From 81567a9d3e61cd83c458b158444b3d7c7536d91e Mon Sep 17 00:00:00 2001
From: hyrodium <hyrodium@gmail.com>
Date: Wed, 26 May 2021 00:21:10 +0900
Subject: [PATCH] fix latex script in the docs

---
 .../py_features_harris/py_features_harris.markdown        | 8 ++++----
 .../py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown     | 4 ++--
 .../py_feature2d/py_sift_intro/py_sift_intro.markdown     | 2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown b/doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown
index e24e692087..60e5686934 100644
--- a/doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown
+++ b/doc/py_tutorials/py_feature2d/py_features_harris/py_features_harris.markdown
@@ -40,12 +40,12 @@ using **cv.Sobel()**).
 Then comes the main part. After this, they created a score, basically an equation, which
 determines if a window can contain a corner or not.
 
-\f[R = det(M) - k(trace(M))^2\f]
+\f[R = \det(M) - k(\operatorname{trace}(M))^2\f]
 
 where
-    -   \f$det(M) = \lambda_1 \lambda_2\f$
-    -   \f$trace(M) = \lambda_1 + \lambda_2\f$
-    -   \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigenvalues of M
+    -   \f$\det(M) = \lambda_1 \lambda_2\f$
+    -   \f$\operatorname{trace}(M) = \lambda_1 + \lambda_2\f$
+    -   \f$\lambda_1\f$ and \f$\lambda_2\f$ are the eigenvalues of \f$M\f$
 
 So the magnitudes of these eigenvalues decide whether a region is a corner, an edge, or flat.
 
diff --git a/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown b/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
index 1229581ce6..c5d29493e4 100644
--- a/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
+++ b/doc/py_tutorials/py_feature2d/py_shi_tomasi/py_shi_tomasi.markdown
@@ -20,7 +20,7 @@ Harris Corner Detector. The scoring function in Harris Corner Detector was given
 
 Instead of this, Shi-Tomasi proposed:
 
-\f[R = min(\lambda_1, \lambda_2)\f]
+\f[R = \min(\lambda_1, \lambda_2)\f]
 
 If it is a greater than a threshold value, it is considered as a corner. If we plot it in
 \f$\lambda_1 - \lambda_2\f$ space as we did in Harris Corner Detector, we get an image as below:
@@ -28,7 +28,7 @@ If it is a greater than a threshold value, it is considered as a corner. If we p
 ![image](images/shitomasi_space.png)
 
 From the figure, you can see that only when \f$\lambda_1\f$ and \f$\lambda_2\f$ are above a minimum value,
-\f$\lambda_{min}\f$, it is considered as a corner(green region).
+\f$\lambda_{\min}\f$, it is considered as a corner(green region).
 
 Code
 ----
diff --git a/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown b/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
index dee4df774a..bbbae6a3e6 100644
--- a/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
+++ b/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.markdown
@@ -156,7 +156,7 @@ sift = cv.SIFT_create()
 kp, des = sift.detectAndCompute(gray,None)
 @endcode
 Here kp will be a list of keypoints and des is a numpy array of shape
-\f$Number\_of\_Keypoints \times 128\f$.
+\f$\text{(Number of Keypoints)} \times 128\f$.
 
 So we got keypoints, descriptors etc. Now we want to see how to match keypoints in different images.
 That we will learn in coming chapters.