So, I have spent the morning working on some histogram plotting routines in Julia – useful if you want to visualize an image histogram, or save it for later use in file format. Now Julia does not come with any native plotting libraries, but there are the likes of Gadfly, and PyPlot. For todays activity I chose PyPlot, which effectively uses the Julia **PyCall** package to call Python’s **matplotlib** directly from Julia (it’s not all a bed of roses though – you do have to make some syntax changes).

Firstly I wanted a function to derive a simple histogram, in the form of a curve. The function **plotHist()** takes an image (grayscale) histogram vector as input, and plots the visual histogram to file: **histogram.png**. This function also has the alternative of plotting to a console window.

function plotHist(hst, show=false) PyPlot.plot(hst) PyPlot.xlabel("Intensities") PyPlot.xlim([0, 256]) PyPlot.yticks([]) if (show) PyPlot.show() end PyPlot.savefig("histogram.png") end

Here is an example of the output produced:

Next, a function** plotRGBhist()** to plot all three components of a colour RGB image in one histogram. This function takes a colour image as input, splits off each of the components, and finds the individual histograms (using **getIMGhist()** ), and then plots them by means of overlaying them, saving the resulting picture of the histogram in **histogramRGB.png**.

function plotRGBhist(img) # Convert image to vector dx, dy, dz = size(img) imgR = img[:,:,1] imgG = img[:,:,2] imgB = img[:,:,3] hstR = getIMGhist(imgR) hstG = getIMGhist(imgG) hstB = getIMGhist(imgB) PyPlot.plot(hstR, color="red", linewidth=2.0, label="Red") PyPlot.plot(hstG, color="green", linewidth=2.0, label="Green") PyPlot.plot(hstB, color="blue", linewidth=2.0, label="Blue") PyPlot.xlabel("Intensities") PyPlot.xlim([0, 256]) PyPlot.yticks([]) PyPlot.legend(fontsize=10) PyPlot.savefig("histogramRGB.png") end

Here is a sample output:

Lastly, a function which takes an alternative approach to plotting the histogram of a grayscale image. In the function **plotIMGhist()**, a grayscale image is input, and the resulting histogram is in the form of a solid histogram, using vertical bars.

function plotIMGhist(img) # Convert image to vector dx, dy = size(img) imgV = vec(reshape(img,1,dx*dy)) common_params = Dict( :bins => 256, :range => (0, 255), :normed => false, :color => "black") PyPlot.yticks([]) PyPlot.xlabel("Intensities") PyPlot.plt[:hist](imgV; common_params... ); PyPlot.savefig("histogramIMG.png") end

The image is first converted to a vector, and then applied using the matplotlib function **hist()**. Note that due to conflicts with Julia’s built-in function **hist()**, in order to use **matplotlib.pyplot.hist**, one has to use **PyPlot.plt[:hist]**. Also notice the use of the Dict expression for the parameters to **hist. **

Here is a sample: