Applied Computer Vision For Beginners

Set up the computer vision environment with Jupyter Notebook and OpenCV, learn image representations in color and grayscale, and practice basic image and video capture.

Set up the Python, Jupyter Notebook, and OpenCV environment, install and verify the tools, and learn to run and test OpenCV in Jupyter for image and video analysis.

Explain how images are stored as pixel matrices: grayscale uses a single intensity per pixel, while color images use blue, green, and red channels in a 2D grid.

Explore how OpenCV handles images as multi-dimensional arrays, generate random grayscale or color images with NumPy, and save or load them using imwrite and imread in a Jupyter notebook.

Load and view images in OpenCV using a Jupyter notebook, compare color and grayscale options, and inspect image shape, size, and data type to understand pixel structure.

Capture an image from a webcam with OpenCV, using a video capture handle to read a frame, display it, and release the camera.

Learn to capture and save webcam video by configuring fps and resolution, selecting an appropriate codec with OpenCV, creating a video writer, and releasing the camera after recording.

Set up a development environment with Python, Jupyter, and OpenCV, learn image representations as matrices (gray and BGR), and create, save, load, and display images and videos.

Explore image manipulation with OpenCV in this section, covering cropping and changing pixels, coloring and drawing lines, drawing shapes and annotating images, extracting color components, and resizing and transposing images.

Learn to crop images by selecting a rectangle defined by corner coordinates to extract a subimage for display, and edit pixels by changing the color matrix to alter colors.

Apply array-based color channel manipulation to image matrices, turning blue, green, or red components on or off, and draw lines by updating pixel values in a Jupyter notebook.

Explore OpenCV drawing functions to annotate images with lines, circles, rectangles, and text using cv2.line, cv2.circle, cv2.rectangle, and cv2.putText.

Learn to annotate images with OpenCV by drawing lines, circles, and rectangles, then add labeled text using cv2 functions, color choices, and thickness settings.

Learn to extract and display color components from images using OpenCV, understand why splitting a BGR image yields grayscale components, and merge them back to reconstruct the color image.

Split an image into blue, green, and red components, display each channel, and merge them back to explore color channel manipulation and swapping effects.

Learn image transformation by resizing with OpenCV in Python, using absolute sizes such as 600 by 600 and half size. Then transpose images, a flip-rotate operation demonstrated on Lena.

Master basic image manipulations with OpenCV, including colors, lines, shapes, and annotating images. Practice resizing and cropping as basic transformations, with more advanced techniques to come.

Explore advanced image operations with OpenCV, including block-based processing and regions of interest. Learn color space conversions, image translations, and how to define the canvas for displaying results.

Explore regions of interest in images, defined by boundaries, using OpenCV to extract and relocate rectangular, circular, or elliptical patches for face-related tasks.

Explore color spaces and conversions in OpenCV, from BGR to grayscale and to YUV/HSV. Understand additive color models and experiment with splitting and displaying color components.

Explore color space conversion in OpenCV by converting a BGR image to gray and back, and by visualizing blue, green, and red channels.

Rotate images around their center using OpenCV’s rotate function with 90° clockwise or counterclockwise and 180° flips, treating the image as a whole.

Rotate an image at an arbitrary angle using a center of rotation, a 2d rotation matrix, and a fine transformation in OpenCV, with options to scale and crop.

Learn to apply linear transformations to translate images in OpenCV, using a translation matrix and an expanded canvas to prevent clipping while preserving pixel integrity during rotation and translation.

Apply translation and rotation using OpenCV transforms to move images within an enlarged canvas, preventing pixel clipping, centering, and rotating with warp affine and proper matrices.

Define the image's major region of interest, reposition regions, and explore color spaces and conversions, transformations for translations and rotations, resizing without information loss, and convolution operations.

Explore image filters and edge detection in section four, applying convolution to blur, sharpen, emboss, and effects while using high and low pass filters to understand image frequencies and contours.

Explore image filtering fundamentals, including filters and convolution, to extract edges, blurs, and objects. See how discrete transforms and a sliding kernel produce output images.

Apply convolutions to sharpen and blur images using central pixel kernels; explore 5x5 sharpening filters and zero-sum kernels in OpenCV examples.

Apply sharpening and blurring with convolution kernels in OpenCV, converting to grayscale, testing 2x2, 3x3, and 5x5 filters, and subtracting blur from the original for edge enhancement.

Explore how low pass and high pass filters affect image frequency to blur or sharpen edges through convolution, applying kernels across grayscale and color (BGR) images with OpenCV.

Explore image filters such as high-pass sharpening, color edge preservation, blur, unbalanced filters, and embossing; apply kernels in a notebook to enhance and analyze image content.

Detect image contours and edges using OpenCV, learn find contours and drawing, apply thresholding and grayscale input, and use the canny edge detector to reveal objects and faces.

Visualize contour detection and edge detection with Garnier's detector in OpenCV, threshold a grayscale image to binary, extract contours, approximate shapes, and draw color contours on images.

Explore image filtering and edge detection through convolution and filters like blurring, sharpening, and embossing to reveal edges and high-intensity areas. Learn to draw contours that define object boundaries.

Section five of the computer vision course shows tightening image controls, using irregular shapes, and detecting faces in images and videos with OpenCV, connecting fundamentals to tasks like phase detection.

Explore fitting contours on images by drawing bounding boxes, minimum area rectangles, and fitted circles around detected controls and contours using OpenCV's hierarchy and bounding box functions.

Convert images to grayscale, threshold them, and extract external contours. Fit and draw bounding rectangles, minimum area rectangles, and circles around detected contours on the color image.

Explore irregular bounding curves to tightly enclose image contours by fitting approximate polygons and convex hulls, using image pyramids, arc length, and epsilon thresholds for adjustable accuracy.

Explore detecting contours with OpenCV, downsampling and grayscale conversion, then thresholding and approximating polygons, and contrasting convex hulls with irregular contours for accurate bounding shapes.

Learn how face detection uses Haar-like features with a Viola-Jones style classifier trained on positive and negative examples to locate faces in images.

Explore robust feature selection and a cascade of classifiers to speed up face detection by focusing on regions of interest in OpenCV.

Detect faces with cascades and Viola John's algorithm in a Jupyter notebook, converting frames to grayscale, using cascade classifiers, and drawing rectangles around faces, with eye detection in ROI.

End of section five tightens image controls near objects and draws contours for non-polygon shapes, applying phase and eye detection in video. Section six introduces neural networks for computer vision.

Switch from OpenCV to neural networks to learn deep learning and machine learning, train and classify with neural networks, and practice image classification and object detection with TensorFlow.

Explore the core concepts of artificial intelligence, machine learning, and deep learning, distinguishing their roles while understanding data, sensors, labeling, and how these ideas enable computer vision applications.

Explore how deep learning powers computer vision by teaching models to recognize handwritten digits from a popular dataset, using input, hidden, and output layers in a feedforward neural network.

Explore how artificial neurons use weighted inputs and activation functions during the training phase to adjust parameters via backward bias, enabling accurate inference on unseen images.

Explore the evolution of deep learning from perceptrons to deep neural networks and transformers, and its impact on computer vision, speech recognition, and real-world applications.

Apply neural networks to computer vision classification using TensorFlow in Google Colab, moving from OpenCV to AI-based workflows with data loading, normalization, training, and deployment for inference.

Load the fashion mnist data set with 60,000 training and 10,000 test images in Colab, map labels to ten clothing categories, and normalize pixels to 0–1.

Initialize a sequential neural network for 28x28 images by flattening inputs, using a dense 128-neuron layer then a 10-neuron output layer, trained with categorical cross entropy and stochastic gradient descent.

Train a neural network, apply softmax normalization to outputs into a probability model, and evaluate test predictions with about 90% accuracy while analyzing misclassifications.

Learn to build and train neural networks for image classification in computer vision using TensorFlow in Google Colab, and explore object detection workflows on Google Cloud.