Counting black pixels by thresholding

Python
,
1

I’d like to count the black pixels and the percentage of those through setting up an automated threshold, so that whichever image I upload, it can calculate the ratio of black pixels. I’ve started with THRESH_BINARY+THRESH_TRIANGLE thresholds, but other types of threshold algorithms may be suitable (I haven’t tried it yet). I have a sample image that I obtained by a software and I know its black-pixel ratio as 10%

f01
f01808×854 20.8 KB
.

The problem is I get 6% for 10%-black pixel ratio image. The code is like this:

img = cv2.imread('f01.jpeg',0)

# thresholding
ret,th = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_TRIANGLE)

# find number of black pixels
white_pix = cv2.countNonZero(th)

# find total number of pixels
tot_pix = img.size

# find number of white pixels
black_pix = tot_pix-white_pix

# calculate black pixel ratio        
ratio = black_pix/tot_pix
2

what is the value of ret?
what is the value of img.size?
what is the value of img.shape?

3

Hi,

How do you get this result?

4

and why do you think your picture contains 10% black pixels? what makes you sure about that?

5

I made this image with a sofware so that it has 10% black phase.

6

Actually, this code calculates as 6%. It should calculate as 10% because I created this image with 10% black phase in another software.

7

ret: 253.0
img.size: 690032
img.shape: (854, 808)

8

you read a jpg image it’s not a black white image, it’s a grey image. Can you post original image?

9

This is original image from software. Maybe I can save it in different format. Or, I don’t know how, maybe I can convert this image to black white image.

10

that picture simply DOES NOT have 10% black area.

the code isn’t at fault really. the threshold calculated to be 253 (near perfect white) is bad however. since your picture has some shades of gray, that’ll give a slightly different result compared to if you had used threshold 128 or something similarly “gray”.

there’s really no reason to use any “adaptive” method. just set the threshold to a fixed 128 and it’s going to be good.

11

Doesn’t suppose to be… I’ll check it anyway :thinking: