I'm attempting to implement a Connected Component Tree structure in openCV. after a series of thresholds from 0..255 with a certain interval i obtain a set of Photos - for in stance:
is there a methodes to detect for each shape in the seconed image if it is contained within another shape in the first image?
Many Thanks!
Take the image with the shape in question (2nd image), and remove all other shapes. Then 'and' the image with the 1st image. If the result is the same as the image with the single shape, it is contained.
Obviously this can be optimized to not go through the entire process for each shape.
Related
Is there any method to create a polygon(not a rectangle) around an object in an image for object recognition.
Please refer the following images:
the result I am looking for
and
the original image
.
I am not looking for bounding rectangles like this.I know the concepts of transfer learning, using pre-trained models for object recognition and other object detection concepts.
The main aim is the object detection but not giving results using bounding box but a fitter polygon instead.Link to some resources or papers will be helpful.
Here is a very simple (and a bit hacky) idea, but it might help: take a per-pixel scene labeling algorithm, e.g. SegNet, and then turn the resulting segmented image into a binary image, where the white pixels are the class of interest (in your example, white for cars and black for the rest). Now compute edges. You can add those edges to the original image to obtain a result similar to what you want.
What you want is called image segmentation, which is different to object detection. The best performing methods for common object classes (e.g. cars, bikes, people, dogs,...) do this using trained CNNs, and are usually called semantic segmentation networks awesome links. This will, in theory, give you regions in your image corresponding to the object you want. After that you can fit an enclosing polygon using what is called the convex hull.
I am trying to implement a motion detection in OpenCV C++. I tried various methods like MOG, Optical flow which work fine but is there a way we can eliminate constant movements in the scene like a constant fan motion etc ? I have opencv accumuateWeighted() in mind but not sure if it works. Is there any better way we can do it ?
I have not got full robust solution and also i don't have any experience with video processing but i would put my idea whatever till now i have got in to this problem:
First consider a few pairs of consecutive image frames from the video and convert them to gray scale for more robust comparison.
Raster scan the image pairs and find the difference of image pairs by comparing corresponding pairs.
The resultant image will give the pixel location where there is a change in image to image in a pair, cluster these pixels locations and make a bounding box over them. So that this bounding box region will mark an object which is translating/rotation.
Now as we have applied the above image difference operation over several pairs. We will have rotating/translating bounding box in each image pair difference.
Now check in each resultant image difference with pixels having bounding box over them.
Compare bounding box central location in a difference image with other difference images. If bounding box with a very slight variation in its central location exists across all difference images then object contained in that bounding box will be having rotational motion like Fan,leaves and remaining bounding boxes will represent the actual translating objects in the video.
A little introduction on what I'm doing ...
For academic purposes I am creating an application in c++ using opencv for the detection of static objects in a scene.
The application is based on a combined approach of background subtraction and tracking, and the detection of events related to the abandonment of the objects works fine.
But at the moment I have a problem that I can't solve; I have to implement a finite state machine for detect the event of object removal, both before and after the entry of the object in the background.
To do this I was ordered by my superiors to use the edges of objects.
And now the problem.
After detecting a vehicle illegally parked along a road, I need to compare the edges of various images (the background captured at the time of the alarm, the current background, the current frame) to understand what the vehicle do (picks up the movement, remains parked or picks up the movement after being in the background).
I run these comparisons on the region of the scene in which there is the vehicle (vehicles typically have different size), I pull the edges using canny algorithm by obtaining a binarized CV_8UC1 cv::Mat.
At this point I have to compare them.
I tried to detect the contours with findContours and compare them with matchShapes, but it does not seem the right way, I'd compare each contour of the first image with every contour of the second, in addition typically the two images to campare have different number of contour (for example original background and current background, because the edges of the current background increased with the entry of the vehicle in the background).
I also tried to create a new image in which each pixel corresponds to the absolute difference of the other two, then I counted the white pixels of the difference image (wPx), and I used this number for comparison in this way: I set two thresholds (thr1 and thr2), and counted the pixels of the bounding rect of the vehicle (perim), if wPxthr2*perim images are different.
(I set percentages thresholds and I moltipy them with the perimeter of the bounding box to adapt the thresholds to the vehicle dimensions.)
This solution, however, seems to be very little robust.
Do you have something simple to suggest me?
Thank you very much in any case, more than once you StackOverflow users have helped me!
PS: THIS is an example of the images that I have to compare
The first is the background without the vehicle stationary, contains the edges of the street;
the second is the original background, the one captured when the stationary vehicle is detected;
the third is the current background (which in this case is equal to the original being the same frame, but then change);
the fourth is the current frame of the video;
You may want to take a look at this paper: A Novel SIFT-Like-Based Approach
for FIR-VS Images Registration. Aguilera et al. propose an Edge Oriented Histogram descriptor (EOH-SIFT).
This paper intends to register multispectral images, visible and infrared image, to each other. Because of the different characteristics of the images, the authors first extract edges/contours in both images, which results in images similiar to yours.
So, you can describe your image patches using this descriptor, illustrated in the following figure (taken from the above paper):
Subdivide your image patch into 4x4 zones
For each of the 16 subregions compose a histogram of contour's orientation (5 bins)
Put the histograms together into one descriptor vector of size 16x5=80 bins
Normalize the feature vector
So, every image you want to compare (in your case 4) is described by its 80-dimensional feature vector. You can compare them to each other by calculating and evaluating the Euclidean distance between them.
Note: Here a patch of size 80x80 or 100x100 (NxN) pixels is suggested. You may have to adjust the sizes to your image sizes.
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 am using OpenCV for a C++ application. I have a 8 bit binary image that has some objects. The objects are all colored 255, whereas everything in the background is colored 0. Each object has no vacant (black) pixels inside it. In other words, each object is fully white. The objects are NOT connected to each other. Here's what I want to extract from this:
I want to extract some kind of list of objects, from which I have some notion of the location of each object in that list. This could be using cvConnectedComponents() or anything else. I need some indication of where each object is located in the image. This could be in the form of bounding rectangle for each object or median or center based on some computation or anything that gives me a measure of the objects location in the image. Any pointers to what OpenCV functions to look into?
Starting from version 3.0, OpenCV has connectedComponents function.
You need to cv::floodFill the contours returned by cv::findCountours. See this example for findContours, and this one for floodFill