I am looking for a haar cascade that can detect hands. I would rather not have to train my own as I am on a regular laptop (I think it would take too long without a powerful computer). Also, I don't know where I would get the images from, and I've never done it before so I have a feeling I would do it wrong and waste a week training a haar cascade that doesn't work.
I know there are similar questions that have been asked already, but they all are from several years ago, so I am wondering if anything new has been created.
Related
This must be a question that is often asked, but I cannot find an answer nor intructions on how to.
Is it possible to train a cascade model from the already train haarcascade frontface default? My goal is for the script to recognise specific people (i.e. me). I know there is a recongniser built in opencv, but it is quite slow on a raspberry pi compared to a cascade xml.
I am tryingto train a model from scratch, but with mixed results.
thanks
(fyi: I've also post this question on answers.opencv.org https://answers.opencv.org/question/231697/train-from-haarcascade-frontface/)
I don't think this is the way stackoverflow is meant to be used. but as I said, I posted the question on the opencv website without knowing if it was often used. I had a very good answer, so if you wish to get the it, go to https://answers.opencv.org/question/231697/train-from-haarcascade-frontface/
I need to compare two images in a project,
The images would be two fruits of the same kind -let's say two different images of two different apples-
To be more clear, the database will have images of the stages which an apple takes from the day it was picked from a tree until it gets rotten..
The user would upload an image of the apple they have and the software should compare it to all those images in the database and retrieve the data of the matching image and tell the user at which stage is it...
I did compare before images using OpenCv emgu but I really don't have much knowledge if it's the best way...
I need an expert advise is what i said in the project even possible? or the whole database images' will match the user's image!
And is this "image processing" or something else?
And is there any suggested tutorials to learn how to do this?
I know it seems not totally clear yet, but it's just a crazy idea that I wish I can get a way to know more how i can bring it to life!
N.B the project will be an android application
This is an example of a supervised image classification problem, which is a pretty broad field. You can read up on image classification here.
The way that you would approach this problem would be to define a few stages of decay (fresh, starting to rot, half rotten, completely rotten), put together a dataset of many images of the fruit in each stage, and train an image classifier on each stage. The sample dataset should contain images of many different pieces of fruit in many different settings. If you want to support different types of fruit, you would need to train a different classifier for each fruit.
There are many image classification tools out there. To name a few:
OpenCV's haar classifier
dlib's hog classifier
Matlab's Computer Vision System Toolbox
VLFeat
It would be up to you to look into which approach would work best for your situation.
Given that this is a fairly broad problem, I wouldn't expect to come up with a solid solution quickly unless you've had experience with image classification. If you are trying to develop a product, I would recommend getting in touch with a computer vision expert that you could contract to solve it.
If you are just looking to learn more about image classification, however, this could be a fun way to play around with different tools and get a feel for what's out there. You may want to start by learning about Machine Learning in general. Caltech offers a free online course that gives a pretty good intro to the subject.
I want to train a new haar-cascade for glasses as I'm not satisfied with the results I'm getting from the cascade that is included in OpenCV.
My main problem is that I'm not sure where to get eyeglasses images. I can manually search and download, but that's not practical for the amount of images I really need. I'm specifically looking for images of people wearing eyeglasses.
As this forum contain many experienced computer vision experts, I hope someone here can guide as to how to obtain images for training.
I'll also be happy to hear other approaches for detecting eyeglasses (on people).
Thanks in advance,
Gil
If you simply want images, it looks like #herhuyongtao pointed you to a good place. Then you can follow opencv's tutorial on training.
Another option is to see what others have trained:
There's a trained data set found here that might be of use, which states simply that it is "better". I'm assuming that it's supposed to be better than opencv.
I didn't immediately see any other places for trained or labeled data.
can someone tell me how i can detect pictures of architecture or sculpture?
I think hough-transforming is a good approach. But i'm new in CV and maybe there a better methods to detect pattern. I heard about haarcascade. can i take this for architecture,too?
For example i want to detect those kind of pictures:
Image Hosted by ImageShack.us http://img842.imageshack.us/img842/4748/resizeimg0931.jpg
If you want an algorithm to detect them, then detecting an object from an image need a description of that object which can be understood by a machine or computer. For a sculpture or architecture, how can you have such uniform definition since they vary a lot in every sense? For example both your input images vary a lot. How can we differentiate between a house and an architecture? A lot of problems will rise in your question. Even with Hough Transforming, how you are supposed to differentiate a big house and a big architecture?
Check out this SOF : Image Processing: Algorithm Improvement for 'Coca-Cola Can' Recognition
He wants to detect coca-cola cans, and not coca-cola bottles. But if you look into it clearly, you will understand can and bottles are almost alike and it will be difficult to differentiate between them. You can find a lot of its difficulties in subsequent answers. Major problem is that, in some cases, it will be difficult for humans as well to differentiate them.
In your second image, even if you train some cascades for second image, there is a change it will detect live lions if they are present in your image, since a sculpture lion and an original lion seems almost same for a machine.
Haar cascades may not be much effective since you have to train for a lot of these kinds of images.
If you have some sample images and want to check if those things are there in your image, may be you can use SURF features etc. But you may need some sample images first to compare. For a demo of SURF, check out this SOF : OpenCV 2.4.1 - computing SURF descriptors in Python
Another option is template matching. But it is slow, and it is not scale and orientation invariant. And you need some template images for this
I think I have seen some papers relating this topic ( but i don't remember now). May be googling will get you them. I will update the answer if I get it.
I want to detect one kind of object such as person in the picture,
who can tell me how to training a kind of people classifier for use,so we can use the classifier to detect people in any picture.
It's a pretty vague question but i think you're looking for a good computer vision library. The gold standard is OpenCV (Open Source Computer Vision). That'll get you started, and there's lots of people that have done facial recognition with it.
If you want to tell two people apart, that's a hugely more complicated problem. You'll likely use some of the same tools, but you'll need much more complicated algos.
you can take a look at viola-jones framework: Viola-Jones Object Detection Framework at Wikipedia
it's cvHaarDetectObjects() in OpenCV.