I have this; (x)=-(x^3+6*x^2-18*x)/(4*(x^2+2));
Have differentiated and tried to solve for x but get too many answers.
graph
Use realroots to discard complex roots:
y:-(x^3+6*x^2-18*x)/(4*(x^2+2));
realroots(diff(y,x)=0),numer;
[x=-1.688117772340774,x=0.8158789575099945]
Related
I am working with the BackgroundSubtractorMOG2 class in OpenCV (Python), and am trying to extract the individual components of the background model. As I understand it, each pixel will be modeled by the mixture of a varying number of gaussian distributions, each defined by a mean and variance. So, how can I determine what all of these components (means and variances) are after feeding the background subtractor a given number of frames?
The documentation here:
https://docs.opencv.org/3.4.3/d7/d7b/classcv_1_1BackgroundSubtractorMOG2.html#adbb1d295befaff88a54a929e50aaf879
Does not seem to discuss doing this.
This information must be contained somewhere in the background subtractor object. Does anyone know how to get to it?
Thanks!
Edit: A little more searching has led me to believe that the cv2.Algorithm class is required to read the parameters from the BackgroundSubtractorMOG2 object. I think the two questions posed here:
http://answers.opencv.org/question/28008/how-to-derive-from-algorithm/
Reading algorithm parameters from file in OpenCV
are similar to what I am asking, but I am unable to interpret the answers. I thought the solution would be something along the lines of:
Parameters = cv2.Algorithm.read('name_of_backgroundsubtractorMOG2_object')
but this returns an error of: 'Required argument 'fn' (pos 1) not found'
Edit 2: Unfortunately I think this question has been answered here:
Save opencv BackgroundSubtractorMOG to file?
Short answer: It cannot be done! Sad!
I'm trying to think of the best way to conduct some sort of analysis between two 3D models of the same object.
The first scan is of the original item and the second scan is after it has been put under some load x.
An example would be trying to find the difference between two types of metal.
I would like to be able to scan the initial metal cylinder, apply a measured load, scan it again, and then finally apply some sort of algorithm to compare the difference.
Is it possible to do this efficiently (maybe using Mablab) over say 50 - 100 items for an object around 5inch^3?
I am assuming I will need to work out some sort of utility function as the total mass should be the same?
Would machine learning be beneficial in this case?
Any suggestions or direction would be amazing.
Thank you :)
EDIT: The scan files are coming through as '.stl'
I've been attempting to export boundary information from an OSM file. My process is nearly there however I have an issue with the polygon I'm generating drawing random lines.
I would appreciate some insight on where I may be going wrong.
Step 1: Export the OSM data into XML
osmfilter -v greater-london-latest.osm --keep="boundary= admin_level= place=" > b.txt
Step 2: Run a script to process the XML.
cycle each relation node
load the member ways
load the nodes from each specified way
record the lat/lon and build a poly set
This produces a series of lat/lon which when I build them as a polygon give the correct overall shape I'm looking for. However, there are issues with the connecting lines I assume..
My polygon output
I'm actually looking for this, which is similar but Im obviously missing something.
Actual Poly Im looking to generate
Again, thanks for any help.
Ways in relations are not necessarily sorted. See answers to this question on how to sort ways, especially the answer by user geocodezip.
Alternatively you can make use of various tools/libraries to do the sorting for you. Unfortunately I can't point you directly to one but there are various tools capable of sorting relation members, including the OSM website itself, JOSM, overpass turbo (I guess), some JS stuff, [...].
Maybe some other user can help out with pointing to some good examples?
I'm working with openCV and I'm a newbie in this field. I'm researching about Camshift. I want to extend this method by using multiple histograms. It means when tracking an object has many than one apperance (ex: rubik cube with six apperance), if we use only one histogram, Camshift will most likely fail.
I know calcHist function in openCV (http://docs.opencv.org/modules/imgproc/doc/histograms.html#calchist) has a parameter is "accumulate", but I don't know how to use and when to use (apply for camshiftdemo.cpp in opencv samples folder). This function can help me solve this problem? Or I have to use difference solution?
I have an idea, that is: create an array histogram for object, for every appearance condition that strongly varies in color, we pre-compute and store all to this array. But when we compute new histogram? It means that the pre-condition to start compute new histogram is what?
And what happend if I have to track multiple object has same color?
Everybody please help me. Thank you so much!
I am writing a histogram like function which looks at vector data and then puts the elements in predefined "histogram" buckets based on which range they are closest to.
I can obviously do this using if condition but I am trying to improve it using NEON because these are image buffers.
One way to do this would be with VCEQ then VBIT but sadly enough I could not find VBIT in the header of neon. Alternatively I figured I could take the VCEQ results and do an exclusive AND with a vector of 1s and then use VBIF :-) but VBIF is not there either!
Any thoughts here?
Thanks
VBIT, VBIF, and VBSL all do the same operation up to permutation of the sources; you can use the vbsl* intrinsics to get any of the three operations.