Learning to Master OpenCV 3 in Python

Learning to Master OpenCV 3 in Python

BEST SELLER 102 Lectures 10h 7m

Prepare for your examination with our training course. The course contains a complete batch of videos that will provide you with profound and thorough knowledge related to certification exam. Pass the test with flying colors.

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Curriculum For This Course

5 lectures 18m
  • 1. Introduction
    2m
  • 2. Introduction to Computer Vision and OpenCV
    3m
  • 3. About this course
    5m
  • 4. Recomended - Setup your OpenCV4.0.1 Virtual Machine
    6m
  • 5. Set up course materials (DOWNLOAD LINK BELOW) - Not needed if using the new VM
    2m

8 lectures 42m
  • 1. What are Images?
    2m
  • 2. How are Images Formed?
    3m
  • 3. Storing Images on Computers
    5m
  • 4. Getting Started with OpenCV - A Brief OpenCV Intro
    9m
  • 5. Grayscaling - Converting Color Images To Shades of Gray
    2m
  • 6. Understanding Color Spaces - The Many Ways Color Images Are Stored Digitally
    12m
  • 7. Histogram representation of Images - Visualizing the Components of Images
    5m
  • 8. Creating Images & Drawing on Images - Make Squares, Circles, Polygons & Add Text
    4m

14 lectures 57m
  • 1. Transformations, Affine And Non-Affine - The Many Ways We Can Change Images
    2m
  • 2. Image Translations - Moving Images Up, Down
    3m
  • 3. Rotations - How To Spin Your Image Around And Do Horizontal Flipping
    3m
  • 4. Scaling, Re-sizing and Interpolations - Understand How Re-Sizing Affects Quality
    4m
  • 5. Image Pyramids - Another Way of Re-Sizing
    2m
  • 6. Cropping - Cut Out The Image The Regions You Want or Don't Want
    3m
  • 7. Arithmetic Operations - Brightening and Darkening Images
    4m
  • 8. Bitwise Operations - How Image Masking Works
    4m
  • 9. Blurring - The Many Ways We Can Blur Images & Why It's Important
    7m
  • 10. Sharpening - Reverse Your Images Blurs
    2m
  • 11. Thresholding (Binarization) - Making Certain Images Areas Black or White
    9m
  • 12. Dilation, Erosion, Opening/Closing - Importance of Thickening/Thinning Lines
    5m
  • 13. Perspective & Affine Transforms - Take An Off Angle Shot & Make It Look Top Down
    4m
  • 14. Mini Project 1 - Live Sketch App - Turn your Webcam Feed Into A Pencil Drawing
    5m

8 lectures 55m
  • 1. Segmentation and Contours - Extract Defined Shapes In Your Image
    11m
  • 2. Sorting Contours - Sort Those Shapes By Size
    13m
  • 3. Approximating Contours & Finding Their Convex Hull - Clean Up Messy Contours
    6m
  • 4. Matching Contour Shapes - Match Shapes In Images Even When Distorted
    5m
  • 5. Mini Project 2 - Identify Shapes (Square, Rectangle, Circle, Triangle & Stars)
    5m
  • 6. Line Detection - Detect Straight Lines E.g
    6m
  • 7. Blob Detection - Detect The Center of Flowers
    3m
  • 8. Mini Project 3 - Counting Circles and Ellipses
    6m

7 lectures 51m
  • 1. Object Detection Overview
    3m
  • 2. Mini Project # 4 - Finding Waldo (Quickly Find A Specific Pattern In An Image)
    3m
  • 3. Feature Description Theory - How We Digitally Represent Objects
    5m
  • 4. Finding Corners - Why Corners In Images Are Important to Object Detection
    7m
  • 5. SIFT, SURF, FAST, BRIEF & ORB - Learn The Different Ways To Get Image Features
    10m
  • 6. Mini Project 5 - Object Detection - Detect A Specific Object Using Your Webcam
    15m
  • 7. Histogram of Oriented Gradients - Another Novel Way Of Representing Images
    8m

3 lectures 23m
  • 1. HAAR Cascade Classifiers - Learn How Classifiers Work And Why They're Amazing
    5m
  • 2. Face and Eye Detection - Detect Human Faces and Eyes In Any Image
    11m
  • 3. Mini Project 6 - Car and Pedestrian Detection in Videos
    7m

4 lectures 35m
  • 1. Face Analysis and Filtering - Identify Face Outline, Lips, Eyes Even Eyebrows
    11m
  • 2. Merging Faces (Face Swaps) - Combine Two Faces For Fun & Sometimes Scary Results
    9m
  • 3. Mini Project 7 - Live Face Swapper (like MSQRD & Snapchat filters!!!)
    6m
  • 4. Mini Project 8 - Yawn Detector and Counter
    9m

3 lectures 41m
  • 1. Machine Learning Overview - What Is It & Why It's Important to Computer Vision
    9m
  • 2. Mini Project 9 - Handwritten Digit Classification
    20m
  • 3. Mini Project # 10 - Facial Recognition - Make Your Computer Recognize You
    12m

6 lectures 34m
  • 1. Filtering by Color
    6m
  • 2. Background Subtraction and Foreground Subtraction
    7m
  • 3. Using Meanshift for Object Tracking
    5m
  • 4. Using CAMshift for Object Tracking
    4m
  • 5. Optical Flow - Track Moving Objects In Videos
    7m
  • 6. Mini Project # 11 - Ball Tracking
    5m

1 lectures 7m
  • 1. Mini Project # 12 - Photo-Restoration
    7m

2 lectures 10m
  • 1. Course Summary and how to become an Expert
    3m
  • 2. Latest Advances, 12 Startup Ideas & Implementing Computer VIsion inm Mobile Apps
    7m

3 lectures 19m
  • 1. Setup your Deep Learning Virtual Machine
    10m
  • 2. Intro to Handwritten Digit Classification (MNIST)
    6m
  • 3. Intro to Multiple Image Classification (CIFAR10)
    3m

12 lectures 1h 35m
  • 1. Neural Networks Chapter Overview
    2m
  • 2. Machine Learning Overview
    8m
  • 3. Neural Networks Explained
    4m
  • 4. Forward Propagation
    9m
  • 5. Activation Functions
    9m
  • 6. Training Part 1 – Loss Functions
    9m
  • 7. Training Part 2 – Backpropagation and Gradient Descent
    10m
  • 8. Backpropagation & Learning Rates – A Worked Example
    14m
  • 9. Regularization, Overfitting, Generalization and Test Datasets
    15m
  • 10. Epochs, Iterations and Batch Sizes
    4m
  • 11. Measuring Performance and the Confusion Matrix
    7m
  • 12. Review and Best Practices
    4m

9 lectures 42m
  • 1. Convolutional Neural Networks Chapter Overview
    1m
  • 2. Introduction to Convolutional Neural Networks (CNNs)
    5m
  • 3. Convolutions & Image Features
    13m
  • 4. Depth, Stride and Padding
    7m
  • 5. ReLU
    2m
  • 6. Pooling
    5m
  • 7. The Fully Connected Layer
    2m
  • 8. Training CNNs
    3m
  • 9. Designing Your Own CNN
    4m

12 lectures 53m
  • 1. Introduction to Keras & Tensorflow
    1m
  • 2. Building a CNN in Keras
    12m
  • 3. Building a Handwriting Recognition CNN
    2m
  • 4. Loading Our Data
    6m
  • 5. Getting our data in ‘Shape’
    4m
  • 6. Hot One Encoding
    3m
  • 7. Building & Compiling Our Model
    4m
  • 8. Training Our Classifier
    5m
  • 9. Plotting Loss and Accuracy Charts
    3m
  • 10. Saving and Loading Your Model
    3m
  • 11. Displaying Your Model Visually
    3m
  • 12. Building a Simple Image Classifier using CIFAR10
    7m

5 lectures 25m
  • 1. Data Augmentation Chapter Overview
    1m
  • 2. Splitting Data into Test and Training Datasets
    10m
  • 3. Train a Cats vs
    4m
  • 4. Boosting Accuracy with Data Augmentation
    5m
  • 5. Types of Data Augmentation
    5m
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