I'm creating Stereo images processing project modeled on Matlab's examples. A copy pasted code from one of them don't works well.
I1 = rgb2gray(imread('viprectification_deskLeft.png'));
I2 = rgb2gray(imread('viprectification_deskRight.png'));
points1 = detectHarrisFeatures(I1);
points2 = detectHarrisFeatures(I2);
[features1, valid_points1] = extractFeatures(I1, points1);
[features2, valid_points2] = extractFeatures(I2, points2);
indexPairs = matchFeatures(features1, features2);
matchedPoints1 = valid_points1(indexPairs(:, 1),:);
matchedPoints2 = valid_points2(indexPairs(:, 2),:);
figure; showMatchedFeatures(I1, I2, matchedPoints1, matchedPoints2);
load stereoPointPairs
[fLMedS, inliers] = estimateFundamentalMatrix(matchedPoints1,matchedPoints2,'NumTrials',4000);
figure;
subplot(121); imshow(I1);
title('Inliers and Epipolar Lines in First Image'); hold on;
plot(matchedPoints1(inliers,1), matchedPoints1(inliers,2), 'go');
An error:
Error using epilineTest (line 24) Invalid indexing operation.
Best regards
Looks like you have an older version of MATLAB. Try doing this:
[fLMedS, inliers] = estimateFundamentalMatrix(...
matchedPoints1.Location, matchedPoints2.Location,'NumTrials',4000);
Generally, look at the example in your own local MATLAB documentation, rather than the one on the website. The website has the doc for the latest release (currently R2014a), and the examples may be using new features that do not exist in the older versions.
Related
Hi I am using public data pbmc to practice single cell analysis
I got stuck at this point with this error message.
Just started with R and having a hard time
Could anyone give a pointer?
Many thanks
Ridge plots - from ggridges. Visualize single cell expression distributions in each cluster
Code: RidgePlot(pbmc3k.final, features = features, ncol = 2)
Error in FetchData.Seurat(object = object, vars = features, slot = slot) :
object 'features' not found
You would need to define what 'features' you want to see.
so :
features = c('Sox2', 'Sox9') #etc, as an example
or, you can add them as a variable which you call features :
features <- c('Sox2', 'Sox9')
RidgePlot(pbmc3k.final, features = features, ncol = 2)
#if they are mouse genes, human genes are all caps.
I am trying to visualise a friedman's test followed by pairwise comparisons using a boxplot with p-values.
Here is an example of how it should look like:
[example graph downloaded from the internet][1]
However, since there are way too many significant comparisons in my case, my graph currently looks like this:
[my graph][2]
[1]: https://i.stack.imgur.com/DO6Vz.png
[2]: https://i.stack.imgur.com/94OXK.png
Here is the code I used to generate the graph with p-value
pwc_IFX_plot <- pwc_IFX %>% add_xy_position(x = "Variant")
ggboxplot(IFX_variant, x = "Variant", y = "Concentration", add = "point") +
stat_pvalue_manual(pwc_IFX_plot, hide.ns = TRUE)+
labs(
subtitle = get_test_label(res.fried_IFX, detailed = TRUE),
caption = get_pwc_label(pwc_IFX)
)+scale_y_log10(breaks = trans_breaks("log10", function(x) 10^x),
labels = trans_format("log10", math_format(10^.x)))
I hope to only show the comparisons of each group to my control group, rather than all the intergroup comparisons.
Thank you for your time.
Any suggestions would be highly appreciated!
How can I convert a point cloud saved in rosbag, in format sensor_msgs/PointCloud2, to .bin files in KITTI format?
I know that it is possible to convert to .pcd (http://wiki.ros.org/pcl_ros#pointcloud_to_pcd) so perhaps even a pcd to bin converter would be enough.
Is there any available tool to do this?
I've found this, but it needs ROS kinetic (legacy ROS version).
A python script to do it:
pc = pypcd.PointCloud.from_msg(msg)
x = pc.pc_data['x']
y = pc.pc_data['y']
z = pc.pc_data['z']
intensity = pc.pc_data['intensity']
arr = np.zeros(x.shape[0] + y.shape[0] + z.shape[0] + intensity.shape[0], dtype=np.float32)
arr[::4] = x
arr[1::4] = y
arr[2::4] = z
arr[3::4] = intensity
arr.astype('float32').tofile('filename.bin')
Where x,y,z and intensity are arrays for a single point cloud. It's not strictly needed to use pypcd. (Source)
Also this conversion tool can actually be used without ROS, using another tool for the conversion to pcd file.
When I tried to use drawMatchesKnn function as mentioned in this tutorial for FLANN feature matching, I get the following error
AttributeError: 'module' object has no attribute 'drawMatchesKnn'
I checked with other resources that drawMatchesKnn method is present in opencv.
Why am I getting this error?
Thanks in advance
The functions cv2.drawMatches and cv2.drawMatchesKnn are not available in newer versions of OpenCV 2.4. #rayryeng provided a lightweight alternative which works as is for the output of DescriptorMatcher.match. The difference with DescriptorMatcher.knnMatch is that the matches are returned as a list of lists. To use the #rayryeng alternative, the matches must be extracted into a 1-D list.
For example, the Brute-Force Matching with SIFT Descriptors and Ratio Test tutorial could be amended as such:
# BFMatcher with default params
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1,des2, k=2)
# Apply ratio test
good = []
for m,n in matches:
if m.distance < 0.75*n.distance:
# Removed the brackets around m
good.append(m)
# Invoke #rayryeng's drawMatches alternative, note it requires grayscale images
gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY)
drawMatches(gray1,kp1,gray2,kp2,good)
You need to use OpenCV version 3. drawMatchesKnn() is present in 3.0.0-alpha but not in 2.4.11
That error is there, because you are using an old version of OpenCV.
Instead of doing good.append(m) try good.append([m])
I have the following problem. I have to compute dense SIFT interest points in a very high dimensional image (182MP). When I run the code in the full image Matlab always close suddently. So I decided to run the code in image patches.
the code
I tried to use blocproc in matlab to call the c++ function that performs the dense sift interest points detection this way:
fun = #(block_struct) denseSIFT(block_struct.data, options);
[dsift , infodsift] = blockproc(ndvi,[1000 1000],fun);
where dsift is the sift descriptors (vectors) and infodsift has the information of the interest points, such as the x and y coordinates.
the problem
The problem is the fact that blocproc just allow one output, but i want both outputs. The following error is given by matlab when i run the code.
Error using blockproc
Too many output arguments.
Is there a way for me doing this?
Would it be a problem for you to "hard code" a version of blockproc?
Assuming for a moment that you can divide your image into NxM smaller images, you could loop around as follows:
bigImage = someFunction();
sz = size(bigImage);
smallSize = sz ./ [N M];
dsift = cell(N,M);
infodsift = cell(N,M);
for ii = 1:N
for jj = 1:M
smallImage = bigImage((ii-1)*smallSize(1) + (1:smallSize(1)), (jj-1)*smallSize(2) + (1:smallSize(2));
[dsift{ii,jj} infodsift{ii,jj}] = denseSIFT(smallImage, options);
end
end
The results will then be in the two cell arrays. No real need to pre-allocate, but it's tidier if you do. If the individual matrices are the same size, you can convert into a single large matrix with
dsiftFull = cell2mat(dsift);
Almost magic. This won't work if your matrices are different sizes - but then, if they are, I'm not sure you would even want to put them all in a single one (unless you decide to horzcat them).
If you do decide you want a list of "all the colums as a giant matrix", then you can do
giantMatrix = [dsift{:}];
This will return a matrix with (in your example) 128 rows, and as many columns as there were "interest points" found. It's shorthand for
giantMatrix = [dsift{1,1} dsift{2,1} dsift{3,1} ... dsift{N,M}];