OpenCV treats all white pixels as foreground and the black ones as background. The green contours visualize the detected foreground.
If you wish to highlight the black circle, you'd need to invert the image beforehand. Alternatively you can use the threshold type "THRESH_BINARY_INV" (http://docs.opencv.org/modules/imgproc/doc/miscellaneous_transformations.html?highlight=threshold#threshold).
Answer from Falko on Stack Overflow
If i has understood your goal correctly, here is my proposal. It uses Otsu's Thresholding as @stateMachine has said.
import cv2
im = cv2.imread("IScDg.png")
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
# Your first method
(_, thresh1) = cv2.threshold(gray, 177, 255, cv2.THRESH_BINARY)
# Your second method
(ret, thresh2) = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)
# Proposed method
(_, thresh3) = cv2.threshold(gray, 0, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
different thresholds on your image
I usually apply OTSU with good results.
ret,gray = cv2.threshold(gray,100,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
Your question and comment are contradictory: Keep everything (significantly) brighter/darker than the mean (+/- constant) of the neighbourhood (question) vs. keep everything within mean +/- constant (comment). I assume the first one to be the correct, and I'll try to give an answer.
Using cv2.adaptiveThreshold is certainly useful; parameterization might be tricky, especially given the example image. First, let's have a look at the output:
We see, that the intensity value range in the given image is small. The upper-halfs of the third and sixth' squares don't really differ from their neighbourhood. It's quite unlikely to find a proper difference there. The upper-halfs of squares #8 and #12 (or also the lower-half of square #10) are more likely to be found.
Top row now shows some more "global" parameters (blocksize = 151, c = 25), bottom row more "local" parameters (blocksize = 51, c = 5). Middle column is everything darker than the neighbourhood (with respect to the paramters), right column is everything brighter than the neighbourhood. We see, in the more "global" case, we get the proper upper-halfs, but there are mostly no "significant" darker areas. Looking, at the more "local" case, we see some darker areas, but we won't find the complete upper-/lower-halfs in question. That's just because how the different triangles are arranged.
On the technical side: You need two calls of cv2.adaptiveThreshold, one using the cv2.THRESH_BINARY_INV mode to find everything darker and one using the cv2.THRESH_BINARY mode to find everything brighter. Also, you have to provide c or -c for the two different cases.
Here's the full code:
import cv2
from matplotlib import pyplot as plt
from skimage import io # Only needed for web grabbing images
plt.figure(1, figsize=(15, 10))
img = cv2.cvtColor(io.imread('https://i.sstatic.net/dA1Vt.png'), cv2.COLOR_RGB2GRAY)
plt.subplot(2, 3, 1), plt.imshow(img, cmap='gray'), plt.colorbar()
# More "global" parameters
bs = 151
c = 25
img_le = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, bs, c)
img_gt = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, bs, -c)
plt.subplot(2, 3, 2), plt.imshow(img_le, cmap='gray')
plt.subplot(2, 3, 3), plt.imshow(img_gt, cmap='gray')
# More "local" parameters
bs = 51
c = 5
img_le = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, bs, c)
img_gt = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, bs, -c)
plt.subplot(2, 3, 5), plt.imshow(img_le, cmap='gray')
plt.subplot(2, 3, 6), plt.imshow(img_gt, cmap='gray')
plt.tight_layout()
plt.show()
Hope that helps โ somehow!
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System information
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Python: 3.8.1
Matplotlib: 3.2.0rc1
OpenCV: 4.1.2
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Another way to look at this is that where abs(mean - image) <= c, you want that to become white, otherwise you want that to become black. In Python/OpenCV/Scipy/Numpy, I first compute the local uniform mean (average) using a uniform 51x51 pixel block averaging filter (boxcar average). You could use some weighted averaging method such as the Gaussian average, if you want. Then I compute the abs(mean - image). Then I use Numpy thresholding. Note: You could also just use one simple threshold (cv2.threshold) on the abs(mean-image) result in place of two numpy thresholds.
Input:
import cv2
import numpy as np
from scipy import ndimage
# read image as grayscale
# convert to floats in the range 0 to 1 so that the difference keeps negative values
img = cv2.imread('squares.png',0).astype(np.float32)/255.0
# get uniform (51x51 block) average
ave = ndimage.uniform_filter(img, size=51)
# get abs difference between ave and img and convert back to integers in the range 0 to 255
diff = 255*np.abs(ave - img)
diff = diff.astype(np.uint8)
# threshold
# Note: could also just use one simple cv2.Threshold on diff
c = 5
diff_thresh = diff.copy()
diff_thresh[ diff_thresh <= c ] = 255
diff_thresh[ diff_thresh != 255 ] = 0
# view result
cv2.imshow("img", img)
cv2.imshow("ave", ave)
cv2.imshow("diff", diff)
cv2.imshow("threshold", diff_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()
# save result
cv2.imwrite("squares_2way_thresh.jpg", diff_thresh)
Result:
