Code  Camp

Code Camp


OpenCV Python Course - Learn Computer Vision and AI

Learn how to use OpenCV for Computer Vision and AI in this full course for beginners. You will learn and get exposed to a wide range of exciting topics like Image & Video Manipulation, Image Enhancement, Filtering, Edge Detection, Object Detection and Tracking, Face Detection and the OpenCV Deep Learning Module.

At the end of the course you will hear from Dr. Satya Mallick (CEO, where he shares his views on the limitless opportunities in the Computer Vision and AI job market and how to confidently prepare yourself in a structured manner for a fulfilling career in AI.

🔗 Course Website:
🔗 Official OpenCV Courses:
🔗 Kickstarter:

💻 Code:

⭐️ Course Contents ⭐️
⌨️ (0:00:00) Intro
⌨️ (0:03:56) Module 1: Getting Started with Images
⌨️ (0:22:22) Module 2: Basic Image Manipulation
⌨️ (0:30:56) Module 3: Image Annotation
⌨️ (0:35:39) Module 4: Image Enhancement
⌨️ (0:52:35) Module 5: Accessing the Camera
⌨️ (0:55:28) Module 6: Read and Write Videos
⌨️ (0:59:08) Module 7: Image Filtering and Edge Detection
⌨️ (1:11:24) Module 8: Image Features and Image Alignment
⌨️ (1:24:16) Module 9: Image Stitching and Creating Panoramas
⌨️ (1:27:13) Module 10: High Dynamic Range Imaging (HDR)
⌨️ (1:38:28) Module 11: Object Tracking
⌨️ (1:49:28) Module 12: Face Detection
⌨️ (1:59:41) Module 13: Object Detection
⌨️ (2:08:33) Module 14: Pose Estimation using OpenPose
⌨️ (2:22:21) Interview with OpenCV CEO, Dr. Satya Mallick

Learn More:

This course was made possible through a grand from OpenCV.

#opencv #python #artificial-intelligence #developer #big-data

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OpenCV Python Course - Learn Computer Vision and AI
Sival Alethea

Sival Alethea


Learn Python - Full Course for Beginners [Tutorial]

This course will give you a full introduction into all of the core concepts in python. Follow along with the videos and you’ll be a python programmer in no time!
⭐️ Contents ⭐
⌨️ (0:00) Introduction
⌨️ (1:45) Installing Python & PyCharm
⌨️ (6:40) Setup & Hello World
⌨️ (10:23) Drawing a Shape
⌨️ (15:06) Variables & Data Types
⌨️ (27:03) Working With Strings
⌨️ (38:18) Working With Numbers
⌨️ (48:26) Getting Input From Users
⌨️ (52:37) Building a Basic Calculator
⌨️ (58:27) Mad Libs Game
⌨️ (1:03:10) Lists
⌨️ (1:10:44) List Functions
⌨️ (1:18:57) Tuples
⌨️ (1:24:15) Functions
⌨️ (1:34:11) Return Statement
⌨️ (1:40:06) If Statements
⌨️ (1:54:07) If Statements & Comparisons
⌨️ (2:00:37) Building a better Calculator
⌨️ (2:07:17) Dictionaries
⌨️ (2:14:13) While Loop
⌨️ (2:20:21) Building a Guessing Game
⌨️ (2:32:44) For Loops
⌨️ (2:41:20) Exponent Function
⌨️ (2:47:13) 2D Lists & Nested Loops
⌨️ (2:52:41) Building a Translator
⌨️ (3:00:18) Comments
⌨️ (3:04:17) Try / Except
⌨️ (3:12:41) Reading Files
⌨️ (3:21:26) Writing to Files
⌨️ (3:28:13) Modules & Pip
⌨️ (3:43:56) Classes & Objects
⌨️ (3:57:37) Building a Multiple Choice Quiz
⌨️ (4:08:28) Object Functions
⌨️ (4:12:37) Inheritance
⌨️ (4:20:43) Python Interpreter
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#python #learn python #learn python for beginners #learn python - full course for beginners [tutorial] #python programmer #concepts in python

Ray  Patel

Ray Patel


Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services


When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

  • revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
  • microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
  • Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

The Best Project to Start in Computer Vision with Python

GrabCut — A Google Colab NoteBook implementation for Image Matting (background removal)

Follow the article along with the complete code implementation on GitHub. Open the notebook in Google Colab, import your image(s), and run the cells!Originally published on, read it 2 days before on my blog!

Image matting is an extremely interesting task where the goal is to find any object of interest, or human, in a picture and remove its background. This task is hard to achieve due to its complexity, finding the person, people, or objects with the perfect contour. This post reviews an exciting technique using basic computer vision algorithms to achieve this task. The GrabCut algorithm. It is swift but not very precise for complex objects like humans or animals. Nonetheless, it can be handy in specific contexts and is a perfect applied first project to start in computer vision and python! As mentioned above, the implementation uses Google Colab, thus having no requirements or setup needed, making it an exciting project to duplicate for learning.

#computer-vision #python #ai #machine-learning #artificial-intelligence #the best project to start in computer vision with python

Gussie  Hansen

Gussie Hansen


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