The line chart view below has too many data points and I'd like to reduce the frequency or smooth the line so that it's more readable, I can't find anything in the Charts documentation on this issue, is it possible?
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I am trying to figure out how to create this overlaid plot of time-series data, where one of the series should "look" like a histogram.
The problem is I could not figure out how to combine/overlay a histogram with time series data and line/scatter plot and get the histogram xbins to work with the date time data, etc.
So I was also trying to use a bar chart, and create a "pseudo histogram" by removing the gaps between bars, adding outlines, and so forth but that seems fruitless as I don't see a way to control all the borders/lines to that level of control.
The result I am looking for is roughly like so;
Which to me looks like the best match for a plot type should be a histogram, but again I could not figure out how to make that work overlaid with the same x axis as the line/scatter time-series data.
Can anyone offer ideas or point me to an example that might help me understand how to do this ?
I guess I also need to figure out how to align the y-axis scales of the two series also, but that I expect is a different topic...
I am specifically using plotly.js / Javascript
I'm trying to highlight a portion of a scatter/line plot, but using separate plots for each highlight and get them overlaid on top of the original plot is not working for me because the original plot is doing cubic curve smoothing and I need more points in the highlight than required for the curve to fit the one in the back.
I haven't found any delegate/data source way of specifying a line style for a given range in the documentation. Is there a way of achieving this?
If not possible, is my approach of multiple plots the way to go or is there something else you'd recommend?
There is no way to specify different line styles for separate data ranges. Your solution of multiple plots is the right one, although as you discovered, it won't work with smoothed lines.
You could do the smoothing yourself by turning off the curved line interpolation and adding additional plot points between the known data points. Then you would know where to separate the data for the individual plots.
I tear up one paper, and its fragments like :
and I want to make these fragments of the paper to recover.
I have done something by using opencv:
take pictures of these fragments and save them into computer disk
read each fragments and get their contour lines
calculate the similarity for each contour lines of the fragment
???
What I should do if I want to recover these fragment to be one whole "paper" which like the paper don't tear up?
Any suggestions or methods would be appreciated, thank you very much!
Although this is like a search problem; due to infinite numbers of states coming from number of numerous points and orientations, it is impossible to implement something like depth first search. You need a proper vision algorithm to reduce these number of states.
How did you compare the "contour lines" ? Where are these lines ? Your contours might be circular, and could have a very big number of "lines". You should look for "how can I create this contour, joining some parts of other contours"
"is this perimeter has a similarity with another one ? which part of the perimeters have highest similarity ?"
Both of these questions yield to one solution that come to my mind, solving it human-way: Select a contour. Connect another one from some point. Rotate it until it hits. If there is a black space between, change the connection point. Try all points. If you find a connection point yielding to "no black space", connect these contours into one contour and continue the operation. If there exist no point, try another contour.
EDIT: Although this method does nothing but searching; it decreases the number of states with black space (contour finding) and hitting (coordinate comparison)
ENHANCEMENT: Use contour finding method with "simple approximation" (CV_APPROX_SIMPLE) to deduce the straight lines of the paper. You can then reduce the number of points you are going to try, by noting these points as paper sides.
EDIT2 : You may want to look at corner detection and stitching methods:
http://tobw.net/index.php?cat_id=2&project=Panorama%20Stitching%20Demo%20in%20Matlab,
http://docs.opencv.org/modules/stitching/doc/introduction.html
Is there anything faster than sliding window? I tried sort of binary search with overlapping rectangles - it kinda works but sometimes cuts off part of the blob (expected, right) - see the video in http://juick.com/lurker/2142051
Binary search makes no sense, because it is an algorithm for searching for specific values in a sorted structure.
Unless you have some apriori knowledge about the image, you need to check all possible locations, which is the sliding window method you suggested.
Chris is correct, unless you can say something about the statistics of the surrounding regions, e.g., "certain arrangements of pixels around the spot I'm looking for are unlikely". Note, this is different from saying "will never happen", and any algorithm based on statistical approaches will have an associated probability of (wrong box found).
If you think the statistics of the larger regions around your desired location might be informative, you might be able to do some block-processing on larger blocks before doing the fine-level sliding window. For example, if you can say with high probability that a certain 64 x 64 region doesn't contain the max, then, you can throw out a lot of [64 x 64] pixel regions, with 32 pixel overlap using (maybe) only a few features.
You can train something like AdaBoost to do this. See the classic Viola-Jones work which does this for face-detection http://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework
If you absolutely need the maxima location, then like Chris said, you need to search everywhere.
In my application I am getting images (captured by a high speed camera) containing projections of some light sources on the screen.
1-My first task is to plot a PDF or intensity distribution plot for the light intensity, which should come as bell shape or Gaussian, since at the center the light intensity will be maximum and at the ends it will be diminishing. Like this(just for example, not the exact case for me):
In worst cases I will be having a series of light sources illuminated simultaneously. In such cases theoretically I should get overlapping bell or Gaussian curves, some what like this:
How do I plot such a curve given the Images of light projection (like the one in the figure)?
2-After the Gaussian curve is drawn, the next job is to analyze the same such as finding width and height of the curve. How do I go for this?
I want an executable for this application, so a solution given by MATLAB or similar tool is not acceptable to my client. Also i want the solution to work in real time or near real time.
I guess OpenCV can be used here. But before I start I would like to know opinions of Image processing gurus on this forum. Especially for the step -1 above, I need some inputs.
Any pointers here?
Rgrds,
Heshsham
Note: Image is taken from http://pentileblog.com.
To get the 1D Gaussian out of the 2D one, you can do a couple of things depending on what you want exactly.
- You could sum over every column of the image;
- You could find the local maximum in intensity and copy the intensity profile of that row of the image only;
- You could threshold the image (in case your maximum will be saturated and therefore a plateau), determine the center of gravity of the remaining blob, and copy that row's intensity profile;
- You could threshold, find contours, determine multiple local maxima, and grab multiple intensity profiles if the application calls for it (e.g. if the blobs are not horizontally aligned).
To get the height and width, it's pretty easy, just find the maximum and the points left and right of it where the curve drops to half of the maximum. The standard deviation is the distance between the two points divided by 2.35 (wikipedia link).
Well I solved it:
Algorithms is as follows:
1-use cvSampleLine for reading a particual line of image
2- use cvMinMaxLoc to know the maximum pixel value in a line
3- Note which of these lines is having highest pixel value. Lets say line no. 150
4- Plot pixel value for line 150.
I used MATLAB for verifying my results and graphs, and the OpenCV result is exactly the same.
Thanks for your suggestions guys.