Computer Vision using Python

Service Description

About the Course: In today’s technology driven world, there is a huge amount of visual data generated across different platforms. Computer vision enables us to interpret and utilize visual data, with applications spanning numerous industries, including autonomous vehicles, social media applications, medical imaging, and many more. As one of the fastest-growing programming languages, Python is ideal for harnessing the power of existing computer vision libraries to make sense of this vast data. Therefore, this course is designed to be your go-to resource for mastering the use of Python for Computer Vision tasks. Here, we’ll dive into how to utilize Python and the OpenCV (Open Computer Vision) library to work with images and video data. What you will learn? In this course, you’ll gain all the skills needed to become proficient in computer vision. We’ll begin by covering numerical processing with the NumPy library, learning to open and manipulate images using NumPy. From there, we’ll dive into OpenCV, focusing on image basics and working through techniques for image processing, such as color mapping, blending, thresholding, and gradients. Next, we’ll shift to video processing fundamentals in OpenCV, including streaming video from a webcam, followed by in-depth topics like optical flow and object detection, including face detection and object tracking. Finally, you will be able to learn the latest deep learning advancements in computer vision, including image recognition and custom image classification. Course Objectives (By the end of this course, participants will be able to): 1. Understand the core concepts of computer vision and its applications in various industries. 2. Use Python libraries such as NumPy and OpenCV to effectively process image and video data. 3. Apply image processing techniques like color mapping, blending, and thresholding to manipulate visual data. 4. Stream and analyze video data, implementing advanced techniques like optical flow and object detection. 5. Utilize deep learning methods for tasks like image recognition and custom image classification. 6. Develop practical computer vision projects to demonstrate and apply learned skills in real-world scenarios. Batch Details: Class Timings: 4:00 pm – 6:00 pm (Saturdays), 10 pm – 12 pm (Sundays) Start Date: 21st Dec 2024 End Date: 9th March 2025 Mode: Online (ILT over Zoom/Webex/GMeet) Certification: Globally accepted Duration: 48 Hours Last Date to Register: 20th Dec 2024