Building a real-time automatic number plate recognition system using YOLO and OpenCV library in Python, order your copy now!Download EBook
Using image processing, machine learning and deep learning methods to build computer vision applications using popular frameworks such as OpenCV and TensorFlow in Python.
Learn how to make a barcode scanner that decodes barcodes and draw them in the image using pyzbar and OpenCV libraries in Python
Learn how to build a deep learning malaria detection model to classify cell images to either infected or not infected with Malaria Tensorflow 2 and Keras API in Python.
Learn how to use transfer learning to build a model that is able to classify benign and malignant (melanoma) skin diseases in Python using TensorFlow 2.
Blurring and anonymizing faces in images and videos after performing face detection using OpenCV library in Python.
Using the state-of-the-art YOLOv8 object detection for real-time object detection, recognition and localization in Python using OpenCV, Ultralytics and PyTorch.
Using K-Means Clustering unsupervised machine learning algorithm to segment different parts of an image using OpenCV in Python.
Learning how to detect contours in images for image segmentation, shape analysis and object detection and recognition using OpenCV in Python.
Learn how to Use Tesseract OCR library and pytesseract wrapper for optical character recognition (OCR) to convert text in images into digital text in Python.
Detecting shapes, lines and circles in images using Hough Transform technique with OpenCV in Python. Hough transform is a popular feature extraction technique to detect any shape within an image.
Learning how to apply edge detection in computer vision applications using canny edge detector algorithm with OpenCV in Python.
Learn what is transfer learning and how to use pre trained MobileNet model for better performance to classify flowers using TensorFlow in Python.
Learning how you can generate and read QR Code in Python using qrcode and OpenCV libraries.
Building and training a model that classifies CIFAR-10 dataset images that were loaded using Tensorflow Datasets which consists of airplanes, dogs, cats and other 7 objects using Tensorflow 2 and Keras libraries in Python.
Performing face detection using both Haar Cascades and Single Shot MultiBox Detector methods with OpenCV's dnn module in Python.