Anil  Sakhiya

Anil Sakhiya

1597956360

Computer Vision for Dummies with OpenCV | OpenCV Tutorial

Great Learning brings you this live session on “Computer Vision for Dummies with OpenCV”. In this session, you will be working on an end-to-end project to understand computer vision. You will also be working on the basics of image processing with Python. We will also give you some ideas about OpenCV library basics and will do some practical task using OpenCV

#opencv #data-science #developer

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Computer Vision for Dummies with OpenCV | OpenCV Tutorial

Computer Vision using Mediapipe

Computer vision can be defined as a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects and then react to what they “see.”

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos.

In today’s world computer vision is very useful in many fields such as — :

*_ Inventory management — : _**In the case of inventory management, the applications can be in the field of security camera image analysis where a computer vision algorithm can generate a very accurate estimate of the items available in the store. Another field can be Analyzing the use of shelf space to identify suboptimal configurations.

* **Manufacturing — : **In the Field of manufacturing Computer vision can help in **predictive maintenance **of the machines.

*** Healthcare — : In the field of healthcare computer Vision can be used in medical image analysis.** Images from CT scans and X-rays are analyzed to find anomalies such as tumors or search for signs of neurological illnesses.

* **Autonomous vehicles — : **The field of computer vision plays a central role in the domain of autonomous vehicles since it allows them to perceive and understand the environment around them in order to operate correctly. One of the most exciting challenges in computer vision is object detection in images and videos. This involves locating a varying number of objects and the ability to classify them, in order to distinguish if an object is a traffic light, a car, or a person, as in the video below.

#computer-vision #opencv #mediapipe #anaconda-navigator #python #computer vision using mediapipe

Gussie  Hansen

Gussie Hansen

1616140422

9 Most important inbuilt functions in OpenCV for Computer Vision

OpenCV is a popular Computer Vision library mostly used for real-time applications. In this blog, we go through the 9 most frequently used OpenCV functions to use the library efficiently along with code examples.

Color to GrayScale

2. Blurring an image using GuassianBlur

3. Edge Cascade

4. Dilation of the cascaded image .

5. Resize and cropping the image

6. Determining contours in an image

7. Splitting an image into its respective RED, GREEN, and BLUE parts .

8. BITWISE operators in OpenCV.

9. Plotting a histogram of an image.

#computer-vision #opencv-python #machine-learning #python #opencv

A Simple HDR Implementation on OpenCV Python

Learn how to create a high dynamic range (HDR) image using Python and OpenCV

HDR images encompass the information of multiple pictures with different exposures. In a scene which the source of light is uneven, a single shot may overexpose certain areas of the image and details will be lost due to elevated brightness. Conversely, this picture may also present underexposed areas which will also lead to information loss.

To create an HDR image you will need:

  1. Take pictures with different exposures. Minimum of 2, generally 3, you can use more than 3 images but it will take a lot of CPU resources.
  2. Align the images. Even if you use a tripod you will need to perform this step (we are talking about pixel level alignment). Not properly aligning your image will lead to artifacts and ‘ghosts’ in your HDR image.
  3. Merge the aligned images into one.
  4. Perform tone mapping on the merged image. In nature the minimum possible brightness is zero but the maximum is not limited to 255, in fact there is no limit to it, it can be infinity. For this reason we need to map the image obtained in the third step to a (0, 255) range. This can be achieved with tone mapping.

#hdr #opencv #computer-vision #python #opencv #opencv python

Larry  Kessler

Larry Kessler

1616729400

Essential OpenCV Functions to Get You Started into Computer Vision

Computer Vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. As such many projects involve the usage of images from cameras and videos and the use of several techniques such as image processing and deep learning models.

OpenCV is a library designed to solve common computer vision problems, it’s super popular among those in the field and it’s great for learning and using in production. The library has interfaces for multiple languages, including Python, Java, and C++.

Throughout this article we will cover different (common) functions inside OpenCV, their applications, and how you can get started with each one. Even though I’ll be providing the examples in Python, the concepts and the functions will be the same for the different supported languages.

What exactly are we going to learn today?

  • Reading, writing and displaying images
  • Changing color spaces
  • Resizing images
  • Image rotation
  • Edge Detection

#opencv #computer vision #essential opencv functions

Alec  Nikolaus

Alec Nikolaus

1597036560

Image Basics Using OpenCV — Lesson 2 of Computer Vision Tutorial

Note from author :

_This tutorial is the foundation of computer vision delivered as “Lesson 2” of the series, there are more Lessons upcoming which would talk to the extend of building your own deep learning based computer vision projects. You can find the _complete syllabus and table of content here

Target Audience_ : Final year College Students, New to Data Science Career, IT employees who wants to switch to data science Career ._

Takeaway_ : Main takeaway from this article :_

  1. Loading an Image from Disk
  2. Obtaining the ‘Height’, ‘Width’ and ‘Depth’ of Image
  3. Finding R,G,B components of the Image
  4. Drawing using OpenCV

Loading an Image from Disk:

Before we perform any operations or manipulations of an image, it is important for us to load an image of our choice to the disk. We will perform this activity using OpenCV. There are two ways we can perform this loading operations. One way is to load the image by simply passing the image path and image file to the OpenCV’s “imread” function. The other way is to pass the image through a command line argument using python module argparse.

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Fig 2.1 Loading an Image from Disk by hard coding the image path and name in code

#Loading Image from disk

import cv2

image = cv2.imread(“C:/Sample_program/example.jpg”)

cv2.imshow(‘Image’, image)

cv2.waitKey(0)

Let’s create a file name Loading_image_from_disk.py in a notepad++. First we import our OpenCV library and contains our image processing functions. we import the library using the first line of code as cv2. The second line of code is where we read our image using cv2.imread function in OpenCV and we pass on the path of image as parameter, the path should also contain the file name with its image format extension .jpg , .jpeg , .png or .tiff .

**syntax **— // image=cv2.imread(“path/to/your/image.jpg”) //

Absolute care has to be taken while specifying the file extension name. we are likely to receive the below error if we provide the wrong extension name

ERROR :

c:\Sample_program>python Loading_image_from_disk.py

Traceback (most recent call last):

File “Loading_image_from_disk.py”, line 4, in

cv2.imshow(‘Image’, image)

cv2.error: OpenCV(4.3.0) C:\projects\opencv-python\opencv\modules\highgui\src\window.cpp:376: error: (-215:Assertion failed) size.width>0 && size.height>0 in function ‘cv::imshow’

The third line of code is where we actually display our image loaded. First parameter is a string, or the “name” of our window. The second parameter is the object to which image was loaded.

Finally, a call to cv2.waitKey pauses the execution of the script until we press a key on our keyboard. Using a parameter of “0” indicates that any keypress will un-pause the execution. Please feel free to run you program without having the last line of code in your program to see the difference.

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#python #machine-learning #artificial-intelligence #computer-vision #opencv