Super 50 Real Projects in Computer Vision and Deep Learning

Create variables to store a name and an age, then print welcome messages. Show how to use f-strings for formatted output, add a decorative separator, and print a multiline message.

Explore variables, data types, and operators in Python, and build a small program step by step to practice basic programming and calculations with numbers and text.

Explore conditional statements in Python by using if and else to make decisions, and learn to check numbers, temperatures, and students' scores with simple code.

Explore how loops power Python code by iterating over lists, using for and while loops, and applying break to stop a loop.

Explore Python lists as a powerful tool for storing multiple pieces of information in one place, access and modify elements, and loop through data to keep code clean and efficient.

Master Python lists by creating and manipulating lists, indexing and slicing, combining and repeating, and modifying with append, insert, remove, and pop, plus looping and nested lists.

Explore tuples, dictionaries, and sets in Python, learning immutable ordered data, fast key-value lookups, and set operations like union, intersection, and difference.

Learn to install and import the psuTL library and monitor real-time system health by checking CPU usage, memory usage, and disk usage in Python.

Set up OpenCV in Visual Studio Code, install and verify libraries, and run your first Python script to load and display an image, establishing the foundation for image processing tasks.

Install OpenCV in Visual Studio Code, import cv2, load an image with cv2.imread, display it with cv2.imshow, wait for a key, and close windows, handling load errors.

Load an image with OpenCV, draw a rectangle with cv2.rectangle and add text with cv2.putText, then display with cv2.impshow and save with cv2.imwrite.

Import the OpenCV library, load an image, split it into blue, green, and red channels in BGR order, and visualize each channel to analyze color intensities.

Apply Otsu's thresholding to convert grayscale images into binary images, loading a grayscale image and separating foreground from background for document scanning and object detection.

Import cv2 and numpy, load a grayscale image, convert it to binary via thresholding, define a 5x5 kernel, apply closing to remove noise and display.

Load a grayscale image and apply the Kanye edge detection algorithm, tuning its parameters to identify sharp changes in intensity as edges for improved object detection and shape analysis.

Learn edge detection with OpenCV and NumPy by loading a grayscale image, applying canny edge detection, and displaying results before closing windows.

Load a color image and perform simple corner detection to identify key points where edges meet. Detecting corners aids object tracking, image alignment, and 3D reconstruction by revealing image structure.

Locate a smaller image inside a larger one using template matching with OpenCV, loading both images to identify where the template appears for object detection, quality control, and automation.

Use template matching with OpenCV and NumPy to load images, convert to grayscale, apply cv2.matchTemplate, threshold, locate matches with NumPy, and draw bounding boxes to visualize detections.

Explore how image histograms depict pixel intensity distributions and apply histogram equalization and CLAHE to enhance contrast in low-contrast images. Visualize histograms with Matplotlib to understand the changes.

Enhance image contrast by applying histogram equalization and Klahe to grayscale images, compute and compare histograms, and visualize results using OpenCV, NumPy, and Matplotlib.

Explore advanced object tracking with OpenCV by detecting and tracking colored objects in real-time video using HSV color space, color masks, bounding boxes, and object labeling.

Use OpenCV and NumPy to load and process video, detecting red, blue, and green objects with HSV color thresholds, drawing bounding boxes and contours, and displaying tracked output.

Extract text from images using Python, OpenCV, and Tesseract OCR through pre-processing, thresholding, and dilation to detect text regions and draw bounding boxes.

Explore advanced shape detection with OpenCV to identify and classify triangles, squares, rectangles, pentagons, and circles using preprocessing, contour detection, and shape recognition in Python.

Learn to build a shape detection pipeline in python with opencv: load an image, convert to grayscale, blur, detect edges, find contours, classify shapes, and annotate the results.

Build a real-time face detection system with MediaPipe FaceMesh and OpenCV by capturing webcam video, detecting facial landmarks, and visualizing results on screen.

Extract and print pixel coordinates for nose tip, left eye, and right eye from the MediaPack face mesh model, converting normalized values to pixels for head position and orientation.

Explore Roboflow to label images, prepare datasets, and train a YOLO model for real-time object detection. Create a workspace, invite teammates, and configure deployment options for collaboration.

Create a new raw flow project, select object detection to train a YOLO model, then upload images and start labeling with bounding boxes to prepare the dataset.

Learn to annotate images for object detection by drawing bounding boxes and naming objects with manual labeling. Use tools like polygon and smart polygon to create accurate training data.

Learn to speed up image labeling with label assist and box prompting using pre-trained models like mscoco, then refine with smart polygon annotations to achieve precise object labels for training.

Annotate and add images to the dataset, then configure train/validation/test splits and finalize the assignment using 70/20/10 for training and evaluation.

Train your model by selecting custom training, defining a dataset version, and configuring source image, train/test split, and pre-processing options like auto-orient, resize, grayscale, and normalization to optimize learning.

Boost contrast using histogram-based auto-adjustment to improve edge detection and detail under uneven lighting. Learn contrast stretching, histogram equalization, adaptive equalization, and tile pre-processing for small objects in aerial imagery.

Apply data augmentation to improve model generalization by increasing training data diversity through random transformations like flip, rotate, crop, grayscale, exposure, and hue.

Augment training images with saturation, brightness, blur, and noise to improve robustness to lighting. Use cutout, mosaic, and bounding box transformations—flip, rotation, crop, and shear.

Open Colab, create a new notebook, and configure runtime to use the GPU; connect to Google Drive and prepare workspace for data processing and training, installing OpenCV and YOLO dependencies.

Open Google Colab, set a Python 3 runtime with a T4 GPU, install YOLOv12 and required libraries, upload an image, and run object detection with YOLOv12.

Create a RoboFlow project, import images, and label objects using smart polygon tools; apply augmentation with rotated, flipped, and scaled images to prepare a structured dataset for training.

Explore smart labeling techniques in computer vision and deep learning with part a from the course Super 50 real projects in computer vision and deep learning.

Advance YOLO training by selecting YOLO version 12x for accuracy, upgrading to Colab Pro for an A100 GPU, and running 100 epochs to download best.pt.

Test a trained YOLO model on local videos using OpenCV, annotate detected objects by drawing bounding boxes, and save the processed output video to evaluate real-world performance.

Test the YOLO model on local videos on a PC, moving testing from Colab to a local environment, learn to run the model, process video files, and troubleshoot deployment issues.

Install the required libraries, load the trained YOLOv12 model, and run object detection on a video, drawing bounding boxes and labels, and save the result as output_underline_detected.mp4.

Enable real-time object detection with a trained YOLO model using your webcam, drawing bounding boxes and labels via annotators, and displaying live results with OpenCV.

Equip yourself to train, test, and deploy object detection models to elevate your computer vision projects. Turn images into actionable ai models that deliver smarter, more impactful results.