sampling

I have a problem to get my head around smoothing and sampling contours in OpenCV (C++ API). Lets say I have got sequence of points retrieved from cv::findContours (for instance applied on this this image: Ultimately, I want To smooth a sequence of points using different kernels. To resize the sequence using different types of interpolations. After smoothing, I hope to have a result like : I also considered drawing my contour in a cv::Mat , filtering the Mat (using blur or morphological operations) and re-finding the contours, but is slow and suboptimal. So, ideally, I could do the job using

After going through the documentation of pyaudio and reading some other articles on the web, I am confused if my understanding is correct. This is the code for audio recording found on pyaudio's site: import pyaudio import wave CHUNK = 1024 FORMAT = pyaudio.paInt16 CHANNELS = 2 RATE = 44100 RECORD_SECONDS = 5 WAVE_OUTPUT_FILENAME = "output.wav" p = pyaudio.PyAudio() stream = p.open(format=FORMAT, channels=CHANNELS, rate=RATE, input=True, frames_per_buffer=CHUNK) print("* recording") frames = [] for i in range(0, int(RATE / CHUNK * RECORD_SECONDS)): data = stream.read(CHUNK) frames.append(data)

I need to get the sample values of sound data of a WAV file so that by using those sample values i need to get the amplitude values of that sound data in every second. Important: Is there any way to get audio data sample values using Naudio library or wmp library? I am getting the sample values in this way: byte[] data = File.ReadAllBytes(File_textBox.Text); var samples=new int[data.Length]; int x = 0; for (int i = 44; i <data.Length; i += 2) { samples[x] = BitConverter.ToInt16(data, i); x++; } But I am getting negative values more like (-326260). so is this right or wrong? I mean can a sample

Here explains the function numpy.random.choice . However, I am confused about the third parameter replace . What is it? And in which case will it be useful? Thanks! It controls whether the sample is returned to the sample pool. If you want only unique samples then this should be false. You can use it when you want sample some elements from a list, and meanwhile you want the elements no repeat , then you can set the " replace=False ". eg. from numpy import random as rd ary = list(range(10)) # usage In[18]: rd.choice(ary, size=8, replace=False) Out[18]: array([0, 5, 9, 8, 2, 1, 6, 3]) # no

Designing my stratified sample library(survey) design <- svydesign(id=~1,strata=~Category, data=billa, fpc=~fpc) So far so good, but how can I draw now a sample in the same way I was able for simple sampling? set.seed(67359) samplerows <- sort(sample(x=1:N, size=n.pre$n)) If you have a stratified design, then I believe you can sample randomly within each stratum. Here is a short algorithm to do proportional sampling in each stratum, using ddply : library(plyr) set.seed(1) dat <- data.frame( id = 1:100, Category = sample(LETTERS[1:3], 100, replace=TRUE, prob=c(0.2, 0.3, 0.5)) ) sampleOne <-

Suppose you have an arbitrary triangle with vertices A , B , and C . This paper (section 4.2) says that you can generate a random point, P , uniformly from within triangle ABC by the following convex combination of the vertices: P = (1 - sqrt(r1)) * A + (sqrt(r1) * (1 - r2)) * B + (sqrt(r1) * r2) * C where r1 and r2 are uniformly drawn from [0, 1] , and sqrt is the square root function. How do you justify that the sampled points that are uniformly distributed within triangle ABC ? EDIT As pointed out in a comment on the mathoverflow question , Graphical Gems discusses this algorithm . You have

I am trying to sample a signal at 10Khz in Python. There is no problem when try to run this code(at 1KHz): import sched, time i = 0 def f(): # sampling function s.enter(0.001, 1, f, ()) global i i += 1 if i == 1000: i = 0 print "one second" s = sched.scheduler(time.time, time.sleep) s.enter(0.001, 1, f, ()) s.run() When I try to make the time less, it starts to exceed one second(in my computer, 1.66s at 10e-6). It it possible to run a sampling function at a specific frequency in Python? ivan_pozdeev You didn't account for the code's overhead. Each iteration, this error adds up and skews the