I want to make a project in which we can make 3D model of a object using sequences of images. So I want to know:
How can I make 3D model using sequences of 2D images?
Is there any tutorial for it either on any website or in PDF format?
I searched on opencv's website but I couldn't find topic related to 3D model.
Here openCV SfM module documentation: link
Related
I want to make an application for counting game statistics automatically. For that purpose, I need some sort of computer vision for handling screenshots of the game.
There are bunch of regions with different skills in always the same place that app needs to recognize. I assume that it should have a database of pictures or maybe some trained samples.
I've started to learn opencv lib, but not sure what will be better for this purpouse.
Would you please give me some hints or algorithms that I could use?
Here is the example of game screenshot.
You can covert it into gray scale and then use any haar cascade classifier to read the words in that image and then save it into any file format (csv) this way you can utilize your game pics for gathering data so that you can train your models
I have an object I'd like to track using OpenCV. In my detection algorithm I can create bounded boxes around the objects it sees, and can create a target object to track properly. My detection algorithm works well, but I want to pass this object to a tracking algorithm.I can't quite get this done without having to re write the detection and image display issues. I'm working with an NVIDA Jetson Nanoboard with an Intel Realsense camera if that helps.
The OpenCV DNN module comes with python samples of state of the art trackers. I've heard good things about the "siamese" based ones. Have a look
Also the OpenCV contrib repo contains a whole module of various trackers. Give those a try first. They have a simple API.
I am using OpenCV library and I can detect multiple faces in a video file or using a webcam. Now, I want to recognize those faces.
If any one guide me step by step means what should I do after detecting faces,it will be great for me. I am using C and C++ language.
#KISHAN, You may follow a tutorial with an example of using OpenFace deep learning network. It takes a 96x96 image of human's face and returns 128-dimensional unit vector called embedding vector. You may match two persons by dot product of these embeddings. So this neural network maps faces to multidimensional unit sphere where similar faces are mapped to the closer points.
NOTE: there is a live demo which downloads models (~35MB) if you pressed a Start button.
So I am working on a project for school and what we are trying to do is to teach a neural network to recognize buildings from non-buildings. The problem I am having right now is representing the data in a form, that would be "readable" by the classifier function.
The training data is a bunch of pictures + .wkt file with coordinates of buildings on a picture. So far we have been able to rescale the polygons, but kinda got stuck there.
Can you give any hints or ideas of how to bring this all to an appropriate form?
Edit: I do not need the code written for me, a link to an article on a similar subject or a book is more of stuff I am looking for.
You did not mention what framework you are using, but I will give an answer for caffe.
Your problem is very close to detecting objects within an image. You have full images with object (building in your case) bounding boxes.
The easiest way of doing this is through a python data layer which reads an image and a file with stored coordinates for that image and feeds that into your network. A tutorial on how to use it can be found here: https://github.com/NVIDIA/DIGITS/tree/master/examples/python-layer
To accelerate the process you may want to store image, coordinate pairs in your custom lmdb database.
Finally a good working example with complete caffe implementation can be found within Faster-RCNN library here: https://github.com/rbgirshick/caffe-fast-rcnn/
You should check roi_pooling_layer.cpp in their custom caffe branch and roi_data_layer on how the data is fed into the network.
I have a 3D matrix (very large, let call it L) and a 3D small one (very small, let call it S) and want to use OpenCV to find the closest pattern in L.
Does OpenCV do it for me? If yes, how I should use it?
Thanks.
What you need is the Point Cloud Library, which is an open source library to work with 3D data. I can tell you from my experience, that learning to use this library is very similar to learning OpenCV because many developers work for Willow Garage, the main sponsor of OpenCV.
If you go to the PCL tutorials you will find three useful sections to solve your problem:
1) finding features in your 3D point cloud, that you can later use for matching
2) 3D object recognition based on correspondence grouping
3) Point cloud registration using methods like iterative closest point, and feature matching
No, OpenCV doesn't have anything for this.
Do you have sparse pointcloud or just 3-dim matrix?
For 3-dim matrix you can use phase correlation using FFT. Good library is FFTW
OpenCV has added some neat tools to accomplish this kind of task
Surface Matching https://docs.opencv.org/master/d9/d25/group__surface__matching.html
Silhouette based 3D tracking https://docs.opencv.org/master/d4/dc4/group__rapid.html
Convolutional Neural Network https://docs.opencv.org/master/d9/d02/group__cnn__3dobj.html