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Introduction to OpenCV putText()Start Your Free Software Development Course
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It is also needed that the starting point for the text has to be defined within the matrix. there is also a requirement for defining the font color for the text, the font style for the text, and the weight that the text has two be specified with. The function is present in the OpenCV library of Python programming language, which is a one-stop solution that has been designed in order to solve problems related to computer vision.
Syntax for OpenCV putText()
cv2.put Text (* image *, text *, org *, font *, fontScale *, color [*, thickness [,line Type [*,* bottom Left Origin *] *] *] *) * Parameters for OpenCV put text functionThe following parameters are accepted by the OpenCV putText() function:
Parameters Description of the parameters:
image this parameter represents the original image that the user has selected to add text by the system
text This parameter represents the text that has to be drawn by the system as specified by the user
org This parameter is used to represent the coordinate with respect to the text present on the bottom left corner of the image. These coordinates are represented with the help of 2 double values that are passed, which represent the X coordinate and the Y coordinate values, respectively
font This parameter is used to represent the type or style of font, which would be denoted for the string text that the user specifies. some instances for the kinds of font types that can be used are FONT * _ * HERSHEY * _ * PLAIN OR FONT * _ * HERSHEY * _ * SIMPLEX
font * scale This parameter represents the best size for the specified font size, which is relevant to the font scale factor, which acts as a multiplying factor further font size of the text that has to be entered
thickness this parameter represents the thickness that has to be given for the line of text that has to be entered by the user. it is measured in terms of pixel size
color this parameter represents the specific color that has to be given to the text string that is being entered into the image that is being drawn on the screen. The color is extracted from the BGR tuple, which is passed to it. for instance, for a text of blue color the tuple to be passed would be * (* 255 *, * 0 *, * 0) *
Line * Type This parameter is used to define the type of line used for the text, which has to be entered into the image. This parameter is an optional parameter.
Bottom * Left * Origin this parameter is used for defining the position for the image data origin with respect to the directional position in the image. this parameter is an optional parameter. If the parameter is taken as true, the image data origin is found to be placed at the bottom left corner of the image. If it is not true, the image data origin is placed at the top left corner of the image.
Return * Value This method is responsible for returning an output image that is to be loaded from the file which has been specified.
Example of OpenCV putText()Following is the example which is used in order to demonstrate how the OpenCV putText() command is utilized in the Python programming language
# command used to import the OpenCV library to utilize OpenCV read image function import cv2 # path being defined from where the system will read the image path = r'C:Users Priyanka Desktop educba OpenCV edu cba logo.png' # command used for reading an image from the disk disk, cv2.imread function is used image1 = cv2.imread(path) # Window name being specified where the image will be displayed window_name1 = 'image' # font for the text being specified font1 = cv2.FONT_HERSHEY_SIMPLEX # org for the text being specified org1 = (50, 50) # font scale for the text being specified fontScale1 = 1 # Blue color for the text being specified from BGR color1 = ( 255 , 0 , 0 ) # Line thickness for the text being specified at 2 px thickness1 = 2 # Using the cv2.putText() method for inserting text in the image of the specified path image_1 = cv2.putText(image1, 'EDU CBA', org1, font1, fontScale1, color1, thickness1, cv2.LINE_AA) # Displaying the output image cv2.imshow(window_name, image_1) cv2.waitKey(0) cv2.destroyAllWindows() ConclusionThe OpenCV putText() method is a very useful function present in the OpenCV library, which allows the system to add text to an image that the user has provided. There are several image processing areas where text needs to be associated with the images that are being processed, and there needs to be a variety in the color, font style, width, and orientation in terms of the position whether the text has to be placed on the image which can easily be utilized by using the put text method. It also reduces the verbosity of the program that is being written and increases the overall processing speed for the program to be executed.
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Learn The Latest Versions Of Pyspark
Introduction to PySpark version
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Versions of PySparkMany versions of PySpark have been released and are available to use for the general public. Some of the latest Spark versions supporting the Python language and having the major changes are given below :
1. Spark Release 2.3.0This is the fourth major release of the 2.x version of Apache Spark. This release includes a number of PySpark performance enhancements including the updates in DataSource and Data Streaming APIs.
Improvements were made regarding the performance and interoperability of python by vectorized execution and fast data serialization.
A new Spark History Server was added in order to provide better scalability for the large applications.
register* for UDFs in SQLContext and Catalog was deprecated in PySpark.
Python na.fill() function now also accepts boolean values and replaces the null values with booleans (in previous versions PySpark ignores it and returns the original DataFrame).
In order to respect session timezone, timestamp behavior was changed for the Panda related functionalities.
From this release, Pandas 0.19.2 or upper version is required for the user to use Panda related functionalities.
Many documentation changes and the test scripts were revised in this release for the Python language.
2. Spark Release 2.4.7This was basically the maintenance release including the bug fixes while maintaining the stability and security of the ongoing software system. Not any specific and major feature was introduced related to the Python API of PySpark in this release. Some of the notable changes that were made in this release are given below:
Now loading of the job UI page takes only 40 sec.
Python Scripts were changes that were failing in certain environments in previous releases.
Now users can compare two dataframes with the same schema (Except for the nullable property).
In the release DockerFile, R language version is upgraded to 4.0.2
Support for the R less than 3.5 version is dropped.
Exception messages at various places were improved.
Error messages were locked when failing in interpreter mode.
Many changes were made in the documentation for the inconsistent AWS variables.
3. Spark Release 3.0.0This is the first release of 3.x version. It brings many new ideas from the 2.x release and continues the same ongoing project in development. It was officially released in June 2023. The top component in this release is SparkSQL as more than 45% of the tickets were resolved on SparkSQL. It benefits all the high level APIs and high level libraries including the DataFrames and SQL. At this stage, Python is the most widely used language on Apache Spark. Millions of users downloaded Apache Spark with the Python language only. Major changes and the features that were introduced in this release are given below:
In this release functionality and usability is improved including the redesign of Pandas UDF APIs.
Various Pythonic error handling were done.
Python 2 support was deprecated in this release.
PySpark SQL exceptions were made more pythonic in this release.
Various changes in the test coverage and documentation of Python UDFs were made.
For K85 Python Bindings, Python 3 was made as the default language.
Validation sets were added to fit with Gradient Boosted trees in Python.
Parity was maintained in the ML function between Python and Scala programming language.
Various exceptions in the Python UDFs were improved as complaints by the Python users.
Now a multiclass logistic regression in PySpark correctly returns a LogisticRegressionSummary from this release.
4. Spark Release 3.0.1
Double catching was fixed in KMeans and BiKMeans.
Apache Arrow 1.0.0 was supported in SparkR.
For the overflow conditions, silent changes were made for timestamp parsing.
Revisiting keywords based on ANSI SQL standard was done.
Regression was done in handling the NaN values in Sql COUNT.
Changes were made for the Spark producing incorrect results in group by clause.
Grouping problems were resolved as per the case sensitivity in panda UDFs.
MLlibs acceleration docs were improved in this release.
Issues related to the LEFT JOIN found in the regression of 3.0.0 producing unexpected results were resolved.
5. Spark Release 3.1.1
Spark Release 3.1.1 would now be considered as the new official release of Apache Spark including the bug fixes and new features introduced in it. Though it was planned to be released in early January 2023, there is no official documentation of it available on its official site as of now.
ConclusionAbove description clearly explains the various versions of PySpark. Apache Spark is used widely in the IT industry. Python is a high level, general purpose and one of the most widely used languages. In order to implement the key features of Python in Spark framework and to use the building blocks of Spark with Python language, Python Spark (PySpark) is a precious gift of Apache Spark for the IT industry.
Recommended ArticlesThis is a guide to PySpark version. Here we discuss Some of the latest Spark versions supporting the Python language and having the major changes. You may also have a look at the following articles to learn more –
Learn The Syntax Of Javascript Tofixed
Introduction to JavaScript tofixed
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In this article, we will study how we can maintain the uniformity in representing the decimal valued numbers representation up to specific precision according to our need using the toFixed() method of javascript. Let us first study the syntax of this method.
Syntax of JavaScript tofixedBelow is the syntax mentioned:
retrievedNumber = numObj.toFixed([digits]) 1. digitsIt represents the number of digits you want to maintain after decimal point while displaying and manipulating the numeric object. It can be any value between 0 to 20. If not mentioned then by default it is considered as zero and the resultant numeric object will be an integral number.
2. retrievedNumberIt is the return value of this method which is the string representation of the passed numeric object following the fixed-point notation.
3. ExceptionsThe toFixed() method may throw two exceptions which are range error and type error. Range error is caused when the number object we are using to call toFixed() method is either too large or too small. While the type error occurs when the used numeric object is not in the number format such as alphanumeric string or some symbolic values or special characters.
ToFixed() method generally returns the string representation of the passed numeric object containing the same number of digits passed as a parameter to it in the non-exponential format. The resultant value is rounded if necessary and extra zeroes are padded if necessary to return the number with specific digits. When the absolute value of the numeric object used to call toFixed() method exceeds the 1e+21, then javascript automatically calls Number.prototype.toString() method to convert the final string in exponential format.
How to Use tofixed in JavaScript?Code:
function toFixedWorking() { let numObj = 89898.56497 let result1 = numObj.toFixed() document.getElementById(“demo1”).innerHTML = “Default working will give you an integer : “ + result1; let result2 = numObj.toFixed(2) document.getElementById(“demo2”).innerHTML = “When specified it will return number with digits after decimal as specified : ” + result2; let result3 = numObj.toFixed(6) document.getElementById(“demo3”).innerHTML = “When more than present digits are specified extra zeroes are padded at the end : ” + result3; let result4 = (0.98e+20).toFixed(2) document.getElementById(“demo4”).innerHTML = “When exponential value is mentioned and used : ” + result4; let result5 = (2.21e-10).toFixed(2) document.getElementById(“demo5”).innerHTML = “When negative exponential (very amal number is used) : ” + result5; let result6 = 98.6.toFixed(1) document.getElementById(“demo6”).innerHTML = “when literal(direct number) used instead of object : ” + result6; let result7 = 9.65.toFixed(1) document.getElementById(“demo7”).innerHTML = “When value less than present digits after decimal point is specified : ” + result7; let result8 = 9.25.toFixed(6) document.getElementById(“demo8”).innerHTML = “When value greater than present digits after decimal point is specified : ” + result8; let result9 = -98.652.toFixed(1) document.getElementById(“demo9”).innerHTML = “When negative number is used to call the method and value less than preesent digits after decimal point is specified : ” + result9; let result10 = (-5.5).toFixed(1) document.getElementById(“demo10”).innerHTML = “When negative number is used to call the method and value greater than preesent digits after decimal point is specified : ” +result10; }
Difference between toFixed() and toPrecision() methodsThe toFixed() method when used without any parameter returns the integral value that means number before the decimal point while toPrecision() method when used without specifying the parameter returns the value of the number up to which it has considerable decimal value. When we specify the parameter value less than the number of the digits present in the number the toFixed()method returns the number of the digits after decimal as specified in the parameter while the toPrecision() method returns the whole number in the digits specified in the parameter. That means the counting for the digits of the parameter starts after the decimal point in case of a toFixed() method while for the toPrecision() method starts from the first digit of the number before the decimal point. The same case happens when we have to add extra zeroes while padding when a specified parameter is greater than the digits after the decimal point.
Code:
function myFunction() { var sampleNumber = 9.54684; var defaultVal = sampleNumber.toFixed(); document.getElementById(“demo1”).innerHTML = defaultVal; var fixedVal = sampleNumber.toFixed(2); document.getElementById(“demo2”).innerHTML = fixedVal; var fixedValGreater = sampleNumber.toFixed(10); document.getElementById(“demo3”).innerHTML = fixedValGreater; var defaultVal1 = sampleNumber.toPrecision(); document.getElementById(“demo4”).innerHTML = defaultVal1; var fixedVal1 = sampleNumber.toPrecision(2); document.getElementById(“demo5”).innerHTML = fixedVal1; var fixedValGreater1 = sampleNumber.toPrecision(10); document.getElementById(“demo6”).innerHTML = fixedValGreater1; }
Recommended ArticlesThis is a guide to JavaScript tofixed. Here we discuss the Syntax of JavaScript tofixed and Difference between toFixed() and toPrecision() methods. You may also have a look at the following articles to learn more –
Synatx Of Example Of Matlab Polyfit()
Introduction of Matlab polyfit()
MATLAB function polyfit() is defined to fit a specific set of data points to a polynomialquickly and easily computing polynomial with the least squares for the given set of data. It generates the coefficients for the elements of the polynomial, which are used for modeling a curve to fit to the given data.
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Polyfit() function uses input vector (x) to form Vandermonde matrix (V ) having n+1 columns and r = length(x) rows, which is nothing but results in a linear system.
Syntax of Matlab polyfit()Syntax of Matlab polyfit() are given below:
Syntax
poly = polyfit(x,y,n) It generates the coefficients of the resultant polynomial p(x) with a degree of ‘n’, for the data set in yas the best fit in the view of a least-square. The coefficients in p are assigned to power in descending order and matching length of p to n+1.
[poly,Struct] = polyfit(x,y,n) It results in a structure S which can be used as input to the function polyval() in order to obtain error estimation.
[poly,Struct,mu] = polyfit(x,y,n) It results in a two-element vector having values-centered and scaled.
mu(1) holds a value of the mean of (x), and
mu(2) ) holds the value of standard of (x).
Using these two values, function polyfit()makes x centered at zero and scaledx to have a unit standard deviation,
Input Arguments
Query Points: Query points are specified as an input of vector type. If x is non-vector element, then this function polyfit() converts x into a column chúng tôi data points in x and their corresponding fitted function values contained in the vector y are formed.
Note
If the vector x has recurring data points or if it needs centering and scaling, warning messages may result out.
Fitted values at query points: Fitted values as inputs are available at query points being specified with the vector data type. The data points in x and their corresponding fitted function values contained in the vector y are formed. If y is the non-vector element, then this function polyfit() converts y into a column vector.
Degree of polynomial fit: Degree of polynomial fit as inputs, are available being specified as any positive integer scalar. In the respective syntax, ‘n’refers to the polynomial power to that of the left-most coefficient in the polynomial ‘p’.
Example of Matlab polyfit()The below code is designed to generate data points placed equally spaced across a sine curve drawn in a specific interval.
Code:
hold off
Output:
Use Cases for polyfit() FunctionUse cases for polyfit() function are given below:
Fitting Polynomial to Set of data Points: The below code snippet carry out the fitting process on the polynomial poly of degree 4 towards 5 points.
Code:
legend(‘ydata’,’ydata1′,’fig1′)
Output:
Fitting the Polynomial function to Error Function: The below code generate a vector having x data points being placed equally in the interval of [0,3.5] and co-efficient are assigned to the polynomial assuming the degree as 6.
Code:
hold off
Output:
Improving Numerical Properties using Centering and Scaling: While solving the equation p = Vy, the condition number for V is usually large for higher-order fits and results in a matrix with singular coefficient, as the columns of V (Vandermonde matrix) are powers of the x vector.
In such cases, functions like scaling and centering are helpful to improve the numerical properties associated with the system in order to find a fit that is more reliable. In the below example polyfit() is called on three outputs to fit a polynomial of degree 5 along with the process of centering and scaling. The data is centered for the quarter, at 0, and scaled to have a unit standard deviation.
Code:
hold off
Output:
Simple Linear Regression: A simple linear regression model can be used to apply a fitting to a set of discrete two-dimensional data points.
legend(‘data’,’func’)
Output:
Combining Linear Regression and Error Estimation: A linear model can be set fit to a set of specified data points and the results can be plotted including an estimation of a prediction interval of 95%.
A few vectors can be created containing sample data points. The function polyfit can be called to fit a polynomial of degree 1 to the given set of data. Dual outputs can be specified to hold the values of coefficients supporting a linear fit as well as a structure containing error estimation.
Code:
[poly,Samp] = polyfit(xdata,ydata,1);
/*The error estimation structure is specified as the third input so that the function polyval()computes an estimated standard error. The estimated standard error estimate is stored in the second output variable delta. */
title(‘Usage of polyfit and polyval’)
Output:
Additional Note
For n number of data points, a polynomial can be fit to that of degree n-1 to passing exactly through the points.
With the increase in the degree of the polynomial, a fitting process using polyfit()loses the accuracy resulting in to a poorer fit for the data. In such cases, a low-order polynomial is preferable to use that tends to be smoother between the data points or apply a different technique, based on the requirement.
Recommended ArticlesThis is a guide to Matlab polyfit(). Here we also discuss the introduction and use cases for polyfit() function along with examples and its code implementation. you may also have a look at the following articles to learn more –
Learn The Various Methods Of Powershell Join
Introduction to PowerShell Join
The join cmdlet is used to join multiple strings into a single string. The order in which the multiple strings are retained during the concatenation operation is the same order in which they are passed to the cmdlet. Join cmdlet is also used to covert text that is present in pipeline objects into a single string value. This article will explain in detail about join cmdlet in PowerShell, its syntax and usage along with appropriate examples.
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The basic syntax of the join operator is as follows
Where string1, string2 and string3 represent the various strings that needs to be merged. The delimiter represents the character that should be present between the strings that are concatenated. If no value is specified, the “” is used by PowerShell.
The following are the other available syntax
For the Join-String cmdlet, the default separator that is used by PowerShell is $OFS if the user doesn’t specify any value. If a property name is specified, then that property’s value can be converted to a string and subsequently concatenated to a string. A script block can also be used in place of a property name. If that is done, then the script block’s result is converted to a string before concatenation. This cmdlet is the latest and was released as part of PowerShell version 6.2
When the comma has used a delimiter with the join operator, the join operator is given a higher priority. In that case, only the first string is considered, in order to avoid that the strings must be enclosed in parentheses.
Example:
Input:
Output:
onetwothreefourfive
Parameters:
DoubleQuote:
This parameter is used to encapsulate each pipeline objects string value inside double quotes. The datatype of this parameter is switch and its default value is false. This parameter doesn’t accept pipeline input and wildcard characters are also not accepted.
FormatString:
This denotes the format structure of the item. The datatype of this parameter is string. None is the default value of this parameter. This parameter doesn’t accept pipeline input and wildcard characters are also not accepted. This is an optional parameter.
InputObject:
This denotes the input texts that are to be joined. It can either be a variable or a command object. The datatype of this parameter is PSObject[]. This parameter’s default value is none. This parameter accepts pipeline input whereas wildcard characters are not allowed. This is an optional parameter.
OutputPrefix:
This denotes the text that will be inserted before the result. It can contain special characters such as newline or a tab. The datatype of this parameter is string. It can be referred using its alias, op. None is the default value of this parameter. This parameter doesn’t accept pipeline input and wildcard characters are also not accepted. This is an optional parameter.
OutputSuffix:
This denotes the text that will be inserted after the result. . It can contain special characters such as newline or a tab. The datatype of this parameter is string. It can be referred using its alias, os. None is the default value of this parameter. This parameter doesn’t accept pipeline input and wildcard characters are also not accepted. This is an optional parameter.
Property:
Separator:
This denotes the character that needs to be inserted between the text that are joined from the pipeline object. It is generally a comma(,) or a semicolon (; ). It is placed at the number one position. None is its default value. Both pipeline input and wild card characters aren’t accepted. This is a mandatory parameter.
SingleQuote:
This parameter is used to wrap the output string value from pipeline object inside single quote. Its datatype is switch. None is its default value. Both pipeline input and wild card characters aren’t accepted. This is an optional parameter.
UseCulture:
This uses the current culture’s separator as the value of the item delimiter. To find this information, Get-Culture).TextInfo.ListSeparator is used. The datatype of this parameter is switch. None is its default value. Both pipeline input and wild card characters aren’t accepted. This is an optional parameter.
Example:
$stringa,$stringb -join “`n”
Output:
Conclusion Recommended ArticlesThis is a guide to PowerShell join. Here we discuss how can PowerShell join achieved using various methods and also explained the various parameters. You may also look at the following article to learn more –
Example And Graphical Representation Of Tail Risk
Definition of Tail Risk
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ExplanationUsually, the financial market follows a normal distribution but with the introduction of tail risk, the idea that the distribution of returns is not normal but has fatter tails crosses the mind. A tail risk or fat tail risk is an event of a risk for the portfolios of assets. It occurs when there is a possibility of fluctuation in the investment from its mean point to three standard deviations. Although the probability of such events to occur is quite low but still, it can cause great negative consequences for the market as well as for the portfolios.
How does It Work?A tail risk occurs when there is a possibility of three standard deviations to move further than the mean point. Usually, in financial markets, it is believed that returns that are generated from the securities and portfolios have a normal distribution. Such distribution is also called bell-shaped distribution. But tail risk suggests that these distributions are not normal but has fatter tails, the left side of the tail represents the lowest return an investor can get on the investment while the right side of the tail represents the highest return an investor can get. It can also be named as left tail risk and right tail risk depending upon the sides; left tail risk shows negative returns while right tail risk shows positive returns of a portfolio.
Example of Tail RiskA tail risk can be understood better by taking a real-life example. In 2007, the health of 30 public companies of the United States of America was contained in the Dow Index, these companies were also a part of the S&P 500 Index. The index showed great performance and went above the 24k mark in December 2023 and continued to show an upward movement since then.
In January 2023, the index which had been continuing to show an upward movement hit the mark of 26k and investors were expecting that this growth will continue to flourish but due to trade wars and economic slowdown, the whole market of the US suffered. It also created an impact on the Dow Index and it reached fall back to the 24k mark in October 2023. It was a massive fall for the market and the Dow index fell to 21k in December 2023. It was a 19℅ downward move in the year. The tail risk in the Dow Index was when it went below the 24k mark in October 2023, as the downfall from 26k to 24k was a behavioral movement but when it went below that, the condition got worse.
Graphical Representation of Tail RiskA tail risk can be better understood with the help of graphical representation. Here,
The bluebell curve indicates the normal distribution that is generated from the securities or the portfolios.
The green bell curve indicates the tail risk fluctuations that could happen in the investment from the mean point to the three normal deviations.
The left tail of the green curve shows how that much loss an investor can suffer while the right tail of the green curve shows how much the investor can gain on investments.
Uses of Tail Risk
It shows investors all the possible risks that could happen which makes them able to gauge the risks.
Tail risk enables investors to take quick and accurate decisions in the investment strategy.
Tail risk is used to support hedging which results in better and increased cash flow in the market.
Tail risk is used to get knowledge about the negative activity that could happen in the market.
Advantages
Enhanced decision making: Tail risk enables the investor in measuring the unforeseen risks which enable the investor to take accurate decisions.
Encouragement of hedging: Tail risk encourages hedging which results in the better and increased flow of funds in the market.
Create awareness: Tail risk creates awareness about all the possible risks that could happen in the future which could create a great negative impact thus creating awareness.
Possibility of not occurring: There is a high possibility for the tail risk that it wouldn’t occur even onetime as there is a quite low chance of such occurring.
The fear among the investors: Tail risk creates a sense of fear among the investors which can result in fewer investments and can create a negative outlook.
ConclusionThe possibility of losses that could happen shortly due to any unforeseen event is known as tail risk. It can be on both the sides of the curve as the right tail and the left tail, the right side indicates gains while the left side indicates losses. It is beneficial for the businesses and the investors as they can study tail risk which will help them to take accurate decisions for investment.
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