I am plotting an oscilloscope type of chart, showing microphone pitch, for example:
There are times when the mic input level is too low to be able to measure pitch accurately. I detect this and have a flag isSquelched.
Is there a way to prevent the G charts from plotting the point(s) e.g. to flag the data array in some way?
Related
I have been working to find temporal displacement between audio signals using a spectrogram. I have a short array containing data of a sound wave (pulses at specific frequencies). Now I want to plot spectrogram from that array. I have followed this steps (Spectrogram C++ library):
It would be fairly easy to put together your own spectrogram. The steps are:
window function (fairly trivial, e.g. Hanning)
FFT (FFTW would be a good choice but if licensing is an issue then go for Kiss FFT or
similar)
calculate log magnitude of frequency domain components(trivial: log(sqrt(re * re + im * im))
Now after performing these 3 steps, I am stuck at how to plot the spectrogram from this available data? Being naive in this field, I need some clear steps ahead to plot the spectrogram.
I know that a simple spectrogram has Frequency at Y-Axis, time at X-axis and magnitude as the color intensity.
But how do I get these three things to plot the spectrogram? (I want to observe and analyze data behind spectral peaks(what's the value on Y-axis and X-axis), the main purpose of plotting spectrogram).
Regards,
Khubaib
Ive got a graph that displays the temperature from my wood pellets stove, what I would like is to get the time the temperature is rising vs cooling down.
Anyone know how to get something like the slope of the curve in RRDTool or something similar?
You can do this in two different ways.
First of all, you could use a "DERIVE" data type. This will log the derivative -- IE, the slope -- of the data instead of the actual data. However, this will not store the actual temperatures, which is probably not what you want.
The next way to do it is to calculate the slope on the fly from the actual data, as we build the graph. You've already stored your temperature using a GAUGE data type. Now, you can use a calculated value to work out the slope.
DEF:temp=myrrdfile.rrd:ds0:AVERAGE
CDEF:slope=temp,PREV(temp),-,STEPWIDTH,/
This calculates slope to be the difference between the current and previous value, divided by the time interval.
However, since all you seem to be interested in is if the temperature is going up or down, you could instead use something like:
CDEF:cooling=temp,PREV(temp),LT,INF,0,IF
CDEF:warming=temp,PREV(temp),GT,INF,0,IF
AREA:cooling#0000cc::skipscale
AREA:warming#cc0000::skipscale
LINE:temp#00cc00:Temperature
This will graph the temperature as a green line, with a background of red if warming, and blue if cooling.
I developed the few Line charts for BMP280 sensor data in powerbi. This is one of the line chart for displaying the temperature value by time and device id.
But I want same line chart with different Color like this below image, whenever temperature value suddenly changes.
Can you please tell me is it possible to develop the Line chart with multiple colors?
If you're willing to consider a vertical bar chart instead of a line chart, you would be able to create a calculation for each row that determines whether the change is significant, potentially by comparing an aggregate of recent measurements to specific thresholds.
Once you do that, you would use this column's value as a legend for your visualization. So if a row has a value of "Significant Positive Change" (or something like that), the bar or bars showing that change can be red.
Your other alternative is to use an R-based visual, of which there are surely examples of this type of visualization. I'll update this answer if I find one that looks promising.
Instead of tending 1 data series, you can split it into 2 data series e.g. one with normal temperatures and one with high temperatures. Then you can just plot these in different colours. Just make sure that the ranges are same i.e. cannot be 'Auto'.
Let's say I have a series of infrared pictures and the task is to isolate human body from other objects in the picture. The problem is a noise from other relatively hot objects like lamps and their 'hot' shades.
Simple thresholding methods like binary and/or Otsu didn't give good results on difficult (noisy) pictures, so I've decided to do it manually.
Here are some samples
The results are not terrible, but I think they can be improved. Here I simple select pixels by hue value of HSV. More or less, hot pixels are located in this area: hue < 50, hue > 300. My main concern here is these pink pixels which sometimes are noise from lamps but sometimes are parts of human body, so I can't simply discard them without causing significant damage to the results: e.g. on the left picture this will 'destroy' half of the left hand and so on.
As the last resort I could use some strong filtering and erosion but I still believe there's a way somehow to told to OpenCV: hey, I don't need these pink areas unless they are part of a large hot cluster.
Any ideas, keywords, techniques, good articles? Thank in advance
FIR data is presumably monotonically proportional (if not linear) to temperature, and this should yield a grayscale image.
Your examples are colorized with a color map - the color only conveys a single channel of actual information. It would be best if you could work directly on the grayscale image (maybe remap the images to grayscale).
Then, see if you can linearize the images to an actual temperature scale such that the pixel value represents the temperature. Once you do this you can should be able to clamp your image to the temperature range that you expect a person to appear in. Check the datasheets of your camera/imager for the conversion formula.
I am trying to write code to calculate the correct exposure time for a camera to capture an image in correct brightness.
what I have is a camera that supply me data in RAW (Bayer raw data) and I can control its exposure time, and I want to control its exposure so when it captured an image, the image is in correct brightness (not too dark (under exposed) or too bright (over exposed).
I think I need an algorithm similar to this:
1-capture a sample image
2-calculate image brightness.
3-calculate correct exposure.
4-capture a new image,
5-check that the image brightness is correct if not go to step 3.
6- capture final image.
My question is:
How can I calculate image brightness?
If I calculate image brightness, how can I calculate exposure? One way of doing this is to do a search (for example start from very fast exposure time increase it till you get a correct exposure, but It is a very time consuming, is there any better way of doing this?)
To do this, I may need to calibrate my camera (as the relationship between brightness and exposure time is different between different sensors), how can I do this?
I am using OpenCV and I can use algorithms which is available in OpenCV (c++)
There are multiple ways to measure the "correct" brightness of the image. A common method is to calculate the intensity histogram and make sure that the values cover the entire range of values, and there is not too much cut-off. I'm not sure if there's a single "one fit all" way for any possible scene.
A faster way than linearly increasing the exposure is to do a binary search, by measuring at low and high exposure, then measuring in the middle, and then continuing to split the sub-range in the middle, until you find the optimum.