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Introduction
I have been developing with PyQT and python-opencv recently, and I just need a fixed threshold for binarization, so I wrote down the following code snippet:
image[image > t] = maxval
image[image <= t] = 0I gave myself full marks for this wave of routine operations. Later, others told me that I could reverse the segmentation according to the threshold, that is, if the value of T is greater than the threshold, it is assigned to 0, and the value of T less than the threshold is assigned to maxval. So, I added an if else operation to it. The code is as follows :
if bin_type == 0:
image[image > t] = maxval
image[image <= t] = 0
else:
image[image > t] = 0
image[image <= t] = 255I feel that this code is written with full marks, so when I debug, I input a grayscale image. As long as I select bin_type=0, the binarization is always performed correctly, and as long as I input bin_type=1, it is all white. After I tested a few images, I felt a little suspicious of life. The code with such good logic was not executed correctly!
1. The truth breaks me down
This question was so depressing at the time, I felt that my IQ had a problem! So I manually wrote the following code:
test = np.zeros((8, 8), dtype=np.uint8)
for i in range(8):
test[i, 0] = i * 32
test[i, 1] = i * 32
test[i, 2] = i * 32
test[i, 3] = i * 32
test[i, 4] = i * 32
test[i, 5] = i * 32
test[i, 6] = i * 32
test[i, 7] = i * 32
print(test)
test[test > 122.2335] = 0
print("output1\n",test)
test[test <= 122.2335] = 255
print("output2\n", test)Print out the result:
I finally solved the case!
It turns out that the first time I assigned image[image>T] = 0, there was no more than T, and then I executed image[image<T]=255, and it turned out to be all 255, of course, it was all white. IQ has been crushed! Later, I remembered that a long time ago, I downloaded someone else's project from github, and his code had similar writing. I remember that when I debugged and found that this code did not work, because the project code was very long at the time, I saved it. The image was found to be incorrect, so I replaced it with the cv.threshod function of opencv-python! I'm dizzy!
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