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)
Related
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 :-)
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.
The MATLAB code I wrote is:
FigHandle = figure;
set(FigHandle, 'Position', [0, 0, 1060, 140]);
load('velocity0.dat')
v=reshape(velocity0,106,14)
vt=transpose(v)
pim=imagesc(vt) ;
set(gca,'XTickLabel',{'1','2','3','4','5','6','7','8','9','10'}, 'FontSize',11)
set(gca,'YTickLabel',{'0.15','0.35','0.55','0.75','0.95','1.15','1.35'}, 'FontSize',11)
xlabel('distance(km)')
ylabel('depth(km)')
C = colorbar('location','EastOutside');
caxis([2928,5553])
set(get(C,'XLabel'),'String','velocity(m/s)', 'FontSize',11)
I am trying to finish my paper for a scientific journal. The reviewers have asked for new images. However, I have not worked with the code for more than a year and I do not have MATLAB installed at my new job. As such, I asked my friend and got this image below.
Previously, the image looked like this in the past:
Should I change the code to get desired colors? Is this a colorbar issue?
Your friend is using R2014b or later where the default colour map is the parula colour map. The results you have previously are using the default colour map before R2014b, which is the jet colour map. As mentioned in the documentation regarding the default colour map:
colormap default uses the default colormap, which is the parula colormap with 64 colors. Versions of MATLAB® prior to R2014b use jet as the default.
Therefore if you want to display results in this colour map in versions of MATLAB that are R2014b or later, simply calling:
colormap jet;
... at the very end of your code will solve your problem. However, don't be tripped out with the colours. What you're concerned about is whether your data follows the distribution of the colour bar on the right. The colours are primarily just for visual display.
I am trying to display images with only builtin functions, and there are plenty of Tkinter examples online. However, none of the libraries work:
import Image # none of these exist.
import tkinter.Image
import _tkinter.Image
etc
However, tkinter does exist, a hellow-world with buttons worked fine.
I am on a MacBook pro 10.6.8 and using PyCharm.
Edit: The best way so far (a little slow but tolerable):
Get the pixel array as a 2D list (you can use a third-party .py to load your image).
Now you make a data array from the pixels like this (this is the weirdest format I have seen, why not a simple 2D array?). This may be sideways, so you may get an error for non-square images. I will have to check.
Imports:
from tkinter import *
import tkinter
data = list() # the image is x pixels by y pixels.
y = len(pixels)
x = len(pixels[0])
for i in range(y):
col_str.append('{')
for j in range(x):
data.append(pixels[i][j]+" ")
data.append("} ")
data = "".join(data)
Now you can create an image and use put:
# PhotoImage is builtin (tkinter).
# It does NOT need PIL, Pillow, or any other externals.
im = PhotoImage(width=x, height=y)
im.put(col_str)
Finally, attach it to the canvas:
canvas = tkinter.Canvas(width=x, height=y)
canvas.create_image(x/2, y/2, image=GLOBAL_IMAGE) # x/2 and y/2 are the center.
tK.mainloop() # enter the main loop and it will be drawn.
Image must be global or else it may not show up because the garbage collector gets greedy.
PIL hasn't been updated since 2009, with Python 3 support being terminally stuck at "later."
Instead, try pillow, which has forked PIL and provides Python 3 support.
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.