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.
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I am required to prevent an extruder from touching the base of a stage. I chose to use opencv on python to detect collision between the two objects. After researching past posts and reading up on opencv's processing tools I decided to try a few methods. The first method was edge detection which proved to be lacking in position detection. Next I tried using color as an advantage to isolate the needle and the stage, place rectangles surrounding the objects, and then prevent the rectangles from touching. This is proving to be a challenge because the background and needle are pretty much the same color. The last method requires using box-box collision but my guess is that if I am having issues with the second method then this method will also prove difficult. I am thinking about trying out deep learning on opencv, taking a bunch of photos, and the training the program but I am not sure how that would play out since ill be getting feed from video. Can anyone give me any tips? Any algorithms that would be helpful here? I see that the needle is clearly defined due to its edges so how can I use that to my advantage here? Any help is appreciated.
Photo of needle and stage:
I am working on a project where I have to find the background of a given gray-scale image.
I did several kinds of research on the internet and I've found some algorithms using OpenCV library (like the following: https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_video/py_bg_subtraction/py_bg_subtraction.html#py-background-subtraction).
This kind of approach doesn't work for me.
The image I want to elaborate is:
As you can see it is in gray-scale and we see the "gray static" background. I would love to see only the nucleus of the cell (the image will improve resolution and quality in the time, this is a pretty raw one)
I tried to subtract the 2D magnitude FFT of the background from the main image but the results is not good:
What I am asking is: What kind of process do you suggest to use to eliminate background?
Did you already try watershed algorithm ? I saw on a paper it's already used and improved for cell image segmentation.
Background subtraction won't work for your images because your background is not consistent. image's SNR is too low!
So you have 2 options:
1) Using deep learning method (like UNET) if you have enough data
2) Using bilateral filter then, some methods like active contour or GLCM Texture Feature or k-means clustering.
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 have a camera that will be stationary, pointed at an indoors area. People will walk past the camera, within about 5 meters of it. Using OpenCV, I want to detect individuals walking past - my ideal return is an array of detected individuals, with bounding rectangles.
I've looked at several of the built-in samples:
None of the Python samples really apply
The C blob tracking sample looks promising, but doesn't accept live video, which makes testing difficult. It's also the most complicated of the samples, making extracting the relevant knowledge and converting it to the Python API problematic.
The C 'motempl' sample also looks promising, in that it calculates a silhouette from subsequent video frames. Presumably I could then use that to find strongly connected components and extract individual blobs and their bounding boxes - but I'm still left trying to figure out a way to identify blobs found in subsequent frames as the same blob.
Is anyone able to provide guidance or samples for doing this - preferably in Python?
The latest SVN version of OpenCV contains an (undocumented) implementation of HOG-based pedestrian detection. It even comes with a pre-trained detector and a python wrapper. The basic usage is as follows:
from cv import *
storage = CreateMemStorage(0)
img = LoadImage(file) # or read from camera
found = list(HOGDetectMultiScale(img, storage, win_stride=(8,8),
padding=(32,32), scale=1.05, group_threshold=2))
So instead of tracking, you might just run the detector in each frame and use its output directly.
See src/cvaux/cvhog.cpp for the implementation and samples/python/peopledetect.py for a more complete python example (both in the OpenCV sources).
Nick,
What you are looking for is not people detection, but motion detection. If you tell us a lot more about what you are trying to solve/do, we can answer better.
Anyway, there are many ways to do motion detection depending on what you are going to do with the results. Simplest one would be differencing followed by thresholding while a complex one could be proper background modeling -> foreground subtraction -> morphological ops -> connected component analysis, followed by blob analysis if required. Download the opencv code and look in samples directory. You might see what you are looking for. Also, there is an Oreilly book on OCV.
Hope this helps,
Nand
This is clearly a non-trivial task. You'll have to look into scientific publications for inspiration (Google Scholar is your friend here). Here's a paper about human detection and tracking: Human tracking by fast mean shift mode seeking
This is similar to a project we did as part of a Computer Vision course, and I can tell you right now that it is a hard problem to get right.
You could use foreground/background segmentation, find all blobs and then decide that they are a person. The problem is that it will not work very well since people tend to go together, go past each other and so on, so a blob might very well consist of two persons and then you will see that blob splitting and merging as they walk along.
You will need some method of discriminating between multiple persons in one blob. This is not a problem I expect anyone being able to answer in a single SO-post.
My advice is to dive into the available research and see if you can find anything there. The problem is not unsolvavble considering that there exists products which do this: Autoliv has a product to detect pedestrians using an IR-camera on a car, and I have seen other products which deal with counting customers entering and exiting stores.