I am trying to plot the contour of an image and get it overlaid over the original image but without filling, I would like it to appear as an edge contour instead of a filled contour like the attached picture.
I used this command but the problem is when I used the LabelOverlay function the image contrast changed! while I need to keep the same image intensity, any idea of how to solve it? The code is : sitk_show(SimpleITK.LabelOverlay(imgOriginal1, SimpleITK.LabelContour(imgOriginal2)))
I would encourage you to check out platipy - a software package for which I am a developer and have built some nice tools for visualisation.
Here is an example:
import SimpleITK as sitk
from platipy.imaging import ImageVisualiser
img = sitk.ReadImage("./CT.nii.gz")
mask = sitk.ReadImage("./MASK_LUNGS.nii.gz")
vis = ImageVisualiser(img)
vis.add_contour(mask)
fig = vis.show()
fig.savefig("example.jpeg", dpi=300)
This tool is highly customisable, check out the documentation on Github :-)
Related
I am using the following code to find the spectrogram of a signal and save it.
spec,freq,t,im = plt.specgram(raw_signal,Fs=100,NFFT=100,noverlap=50)
plt.axis('off')
figure = plt.gcf()
figure.set_size_inches(12, 1)
plt.savefig('spectrogram',bbox_inches = 'tight',pad_inches=0)
But I have multiple spectrograms like this and the end product I need is a concatenation of all these. Right now, what I am doing is, I am saving all these individual images using plt.savefig() as earlier and reading them back using cv2.imread() and concatenating them. But this process is not very good I think. So is there any other way I can do this without saving it and re-reading it?
One possible idea I have is, somehow converting matplotlib.figure.Figure into a format that can be handled by OpenCV (specifically cv2). However, it should also not have white padding.
You can get the image as an array using buffer_rgba (don't forget to draw the image first). Then in OpenCV, you need to convert the image from RGB to OpenCV's BGR channel ordering.
import matplotlib.pyplot as plt
import numpy as np
import cv2
raw_signal = np.random.random(1000)
spec,freq,t,im = plt.specgram(raw_signal,Fs=100,NFFT=100,noverlap=50)
plt.axis('off')
figure = plt.gcf()
figure.set_size_inches(12, 1)
figure.set_dpi(50)
figure.canvas.draw()
b = figure.axes[0].get_window_extent()
img = np.array(figure.canvas.buffer_rgba())
img = img[int(b.y0):int(b.y1),int(b.x0):int(b.x1),:]
img = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
cv2.imshow('OpenCV',img)
Top: matplotlib, bottom OpenCV:
don't save the figure. matplotlib happens to have a convenience function for displaying time series data in this way but that's not how you deal with spectrograms. any handling of spectrogram "pictures" is a kludge.
use scipy.signal.spectrogram to get the actual spectrogram.
I am following the Semantic Segmentation Examples tutorial by MathWorks.
I understand that I can load pixel labeled images
pxDir = fullfile(dataDir,'buildingPixelLabels');
Define the class names.
classNames = ["sky" "grass" "building" "sidewalk"];
Define the label ID for each class name.
pixelLabelID = [1 2 3 4];
and create a pixelLabelDatastore.
pxds = pixelLabelDatastore(pxDir,classNames,pixelLabelID);
But, how do I create a custom pixel labelled image where every pixel value represents the categorical label of that pixel?
I would then proceed by writing:
pxDir = fullfile(dataDir,'myCustomPixelLabels');
If I understood correctly, imageDatastore holds the actual image and not the pixel labels for that image.
EDIT:
On my system pxDir points to 'C:\Program Files\MATLAB\R2017a\toolbox\vision\visiondata\buildingPixelLabels'. Since I am on Matlab2017a so this example is not included by default, and I cannot compare or view the file to get a better understanding of what I need to do to reproduce this example.
The answer can be found here.
Matlab 2017a
Go to the APPS tab, and search for Image Labeler, or Training Image Labeler
Click on Add Images to add your training images.
Click on Add ROI Labels to add class names for the regions of interest.
Proceed to select the regions of interest manually from the uploaded images.
Once areas are selected, the data can be exported to workspace as a Ground Truth object.
I have a image like below,
I would like to remove background watermark.
So far I tried, inpainting method in opencv. It didn't help me.
I tried following script:
edges = cv2.Canny(img,50,150,apertureSize = 3)
dst = cv2.inpaint(img,edges,3,cv2.INPAINT_TELEA)
I am new to image processing and opencv. So, I don't know whether I'm doing in the correct way or not for performing inpainting. What method should I do for removing background watermarks.
I would like to remove green quoted watermark from my image.
any help would be more appreciable.
Text here has a different intensity than the watermark. You could play around with a simple brightness/contrast transformation, i.e. increasing gain/contrast until the watermark vanishes and reducing brightness to compensate.
See OpenCV docs for a simple tutorial.
Here's a quick attempt in Python, not really using OpenCV because it's not needed IMHO for such a simple linear transformation. Play around with alpha (contrast) and beta (brightness) parameters until you get the result you want
import cv2
import numpy as np
img = cv2.imread("veidz.jpg")
alpha = 2.0
beta = -160
new = alpha * img + beta
new = np.clip(new, 0, 255).astype(np.uint8)
cv2.imwrite("cleaned.png", new)
ImageJ has a nice contour plot plugin that might be downloaded from this link: http://imagej.nih.gov/ij/plugins/contour-plotter.html , it works fine and easily.
However, saving the image including the contour plot is not trivial, Using the save or save as options will save the image without the contour. It sound that the problem is in my understanding of how to merge the contour layer with the image layer.
I would be thankful if you can instruct me how to save the contour plot and the image in one file rather than taking a snapshot of the screen.
According to the source contained in Contour_Plotter.jar, the contours are drawn directly onto the Graphics object of the parent ImageCanvas:
private ImageCanvas Parent_Canvas;
private Graphics Parent_Graphics;
[...]
Parent_Graphics = Parent_Canvas.getGraphics();
[...]
Parent_Graphics.drawLine(
(int)(Parent_Canvas.screenX((int)(pts[pt_right][X]+0.5))),
(int)(Parent_Canvas.screenY((int)(pts[pt_right][Y]+0.5))),
(int)(Parent_Canvas.screenX((int)(pts[pt_at][X]+0.5))),
(int)(Parent_Canvas.screenY((int)(pts[pt_at][Y]+0.5))));
It seems that the plugin was written before ImageJ had introduced the concept of Overlays, and apparently it has not been maintained for a decade now. You can write to the ImageJ mailing list however and ask if the plugin is still maintained.
Unless you want to take a screenshot of the current image and save this, I would suggest you use a different approach using a sequence such as:
set a threshold (Image > Adjust > Threshold...)
convert it to a selection (Edit > Selection > Create Selection)
draw the contour of the selection (Edit > Draw) or add it as a new overlay (Image > Overlay > Add Selection...)
You can automate this process for various intensity levels (i.e. threshold values) using the macro recorder, see also this tutorial.
I'm having an issue with attempting to save some plots with transparent ellipsoids on them if I attempt to save them with .ps/.eps extensions.
Here's the plot saved as a .png:
If I choose to save it as a .ps/.eps here is what it looks like:
How I got around this, was to use ImageMagick to convert the original png to a ps. The only problem is that the image in png format is about 90k, and it becomes just under 4M after conversion. This is not good since I have a lot of these images, and it will take too much time to compile my latex document. Does anyone have a solution to this?
The problem is that eps does not support transparencies natively.
There are few options:
rasterize the image and embed in a eps file (like #Molly suggests) or exporting to pdf and converting with some external tool (like gs) (which usually relies as well on rasterization)
'mimic' transparency, giving a colour that looks like the transparent one on a given background.
I discussed this for sure once on the matplotlib mailing list, and I got the suggestion to rasterize, which is not feasible as you get either pixellized or huge figures. And they don't scale very nicely when put into, e.g., a publication.
I personally use the second approach, and although not ideal, I found it good enough. I wrote a small python script that implements the algorithm from this SO post to obtain a solid RGB representation of a colour with a give transparency
EDIT
In the specific case of your plot try to use the zorder keyword to order the parts plotted. Try to use zorder=10 for the blue ellipse, zorder=11 for the green and zorder=12 for the hexbins.
This way the blue should be below everything, then the green ellipse and finally the hexbins. And the plot should be readable also with solid colors. And if you like the shades of blue and green that you have in png, you can try to play with mimic_alpha.py.
EDIT 2
If you are 100% sure that you have to use eps, there are a couple of workarounds that come to my mind (and that are definitely uglier than your plot):
Just draw the ellipse borders on top of the hexbins.
Get centre and amplitude of each hexagon, (possibly discard all zero bins) and make a scatter plot using the same colour map as in hexbin and adjusting the marker size and shape as you like. You might want to redraw the ellipses borders on top of that
Another alternative would be to save them to pdf
savefig('myfigure.pdf')
That works with pdflatex, if that was the reason why you needed to use eps and not svg.
You can rasterize the figure before saving it to preserve transparency in the eps file:
ax.set_rasterized(True)
plt.savefig('rasterized_fig.eps')
I had the same problem. To avoid rasterizing, you can save the image as a pdf and then run (on unixish systems at least) in a terminal:
pdftops -eps my.pdf my.eps
Which gives a .eps file as output.
I solved this by:
1) adding a set_rasterization_zorder(1) when defining the figure area:
fxsize=16
fysize=8
f = figure(num=None, figsize=(fxsize, fysize), dpi=180, facecolor='w',
edgecolor='k')
plt.subplots_adjust(
left = (18/25.4)/fxsize,
bottom = (13/25.4)/fysize,
right = 1 - (8/25.4)/fxsize,
top = 1 - (8/25.4)/fysize)
subplots_adjust(hspace=0,wspace=0.1)
#f.suptitle('An overall title', size=20)
gs0 = gridspec.GridSpec(1, 2)
gs11 = gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs0[0])
ax110 = plt.Subplot(f, gs11[0,0])
f.add_subplot(ax110)
ax110.set_rasterization_zorder(1)
2) a zorder=0 in each alpha=anynumber in the plot:
ax110.scatter(xs1,ys1 , marker='o', color='gray' , s=1.5,zorder=0,alpha=0.3)#, label=label_bg)
and
3) finally a rasterized=True when saving:
P.savefig(str(PLOTFILENAME)+'.eps', rasterized=True)
Note that this may not work as expected with the transparent keyword to savefig because an RGBA colour with alpha<1 on transparent background will be rendered the same as the RGB colour with alpha=1.
As mentioned above, the best and easiest choice (if you do not want to loose resolution) is to rasterized the figure
f = plt.figure()
f.set_rasterized(True)
ax = f.add_subplot(111)
ax.set_rasterized(True)
f.savefig('figure_name.eps',rasterized=True,dpi=300)
This way, you can manage the size by dpi option as well. In fact, you can also play with the zorder below you want to apply the rasterization:
ax.set_rasterization_zorder(0)
Note: It is important to keep f.set_rasterized(True) when you use plt.subplot and plt.subplot2grid functions. Otherwise, label and tick area will not appear in the .eps file
My solution is to export the plot as .eps, load it up to Inkscape for example, then Ungroup the plot, select the object that I want to set the transparency and just edit the Opacity of the Fill in the "Fill and Stroke" tab.
You can save the file as .svg if you want to tweak it later, or export the image for a publication.
If you are writing the academic paper in latex, I would recommend you export the .pdf file rather than .eps. The .pdf format supports transparency perfectly and has good compression efficiency, and most importantly, can be easily edited in Adobe Illustrator.
If you wanna further edit the graph (NOT EDITING DATA! I MEAN, FOR GOOD-LOOKING), you could open the exported graph, in Adobe Acrobat - Edit - Copy elements into Adobe Illustrator. The two software can handle everything perfectly.
I work happily with this method. Everything clear, editable and small-size. Hope can help.