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Image Processing & Computer Vision with Python
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Week 0 (4 hours) FREECourse Introduction18 Topics
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0.01 Welcome to the course
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0.02 Course Structure
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0.03(a) What is Computer Vision?
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0.03(b) Computer Vision Sub Domains
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0.04 Computer Vision Vs Image Processing
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0.05 About Opencv & learning resources
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0.06 Resources to learn Python
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0.07 Anaconda & Why we need Virtual Environments
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0.08 Installing Anaconda and checking it
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0.09 Creating Python Scripts and running it
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0.10 How to use a Jupyter notebook
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0.11 Installing Opencv and testing it
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0.12 Troubleshooting Installation problems
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0.13 Downloading Files in the working directory
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0.14 How to Solve Programming Errors
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0.15 Using Google Colab
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0.16 (Optional but recommended) Creating a Virtual Environment & Installing Jupyter notebook
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0.17 (Optional) Installing OpenCV from Source
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0.01 Welcome to the course
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Week 1 (5 hours) FREEPython Crash Course8 Topics|1 Quiz
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Introduction To The Week
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1.00 Download Code
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1.01 Datatypes, Variables, Strings, Printing
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1.02 Input, Eval, Lists, Dict
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1.03 Booleans, tuples, sets, operators, Conditional statements
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1.04 Loops, Enumerate, List Comprehension
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1.05 Functions, Lambda, Map, Filter, Methods
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1.06 Interospection, Try-Except, Help, Import, OOP
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Introduction To The Week
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Numpy Crash Course3 Topics|1 Quiz
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Week 2 (8 hours) FREEOpencv Basics13 Topics|1 Quiz
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Introduction To The Week
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2.00 Download Code
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2.01 Opencv Fundamentals
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2.02 Imshow, Resizing, Imwrite & Conditional Exit
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2.03 Drawing Shapes & Text On Image
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2.04 Working with Videos
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2.05 Using Mouse & Trackbar
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2.06 Manipulating Image ROI & Channels
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2.07 Image Addition, Resizing & Blending
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2.08 Making an Image Transition Application
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2.09 Replacing ROIs & Transparent Images
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2.10 Bitwise Operations & Basic Thresholding
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2.11 Overlaying Logo With Removed Background
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Introduction To The Week
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Week 3 (6 hours) FREEImage Processing Pt 113 Topics|1 Quiz
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Introduction To The Week
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3.00 Download Code
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3.01 Brightness & Contrast Enhancement
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3.02 Advance Thresholding
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3.03 Image Filtering & Convolution
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3.04 Blurring Methods
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3.05 Cartoonify Photos
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3.06 Color Spaces & Color Models Theory
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3.07 Segmenting Colored Objects
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3.08 Image Gradients
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3.09 Edge Detection with First & Second Order Derivatives
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3.10 Canny Edge Detector Theory
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3.11 Canny Edge Detector in OpenCV
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Introduction To The Week
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Week 4 (6 hours)Image Processing Pt 214 Topics|1 Quiz
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Introduction To The Week
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4.00 Download Code
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4.01 Morphological Operations
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4.02 Blur Detection
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4.03(a) Haar Cascades Theory
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4.03(b) Haar Cascades, Face & Eye Detection
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4.04 Cat, Car & Pedestrian Detection With Optimization
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4.05 Histograms
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4.06 Histogram Equalization
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4.07 Histogram Backprojection
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4.08 Finding Dominant Color in Image
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4.09 LookUpTables & Gamma Correction
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4.10 ColorMaps
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4.11 Advance Color Adjustment
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Introduction To The Week
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Week 5 (12 hours)Image Processing Pt 314 Topics|1 Quiz
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Introduction To The Week
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5.00 Download Code
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5.01 Geometric Transformations
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5.02 Image Pyramids
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5.03 Pyramid Blending
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5.04 Seamless Cloning
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5.05 Facial Cloning
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5.06 Blob Detection
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5.07 Contours
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5.08 Contours, More Functions
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5.09 Contour Analysis
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5.10 Static Background Subtraction
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5.11 Smart Background Subtraction
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5.12 Car detection with Background Subtraction
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Introduction To The Week
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Week 6 (6 hours)Classical Vision Applications7 Topics
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Week 7 (7 hours)GUI Automation, Segmentation & Hough Transforms10 Topics|1 Quiz
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Introduction To The Week
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7.00 Download Code
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7.01 Template Matching
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7.02 GUI Automation with PyAutoGUI
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7.03 Making Computer Vision Game Bots
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7.04 Image Inpainting
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7.05 Image Segmentation with GrabCut Algorithm
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7.06 Image Segmentation with Watershed Algorithm
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7.07 Hough Transforms
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7.08 Hough Lines & Circles
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Introduction To The Week
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Week 8 (5 hours)Feature Detectors & Descriptors11 Topics|1 Quiz
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Introduction To The Week
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8.00 Download Code
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8.01 What Are Image Features?
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8.02 Corner Detection
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8.03 Feature Detectors & Descriptors In OpenCV
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8.04 ORB (Object Oriented FAST & Rotated BRIEF) Theory
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8.05 Feature Matching
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8.06 Real Time Image Classification With Feature Matching
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8.07 Real Time Object Detection With Image Features
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8.08 Creating A Panorama
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8.09 Feature Based Image Alignment
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Introduction To The Week
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Week 9 (8 hours)Machine Learning9 Topics|1 Quiz
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Introduction To The Week
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9.00 Download Code
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9.01 Introduction To Artificial Intelligence
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9.02 Image Classification With AI, ML and DL
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9.03 Histogram Of Oriented Gradients
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9.04 Support Vector Machines (SVMS)
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9.05 Image Classification With HOG + SVM
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9.06 Custom ASL Classification
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9.07 Pedestrian Detection With HOG+SVM
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Introduction To The Week
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Week 10 (5 hours)Object Tracking12 Topics|1 Quiz
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Introduction To The Week
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10.00 Download Code
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10.01 Object Tracking Introduction
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10.02 Meanshift & Camshift
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10.03 Optical Flow Theory
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10.04 Optical Flow In OpenCV
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10.05 Modern Trackers In OpenCV
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10.06 Object Tracking In OpenCV With Tracker API
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10.07 Multiple Object Tracking In OpenCV
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10.08 Comparing Different Trackers
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10.09 Video Stabilization In OpenCV
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10.10 Video Stabilization With Vidstab
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Introduction To The Week
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Week 11 (10 hours)Deep Learning16 Topics|1 Quiz
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Introduction To The Week
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11.00 Download Code
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11.01 Deep Learning In OpenCV With DNN Module
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11.02 Image Classification WIth DNN Module
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11.03 Face Detection With DNN Module
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11.04 SSD Based Object Detection
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11.05 Object Detection With YOLO v3
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11.06 Image Segmentation With Mask R-CNN
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11.07 Age Detection
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11.08 Gender Detection With DNN
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11.09 Facial Expression Recognition
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11.10 Image Colorization
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11.11 Super Resolution With DNN
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11.12 Neural Style Transfer
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11.13 Human Body Pose Estimation With OpenPose
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11.14 Hand Keypoint Detection
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Introduction To The Week
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Instructions For Getting Certificate Of Completion
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Thank You2 Topics
Participants 242
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Question 1 of 9
1. Question
Why use Numpy instead of python lists? (Choose all that apply)
CorrectIncorrect -
Question 2 of 9
2. Question
if array = np.arange(3,7) What is the output of len(array)
CorrectIncorrect -
Question 3 of 9
3. Question
How many dimensions are there in array ‘a’?
a = np.array([[3,3,0,3,3]])
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Question 4 of 9
4. Question
What will be the output of b.flatten() if
b = np.full((3, 2), 7)
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Question 5 of 9
5. Question
What is the result of np.vstack((a, b,c))
a = np.arange(4).reshape(2 , 2)
b = np.arange(4,8).reshape(2,2)
c = np.arange(9,13).reshape(2,2)CorrectIncorrect -
Question 6 of 9
6. Question
What is the value of nums after we do:nums[2:4] = [5, 6]
Suppose
nums = list(range(5))CorrectIncorrect -
Question 7 of 9
7. Question
What value is at a[1][2]
a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]])
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Question 8 of 9
8. Question
What is the result of np.sum(x)
x = np.array([[1, 2, 3], [4, 5, 6]])
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Question 9 of 9
9. Question
What is the result of x + y
x = np.array([[1, 2],[3, 4]])
y = np.array([[5, 6],[7, 8]]).
CorrectIncorrect