I am using OpenCV's solvePnPRansac function to estimate the pose of my camera given a pointcloud made from tracked features. My pipeline consists of multiple cameras where I form the point cloud from matched features between two cameras, and use that as a reference to estimate the pose of one of the cameras as it starts moving. I have tested this in multiple settings and it works as long as there are enough features to track while the camera is in motion.
Strangely, during a test I did today, I encountered a failure case where solvePnP would just return junk values all the time. What's confusing here is that in this data set, my point cloud is much denser, it's reconstructed pretty accurately from the two views, the tracked number of points (currently visible features vs. features in the point cloud) at any given time was much higher than what I usually have, so theoretically it should have been a breeze for solvePnP, yet it fails terribly.
I tried with CV_ITERATIVE, CV_EPNP and even the non RANSAC version of solvePnP. I was just wondering if I am missing something basic here? The scene I am looking at can be seen in these images (image 1 is the scene and feature matches between two perspectives, image 2 is the point cloud for reference)
The part of the code doing PNP is pretty simple. If P3D is the array of tracked 3Dpoints, P2D is the corresponding set of image points,
solvePnpRansac(P3D, P2D, K, d, R, T, false, 500, 2.0, 100, noArray(), CV_ITERATIVE);
EDIT: I should also mention that my reference poincloud was obtained with a baseline of 8 feet between the cameras, whereas the building I am looking at was probably like a 100 feet away. Could the possible lack of disparity cause issues as well?
For school, I have to do an augmented reality project. ARToolkit is good for tracking markers but my problem is my procamcalib calibration can't be used by artoolkit (distortion coefficient in procamcalib and distortion factor in artoolkit).
I see that with openCV i can calibrate my ps eye and can apply the undistortion directly.
So my question is can get the ps eye image, undistorted it and give it to artoolkit after to have my markers's positions?
Thanks
(Sorry for my english, I'm a french student, if you've got some troubles to read I can explain again)
might be a bit of work to de-couple the video code, but in the end you can use just:
arDetectMarker(dataPtr, thresh, &marker_info, &marker_num)
with pixels from anywhere ( e.g an undistorted opencv-Mat from your pseye )
Not entirely sure if I understanded your question. But you could run the example calibration program that comes with ARToolKit. More information could be found here: Calibrating Your Camera
Then you would be able to get the calibration result "camera_para.dat" at ARToolKit's bin/Data, which could be used later in your project.
If by any chance you are using Unity for your AR project (If not, ignore below), simply import the ARToolKit, then in the AR Controller inspector, give your .dat file a unique name, then include it in "Camera Parameters" option.
I'm trying to determine skeleton joints (or at the very least to be able to track a single palm) using a regular webcam. I've looked all over the web and can't seem to find a way to do so.
Every example I've found is using Kinect. I want to use a single webcam.
There's no need for me to calculate the depth of the joints - I just need to be able to recognize their X, Y position in the frame. Which is why I'm using a webcam, not a Kinect.
So far I've looked at:
OpenCV (the "skeleton" functionality in it is a process of simplifying graphical models, but it's not a detection and/or skeletonization of a human body).
OpenNI (with NiTE) - the only way to get the joints is to use the Kinect device, so this doesn't work with a webcam.
I'm looking for a C/C++ library (but at this point would look at any other language), preferably open source (but, again, will consider any license) that can do the following:
Given an image (a frame from a webcam) calculate the X, Y positions of the visible joints
[Optional] Given a video capture stream call back into my code with events for joints' positions
Doesn't have to be super accurate, but would prefer it to be very fast (sub-0.1 sec processing time per frame)
Would really appreciate it if someone can help me out with this. I've been stuck on this for a few days now with no clear path to proceed.
UPDATE
2 years later a solution was found: http://dlib.net/imaging.html#shape_predictor
To track a hand using a single camera without depth information is a serious task and topic of ongoing scientific work. I can supply you a bunch of interesting and/or highly cited scientific papers on the topic:
M. de La Gorce, D. J. Fleet, and N. Paragios, “Model-Based 3D Hand Pose Estimation from Monocular Video.,” IEEE transactions on pattern analysis and machine intelligence, vol. 33, Feb. 2011.
R. Wang and J. Popović, “Real-time hand-tracking with a color glove,” ACM Transactions on Graphics (TOG), 2009.
B. Stenger, A. Thayananthan, P. H. S. Torr, and R. Cipolla, “Model-based hand tracking using a hierarchical Bayesian filter.,” IEEE transactions on pattern analysis and machine intelligence, vol. 28, no. 9, pp. 1372–84, Sep. 2006.
J. M. Rehg and T. Kanade, “Model-based tracking of self-occluding articulated objects,” in Proceedings of IEEE International Conference on Computer Vision, 1995, pp. 612–617.
Hand tracking literature survey in the 2nd chapter:
T. de Campos, “3D Visual Tracking of Articulated Objects and Hands,” 2006.
Unfortunately I don't know about some freely available hand tracking library.
there is a simple way for detecting hand using skin tone. perhaps this could help... you can see the results on this youtube video. caveat: the background shouldn't contain skin colored things like wood.
here is the code:
''' Detect human skin tone and draw a boundary around it.
Useful for gesture recognition and motion tracking.
Inspired by: http://stackoverflow.com/a/14756351/1463143
Date: 08 June 2013
'''
# Required moduls
import cv2
import numpy
# Constants for finding range of skin color in YCrCb
min_YCrCb = numpy.array([0,133,77],numpy.uint8)
max_YCrCb = numpy.array([255,173,127],numpy.uint8)
# Create a window to display the camera feed
cv2.namedWindow('Camera Output')
# Get pointer to video frames from primary device
videoFrame = cv2.VideoCapture(0)
# Process the video frames
keyPressed = -1 # -1 indicates no key pressed
while(keyPressed < 0): # any key pressed has a value >= 0
# Grab video frame, decode it and return next video frame
readSucsess, sourceImage = videoFrame.read()
# Convert image to YCrCb
imageYCrCb = cv2.cvtColor(sourceImage,cv2.COLOR_BGR2YCR_CB)
# Find region with skin tone in YCrCb image
skinRegion = cv2.inRange(imageYCrCb,min_YCrCb,max_YCrCb)
# Do contour detection on skin region
contours, hierarchy = cv2.findContours(skinRegion, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Draw the contour on the source image
for i, c in enumerate(contours):
area = cv2.contourArea(c)
if area > 1000:
cv2.drawContours(sourceImage, contours, i, (0, 255, 0), 3)
# Display the source image
cv2.imshow('Camera Output',sourceImage)
# Check for user input to close program
keyPressed = cv2.waitKey(1) # wait 1 milisecond in each iteration of while loop
# Close window and camera after exiting the while loop
cv2.destroyWindow('Camera Output')
videoFrame.release()
the cv2.findContour is quite useful, you can find the centroid of a "blob" by using cv2.moments after u find the contours. have a look at the opencv documentation on shape descriptors.
i havent yet figured out how to make the skeletons that lie in the middle of the contour but i was thinking of "eroding" the contours till it is a single line. in image processing the process is called "skeletonization" or "morphological skeleton". here is some basic info on skeletonization.
here is a link that implements skeletonization in opencv and c++
here is a link for skeletonization in opencv and python
hope that helps :)
--- EDIT ----
i would highly recommend that you go through these papers by Deva Ramanan (scroll down after visiting the linked page): http://www.ics.uci.edu/~dramanan/
C. Desai, D. Ramanan. "Detecting Actions, Poses, and Objects with
Relational Phraselets" European Conference on Computer Vision
(ECCV), Florence, Italy, Oct. 2012.
D. Park, D. Ramanan. "N-Best Maximal Decoders for Part Models" International Conference
on Computer Vision (ICCV) Barcelona, Spain, November 2011.
D. Ramanan. "Learning to Parse Images of Articulated Objects" Neural Info. Proc.
Systems (NIPS), Vancouver, Canada, Dec 2006.
The most common approach can be seen in the following youtube video. http://www.youtube.com/watch?v=xML2S6bvMwI
This method is not quite robust, as it tends to fail if the hand is rotated to much (eg; if the camera is looking at the side of the hand or at a partially bent hand).
If you do not mind using two camera's you can look into the work Robert Wang. His current company (3GearSystems) uses this technology, augmented with a kinect, to provide tracking. His original paper uses two webcams but has much worse tracking.
Wang, Robert, Sylvain Paris, and Jovan Popović. "6d hands: markerless hand-tracking for computer aided design." Proceedings of the 24th annual ACM symposium on User interface software and technology. ACM, 2011.
Another option (again if using "more" than a single webcam is possible), is to use a IR emitter. Your hand reflects IR light quite well whereas the background does not. By adding a filter to the webcam that filters normal light (and removing the standard filter that does the opposite) you can create a quite effective hand tracking. The advantage of this method is that the segmentation of the hand from the background is much simpler. Depending on the distance and the quality of the camera, you would need more IR leds, in order to reflect sufficient light back into the webcam. The leap motion uses this technology to track the fingers & palms (it uses 2 IR cameras and 3 IR leds to also get depth information).
All that being said; I think the Kinect is your best option in this. Yes, you don't need the depth, but the depth information does make it a lot easier to detect the hand (using the depth information for the segmentation).
My suggestion, given your constraints, would be to use something like this:
http://docs.opencv.org/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html
Here is a tutorial for using it for face detection:
http://opencv.willowgarage.com/wiki/FaceDetection?highlight=%28facial%29|%28recognition%29
The problem you have described is quite difficult, and I'm not sure that trying to do it using only a webcam is a reasonable plan, but this is probably your best bet. As explained here (http://docs.opencv.org/modules/objdetect/doc/cascade_classification.html?highlight=load#cascadeclassifier-load), you will need to train the classifier with something like this:
http://docs.opencv.org/doc/user_guide/ug_traincascade.html
Remember: Even though you don't require the depth information for your use, having this information makes it easier for the library to identify a hand.
At last I've found a solution. Turns out a dlib open-source project has a "shape predictor" that, once properly trained, does exactly what I need: it guesstimates (with a pretty satisfactory accuracy) the "pose". A "pose" is loosely defined as "whatever you train it to recognize as a pose" by training it with a set of images, annotated with the shapes to extract from them.
The shape predictor is described in here on dlib's website
I don't know about possible existing solutions. If supervised (or semi-supervised) learning is an option, training decision trees or neural networks might already be enough (kinect uses random forests from what i have heard). Before you go such a path, do everything you can to find an existing solution. Getting Machine Learning stuff right takes a lot of time and experimentation.
OpenCV has machine learning components, what you would need is training data.
With the motion tracking features of the open source Blender project it is possible to create a 3D model based on 2D footage. No kinect needed. Since blender is open source you might be able to use their pyton scripts outside the blender framework for your own purposes.
Have you ever heard about Eyesweb
I have been using it for one of my project and I though it might be usefull for what you want to achieve.
Here are some interesting publication LNAI 3881 - Finger Tracking Methods Using EyesWeb and Powerpointing-HCI using gestures
Basically the workflow is:
You create your patch in EyesWeb
Prepare the datas you want to send with a network client
Use theses processed datas on your own server (your app)
However, I don't know if there is a way to embed the real time image processing part of Eyes Web into a soft as a library.
I have 2 images sourceImg, refImg.
I've extracted the features like so:
cv::GoodFeaturesToTrackDetector detector;
std::vector<cv::KeyPoint> sourceKeyPoints, refKeyPoints;
detector.detect(sourceImg, sourceKeyPoints);
detector.detect(refImg, refKeyPoints);
I want to find the translation of an object from refImg to sourceImg. There is no rotation or perspective change, only simple 2d translation. There may be some noise.
findHomography() works fine when both sets have the same number of features extracted, even handling noise quite well.
My question is, what do I do when the number of features differs?
Can someone point me in the right direction regarding DescriptorExtractor and Matching?
Note: I can't use SURF/SIFT for patent reasons.
You could try the FlannBasedMatcherclass from OpenCV. Use this to match descriptors (of any type) and then use the best matches to find your homography.
Hi (sorry for my english) .. i'm working in a project for University in this project i need to use the MBA (Multilevel B-Spline Approximation) algorithm to get some points (control points) of a image to use in other operations.
I'm reading a lot of papers about this algorithm, and i think i understand, but i can't writing.
The idea is: Read a image, process a image (OpenCV), then get control points of the image, use this points.
So the problem here is:
The algorithm use a set of points {(x,y,z)} , this set of points are approximated with a surface generated with the control points obtained from MBA. the set of points {(x,y,z)} represents de data we need to approximate (the image)..
So, the image is in a cv::Mat format , how can transform this format to an ordinary array to simply access to the data an manipulate...
Here are one paper with an explanation of the method:
(Paper) REGULARIZED MULTILEVEL B-SPLINE REGISTRATION
(Paper)Scattered Data Interpolation with Multilevel B-splines
(Matlab)MBA
If someone can help, maybe a guideline, idea or anything will be appreciate ..
Thanks in advance.
EDIT: Finally i wrote the algorithm in C++ using armadillo and OpenCV ...
Well i'm using armadillo a C++ linear algebra library to works with matrix for the algorithm