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 compute and detect SIFT features for feature matching and more using OpenCV library in Python.
Learn how to use scikit-image library to extract Histogram of Oriented Gradient (HOG) features from images in Python.
Learn how to perform perspective image transformation techniques such as image translation, reflection, rotation, scaling, shearing and cropping using OpenCV library 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 YOLOv3 object detection for real-time object detection, recognition and localization in Python using OpenCV 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.
