Detailed Course Curriculum
Lesson by lesson
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Welcome to the Computer Vision for Autonomous Vehicles program
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How to use Discussions option
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Course access duration
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Piracy & infringement warning
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1_Introduction
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2_What is Python ?
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3_Is Python really needed ?
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4_Installation
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5_Jupyter Notebook
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6_Your First Code
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7_Lab: Your First Code
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8_Data types
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9_Lab: Data types
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10_Operations
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11_Lab: Operations
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12_Type Conversion
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13_ Lab: Type Conversion
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14_Input statement
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15_Lab: Input statement
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16_IF
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17_else
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18_Lab: If else
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19_While
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20_ break and continue
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21_Lab: While, break and continue
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22_Functions
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23_Lab: Function
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24_for loop
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25_Lab: for loop
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26_Practice
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1_List
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2_Lab: List
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3_Slicing
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4_Lab: Slicing
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5_Dictionaries
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6_Lab: Dictionaries
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7_Tuples
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8_Lab: Tuples
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9_Exceptions
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10_Module, Package & Library
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11_Lab: Numpy Part: 1
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12_Lab: Numpy Part: 2
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13_Lab: Numpy Part: 3
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14_Lab: Numpy Part: 4
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Dataset for Pandas
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15_Lab: Pandas Part: 1
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16_Lab: Pandas Part: 2
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17_Lab: Matplotlib
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18_pip
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Introduction to Computer Vision
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Introduction to Camera and Camera Parameters
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Image, Gray Scale Image, RGB Color Space and Image Resolution
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Install OpenCV package
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Computer Vision Basics (Draw Line, Rectangle, Circle and text)
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Computer Vision Basics (Add Images and Bitwise Operation)
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Rotation, Translation and Scale Matrix Theory
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Image Rotation and Translation
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Image Blur and Sharpening Theory
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Blurring and Sharpening Image
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Canny Edge Detection theory
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Canny Edge Detection 1
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Canny Edge Detection 2
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HSV Color Space
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Programming HSV Color format edge detection
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Image Similarity
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Scale Invariant Fourier Transform
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Feature Matching
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Programming Image Similarity 1
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Programming Image Similarity 2
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Programming Image Similarity 3
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Programming Image Similarity 4
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About this course
- 91 lessons
- 18.5 hours of video content
- Mentor guided projects
- 4.1 course rating & 1100+ learners
Software tools used
About the Course
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.
Computer vision is a strong tool that may be used in conjunction with a variety of applications and sensing devices to serve a variety of practical use cases. Computer vision technology is used in the Content organization, Text extraction, Augmented reality, Agriculture, Autonomous vehicles, Healthcare, Sports, Manufacturing, Spatial analysis, Face recognition, etc.
The 4-week course will involve you to build real-world applications using Python and computer vision. It will be 100% practical & hands-on oriented content, taught online to meet the industry requirements. Learning this course will give you the opportunity to extend yourself to build any system and take up the projects of your interest.
Start with Python Fundamentals & Motivation to Master Python
Week 1
Kickstart the Python learning, and lay a solid foundation as a beginner. We will start with understanding the wide range of applications Python is being used in Industries, Start-up & Governments, etc., We shall build a clear motive & motivation to learn the language during this course & the outcome you can get. You will begin to learn Python programming with live coding sessions on topics such as operators, data types, Operations in Python (Integers and floats, Strings, Type Conversions & Booleans), Statements and Loops (IF, IF - else & elif), Functions (Arguments, Return & In-built functions), Loops (for & while), break & continue.
Practice With Examples & Gain Confidence to Start Projects
Week 2
During Week 2, we will be exploring the vast and fabulous domains of Intermediate and Advanced Python programming with hands-on coding. You will go through solid theory as well as practical sessions for each new topic to gain confidence & expertise to build the real-world projects in weeks 3 & 4. Topics such as Lists (Operations, Functions, Slices & Comprehensions), Dictionaries, Tuples, Sets, Arrays, Exceptions, Advanced File Handling, Modules & Packages, Libraries in Python (Numpy, Pandas & Matplotlib), Plots & Matrix Operations will be covered during the week 2.
Projects
Week 3
Practice during week-3 will focus on projects such as Edge Detection and Sift-based Image Similarity Identification using python. We will start the week by understanding computer vision, camera, images, and program Image Rotation, Translation, Blurring & Sharpening. Further, You will be guided to understand edge detection, specifically the canny edge detection method. We will be using image data of a road taken by an autonomous vehicle, then process it to detect edges in the image and visualize the edges on the image. Image similarity is a way to consistently quantify the similarity in images. In the image similarity project, You will identify the key points in an image and identify similar images in the given set of images.
Projects
Week 4
During week 4, we will focus on two major projects, one is on O-Ring manufacturing line inspection and another is on fire detection using real-time video analysis. You will understand and program dynamic thresholding, Binary morphism, and Semantic segmentation to inspect the O-Rings by using the given set of images of the O-Rings. Finally, classify whether the O-ring meets the required size, and shape, and is not snapped. In the fire detection project, You would work on detecting a fire in a video feed by typing a program that checks for fire in each frame of the video. For the process of fire detection, we would be using Chromatic analysis and dynamic analysis to detect fire based on flame color and intensity of the flame.
Projects
Edge Detection:
The concept of edge detection is used to detect the location and presence of edges by making changes in the intensity of an image. Different operations are used in image processing to detect edges. It can detect the variation of grey levels but it quickly gives a response when a noise is detected. In image processing, edge detection is a very important task. Edge detection is the main tool in pattern recognition, image segmentation, and scene analysis. In the project, you would understand canny edge detection by using image thresholding and HSV color space.
SIFT Image Similarity Analysis:
Image similarity is a way to consistently quantify the similarity between images. Image similarity search is fast since the data becomes so compressed. In the project, you will understand on to identify key points in an image and identify similar images in the given set of images by using the identified key points or features.
Manufacturing Line Inspection:
A computer vision system is implemented to analyze the captured images of O-rings and perform the measurement and inspection processes. The system is evaluated by inspecting a series of O-rings. Experimental results show that the proposed vision system is capable of measuring and inspecting the O-ring seal with good accuracy and efficiency. The project is focused on building an algorithm for analyzing the shape and size of the O-Ring to detect faulty O-rings.
Fire Detection using Real-Time Video Analysis:
The camera-based fire monitoring system can monitor the specified area in real-time through video processing. When a fire is detected based on the video, it will send a captured alarm image to the administrator. The administrator makes a final confirmation based on the submitted alarm image. Based on chromatic fire analysis and Dynamic Analysis of flame, you would program an algorithm to detect fire in the given video feed.
