Learn OpenCV for Computer Vision

Explore six trending applications of computer vision, including robotics, self-driving cars, medical imaging, facial recognition, machine vision, and surveillance, and learn how CV enables object detection, tumor detection, and tracking.

Differentiate digital image processing from computer vision by contrasting transformation with information extraction. Digital image processing outputs transformed images, while computer vision interprets images or videos to extract meaningful information.

Explore tools for computer vision, including OpenCV, TensorFlow, OpenVINO, MATLAB, and Rekognition, and learn operations like image enhancement, restoration, segmentation, morphological processing, feature matching, and recognition.

learn to install the open-source Anaconda distribution on Windows 10 by checking system type, downloading the 64-bit graphical installer, and completing the setup with default options.

Navigate the Anaconda Navigator interface, launch Jupyter Notebook and Spyder, explore optional tools like Louis, Orange Tree, and RStudio, and manage environments across the learning, environment, and community tabs.

Learn two methods to open Jupyter Notebook: via Anaconda Navigator and via the start menu, observing the launch process and the notebook home page in your browser.

Learn essential Jupyter notebook shortcuts for efficient coding, including running cells with shift enter, switching between command and markdown modes, deleting and inserting cells, and using tooltips with shift tab.

Import the OpenCV module, read an image with cv2.imread into img, display it with cv2.imshow, and use cv2.waitKey and cv2.destroyAllWindows to keep the window open until closed.

Learn how to load videos in OpenCV using cv2, reading from a file or capturing from a webcam, then display, handle frames, and release resources.

Explore how to convert from one color space to another in OpenCV using cvtColor, covering common spaces like gray and HSV and noting that images use BGR as the base.

Explore changing color spaces with OpenCV by reading an image in BGR, converting to grayscale and HSV with cv2.cvtColor, and displaying results to compare color space transformations.

Explore pixel manipulation in OpenCV for computer vision by examining image shape, size, pixel values, ROI, and splitting and merging channels, including RGB/BGR and CMYK considerations using cv2.

Explore pixel manipulation in OpenCV by examining image shape (height, width, channels), size, and a sample pixel value, and practice indexing pixels in a Jupyter notebook.

Learn to define and crop a region of interest in an image using cv2.selectROI, read the image, crop the region, and display the cropped output for further processing.

Learn image resizing in the image processing module by scaling images using a scaling factor or manual method, with cv2 interpolation options like Arya cubic and linear.

Resize images in OpenCV by reading an image, checking its shape, and resizing with fx and fy using cv2 dot resize and cv2 dot inter underscore linear.

Explore image filtering and blurring in OpenCV, applying low-pass kernels to smooth images, remove noise, and blur edges with averaging (box) filters, Gaussian, and median filters, using cv2.

Blur the image in a Jupyter notebook using cv2.blur with a 5x5 kernel, and compare it to the original image.

Understand image pyramids for multi-scale image representation by applying smoothing and subsampling to create a pyramid with varying resolutions, enabling upsampling, downsampling, edge detection, and image blending with high performance.

Explore image pyramid techniques in OpenCV by applying cv2.pyrUp and cv2.pyrDown to progressively scale an image, and visualize the results with matplotlib.

Explore image blending as an arithmetic operation that creates transparency by combining two images with adjustable alpha weights using cv2.addWeighted.

Dive into image subtraction by subtracting pixel values to merge two images, ensuring equal size and depth, using the cv2 subtraction syntax assigned to a variable.

Learn to implement image subtraction using cv2.subtract with two input images, display original images and the subtracted result, and understand how pixel values are merged in the output.

Explore bitwise operations in OpenCV to perform image masking and enhancement. Learn how and, or, xor, and not gates work with cv2 bitwise functions to manipulate image pixels.

Explore edge detection and its role in revealing boundaries between image segments. Learn the canny edge detection process with gaussian smoothing, gradient computation, non-maximum suppression, and hysteresis thresholding.

Explore edge detection with OpenCV by live camera capture, applying canny edge detection with adjustable min and max aperture values, and comparing multiple outputs.

Learn how morphological transformations apply non-linear neighborhood operations to binary images after thresholding to remove noise, clean up shapes, and merge gaps, using erosion, dilation, opening, closing, and gradient.

Explore erosion and dilation in OpenCV by coding cv2.erode and cv2.dilate with a kernel and iteration counts, observe original versus eroded and dilated images in a Jupyter notebook.

Master morphological operations in OpenCV, including erosion and dilation inside opening and closing, then compute image gradient and apply top hat and black hat to reveal features.

Apply morphological opening and closing using cv2 morphology operations on an image, compare with original, and explore eroded, dilated, and other operations like gradient, top hat, and black hat.

Explore simple thresholding for image segmentation by converting grayscale images to binary, and apply various binary threshold operations such as binary, binary inverted, truncated, set-to-zero, and set-to-zero inverted.

Learn what image noise is, caused by electronics, sensor heating, and ISO settings, and explore impulse noise—salt, pepper, and salt-and-pepper—along with brief notes on Gaussian, Poisson, and speckle.

Explore image gradients as measures of change in x and y, revealing magnitude and direction. Use the Sobel operator, a Gaussian smoothing differentiator, to compute x, y, and mixed derivatives.

Apply the Sobel filter in OpenCV to compute image gradients, adjust dx and dy and kernel size, and visualize the original and Sobel x images in a Jupyter notebook.

Explore image gradients using the scar operator for x and y derivatives, compare it with Sobel, and apply the Laplacian filter to compute the image's second derivatives.

Learn how image segmentation clusters an image into regions from pixel similarities and grayscale values to form connected regions. Explore techniques including threshold-based, edge-based, clustering, deep learning, and region growing.

Discover cluster based segmentation by creating pixel value based clusters and classifying pixels to the nearest centroids using k-means, hierarchical, or mean shift.

Learn how to perform image segmentation with OpenCV using k-means clustering, including reading and converting the image, setting termination criteria, and displaying the segmented result.