I have worked on some project which use computer vision software like Cognex's VisionPro. I am really impressing the way how Cognex's VisionPro detecting an object on image.
Let me descript about how it work.
First, the master image is needed to be trained. In that training process VisionPro will extract some features of the train image.
Second, Every time we want to detect that object in another query image, query image features will be extracted, and compare with the features of the train image to get it's location and rotated angle.
My question
Can we implement this method on opencv? Which algorithms could work in this case?
What I have done
I did some research and I know that we have keypoints detection methods like SIFT, SURF, or ORB and there is FLANN for keypoints matching. I have tried to run some example (most on opencv official tutorial), but the result it's not really as good as I want (it's to bad). The keypoints matching not really match on rotated object and it's really hard to get the rotated angle on query image.
This image below can better help you understand Cognex VisionPro's object dection.
Related
I'm trying to learn computer vision and more specifically open-cv in python.
I want to make a program that would track my barbell in a video and show me its path. (I know apps like this exists but I want to make it myself). I tried using the Canny edge detection and the HoughCircles functions but I seem to get everything but a good result.
I have been using this code to find the edges of my image:
gray = cv.cvtColor(src=img, code=cv.COLOR_BGR2GRAY)
blur = cv.blur(gray, (2,2))
canny = cv.Canny(blur, 60, 60)
And then this code to find the circle:
circles = cv.HoughCircles(canny, cv.HOUGH_GRADIENT, dp=2, minDist=1000, circles=None,maxRadius=50)
This is the result:
Result
left = original image with detected circle // right = canny image
Is this the right way to go or should I use another method?
Train the YOLO model for the barbell to detect barbel object is better than anything you tried with OpenCV. You need at least 500 images. Those images can be found on the internet easily. This tutorial is kick start tutorial on YOLO. Let's give a try.
If you tweak the parameters of HoughCircles it may recognize the barbell [EDIT: but with more preprocessing, gamma correction, blurring etc., so better not], however OpenCV has many algorithms for such object tracking - only a region from the image has to be specified first (if that's OK).
In your case the object is always visible and is not changing much, so I guess many of the available algorithms would work fine.
OpenCV has a built-in function for selection:
initBB = cv2.selectROI("Frame", frame, fromCenter=False, showCrosshair=True)
See this tutorial for tracking: https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/
The summary from the author suggestion is:
CSRT Tracker: Discriminative Correlation Filter (with Channel and Spatial Reliability). Tends to be more accurate than KCF but slightly slower. (minimum OpenCV 3.4.2)
Use CSRT when you need higher object tracking accuracy and can tolerate slower FPS throughput
I guess accuracy is what you want, if it is for offline usage.
Can you share a sample video?
What's your problem exactly? Why do you track the barbell? Do you need semantic segmentation or normal detection? These are important questions. Canny is a very basic approach It' needs a very stable background to use it. That's why there is deep learning to handle that kind of problem If we need to talk about deep learning you can use MaskRCNN, yolvoV4, etc. there are many available solutions out there.
I am trying to crop out features from a photo using opencv and haven't quite been able to find anything that helps to do so. I have photos in which I am trying to crop out rivets from metal panels to create a dataset of rivets that focus in on just the rivets. I have been able to use feature detection and feature matching using Orb to match features but I am unsure of how to then crop out those features. Ideally each photo should provide me with multiple cropped out photos of the rivets. Does anyone have any experience with anything such as this?
For template matching with OpenCV, you can use template matching (which is nicely described here)
If your template is skewed, rotated, etc. in the photo, you can use feature homography
For cropping the part of the image, you can look at this previously answered question.
I am trying to create an application that is able to detect and track the iris of an eye in a live video stream. In order to do that, I want to use Python and OpenCV. While researching for this on the internet, it seemed to me that there are multiple possible ways to do that.
First Way:
Run a Canny Filter to get the edges, and then use HoughCircle to find the Iris.
Second Way:
Use Otsus-Algorithm to find the perfect threshold and then use cv2.findContours() to find the Iris.
Since I want this to run on a Raspberry Pi (4B), my question is which of these methods is better, especially in terms of reliability and performance?
I would take a third path and start from a well enstablished method for facial landmark detection (e.g. dlib). You can use a pre-trained model to get a reliable estimate on the position of the eye.
This is an example output from a facial landmark detector:
Then you go ahead from there to find the iris, either using edge detection, Hough or whathever.
Probably you can simply use an heuristic as you can assume the iris to be always in the center of mass of the keypoints around each eye.
There are also some good tutorials online in a similar setting (even for Raspberry) for example this one or this other one from PyImageSearch.
I am trying to detect a vehicle in an image (actually a sequence of frames in a video). I am new to opencv and python and work under windows 7.
Is there a way to get horizontal edges and vertical edges of an image and then sum up the resultant images into respective vectors?
Is there a python code or function available for this.
I looked at this and this but would not get a clue how to do it.
You may use the following image for illustration.
EDIT
I was inspired by the idea presented in the following paper (sorry if you do not have access).
Betke, M.; Haritaoglu, E. & Davis, L. S. Real-time multiple vehicle detection and tracking from a moving vehicle Machine Vision and Applications, Springer-Verlag, 2000, 12, 69-83
I would take a look at the squares example for opencv, posted here. It uses canny and then does a contour find to return the sides of each square. You should be able to modify this code to get the horizontal and vertical lines you are looking for. Here is a link to the documentation for the python call of canny. It is rather helpful for all around edge detection. In about an hour I can get home and give you a working example of what you are wanting.
Do some reading on Sobel filters.
http://en.wikipedia.org/wiki/Sobel_operator
You can basically get vertical and horizontal gradients at each pixel.
Here is the OpenCV function for it.
http://docs.opencv.org/modules/imgproc/doc/filtering.html?highlight=sobel#sobel
Once you get this filtered images then you can collect statistics column/row wise and decide if its an edge and get that location.
Typically geometrical approaches to object detection are not hugely successful as the appearance model you assume can quite easily be violated by occlusion, noise or orientation changes.
Machine learning approaches typically work much better in my opinion and would probably provide a more robust solution to your problem. Since you appear to be working with OpenCV you could take a look at Casacade Classifiers for which OpenCV provides a Haar wavelet and a local binary pattern feature based classifiers.
The link I have provided is to a tutorial with very complete steps explaining how to create a classifier with several prewritten utilities. Basically you will create a directory with 'positive' images of cars and a directory with 'negative' images of typical backgrounds. A utiltiy opencv_createsamples can be used to create training images warped to simulate different orientations and average intensities from a small set of images. You then use the utility opencv_traincascade setting a few command line parameters to select different training options outputting a trained classifier for you.
Detection can be performed using either the C++ or the Python interface with this trained classifier.
For instance, using Python you can load the classifier and perform detection on an image getting back a selection of bounding rectangles using:
image = cv2.imread('path/to/image')
cc = cv2.CascadeClassifier('path/to/classifierfile')
objs = cc.detectMultiScale(image)
I am trying to detect the objects in an image which look similar to the reference image. Here is how i'm trying to accomplish it:
Here is the sample Image:
and here is the image with SURF keypoints:
The rectangle is drawn based on Clustering method like "Hierarchial Clustering".
The main problem is, in this case it doesnt detect the objects individually, it detects everything as one object.
Is there a way to seperate these keypoints, so as to detect each vehicle seperately?
Is this a good way to detect objects or if there is a better way please suggest.
SURF keypoints are useful in detecting similar images, or images taken of the same place from different perspectives. Although you can use Haar classifiers for the purpose of object detection. It is also a part of OpenCV library.
Here is another great tutorial regarding object detection using OpenCV.