generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Doxygen tutorials: python final edits

Browse Source
This commit is contained in:
Maksim Shabunin
2014-12-01 15:46:05 +03:00
parent 875f922332
commit 812ce48c36
49 changed files with 426 additions and 353 deletions
Download Patch File Download Diff File Expand all files Collapse all files

7
doc/py_tutorials/py_imgproc/py_thresholding/py_thresholding.markdown
View File

@@ -54,7 +54,9 @@ for i in xrange(6):
plt.show()
@endcode
@note To plot multiple images, we have used plt.subplot() function. Please checkout Matplotlib docs
for more details. Result is given below :
for more details.
Result is given below :
![image](images/threshold.jpg)
@@ -70,7 +72,7 @@ results for images with varying illumination.
It has three ‘special’ input params and only one output argument.
**Adaptive Method** - It decides how thresholding value is calculated.
- cv2.ADAPTIVE_THRESH_MEAN_C : threshold value is the mean of neighbourhood area.
- cv2.ADAPTIVE_THRESH_MEAN_C : threshold value is the mean of neighbourhood area.
- cv2.ADAPTIVE_THRESH_GAUSSIAN_C : threshold value is the weighted sum of neighbourhood
values where weights are a gaussian window.
@@ -229,4 +231,3 @@ Exercises
---------
-# There are some optimizations available for Otsu's binarization. You can search and implement it.