I need to extract an object from an image where the background is almost flat...
Consider for example a book over a big white desktop.. I need to get the coordinates of the 4 corners of the book to extract a ROI.
Which technique using OpenCV would you suggest? I was thinking to use k Means but I can't know the color of the background a priori (also the colors inside the object can be vary)
If your background is really low contrast, why not try a flood fill from the image borders, then you can obtain bounding box or bounding rect afterwards.
Another option is to apply Hough transform and take intersection of most outer lines as corners. This is, if your object is rectangular.
Related
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'm at a point where I need to mix the DICOM Region of Interest (ROI) Relative Electron Density (RED) with the information from DICOM CT's where some of the ROIs should override the CT info. [I'm working in C# by the way.] My question is that I need to draw the ROI's filled, in the correct way such that lungs for instance are shown with low RED while the body is water eq. I can use the bounding rectangle to gain an idea if one is possibly inside the other, but once that is known, I still need to determine if they overlap or if one is completely contained within another. I can do a raw draw of each ROI on a separate bitmap and do a slice voxel by voxel comparison, but this seems likely to be slow. I have not found a good answer and I'm hoping someone knows a better way to determine ordering of drawing (painting filled) that works in a fast manner.
Thanks
ROI in DICOM is normally defined as a list of points to form a polygon (or several) on a plane of related CT-scan slice (they share the same frame of reference UID). So, you can draw your CT slice and then on top draw ROI polygons, or you can query every CT point you draw whether it belongs or not to ROI polygons set, and change the color correspondingly.
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 am using open CV and C++. I have a completely dark image which has 3 colored points on it. I need their center coordinates. If I have only one colored point in the dark image, it will automatically display its center coordinate. However,if I take as input the dark image with the 3 colored points,my program will make an average if those 3 coordinates and return the center of the 3 colored points together,which is my exact problem. I need their individual center coordinates.
Can anyone suggest a method to do that please. Thanks
Here is the code http://pastebin.com/RM7chqBE
Found a solution!
load original image to grayscale
convert original image to gray
set range of intensity value depending on color that needs to be detected
vector of contours and hierarchy
findContours
vector of moments and point
iterate through each contour to find coordinates
One of the ways to do this easily is to use the findContours and drawContours function.
In the documentation you have a bit of code that explains how to retrieve the connected components of an image. Which is what you are actually trying to do.
For example you could draw every connected component you will find (that means every dot) on it's own image and use the code you already have on every image.
This may not be the most efficient way to do this however but it's really simple.
Here is how I would do it
http://pastebin.com/y1Ae3e2V
I'm not sure this works however as I don't have time to test it but you can try it.
i have several contours that consist of several black regions in my image. Directly adjacent to these black regions are some brighter regions that do not belong to my contours. I want to add these brighter regions to my black region and therefor extend my contour in OpenCv.
Is there a convenient way to extend a contour? I thought about looking at intensity change from my gradient-image created with cv::Sobel and extend until the gradient changes again, meaning the intensity of pixel is going back to the neither black nor bright regions of the image.
Thanks!
Here are example images. The first picture shows the raw Image, the second the extracted Contour using Canny & findContours, the last one the Sobel-Gradient intensity Image of the same area.
I want to include the bright boundaries in the first image to the Contour.
Update: Now i've used some morphological operations on the Sobelgradients and added a contour around them (see Image below). Next step could be to find the adjacent pair of purple & red contours, but it seems very much like a waste of procession time to actually have to search for directly adjacent contours. Any better ideas?
Update 2: My solution for now is to search for morphed gradient (red) contours in a bounding box around my (purple) contours and pick the one with correct orientation & size. This works for gradient contours where the morphological operation closes the "rise" and "fall" gradient areas like in Figure 3. But it is still a bad solution for cases in which the lighted area is wider then in the image above. Any idea is still very much appreciated, thanks!
What you're trying to do is find two different features and merge them. It's not terribly difficult but you have to use multiple copies of the image to make it happen.
Make one copy, and threshold it for the dark portion
Make another copy and threshold it for the light portion
Merge both thresholded images into a new image
Apply a morphological operation like opening or closing (depending on how you threshold) This will connect nearby components
Find contours in the resultant image
Use those contours on your original image. This will work since all the images are the same size and all based off of the original.