How to change intensity threshold in an image using python

Question

I want to remove the background noise from microscopy images. I have tried different methods (hist equalization and morphological transformation methods) but I got the conclusion the best method is to remove low intensity pixels. I can do this using photoshop: As you can see, figure A is the original one. I have included the histogram, shown in the bottom insert. Applying the transformation in B, I get the desired final image, where background is removed. See the transformation I have applied in the bottom insert from B.

I start working on the python code:

import cv2
import numpy as np
import matplotlib.pyplot as plt


img = cv2.imread('lamelipodia/Lam1.jpg', 1)



#get green channel to gray
img_g = img[:,:,1]

#get histogram
plt.hist(img_g.flatten(), 100, [0,100], color = 'g')

cv2.imshow('b/w',img_g)
#cv2.imwrite('bw.jpg',img_g)
plt.show()
cv2.waitKey(0)
cv2.destroyAllWindows()

I converted the figure to black and white

and got the histogram: Which is similar to the one from photoshop. I have been browsing google and SO but although I found similar questions, I could not find how to modify the histogram as I described.

How can I apply this kind of transformations using python (numpy or openCV)? Or if you think this has been responded before, please let me know. I apologize, but I have been really looking for this.

google "threshold opencv python", click the first hit, read docs.opencv.org/3.3.1/d7/d4d/tutorial_py_thresholding.html, apply :) or maybe I don't understand what your problem is. you sound like a guy who should be able operate google — Piglet
– Piglet, Commented Nov 21, 2017 at 17:03
I saw this page before, but none of the methods helped me... let me read it again and see if this time I am able to find the way. Either way, I will come back here — daniel_hck
– daniel_hck, Commented Nov 21, 2017 at 17:12
Ok, you were right. I used : ret,thresh5 = cv2.threshold(img_g,150,255,cv2.THRESH_TOZERO). Now I understand. If any pixel is minor than 150 make it zero. I guess this is difficult to read from the function, since the THRESH_TOZERO change the meaning of the transformation. I will update the post, so it is clear the result, thanks — daniel_hck
– daniel_hck, Commented Nov 21, 2017 at 17:23
they actually give a visual example for each mode ;) write an answer to your question or delete the question. you should not add solutions to a question — Piglet
– Piglet, Commented Nov 21, 2017 at 17:28
not sure what page you read but the link I gave you shows a specific example for evey mode: input image, output image and even the code that produced the example. but you're right, reading that stuff needs practice and you have to read carefully. — Piglet
– Piglet, Commented Nov 21, 2017 at 17:35

daniel_hck · Accepted Answer · 2017-11-21 17:46:08Z

1

Following Piglet link: docs.opencv.org/3.3.1/d7/d4d/tutorial_py_thresholding.html,the function is needed for the goal is:

ret,thresh5 = cv2.threshold(img_g,150,255,cv2.THRESH_TOZERO)

This is not easy to read. We have to understand as: if any pixel in the image_g is less than 150 then make it ZERO, keep the rest the same value as it was. If we apply this to the image, we get:

The trick on how to read the function is by the added style. For example, cv2.THRESH_BINARY makes it read it as: if any pixel in the image_g is less than 150 then make it ZERO (black), the rest make it 255 (white)

answered Nov 21, 2017 at 17:46

daniel_hck

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