Learn OpenCV 3. Become a Pro in Image Processing and Computer Vision. Welcome to my OpenCV Tutorial. In this tutorial, we will learn OpenCV 3 with Python 3 on various platforms like Windows and Raspbian on Raspberry Pi 3. We will also learn to use various image capture devices like web camera and Pi Camera module. Follow the entire series without missing a video or an exercise and you will become pro in computer vision in no time.

• 课时1：Installing Anaconda (Windows) and Basic Conda commands (15分50秒)
• 课时2：Basics of SPYDER IDE for Python Programmers (11分39秒)
• 课时3：Installing OpenCV 3 for Python 3 on Windows (11分59秒)
• 课时4：Databases of Images for Computer Vision Programming (5分0秒)
• 课时5：Reading and Displaying images with OpenCV (12分1秒)
• 课时6：Image reading modes and Writing an image to disc (7分36秒)
• 课时7：Images, Numbers, and NumPy (27分0秒)
• 课时8：Drawing Geometric Shapes (24分14秒)
• 课时9：Mini Project_ OpenCV BGR Palette with Trackbars (14分25秒)
• 课时10：Mini Project_ Mouse Events and Interactive Drawing (17分18秒)
• 课时11：Installing and Updating matplotlib with conda (6分44秒)
• 课时12：Matplotlib and Colormaps (12分40秒)
• 课时13：Colorspaces in OpenCV (17分54秒)
• 课时14：OpenCV Webcam Image Capture (11分49秒)
• 课时15：OpenCV Live Video Feed Capture and Processing (9分31秒)
• 课时16：OpenCV and Webcam Resolution (8分19秒)
• 课时17：OpenCV Webcam Video Recording to a file (12分59秒)
• 课时18：OpenCV Video Player (13分47秒)
• 课时19：OpenCV Object Tracking by Color (17分46秒)
• 课时20：Displaying Multiple Images with Matplotlib (15分36秒)
• 课时21：Arithmetic Operations on Images (15分5秒)
• 课时22：Blending and transitioning Images (14分36秒)
• 课时23：OpenCV Mini Project _ Trackbar Image Blending App (8分10秒)
• 课时24：Splitting and Merging channels of a color image (7分45秒)
• 课时25：Computing Negative of an Image (7分49秒)
• 课时26：Logical Operations on Images (8分40秒)
• 课时27：Scaling Operation on an Image (10分43秒)
• 课时28：Shifting or Translation Operation in Images (10分46秒)
• 课时29：Rotating an Image (8分56秒)
• 课时30：OpenCV Mini Project - Rotation Effect on live Webcam Feed (14分30秒)
• 课时31：Affine Transformations (10分8秒)
• 课时32：Perspective Transform (8分58秒)
• 课时33：Thresholding and basic Segmentation (12分24秒)
• 课时34：Otsu's Binarization Thresholding (8分20秒)
• 课时35：Adaptive Thresholding (10分5秒)
• 课时36：Thresholding Live Webcam Feed (8分17秒)
• 课时37：Noise (21分35秒)
• 课时38：Kernel and Convolution (17分21秒)
• 课时39：Low Pass Filters (11分14秒)
• 课时40：Median Blur Filter (4分46秒)
• 课时41：High Pass Filters (15分27秒)
• 课时42：Canny Edge Detector (11分53秒)
• 课时43：Hough Line Transform on a Live Video (14分14秒)
• 课时44：Image Restoration by Inpainting (11分58秒)
• 课时45：Image Quantization with K Means Clustering (39分50秒)
• 课时46：Matplotlib Histograms (17分40秒)
• 课时47：NumPy Histograms (9分41秒)
• 课时48：OpenCV Histograms (6分4秒)
• 课时49：Histogram Equalization (10分33秒)
• 课时50：Morphological Operations (22分27秒)
• 课时51：Finding and Drawing Contours (9分35秒)
• 课时52：Motion Tracking and Detection (21分23秒)
• 课时53：Max RGB Filter (7分54秒)

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