I compute a histogram server-side using numpy which gives the histogram data in the arrays bins and edges. How can I show this in highcharts so that the tick-labels showing edges are in-between the columns showing bins? The histogram graph does not work since it computes the histogram on the client side.
Related
I want to extract the peek value from a plot automatically.
I searched web plot digitizer and other programs and packages, however none of them gives points on the plot automatically. Is there any way to achieve this by using image processing such as CNN ?
I am thinking to make custom filters to find peek point.
Thanks in advance.
Sample plot
Algorithm
convert to gray-scale and binarize
find coorditates of a white pixel (x,y) where y is minimal nonzero values
add to y the blob radius y=y+r
make the scale transformation from range [0,image_height] to your range [0,25]
calculate new value of y under the transformation
How can I calculate color moments with skimage? measure.moments provides some moments but I don't know how to calculate mean, variance and skewness in terms of them. For example I can use M00 returned from measure.moments and with dividing it by number of pixels I can obtain mean, but I'm not sure it's the right way.
You can compute sample statistics for the pixels in an image using standard NumPy functions and function in the SciPy statistics package:
numpy.mean for the mean,
numpy.var for the variance,
scipy.stats.skew for the skewness, or
scipy.stats.moment for arbitrary central moments.
You will have to compute these per channel.
I know that we take a 16x16 window of "in-between" pixels around the key point. we split that window into sixteen 4x4 windows. From each 4x4 window, we generate a histogram of 8 bins. Each bin corresponding to 0-44 degrees, 45-89 degrees, etc. Gradient orientations from the 4x4 are put into these bins. This is done for all 4x4 blocks. Finally, we normalize the 128 values you get.
Where they get their value
but I misunderstand where the 128 number get their value from? did it refer to the corresponding magnitude of the orientation value or what?
I would be grateful if anyone describes any numerical example Regards!
In SIFT (Scale-Invariant Feature Transform), the 128 dimensional feature vector is made up of 4x4 samples per window in 8 directions per sample -- 4x4x8 = 128.
For an illustrated guide see A Short introduction to descriptors, and in particular this image, showing 8-direction measurements (cardinal and inter-cardinal) embedded in each of the 4x4 grid squares (center image) and then a histogram of directions (right image):
From your question I believe you are also unclear on what the information inside the descriptor is -- it is called Histograms of Oriented Gradients (HOG). For further reading, Wikipedia has an overview of HOG gradient computation:
Each pixel within the cell casts a weighted vote for an orientation-based histogram channel based on the values found in the gradient computation.
Everything is built on those per-pixel "votes".
I have a photo editing app that is built around Brad Larson's amazing
GPUImage framework.
I need a way to analyze an image's histogram so i can return it's range.
Meaning the real range of "activity".
I need this to improve a tool i have in the app that controls the RGB composite curve.
Is there a way to analyze or retrieve hard numbers from the histogram filter in GPUImage ? any other way to do it?
As I document in the Readme.md for the GPUImageHistogramFilter:
The output of this filter is a 3-pixel-high, 256-pixel-wide image with
the center (vertical) pixels containing pixels that correspond to the
frequency at which various color values occurred. Each color value
occupies one of the 256 width positions, from 0 on the left to 255 on
the right. This histogram can be generated for individual color
channels (kGPUImageHistogramRed, kGPUImageHistogramGreen,
kGPUImageHistogramBlue), the luminance of the image
(kGPUImageHistogramLuminance), or for all three color channels at once
(kGPUImageHistogramRGB).
To get the numerical values for the histogram, have the GPUImageHistogramFilter output to a GPUImageRawDataOutput and grab bytes from that. The result will be a 256x3 (width, height) array of 0-255 values indicating the intensity at each color component. You can ignore the first and last rows, as the values are only present in the center row.
From there, you can analyze the histogram obtained by this operation.
Is there any easy way of finding the median value of a RGB image in OpenCV using C?
In MATLAB we can just extract the arrays corresponding to the three channels and compute median values for each of the arrays by median(median(array)). Finally, the median value of these three medians (for three channels) can be calculated for the final median value.
You can convert the matrix to a histogram via the calcHist function (once for each channel), then calculate the median for a given channel by using the function available here.
Note: I have not tested that linked code, but it should at least give you an idea of how to get started.