Master Computer Vision & Deep Learning in OpenCV and Keras

Install and configure Anaconda Navigator and PyCharm Community Edition, then launch Jupyter Notebook and install and use libraries like NumPy, Pandas, and TensorFlow.

Explore online coding without installs by using a Python online compiler and Google Colab; learn to create a new notebook, connect a runtime, and import TensorFlow and NumPy.

Master Python conditional statements by solving homework on age-based messages using if and elif. Learn how to declare an age variable, print corresponding messages, and prevent duplicates by using elif.

Learn how the for loop uses Python's range to repeat tasks, with start, end, and step, noting end is exclusive and examples use 11 to 20 and 85 to 99.

Master the Python while loop and how it runs while a condition holds. See how not incrementing age causes an infinite loop, then fix it with age += 1.

Discover how Python treats lists like arrays, declare and index lists with square brackets, print elements, and loop with range, len, and replace and append to update values.

Explore python function and method concepts, declaring with div, passing parameters and arguments, and returning values. Distinguish function from method inside a class and how to call and test them.

Master Python list basics and operations, including indexing with len or minus-one and slicing ranges. Learn removing and popping items, sorting and joining lists, and clearing for multi-dimensional list handling.

Learn how to create two-dimensional and higher dimensional lists in python, index with zero or negative indices, and print elements using nested for loops.

Explore Python tuple, set and dictionary basics, including tuple immutability, set uniqueness, and dictionary key-value pairs with indexing, updating, looping, and element removal.

Master python strings by using single or double quotes and creating multi-line values. Explore indexing, length, slicing, and string methods like upper, lower, find, replace, split, and format with variables.

Master Python date time and math modules to print date and time, format day and month names with format codes, and use max, min, abs, pow, sqrt, ceil, and floor.

Define a custom data type in Python using a class, declare attributes id, name, age, and fee, instantiate it, and access its attributes with dot notation.

Learn to define a Python class and create an object, with properties and a constructor, using self, and implement a get employee info method to return formatted employee data.

Learn Python scope, including global and local variables, the use of the global keyword, and how private fields and methods with underscores control access inside and outside a class.

Learn Python inheritance by creating a base person class and student, teacher, and peon subclasses with shared get info and constructors. See how inheritance reduces duplication and explore polymorphism.

Learn how polymorphism and abstraction shape object-oriented design in Python, using built-in polymorphism examples like len for lists and strings, and building abstract methods with ABC.

Learn how to create and use Python iterators, including the iter and next methods, and implement a custom iterator class with __iter__ and __next__ that stops at a limit.

Master Python lambda functions: small anonymous functions that take multiple arguments but contain only a single expression, shown through defining, assigning, and calling with various arguments.

Open, read, write, and create python files with open and os.path.exists, using modes r, a, w, and x, then manage files and directories.

Learn how Python handles errors with try and except, including else and finally, to print custom messages and control program flow.

Learn to create numpy arrays using array, zeros, ones, empty, arange, and random methods; specify data types and shapes like rows and columns, including multidimensional arrays, with rand and randint.

Explore numpy array attributes such as ndim, size, shape, and dtype, then sort, concatenate, and split arrays, while using min, max, and their indices with argmin and argmax.

Explore indexing and slicing a numpy array, using one-dimensional and two-dimensional arrays, zero-based indices, and range selection with a colon; print, subset, and modify borders.

Demonstrates numpy copy and view: view shares data with the original so changes propagate; copy creates an independent array, leaving the main array unchanged.

Master reshaping and resizing numpy arrays, from 3x6 to 6x3, and distinguish ravel (view) from flatten (copy) for one-dimensional conversions.

Explore numpy arithmetic on arrays with operators and the add method, check shapes, and apply aggregation like sum, mean, std, prod, var, and cumsum.

Set up a PyCharm project, install OpenCV, and learn to read, display, and save images using cv2.imread, cv2.imshow, cv2.waitKey, cv2.destroyAllWindows, and cv2.imwrite.

Explore how OpenCV represents images as numpy ndarrays, inspect their shape, height, width, and color channels, and separately visualize blue, green, and red channels.

OpenCV image manipulation covers resizing with cv2.resize and interpolation options (linear, area, cubic), applying blur (cv2.blur, Gaussian, median), and color conversions using cv2.cvtColor (BGR to RGB and grayscale).

Display text on an image using cv2.putText with font Hershey simplex, scale, color in BGR, and thickness; then draw shapes like lines, arrowed line, circles, and filled rectangles.

Master how to play video from a file with OpenCV using cv2.VideoCapture, read frames, display in a 'video' window, resize the window, and save frames as images with imwrite.

Learn to use OpenCV for webcam and security cam capture, handle IP cameras, read frames, display live video, and save video or images with a codec and specified dimensions.

Apply text on video using OpenCV putText and learn to draw shapes—lines, circles, ellipses, rectangles, and arrows—on webcam and security camera feeds.

Apply template matching in OpenCV to detect objects in an image. Convert to grayscale, load a template, run cv2.matchTemplate, locate the best match with minMaxLoc, and draw a detection rectangle.

Experiment with image thresholding in OpenCV to enhance images for OCR, medical imaging, surveillance, and robotics, using grayscale conversion, binary thresholding, and adaptive thresholding with varying block sizes and constants.

Explore edge detection in OpenCV using Canny, Laplacian, Sobel, and Scharr methods, with Gaussian blur, kernel sizes, and magnitude computation to compare edges against the original image.

Learn to find and draw contours with OpenCV using thresholding, grayscale conversion, and Canny edge detection, enabling shape analysis, object detection, and image recognition.

Explore how to read, display, and inspect an image with OpenCV, understand NumPy array shapes, and manipulate color channels to create and save multicolor images.

Detect colors in images using OpenCV by converting to HSV, creating lower and upper color ranges, applying inRange and bitwise operations to reveal blue, green, yellow, red, and magenta regions.

Detect shapes with OpenCV by thresholding and contour finding. Identify circles, triangles, rectangles, pentagons, hexagons, and stars, then draw contours and label shapes with cv2 in Python.

OpenCV corner detection covers Harris on grayscale images with block size, kernel size, and k, and Shi-Tomasi via goodFeaturesToTrack for up to corners, quality level, and distance, with visualization.

Detect motion and track objects in video using contour-based detection with thresholding and grayscale frames, plus gaussian mixture background subtraction for improved accuracy.

Demonstrate object detection with feature matching in OpenCV by detecting and computing keypoints and descriptors with ORB, matching via brute-force and the k nearest neighbor method, and visualizing good matches.

Detect faces and eyes with Python using OpenCV and Haar cascade classifiers; build a PyCharm project, load XML cascades, and apply real-time face and eye detection on images and video.

Explore road lane detection with OpenCV in Python by processing a video, applying grayscale and canny edges, masking a region of interest, and drawing detected lanes using Hough lines.

learn to visualize data with matplotlib's pyplot, including imread for images, plotting lines, and creating scatter, pie, and histogram charts with customizable colors, labels, and titles.

Explains how image kernels, as tensors, act as filters to blur, sharpen, edge detect, and reveal features. Shows how kernels slide over images with strides and padding to produce images.