C++

Scripting is a critical part of Unity 2D racing. It is a crucial element that handles the input from a player and then holds an object accordingly. 

Playing games has always been fun, just like coding! There is something about games, which grabs everyone towards it with its fascinating visuals, engaging obstacles to reach victory. You could have played plenty of games like these which were terrific, but how about creating your very own game? How about designing a racing game? Here is one of the most exciting games called Unity 2D Racing. Unity 2D Racing is a single-player game in which a player controls the car on a road path. Some hazards like clouds will pass by the road to make the game bit tough for the player. Besides, a player must also be cautious about not getting into the potholes that appear on the road. Listed below are the best libraries that can be reused. This kit aids the development of the Unity 2D racing game by following the below steps : 1. Pick a Development Environment! 2. Begin with Unity 2D project 3. Set up background sprite 4. Create a script to do the background scroll 5. Add cloud materials 6. Create a 2D controller 7. Sort the layers 8. Create a script for the controller 9. Add Canvas in the UI 10. Create a script for managing the UI 11. Design a Graphic Raycaster in Unity.

Controller for our unity 2d racing game

Scripting is one of the critical parts of Unity 2D racing, in which we do scripting for controlling an object. A controller is a crucial element that handles the input from a player and then holds an object in a game accordingly.

Development Environment

Unity Hub is used for development. Unity is generally a game engine framework that allows you to create two-dimensional(2D) and three-dimensional (3D) games. C# programming language has been used for scripting in Unity.

Graphic Raycaster

The graphic Raycaster will decide on the end of the game. Generally, Raycaster inspects the graphics to determine if any objects got hit by the canvas. If an object hits the hazard, the Graphic Raycaster will block that object and finish the game.

OpenCV is a library for image processing and computer vision that can be used to resize images. Resizing images using OpenCV can be useful in a number of ways, some of which include:  

• Image compression: to reduce the file size of an image.  
• Image processing: as a pre-processing step in image processing algorithms, such as object detection, segmentation, and feature extraction.  
• Computer vision: to adjust the resolution of an image to match the requirements of a computer vision algorithm, such as object detection or image recognition.  
• Data augmentation: as a data augmentation technique to increase the diversity of the training data, which can improve the performance of machine learning models  
• Printing: To adjust the resolution of an image to match the requirements of a printing device.  
• Video editing: To adjust the resolution of an image to match the requirements of video editing software.  

Here is how you can print coloured text in Terminal:  

Preview of the output that you will get on running this code in your ide

Code

In this solution we have used Imread function in python,

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Modify the name, location of the image to be read in the code.
3. Run the file to resize the image.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for " Re-Size the image in Open Cv using python" in kandi. You can try any such use case!

Environment Tested

I have tested this solution in the following version. Be mindful of changes when working with other versions.

1. This solution is created and executed in Python 3.7.15 version
2. This solution is tested in Opencv 4.6.0 version

Using this solution we able to re size the image in python with the help of OpenCv library with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us resize an image in Python.

Dependent Library

If you don't have the opencv library which is required to run this code, click the above link and install opencv and copying the pip install command from the OpenCv page in Kandi. You can search for any dependent library like Opencv using kandi.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Good health is critical to lead a happy life. Pandemic has caused drastic changes in the existing health care system. These times, there are many difficulties in taking care of patients in person because of safety. It is hard to treat a lot of patients in person. A doctor can consult patients remotely without the risk of transmitting the infection. Thus we need to appraise the situation with remote monitoring systems and video consultation of patients. These are not only for the pandemic situation. Patients from another country can consult a doctor present in another country swiftly with this system. Build Smart Healthcare Patient Monitoring using below steps: 1. Development Environment 2. Data Analysis 3. Mobile Application

Data Analysis

Following are the libraries which help in data analysis. We need an ESP8266 wifi module, MAX30100(Heart rate and Blood Pressure), Dallas temperature Sensor, DTH11 Sensor.

Development Environment

Arduino is used for writing and uploading the program and storing the data in the cloud. So Arduino is used for getting the typical experience of IDE for the developer.

Mobile Application

Users can use the following libraries to check the patient's temperature, blood pressure, heart rate, humidity level.

Doorbells are the signaling device that helps alert the person outside the home and get permission to come inside. Traditionally, the doorbells were simple buttons, and when pressed, the bell rings. Let’s think of making it automatic detection and alarm with pictures or videos. Here are best libraries that will help you make the alarms or doorbell automatic, which will detect someone in front of the door and automatically ring. It is beneficial because it’s not always the case that a person can reach the doorbell, and it also comes with the additional feature of a picture or video to see and confirm to avoid intruders. This kit helps in the rapid development of automatic door alarms by the following steps: 1. Development Environment 2. Data Analysis

Development Environment

Arduino is used for writing and uploading the program and storing the data in the cloud.

Data Analysis

Following are the libraries which help in data analysis. These libraries use Ultrasonic sensors and IoT technology.

Searching for parking spaces in the cities, especially during the rush hours, is an arduous job for daily commuters. The demanding situation arises from not knowing if the parking space is available or not. A lot of time and energy is drained in searching for a car parking space. Fuels consumption on searching parking space is also high. Even if we knew the parking place, many vehicles might pursue minimal parking space to cause severe traffic congestion. With open-source libraries, we can build our own intelligent parking system. Build your own intelligent parking system by following below steps: 1. Development Environment 2. Data Analysis 3. Mobile Application

Development Environment

Arduino is used for writing and uploading the program and storing the data in the cloud.

Mobile Application

Users can use the following libraries to check the status of parking spaces using mobile instantly.

Data Analysis

Following are the libraries which help in data analysis. These libraries use IoT(Internet Of Things) and some hardware tools like Radar sensors, Arduino, Raspberry Pi.

Worldwide, traffic on the roadways is a concern due to increasing automobile users. It also causes wastage of fuels and increases pollution. Traffic lights have become redundant in controlling vehicles. There are hundreds of accidents due to malfunctions of traffic lights in India alone for a day. Mentally, traffic increases the stress and frustration among people. Let us think of making it innovative and efficient in controlling the motorway. Are you thinking of providing a solution to this issue? Following libraries will help you on this, 1. Development Environment 2. Data Analysis

Data Analysis

Following are the libraries which help in data analysis. These libraries use ultrasonic sensors and IoT technology.

Development Environment

Arduino is used for writing and uploading the program and storing the data in the cloud. So Arduino is used for getting the typical experience of IDE for the developer.

A Bluetooth robotic arm is used to reduce the errors made by humans and also minimize their efforts. Around the globe, with upcoming technologies, robotics provides improvements in various engineering departments for a use case. There is a need to communicate with the robot remotely in order to control its movements and pass critical data—a mechanical arm with a movable base controlled by an application through Smartphone via Bluetooth. A robotic arm can function similarly to the human arm as specified by the controller or can be used for automation purposes. Listed below are the best libraries that can be reused and create a custom robotic arm.

Water is the vital source given to us by our planet. Global warming has reached to alarming state such every living or non-living or non-living organism will become a victim. Although Propagandas are done at scale, every human being on the planet should drive the change. One of the driving factors in reducing water wastage. Wastage of water occurs at various places, and one among them is overflowing water tanks and leakage in water tanks which is not affordable. Water tanks can neither monitor nor control the water level in the tank, leading to a large amount of water wastage. Listed below are the best libraries that can help you create applications to reduce water wastage.

Build an automatic solar tracking system that can effectively harness the sun’s rays throughout the day using the open-source libraries and frameworks.

With a limited number of conventional sources of energy like coal, petroleum, natural gas, which creates a lot of pollution too, we are in great danger of complete depletion of these resources as alternative solar energy has a huge potential to meet the ever-growing demands of the human race.

Solar cells are used to convert the sun’s energy into electrical energy. But the sun’s direction and position are different from sunset and sunrise. And it keeps changing throughout the day. Therefore, a stationary system cannot harness the maximum energy on the sun. Thus, building a customized solar energy tracking system can effectively solve the problem. And you can do so using the open-source code libraries and frameworks.

Scroll down to check out the best and trending open-source libraries to enable functions like automatic solar tracking in your next application development project:

Denoising colored images remove unwanted noise or artifacts from an image to improve its visual quality or make it more suitable for further analysis or processing. In computer vision and image processing, noise can arise from various sources, such as image sensors, transmission or storage errors, or digital processing algorithms. 

OpenCV is a popular open-source computer vision library with many functions and methods to denoise colored images in Python. Some of the most commonly used techniques for denoising include: 

• Bilateral filtering: a non-linear filtering method that preserves edges while smoothing noise by applying a weighted average of nearby pixels. 
• Non-local means denoising: a method that replaces the value of each pixel with a weighted average of similar pixels in the image based on their distance and similarity. 
• Median filtering: a simple but effective method that replaces every pixel with the median value of its neighboring pixels. 

OpenCV provides implementations of these methods in Python, which can be used to denoise colored images. To apply these methods, one needs first to read the image in OpenCV, then apply the denoising method of choice with appropriate parameters, and finally, display or save the denoised image. 

Denoising colored images using OpenCV in Python can help improve the quality and clarity of images, making them more suitable for various computer vision applications, such as object recognition, segmentation, or tracking. It can also help reduce the effects of noise on subsequent image analysis or processing, leading to more accurate and reliable results. 

Here is an example of how to Denoise the colored image using an open cv in Python: 

Preview of the output that you will get on running this code from your IDE

Code

In this solution we use the numPy and openCV library.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Modify the name, location of the image to be read in the code.
3. Run the file to get the Output

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "How to denoise the image" in kandi. You can try any such use case!

Note

Add your Image path in line 14 .

Dependent Library

If you do not have openCv and numPy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the Spacy page in kandi.

You can search for any dependent library on kandi like numPy and OpenCv

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python 3.7.15. Version
2. The solution is tested on numPy 1.21.6 Version
3. The solution is tested on OpenCV 4.6.0 Version

Using this solution, we can able to Denoise the image using python with the help of OpenCV library. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us Denoise the image in python.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

OpenCV is a library of programming functions mainly aimed at real-time computer vision. It is written in C, C++, and Python, and runs on Windows, Linux, Android, and macOS.OpenCV is widely used in the field of computer vision for tasks such as object recognition, face detection, and image and video analysis. It has a large community of developers and users and is continuously updated and improved.

OpenCV provides a large collection of algorithms and functions for image and video processing, including:

• Image processing operations like filtering, morphological transformations, thresholding, etc.
• Object detection and recognition, including face detection and recognition, object tracking, etc.
• Image and video analysis, including edge detection, feature extraction, and optical flow.
• Camera calibration and 3D reconstruction.
• Machine learning algorithms, including support for deep learning frameworks like TensorFlow and Caffe.

You can divide an image into two equal parts vertically or horizontally using OpenCV by simply slicing the image array. Here's an example of how you could divide an image into two equal parts horizontally in Python using OpenCV:

This code splits the image into two equal parts, horizontally. It first retrieves the shape of the image and calculates the height and width of the image. It then calculates the starting and ending row and column pixel coordinates for the top and bottom halves of the image. The image is then sliced and each half is stored in the cropped_top and cropped_bot variables. Finally, each of the two cropped images is displayed using the OpenCV function cv2.imshow() and is shown until a key is pressed using the cv2.waitKey(0) function

Here is an example of how you can Divide the image into two equal parts using OpenCV

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Modify the name, location of the image to display in the code.
3. Run the file to divide the image to Top and Bottom

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "divide image into tow equal parts python opencv" in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are able to divide an image using the OpenCV library in Python with simple steps. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us divide an image in Python

Dependent Library

If you do not have OpenCV and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV and numpy

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

OpenCV is a computer vision library written in C++ and widely used for image and video processing. It offers a range of features for working with photographs and movies, including the ability to load and save images, use filters, find edges, and find and track objects. In collaboration, applications involving image and video processing are frequently created using Python and OpenCV. This combination enables you to develop solid and adaptable programs that can address various computer vision issues.  

In our work as developers, we frequently must read and rotate the photos in our applications to complete various image processing activities, such as recognition, upload, augmentation, training, and many more. There are numerous libraries for Python that enable working with images. Python has features for manipulating, enhancing, and creating more images. In addition to using additional OpenCV functions to apply other transformations to the image, such as scaling, cropping, and applying filters, you can modify the angle of rotation and the image's size to get the desired effect.  

Here is an example of how we can draw a line beyond the second point using opencv

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the numpy and open cv library

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and Numpy library.
3. Modify the name and Length of the points.
4. Run the file to draw a line.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

ifound this code snippet by searching for "Draw a line in open cv and python beyond given points" in kandi. You can try any such use case!

Dependent Library

If you do not have OpenCV and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV and numpy

Environment Test

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0 version
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to draw a line beyond the second given point using the OpenCv library and numpy library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us draw a image in Python

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

In Python text can be represented using many colors. we have some Python libraries for colors and formatting in the terminal. The programmer gets better by printing colored texts. Colorama is a Python library that makes it easy to add color to your text in the terminal. 

Colorama is designed to work on Windows, macOS, and Linux, and provides a simple and consistent interface for adding color to your text output. With Colorama, you can add ANSI escape sequences to your text to specify its color, background, and formatting, making it easier to create colorful and attractive terminal-based applications. The library provides several modules, such as Fore, Back, and Style, which allow you to specify the text color, background color, and formatting, respectively. Some of the formatting options supported by Colorama include bright, dim, normal, bold, and underlined. Colorama also supports 256-color mode, which provides a wider range of colors compared to the basic 8-color mode.

termcolor supports a range of colors, including red, green, yellow, blue, magenta, cyan, and white, as well as a number of formatting options, such as bold, dim, and underlined. The library is easy to use and provides a simple and intuitive interface for adding color to your text in the terminal.

Here are examples for printing color in a terminal.

Preview of the output that you will get on running this code in your IDE

Code

In this solution we have used Colorama library from python

1. Copy this code using "Copy "button above and paste it in your python file IDE
2. Add Colorama Library to run this code
3. Run the code to get the texts coloured.

I hope you found this useful i have added the Dependent libraries , versions in the following sections

I have searched using "Add text Color while print" in Kandi. you can try any use case

Dependent Library

If you don't have this colorama and termcolor Library that required to run this code. You can install by clicking the above link and copying the pip install command from the Colorama page in Kandi. You can search any Library Like colorama and termcolor in kandi

Environment Test

I have tested this solution with following versions. Be mindful of changes when working with other versions

1. This solution is created and executed in Python 3.7.15 version
2. This solution is tested on Colorama 0.4.6 version
3. This solution is tested on termcolor 2.1.1 version

Using this solution we able to change the style , color, Brightness of the Text in Terminal using Colorama Library in python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us color the text in Python.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Converting RGB to YCbCr can provide better results for image and video compression, color space conversions, and HDR processing.  There are several reasons why we might need to convert RGB to YCbCr

Compression efficiency: YCbCr provides better compression results compared to RGB, especially in preserving image quality after compression. This is because the human visual system is more sensitive to changes in brightness (luma, Y) than to changes in color (chroma, Cb and Cr). Color space conversion: Some image processing tasks, such as color correction and color space conversion, may require transforming the image from one color space to another. For example, many image sensors capture the image in the YCbCr color space, and it may be necessary to convert it to RGB for display purposes. 

OpenCV (Open Source Computer Vision Library) is an open-source and machine-learning software library. OpenCV is a computer vision library written in C++ and widely used for image and video processing. OpenCV provides a vast array of image and video processing functions that can be used in various domains such as:

• Object detection and recognition
• Image and video segmentation
• Face and feature detection
• Object tracking
• Image restoration and enhancement
• Stereoscopic vision
• Motion analysis and object tracking
• 3D reconstruction

RGB and YCbCr are color spaces used in digital image processing.

RGB stands for Blue, Green, Red, and is an encoding of the RGB (Red, Green, Blue) color space. BGR is used in computer vision and image processing applications and is the default color format for the OpenCV library in Python.

YCbCr, on the other hand, stands for Luma (Y) and Chrominance (Cb, Cr), and is a color space used in digital video processing. YCbCr separates the brightness information (luma) from the color information (chroma), which allows for more efficient compression. YCbCr is used in many image and video compression standards, such as JPEG and MPEG. In summary, BGR is used in computer vision and image processing, while YCbCr is used in video processing and compression.

In this solution, we are going to learn how to convert the RGB image to YcbCr using opencv.

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and numpy library
3. Modify the name, and location of the image in the code.
4. Run the file to get the Output

I hope you found this useful. I have added the link to dependent libraries, and version information in the following sections.

i found this code snippet by searching for "OpenCV Python converting color-space image to YCbCr" in kandi. You can try any such use case!

Note:-

If the user wants to Display the output use this command

cv2.imshow('after', YCrbCrImage)

cv2.waitkey(0)

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to convert BGR image to YCBCR using the OpenCv library in Python with simple steps. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us convert BGR to YCBCR in Python

Dependent Library

If you do not have OpenCV and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV and numpy

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Converting a black image to white means changing the color of the image from black to white. This can be done using increasing and decreasing the pixels of the color. The specific steps will vary depending on the software being used, but the basic idea is to increase the brightness until the black pixels become white.

OpenCV is widely used in the field of computer vision for tasks such as object recognition, face detection, and image and video analysis. It has a large community of developers and users and is continuously updated and improved. cv2.threshold is a function in OpenCV (Open Source Computer Vision Library) that applies a threshold to an image. Thresholding is a process of converting an image into a binary image by dividing the image into two regions, foreground, and background. The foreground pixels have a certain intensity level and all other pixels are set to the background. The threshold is the value that separates the foreground and background pixels.

• Reads an image using cv2.imread and stores it in the img variable.
• Converts the image to grayscale using cv2.cvtColor and stores it in the gray variable.
• Applies a binary threshold to the grayscale image using cv2.threshold, which means that all pixels with a value greater than or equal to 240 are set to 255 (white) and all other pixels are set to 0 (black). The thresholded image is stored in the thresh variable.
• Defines two arrays white_px and black_px to represent the white and black pixels, respectively.
• Loops through each row and column of the thresh image and if the pixel value is equal to white, it sets the corresponding pixel in the img_array to black.
• Creates a structuring element using cv2.getStructuringElement with an elliptical shape and size 5x5.
• Applies morphological erosion to the img_array using the created structuring element and 1 iteration. The result is stored in the erosion variable.
• Displays the erosion image in a window using cv2.imshow and waits for a key press using cv2.waitKey. The window is closed using cv2.destroyAllWindows..

Here is an example of how you can convert white pixels to Black using openCV.

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and Numpy library.
3. Modify the name, location of the image in the code.
4. Run the file to get Output

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "Convert White Pixels to Black in OpenCV python" in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to convert white Pixels to Black using the OpenCV library in Python with simple steps. This process also facilities an easy-to-use, hassle-free method to create a hands-on working version of code which would help us change the pixels in Python

Dependent Library

If you do not have OpenCV and numpy that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV and numpy

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

This code demonstrates how to use the OpenCV library in Python to modify a transparent image. It's useful for various applications that require the manipulation of transparent images, such as image editing software or computer vision projects. 

This code imports the cv2 (OpenCV) module, a Python wrapper for the OpenCV library, and the NumPy module, which supports multi-dimensional arrays and matrices. This code prepares the image file for further processing by loading it into a numpy array and obtaining its dimensions. The loaded image can then be modified or processed in various ways, such as resizing, cropping, or applying image filters. 

This code draws a straight line on the image result using the line() function from the cv2 module. These modules can perform various image processing tasks, such as loading, manipulating, and saving images and applying various image processing operations like filtering, thresholding, and segmentation. This code modifies the image result by adding a blue line to it. The resulting array can then be saved as a new image file or used for further image processing. 

Here is an example of how to draw a line in a transparent Image 

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and Numpy library.
3. Modify the name, location of the image in the code.
4. Run the file to draw a line.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "Drawing a line on PNG image OpenCV2 Python' in kandi. You can try any such use case!

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to draw a line in PNG image using the OpenCv library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us draw a line in a image in Python

Dependent Library

If you do not have OpenCV that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV ,numpy

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

This script is a simple example of how to create a basic drawing program using the OpenCV library. Such drawing programs can be used in various real-world applications, such as computer-aided design (CAD) software, image annotation and labeling tools, and whiteboard applications for online education and collaboration. 

This Python script uses the OpenCV library to create a simple drawing program that allows the user to draw lines on an image using the mouse. The script sets up a mouse event handler, which listens for mouse clicks, mouse movement, and mouse button releases.:

• OpenCV library is a popular computer vision library that provides various image and video processing functions. 
• NumPy is a numerical computing library that supports working with large, multi-dimensional arrays and matrices. 

The draw_shape function is responsible for drawing the lines on the image based on the user's mouse actions, such as mouse clicks, mouse movement, and mouse button releases. The function updates the global variables based on the mouse events and draws lines on the image using OpenCV's cv2.line function. 

The function takes five arguments: 

• event: An integer value representing the mouse event type, such as cv2.EVENT_LBUTTONDOWN for a left mouse button click event. 
• x: An integer value that represents the x-coordinate of the mouse position relative to the image. 
• y: An integer value that represents the y-coordinate of the mouse position relative to the image. 
• flag: An integer value that provides additional information about the mouse event, such as whether the mouse buttons were pressed or released. 
• param: An optional parameter that can pass additional user-defined data to the event handler. 
• cv2.setMouseCallback is a function in the OpenCV library that allows the user to associate a mouse event handler function with a particular window.  

This script provides a starting point for building more complex drawing tools with more advanced features, such as the ability to draw different shapes, fill areas with color, use different colors and line styles, and save the drawing to a file. 

Here is an example of how to draw a line using the mouse in Opencv: 

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv library and Numpy library.
3. Modify the name, location of the image in the code.
4. Run the file to draw a line.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "how to draw pefect line in python using open Cv" in kandi. You can try any such use case!

Dependent Library

If you do not have OpenCV that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV, numpy

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0
3. The solution is tested on numpy 1.21.6

Using this solution, we are going to draw a perfect line using the OpenCv library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us draw a image in Python

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Adding colors to text output in a Python script can make the output more visually appealing and easier to read. It can also be used to highlight important information, such as errors or warning messages, making them stand out and more noticeable. In general, colors can be used to add meaning to text-based interfaces and make them more user-friendly. A collection of methods for appending ANSI color codes to strings are available in the termcolor library. 

• Termcolor: Python's termcolor package may colorize terminal output. It may be used to add ANSI color codes to strings, which will cause them to print to the terminal in a variety of colors. Windows, Linux, and MacOS all support it. This can be helpful for making visual distinctions in command-line interfaces that employ text. 

Colors can also be used in text-based games, and other interactive command-line programs to make the interface more engaging and fun. When creating a text-based game, adding colors can make the game feel more immersive, and more like a 'real' game. 

For more information about adding colors using Termcolor, refer to the code given below. 

Preview of the output that you will get on running this code in your IDE

Code

In this solution we have used Termcolor from python

1. Copy this code using "Copy "button above and paste it in your python file IDE
2. Add Termcolor Library to run this code
3. Run the code to get the texts coloured.

I hope you found this useful i have added the Dependent libraries , versions in the following sections.

I have searched using "How to add colors to printed text " in Kandi. you can try any use case

Dependent Library

If you don't have this termcolor and colorama Library that required to run this code. You can install by clicking the above link and copying the pip install command from the termcolor page in Kandi. You can search any Library Like termcolor and colorama in kandi

Environment Test

I have tested this solution with following versions. Be mindful of changes when working with other versions

1. This solution is created and executed in Python 3.7.15 version
2. This solution is tested on termcolor 2.1.1 version
3. This solution is tested on colorama 0.4.6 version

Using this solution we able to color the text with termcolor Library in python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us color the text in Python.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Attempting to create a new image from an input image by swapping the positions of the rows and columns using nested loops, and then writing the resulting image to a file using the OpenCV library

OpenCV (Open Source Computer Vision) and NumPy are two powerful libraries in Python that are widely used in computer vision, image processing, and machine learning applications. Here's a brief overview of how each library can be used. OpenCV provides a variety of computer vision algorithms and functions for image and video processing.

• These functions range from basic image filtering, resizing, and rotation to advanced feature detection, object recognition, and video analysis.
• OpenCV can read and write a variety of image and video formats, making it easy to work with different types of media.
• OpenCV has interfaces for several programming languages, including

h, w, c where h, w, and c represent the height, width, and number of color channels in the new array. A nested loop is a loop inside another loop. It is a common programming construct used to iterate over multiple levels of data, such as two-dimensional arrays or matrices. cv2.imwrite is a function provided by the OpenCV library that is used to write an image to a file on a disk. The function takes two arguments: the filename of the image to be saved, and the image data to be written.

Here is the example how to rotate the image:

Preview of the output that you will get on running this code from your IDE

CODE

In this solution we use the Imread function of the OpenCV.

1. Copy the code using the "Copy" button above, and paste it in a Python file in your IDE.
2. Import open Cv and Numpy library
3. Modify the name, location of the image to be rotate in the code.
4. Run the file to rotate the image.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

i found this code snippet by searching for "Image rotation using OpenCV" in kandi. You can try any such use case!

Dependent Libraries

If you do not have OpenCV that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the OpenCV page in kandi.

You can search for any dependent library on kandi like OpenCV, numpy

Envorinment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions

1. The solution is created and executed in python version 3.7.15 .
2. The solution is tested on OpenCV 4.6.0

Using this solution, we are able to rotate an image using the OpenCv library in Python with simple steps. This process also facilities an easy to use, hassle free method to create a hands-on working version of code which would help us rotate an image in Python

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.

Indexing and slicing a tensor in PyTorch refers to selecting a specific part of a tensor, which can be done using a combination of indices and slices. This is useful for selecting tensor parts, such as a subset of rows or columns or a certain number of elements along a certain dimension. Indexing and slicing can be used to select and manipulate tensor parts, which can be used for various operations, such as creating sub-tensors from a larger tensor or applying certain operations to only a subset of elements in a tensor. 

A tensor in Python is a multi-dimensional array used to store numerical data. It is a fundamental data structure in deep learning models like convolutional neural networks (CNNs). Tensors are usually represented as a matrix of numbers and can be manipulated using various operations such as addition, multiplication, and division. 

Indexing and slicing of tensors in PyTorch are the same as indexing and slicing lists in Python. 

• To retrieve a single tensor element, use the indexing operator [] with the corresponding indices. 
• To slice a tensor, use the slicing operator: with the corresponding indices. 

Here is an example of indexing and slicing a tensor in PyTorch. 

Fig 1: Preview of the output that you will get on indexing a tensor in PyTorch.

Fig 2: Preview of the output that you will get on slicing a tensor in PyTorch.

Codes

In this solution, we use the torch.tensor Function of the PyTorch library

Instructions

Follow the steps carefully to get the output easily.

1. Install Jupyter Notebook on your computer.
2. Open terminal and install the required libraries with following commands.
3. Install pytorch - pip install torch.
4. Copy the codes using the "Copy" button above, and paste it into your IDE's Python file.
5. Print Result in slicing.
6. Run the file to perform Indexing and slicing a tensor in PyTorch.

I hope you found this useful. I have added the link to dependent libraries, version information in the following sections.

I found this code snippet by searching for "Indexing and slicing a tensor in PyTorch" in kandi. You can try any such use case!

Dependent Libraries

If you do not have PyTorch that is required to run this code, you can install it by clicking on the above link and copying the pip Install command from the PyTorch page in kandi.

You can search for any dependent library on kandi like PyTorch

Environment Tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

1. The solution is created in Python 3.9.6
2. The solution is tested on PyTorch 2.0.0+cpu version.

Using this solution, we are able to perform indexing and slicing of tensor in PyTorch in Python with simple steps. PyTorch is also used in Computer Vision and Generative Adversarial Networks.

Support

1. For any support on kandi solution kits, please use the chat
2. For further learning resources, visit the Open Weaver Community learning page.