How to allow 180 degree angles in X3DOM IndexedFaceSet - x3dom

I'm trying to make a simple planar 2D shape in X3DOM, but since the coordinates are autogenerated, some of them are on a straight line, and X3DOM seems to fail in this case. A trivial example is below. What am I doing wrong here?
<X3D width='800px' height='600px'>
<Scene>
<Viewpoint description='Front view' orientation='0 1 0 1.57' position='8 0 0'/>
<Shape DEF='Front'>
<IndexedFaceSet coordIndex='0 1 2 3' solid='false', convex='false'>
<Coordinate DEF='Points' point='
1 1 1
1 1 2
1 1 3
1 1 4
2 1 4
2 1 3
2 1 2
2 1 1
1 1 1'/>
</IndexedFaceSet>
<Appearance>
<Material diffuseColor="0 0 1" specularColor=".5 .5 .5" DEF="edgecolour" />
</Appearance>
</Shape> </Scene>
</X3D>
This works fine if I cut out the middle 4 points (1 1 3, 1 1 4, 2 1 4, 2 1 3), but I can't easily change this in my script (the real shapes are much more complex)

First of all, the x3d snippet you posted was malformed:
<IndexedFaceSet coordIndex='0 1 2 3' solid='false', convex='false'>
^
Second, your IndexedFaceSet only uses the first 4 coordinates of the Coordinate node:
coordIndex='0 1 2 3'
And the first 4 point only make a straight line, which is invisible as a Face (Area of 0, nothing to render). Only Z changes:
1 1 1
1 1 2
1 1 3
1 1 4
But after adding all points to the coordIndex of the IndexedFaceSet and setting convex to true my X3D viewer was able to render a blue rectangle:
<?xml version="1.0" encoding="UTF-8"?>
<X3D>
<Scene>
<Viewpoint description='Front view' orientation='0 1 0 1.57' position='8 0 0'/>
<Shape DEF='Front'>
<IndexedFaceSet coordIndex='0 1 2 3 4 5 6 7' solid='false' convex='true'>
<Coordinate DEF='Points' point='
1 1 1
1 1 2
1 1 3
1 1 4
2 1 4
2 1 3
2 1 2
2 1 1
1 1 1'/>
</IndexedFaceSet>
<Appearance>
<Material diffuseColor="0 0 1" specularColor=".5 .5 .5" DEF="edgecolour" />
</Appearance>
</Shape>
</Scene>
</X3D>
Hope that helps :)

Related

How to stitch two images have different absolute coordinates?

for example, stitch
first image
1 1 1
1 1 1
1 1 1
second image
2 2 2 2
2 2 2 2
2 2 2 2
and What I want
0 0 0 2 2 2 2
1 1 1 2 2 2 2
1 1 1 2 2 2 2
1 1 1 0 0 0 0
or
1 1 1 0 0 0 0
1 1 1 2 2 2 2
1 1 1 2 2 2 2
0 0 0 2 2 2 2
In python, that is easy to make like..
temp_panorama = np.zeros((1's height+abs(2's upper part length), 1's width+2's width))
temp_panorama[(2's upper part length) : 1's height, 0 : 1's width] = img1[:]
temp_panorama[0 : 2's height, 1's width +1 :] = img2[:, :]
but how can I implement the same function in C++'s opencv?
use subimages:
// ROI where first image will be placed
cv::Rect firstROI = cv::Rect(x1,y2, first.cols, first.height);
cv::Rect secondROI = cv::Rect(x2,y2, second.cols, second.height);
// create an image big enought to hold the result
cv::Mat canvas = cv::Mat::zeros(cv::Size(std::max(x1+first.cols, x2+second.cols), std::max(y1+first.rows, y2+second.rows)), first.type());
// use subimages:
first.copyTo(canvas(firstROI));
second.copyTo(canvas(secondROI));
in your example:
x1 = 0,
y1 = 1,
x2 = 3,
y2 = 0
first.cols == 3
first.rows == 3
second.cols == 4
second.rows == 3

Transform string variable into 0-1 columns

As a very begginer in SPSS I would ask you for help with some transformation from table A into table B. I have to recode values of "brand" variable into columns and make 0-1 variables.
#table A#
nr brand
1 GREEN CARE PROFESSIONAL
1 GREEN CARE PROFESSIONAL
1 GREEN CARE PROFESSIONAL
2 HENKEL
3 HENKEL
3 HENKEL
3 HENKEL
3 VIZIR
4 BIEDRONKA
4 BOBINI
4 BOBINI
4 BOBINI
4 BOBINI
4 BOBINI
4 HENKEL
5 VIZIR
6 HENKEL
#table B#
nr GREEN HENKEL VIZIR BIEDR BOBINI
1 1 0 0 0 0
1 1 0 0 0 0
1 1 1 0 0 0
2 0 1 0 0 0
3 0 1 0 0 0
3 0 1 0 0 0
3 0 1 0 0 0
3 0 0 1 0 0
4 0 0 0 1 0
4 0 0 0 0 1
4 0 0 0 0 1
4 0 0 0 0 1
4 0 0 0 0 1
4 0 0 0 0 1
4 0 1 0 0 0
5 0 0 1 0 0
6 0 1 0 0 0
I can do it in this particular case in this simple way:
compute HENKEL=0.
...
do if BRAND='GREEN_CARE' .
compute GREEN_CARE=1.
else if ....
but the loop has to be usable with another variable and different number of values ect. I was trying to make it all day and gave up.
Do you have any idea to make it in a easy way?
Thanks!
The following syntax does the job on the sample data you provided.
First, let's recreate the sample data to demonstrate on:
Data list list/nr (f1) brand (a30).
begin data
1 "GREEN CARE PROFESSIONAL"
1 "GREEN CARE PROFESSIONAL"
1 "GREEN CARE PROFESSIONAL"
2 "HENKEL"
3 "HENKEL"
3 "HENKEL"
3 "HENKEL"
3 "VIZIR"
4 "BIEDRONKA"
4 "BOBINI"
4 "BOBINI"
4 "BOBINI"
4 "BOBINI"
4 "BOBINI"
4 "HENKEL"
5 "VIZIR"
6 "HENKEL"
end data.
dataset name originalDataset.
Now for the restructure.
sort cases by nr brand.
* creating an index to enumerate cases for each combination of `nr` and `brand`.
* This is necessary for the `casestovars` command to work later.
compute ind=1.
if $casenum>1 and lag(nr)=nr and lag(brand)=brand ind=lag(ind)+1.
exe.
* variable names can't have spaces in them, so changing the category names accordingly.
compute brand=replace(rtrim(brand)," ","_").
sort cases by nr ind brand.
compute exist=1.
casestovars /id=nr ind /index= brand/autofix=no.

Count unique values across multiple columns

I have data in this format
A B C D
1 1 1 1
1 1 1 2
1 1 1 3
1 1 1 4
...
4 4 4 4
I want to count number of unique values in each row and print it
output:
A B C D unique-count
1 1 1 1 4
1 1 1 2 3
1 1 1 3 3
1 1 1 4 3
...
4 4 4 4 4

OneVsRestClassifier(svm.SVC()).predict() gives continous values

I am trying to use y_scores=OneVsRestClassifier(svm.SVC()).predict() on datasets
like iris and titanic .The trouble is that I am getting y_scores as continous values.like for iris dataset I am getting :
[[ -3.70047231 -0.74209097 2.29720159]
[ -1.93190155 0.69106231 -2.24974856]
.....
I am using the OneVsRestClassifier for other classifier models like knn,randomforest,naive bayes and they are giving appropriate results in the form of
[[ 0 1 0]
[ 1 0 1]...
etc on the iris dataset .Please help.
Well this is simply not true.
>>> from sklearn.multiclass import OneVsRestClassifier
>>> from sklearn.svm import SVC
>>> from sklearn.datasets import load_iris
>>> iris = load_iris()
>>> clf = OneVsRestClassifier(SVC())
>>> clf.fit(iris['data'], iris['target'])
OneVsRestClassifier(estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf', max_iter=-1, probability=False, random_state=None,
shrinking=True, tol=0.001, verbose=False),
n_jobs=1)
>>> print clf.predict(iris['data'])
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
maybe you called decision_function instead (which would match your output dimension, as predict is supposed to return a vector, not a matrix). Then, SVM returns signed distances to each hyperplane, which is its decision function from mathematical perspective.

How to encode video 3840x2160 with 32x32 and 16x16 CU with depth 2 and 1 in HEVC Encoder HM 13

When I try to encode a video the encoder crashes after finishing first GOP.
This is the configuration I'm using:
MaxCUWidth : 16 # Maximum coding unit width in pixel
MaxCUHeight : 16 # Maximum coding unit height in pixel
MaxPartitionDepth : 2 # Maximum coding unit depth
QuadtreeTULog2MaxSize : 3 # Log2 of maximum transform size for
# quadtree-based TU coding (2...5) = MaxPartitionDepth + 2 - 1
QuadtreeTULog2MinSize : 2 # Log2 of minimum transform size for
# quadtree-based TU coding (2...5)
QuadtreeTUMaxDepthInter : 1
QuadtreeTUMaxDepthIntra : 1
#======== Coding Structure =============
IntraPeriod : 8 # Period of I-Frame ( -1 = only first)
DecodingRefreshType : 1 # Random Accesss 0:none, 1:CDR, 2:IDR
GOPSize : 4 # GOP Size (number of B slice = GOPSize-1)
# Type POC QPoffset QPfactor tcOffsetDiv2 betaOffsetDiv2 temporal_id #ref_pics_active #ref_pics reference pictures predict deltaRPS #ref_idcs reference idcs
Frame1: P 4 1 0.5 0 0 0 1 1 -4 0
Frame2: B 2 2 0.5 1 0 1 1 2 -2 2 1 2 2 1 1
Frame3: B 1 3 0.5 2 0 2 1 3 -1 1 3 1 1 3 1 1 1
Frame4: B 3 3 0.5 2 0 2 1 2 -1 1 1 -2 4 0 1 1 0
This also happens with CU=16x16 with depth=1
Note: I encoded CU=64x64 with depth=4 with the same GOP configuration and every thing went fine.
This is most probably due to the fact that you have compiled the binary for a 32-bit system?
Please rebuild it for a 64-bit system and the problem will go away.

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