Master Computer Vision: From Basics to Advanced Projects

Explore computer vision as the technology that enables computers to perceive like humans, detecting, identifying, and recognizing objects by learning features, patterns, and neural networks from visual data.

Explore why computer vision matters for security and data capture. Learn how face recognition enhances cyber security and how handwriting recognition prevents forgery and enables digitization of handwritten notes.

Explore object detection and its applications, including self-driving cars, visual editing, and voice recognition integration. Discover how computer vision enables smart homes, movement and light detection, and medical imaging analysis.

Learn how object detection locates and identifies objects in images, draws bounding boxes, and tracks them across video frames within a predefined set of classes.

Tackle the major challenges in object detection, from recognizing highly varied objects across shapes and colors to managing class imbalance and reducing false positives.

Object recognition goes beyond bounding boxes to identify and name objects in image or video data, answering what is inside the image and who is present, as cats and dogs.

Explore the challenges in object recognition, including limited positive data, vast numbers of objects to recognize, and persistent class imbalance due to scarce positive samples versus abundant negatives.

Learn object segmentation by classifying each pixel into a class and color-coding components, with applications such as foreground extraction on green screens in film production.

Install OpenCV Python in your Jupyter notebook to start the image collection for the face recognition project. Build a data set, train the model, and test it on new data.

Install OpenCV Python using the command prompt, Anaconda, or Jupyter notebook, then troubleshoot common installation issues, including connection errors, slow internet, and read timeout during package downloads.

Install and import cv2, capture a webcam image, and save it under a name into a database folder, while addressing common cv2 import errors and numpy version issues.

Write and run the project code to capture webcam images, initialize a counter, and use OpenCV's cascade classifier with an XML model for face detection, while debugging together.

Capture webcam video, detect faces with a cascade classifier, crop and save face images, and control the loop with break conditions like pressing q or reaching 100 captures.

Capture the face portion, extend the boundary box by 30 units, crop and resize to 400x400, and save as jpgs in an images folder. Show the bounding box and counter on the final image, then release the webcam and resources.

Troubleshoot code execution and not found errors, create an images folder, manage the webcam, and use the CB2 cascade classifier for face detection to build a dataset from your images.

Introduce core computer vision concepts such as object detection, recognition, and pose estimation, then train a face recognition model on collected images and test it with new data.

Address doubts about collecting face images, guiding students to verify the images folder, download data from the portal, and troubleshoot webcam issues.

Master computer vision by revising a session on building a face data collection pipeline with OpenCV, including cascade classifier face detection, video capture, image cropping, resizing, saving samples, and cleanup.

Explore the basics of training and testing data in computer vision, learn how to split datasets into training and testing sets, and evaluate model accuracy against test data.

Split data into training and testing sets, creating train and test folders with per-person facial images. Follow a 70/30 split to build robust computer vision models.

Explore using a pre-trained VGG-16 convolutional neural network for object recognition, and leverage TensorFlow, Keras, NumPy, and glob to read data from dataset folders.

Resize images to 224 by 224 for VGG16 input, use datasets/train and datasets/test paths, initialize VGG16 with 224x224x3 and ImageNet weights, include_top false, freeze layers, add layer for three classes.

Explore why we use a pretrained VGG16 as a fixed feature extractor, apply transfer learning by replacing the top layer, and set up a dense softmax classifier for multiple classes.

Demonstrates flattening the VGG-16 final layer with vg dot output to a 7x7x512 vector and attaching a dense output layer, and explains two ways to define models.

Build the model by connecting input to the final output layer, inspect architecture with model.summary, and compile using categorical_crossentropy loss and Adam optimizer to tune metrics.

Explore data augmentation with an on-the-fly image data generator to create varied training and validation data, rescaling 0–255 to 0–1 and using 224×224 images.

Explore how data augmentation in computer vision uses the image data generator to normalize pixel values from 0 to 255 to 0 to 1 via rescale 1./255.

Train the defined model with data augmentation using fit_generator, tune epochs and steps per epoch, monitor loss and accuracy with plots, and save the trained model for later loading.

Execute and train a VGG16-based image classifier with 224 by 224 by 3 input and a final dense layer. Review the model summary and save the trained model as VGG16_fr.h5.

Address doubts during code execution by guiding you to install TensorFlow and Keras, run setup commands, and load the VGG16_fr.h5 model for testing face recognition.

learn to load a saved model with Keras, import libraries, and test new images for face recognition using a face detector to identify faces and assess prediction accuracy.

Read test images, detect and crop faces, resize to 224 by 224 RGB, expand dimensions to a four-dimensional tensor, and predict among three classes with a 0.7 threshold, handling unknowns.

Learn how facial recognition systems use folder-based data organization, biometric enrollment for new employees, and retraining practices to distinguish existing staff from new entrants and non-employees.

Observe a face recognition demo that loads the model, runs predictions, and highlights misidentifications due to limited training data and few epochs, emphasizing dataset preparation and model evaluation.

Use transfer learning with VGG16 to build and save a face recognition model, then test and identify whether faces belong to trained classes. Analyze results to explain successes and failures.

Troubleshoot and debug a TensorFlow script in an Anaconda environment. Verify the TensorFlow installation, run code, and address a missing dataset while generating image data batches.