potok/opencv
1
0
Fork 0
mirror of https://github.com/opencv/opencv.git synced 2026-07-30 15:53:03 +04:00
Code Packages Releases Wiki Activity

Merge pull request #22754 from mshabunin:c-cleanup

Browse Source 
C-API cleanup for OpenCV 5.x (imgproc, highgui)

* imgproc: C-API cleanup

* imgproc: increase cvtColor test diff threshold

* imgproc: C-API cleanup pt.2

* imgproc: C-API cleanup pt.3

* imgproc: C-API cleanup pt.4

* imgproc: C-API cleanup pt.5

* imgproc: C-API cleanup pt.5

* imgproc: C-API cleanup pt.6

* highgui: C-API cleanup

* highgui: C-API cleanup pt.2

* highgui: C-API cleanup pt.3

* highgui: C-API cleanup pt.3

* imgproc: C-API cleanup pt.7

* fixup! highgui: C-API cleanup pt.3

* fixup! imgproc: C-API cleanup pt.6

* imgproc: C-API cleanup pt.8

* imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9

* fixup! imgproc: C-API cleanup pt.9
This commit is contained in:
Maksim Shabunin
2022-12-14 21:57:08 +03:00
committed by GitHub
parent d49958141e
commit 8a62b03761
87 changed files with 1374 additions and 12887 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.markdown
+3 -3
View File
@@ -204,9 +204,9 @@ receives three arguments:
We can choose any of three shapes for our kernel:
- Rectangular box: CV_SHAPE_RECT
- Cross: CV_SHAPE_CROSS
- Ellipse: CV_SHAPE_ELLIPSE
- Rectangular box: Imgproc.SHAPE_RECT
- Cross: Imgproc.SHAPE_CROSS
- Ellipse: Imgproc.SHAPE_ELLIPSE
Together with the shape we specify the size of our kernel and the *anchor point*. If the anchor point is not
specified, it is assumed to be in the center.
doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.markdown
+4 -4
View File
@@ -27,19 +27,19 @@ Theory
(\f$d(H_{1}, H_{2})\f$) to express how well both histograms match.
- OpenCV implements the function @ref cv::compareHist to perform a comparison. It also offers 4
different metrics to compute the matching:
-# **Correlation ( CV_COMP_CORREL )**
-# **Correlation ( cv::HISTCMP_CORREL )**
\f[d(H_1,H_2) = \frac{\sum_I (H_1(I) - \bar{H_1}) (H_2(I) - \bar{H_2})}{\sqrt{\sum_I(H_1(I) - \bar{H_1})^2 \sum_I(H_2(I) - \bar{H_2})^2}}\f]
where
\f[\bar{H_k} = \frac{1}{N} \sum _J H_k(J)\f]
and \f$N\f$ is the total number of histogram bins.
-# **Chi-Square ( CV_COMP_CHISQR )**
-# **Chi-Square ( cv::HISTCMP_CHISQR )**
\f[d(H_1,H_2) = \sum _I \frac{\left(H_1(I)-H_2(I)\right)^2}{H_1(I)}\f]
-# **Intersection ( method=CV_COMP_INTERSECT )**
-# **Intersection ( method=cv::HISTCMP_INTERSECT )**
\f[d(H_1,H_2) = \sum _I \min (H_1(I), H_2(I))\f]
-# **Bhattacharyya distance ( CV_COMP_BHATTACHARYYA )**
-# **Bhattacharyya distance ( cv::HISTCMP_BHATTACHARYYA )**
\f[d(H_1,H_2) = \sqrt{1 - \frac{1}{\sqrt{\bar{H_1} \bar{H_2} N^2}} \sum_I \sqrt{H_1(I) \cdot H_2(I)}}\f]
Code