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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 17
1. Question
How to load an image?
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Question 2 of 17
2. Question
Which function is used to display an image in a window?
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Question 3 of 17
3. Question
How to save an image?
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Question 4 of 17
4. Question
Which function do we use for an adjustable frame?
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Question 5 of 17
5. Question
What can you do if you have a Colored image and you want to change it into Grayscale?
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Question 6 of 17
6. Question
What happens when you pass ‘2’ in cv2.waitkey function?
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Question 7 of 17
7. Question
You can create as many windows as you wish, but with different window names.
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Question 8 of 17
8. Question
If you wanted to destroy a specific window, what function would you use ?
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Question 9 of 17
9. Question
Color image loaded by OpenCV is in BGR mode, but Matplotlib displays it in RGB mode, so color images will not be displayed correctly in Matplotlib if the image is read with OpenCV.
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Question 10 of 17
10. Question
What np.zeros((255,255), np.uint8) will create?
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Question 11 of 17
11. Question
What will the result be if we modify the ROI like this: img[100:150,100:150] = [255,0,0]
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Question 12 of 17
12. Question
What will this do:
img[:,:,1] = 255
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Question 13 of 17
13. Question
What will happen if we load an image into grayscale then split it into 3 like: a,b,c = cv2.split(img)
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Question 14 of 17
14. Question
How can we add two images? (choose all that apply)
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Question 15 of 17
15. Question
What are the several techniques and coding principles to exploit maximum performance of Python and Numpy. (Choose all that apply)
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Question 16 of 17
16. Question
Total number of pixels is accessed by img.size
The answer option of this question is in python instead of English
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Read the Options carefully
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Question 17 of 17
17. Question
If an image is grayscale, the tuple returned with `img.shape` contains the number of rows, columns and channels.
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