Fix spelling typos

backport commit 659ffaddb4
2019-12-26 06:45:03 -05:00
parent 5e2bcc9149
commit f9c514b391
70 changed files with 89 additions and 89 deletions
@@ -145,7 +145,7 @@ no child, parent is contour-3. So array is [-1,-1,-1,3].
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.

-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.

@@ -17,7 +17,7 @@ In short, we found locations of some parts of an object in another cluttered ima
 is sufficient to find the object exactly on the trainImage.

 For that, we can use a function from calib3d module, ie **cv.findHomography()**. If we pass the set
-of points from both the images, it will find the perpective transformation of that object. Then we
+of points from both the images, it will find the perspective transformation of that object. Then we
 can use **cv.perspectiveTransform()** to find the object. It needs atleast four correct points to
 find the transformation.

@@ -68,7 +68,7 @@ Now we set a condition that atleast 10 matches (defined by MIN_MATCH_COUNT) are
 find the object. Otherwise simply show a message saying not enough matches are present.

 If enough matches are found, we extract the locations of matched keypoints in both the images. They
-are passed to find the perpective transformation. Once we get this 3x3 transformation matrix, we use
+are passed to find the perspective transformation. Once we get this 3x3 transformation matrix, we use
 it to transform the corners of queryImage to corresponding points in trainImage. Then we draw it.
@code{.py}
 if len(good)>MIN_MATCH_COUNT:
@@ -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 conidered as a corner(green region).
+\f$\lambda_{min}\f$, it is considered as a corner(green region).

 Code
 ----
@@ -144,7 +144,7 @@ cv.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)
 ### 7.b. Rotated Rectangle

 Here, bounding rectangle is drawn with minimum area, so it considers the rotation also. The function
-used is **cv.minAreaRect()**. It returns a Box2D structure which contains following detals - (
+used is **cv.minAreaRect()**. It returns a Box2D structure which contains following details - (
 center (x,y), (width, height), angle of rotation ). But to draw this rectangle, we need 4 corners of
 the rectangle. It is obtained by the function **cv.boxPoints()**
@code{.py}
@@ -185,7 +185,7 @@ array([[[ 3, -1,  1, -1],
 And this is the final guy, Mr.Perfect. It retrieves all the contours and creates a full family
 hierarchy list. **It even tells, who is the grandpa, father, son, grandson and even beyond... :)**.

-For examle, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
+For example, I took above image, rewrite the code for cv.RETR_TREE, reorder the contours as per the
 result given by OpenCV and analyze it. Again, red letters give the contour number and green letters
 give the hierarchy order.

@@ -381,7 +381,7 @@ Here is explained in detail the code for the real time application:
    as not, there are false correspondences or also called *outliers*. The [Random Sample
    Consensus](http://en.wikipedia.org/wiki/RANSAC) or *Ransac* is a non-deterministic iterative
    method which estimate parameters of a mathematical model from observed data producing an
-    approximate result as the number of iterations increase. After appyling *Ransac* all the *outliers*
+    approximate result as the number of iterations increase. After applying *Ransac* all the *outliers*
    will be eliminated to then estimate the camera pose with a certain probability to obtain a good
    solution.

@@ -499,7 +499,7 @@ using the following OpenCV methods:
 -   the imwrite static method from the Highgui class to write an image to a file
 -   the GaussianBlur static method from the Imgproc class to apply to blur the original image

-We're also going to use the Mat class which is returned from the imread method and accpeted as the
+We're also going to use the Mat class which is returned from the imread method and accepted as the
 main argument to both the GaussianBlur and the imwrite methods.

 ### Add an image to the project
@@ -10,7 +10,7 @@ In this tutorial,
 -   We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers
 -   We will use the @ref cv::CascadeClassifier class to detect objects in a video stream. Particularly, we
    will use the functions:
-    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifer
+    -   @ref cv::CascadeClassifier::load to load a .xml classifier file. It can be either a Haar or a LBP classifier
    -   @ref cv::CascadeClassifier::detectMultiScale to perform the detection.

 Theory
@@ -168,7 +168,7 @@ Command line arguments of opencv_traincascade application grouped by purposes:
  - `-w <sampleWidth>` :  Width of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).
  - `-h <sampleHeight>` : Height of training samples (in pixels). Must have exactly the same value as used during training samples creation (opencv_createsamples utility).

- Boosted classifer parameters:
+- Boosted classifier parameters:
  - `-bt <{DAB, RAB, LB, GAB(default)}>` : Type of boosted classifiers: DAB - Discrete AdaBoost, RAB - Real AdaBoost, LB - LogitBoost, GAB - Gentle AdaBoost.
  - `-minHitRate <min_hit_rate>` : Minimal desired hit rate for each stage of the classifier. Overall hit rate may be estimated as (min_hit_rate ^ number_of_stages), @cite Viola04 §4.1.
  - `-maxFalseAlarmRate <max_false_alarm_rate>` : Maximal desired false alarm rate for each stage of the classifier. Overall false alarm rate may be estimated as (max_false_alarm_rate ^ number_of_stages), @cite Viola04 §4.1.
@@ -43,7 +43,7 @@ VideoCapture can retrieve the following data:
    -   CAP_OPENNI_POINT_CLOUD_MAP - XYZ in meters (CV_32FC3)
    -   CAP_OPENNI_DISPARITY_MAP - disparity in pixels (CV_8UC1)
    -   CAP_OPENNI_DISPARITY_MAP_32F - disparity in pixels (CV_32FC1)
-    -   CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not ocluded, not shaded etc.)
+    -   CAP_OPENNI_VALID_DEPTH_MASK - mask of valid pixels (not occluded, not shaded etc.)
        (CV_8UC1)

 -#  data given from BGR image generator: