be done to enhance the tiles inside the grid (for example in this game it's enough to take the V channel out of HSV color space). Then I can enlarge the tiles so that they overlap, find the largest contour of the image and get the bound box from it.
The problem with above approach is that every game (or even level inside the same game) may need a different transformation to get hold of the tiles. So the question is - is there a standard way to enhance either tiles inside the grid or grid's lines (I've tried finding lines with Hough transform, but, although the grid seems pretty visible to the eye, Hough doesn't find it)?
Also, what if the screenshot is obtained using the phone camera instead of taking a screenshot of a desktop? From my experience, captured images have less defined colors (which depends on lighting), and also can be distorted a little, as there is no way to hold the phone exactly in front of the screen.
I would go with the following approach for a screenshot:
Find corners in the image using for example a canny like edge detector.
Perform a hough line transform. This should work quite nicely on the edge image.
If you have some information about size of the tiles you could eliminate false positive lines using some sort of spatial model of the grid (eg. lines only having a small angle to x/y axis of the image and/or distance/angle of tile borders.
Identifiy tile borders under the found hough lines by looking for edges found by canny under/next to the lines.
Which implementation of the hough transform did you use? How did you preprocess the image?
Another approach would be to use some sort of machine learning approach. As you are working in OpenCV you could use either a Haar like feature detector. An example for face detection using Haar like features can be found here:
OpenCV Haar Face Detector example
Another machine learning approach would be to follow a Histogram of Oriented Gradients (Hog) approach in combination with a Support Vector Machine (SVM). An example is located here:
HOG example
You can find general information about HoG detection at:
Hog detection