Software Development
Image Processing with OpenCV
Final Exam: Resource Optimization with Python
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OpenCV: Advanced Image Operations
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OpenCV: Introduction
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OpenCV: Manipulating Images
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Final Exam: Resource Optimization with Python
Lesson Objectives
Final Exam: Resource Optimization with Python
- add noise to an image
- add noise to an image and apply a blur
- add noise to an image and apply a blur which obscures minute details in an image
- aggregate data on a per-key, per-window basis
- apply cv2.resize to scale up an image along individual dimensions
- apply the Laplacian operator to detect the edges in an image
- apply the Laplacian, Sobel and Canny operators to detect the edges in an image
- compute aggregations on streaming data
- contrast tumbling windows and hopping windows
- create a workspace for the demos and install OpenCV from a Jupyter notebook
- create models with multiple fields and different data types
- draw a polygon and an arrow in an OpenCV image and introduce a text element
- forward messages to destination topics
- handle GET, PUT, POST, DELETE, HTTP requests with web views
- identify attributes of hopping tumbling
- identify attributes of tumbling windows
- identify the components that make up the architecture of a stream processing system
- identify the differences between event time, ingestion time, and processing time
- identify the different kinds of sinks that can be used with a Faust agent
- identify the results of bitwise AND, OR, NOT and XOR operations on images
- implement event time hopping windows
- implement gaussian and median blur operations in order to smooth an image
- implement processing time tumbling windows
- implement the cv2.resize method to reduce the size of a color image
- implement the "faust" command to run workers and send messages to agents
- implement the key index to iterate over keys, values, and items in windowed tables
- implement the subtract method in OpenCV to perform a subtract operation between two images
- implement trained classifiers to detect eyes, faces and people in images
- invoke the cast() method to await processing results from an agent
- list the components that make up the architecture of a stream processing system
- load images from your file system into an OpenCV array and then perform the reverse operation by saving an array into a local file
- perform a variety of translations and rotations in increments of 90 degrees in order to orient an image according to your specifications
- perform gaussian and median blur operations in order to smooth an image
- perform group-by operations on streams
- perform grouping operations and understand table sharding
- plot a circle, line, rectangle and ellipse in an image
- publish messages to a Kafka topic using the pykafka library
- read a color image into your Python source
- read a color image into your Python source as a grayscale image
- read a color image into your Python source as a grayscale image and view it using an interactive window
- recall the differences between event time, ingestion time, and processing time
- recall the different kinds of sinks that can be used with a Faust agent
- recall the important characteristics of the Faust stream processing applications
- recognize the results of bitwise AND, OR, NOT and XOR operations on images
- recognize the use of the BGR and RGB color spaces used by OpenCV and the Pillow libraries
- save table state to an embedded RocksDB database
- send and receive messages using channels
- separate a color image into blue, green and red channels
- use channels to send and receive messages
- use models to represent stream elements
- use the add and addWeighted methods in OpenCV to combine two images
- use the cv2.resize method to reduce the size of a color image
- use the "faust" command to run workers and send messages to agents
- use the key index to iterate over keys, values, and items in windowed tables
- use the pykafka library to publish messages to a Kafka topic
- use the subtract method in OpenCV to perform a subtract operation between two images
- use trained classifiers to detect eyes, faces and people in images
- use trained classifiers to detect faces and people in images
- use web views to handle GET, PUT, POST, DELETE, HTTP requests
- using trained classifiers to detect faces, eyes and people in images
Overview/Description
Final Exam: Resource Optimization with Python will test your knowledge and application of the topics presented throughout the Resource Optimization with Python track of the Skillsoft Aspire Pythonista to Python Master Journey.
Target
Prerequisites: none
OpenCV: Advanced Image Operations
Lesson Objectives
OpenCV: Advanced Image Operations
- discover the key concepts covered in this course
- apply and blur noise in an image
- perform Gaussian and median blur operations to smoothen an image
- apply the Laplacian, Sobel, and Canny operators to detect edges in an image
- plot a circle, line, rectangle, and ellipse in an image
- introduce a text element, polygon, and arrow to an OpenCV image
- use trained classifiers to detect eyes, faces, and people in images
- apply morphological transformations such as erosion and dilation to emphasize specific features of an image
- summarize the key concepts covered in this course
Overview/Description
Many image processing operations involve complex math, but when using OpenCV, much of that is abstracted from the developer. In this course, you'll gain a high-level understanding of advanced image operations in OpenCV. You'll begin by recognizing how to apply different blur operations to an image. These range from simple blurs to Gaussian and median blurs. While doing so, you'll examine their specific advantages and disadvantages and how to distinguish between them. Moving on, you'll outline how to highlight objects in an image using edge detection and augment images by adding shapes and objects to them. Finally, you'll discover how to work with pre-trained classifiers to detect people in an image and perform morphological transformations to emphasize or suppress specific parts of an image.
Many image processing operations involve complex math, but when using OpenCV, much of that is abstracted from the developer. In this course, you'll gain a high-level understanding of advanced image operations in OpenCV. You'll begin by recognizing how to apply different blur operations to an image. These range from simple blurs to Gaussian and median blurs. While doing so, you'll examine their specific advantages and disadvantages and how to distinguish between them. Moving on, you'll outline how to highlight objects in an image using edge detection and augment images by adding shapes and objects to them. Finally, you'll discover how to work with pre-trained classifiers to detect people in an image and perform morphological transformations to emphasize or suppress specific parts of an image.
Target
Prerequisites: none
OpenCV: Introduction
Lesson Objectives
OpenCV: Introduction
- discover the key concepts covered in this course
- install OpenCV from a Jupyter notebook
- load images into an OpenCV array from your local storage and also save an array into a local file
- read a color image into your Python source as a grayscale image and view it using an interactive window
- recognize the use of BGR and RGB color spaces in OpenCV and Pillow libraries
- separate a color image into blue, green, and red channels
- use the add and add weighted operations in OpenCV to combine two images
- perform OpenCV's subtract operation between two images
- summarize the key concepts covered in this course
Overview/Description
A cross-platform library, OpenCV facilitates image processing and analysis. In this course, you'll discover fundamental concepts related to computer vision and the basic operations which can be performed on images using OpenCV. You'll begin by outlining how to read images from your file system into your Python source in the form of arrays and then save an image array into a local file. Next, you'll explore color images represented as a combination of blue, green, and red channels, how to convert color images to grayscale, and how grayscale images are defined. Finally, you'll perform basic operations on images by investigating how to combine two images using an add operation and make one of the added images more prominent than the other using a weighted addition. Conversely, you'll also perform a subtract operation using two images.
A cross-platform library, OpenCV facilitates image processing and analysis. In this course, you'll discover fundamental concepts related to computer vision and the basic operations which can be performed on images using OpenCV. You'll begin by outlining how to read images from your file system into your Python source in the form of arrays and then save an image array into a local file. Next, you'll explore color images represented as a combination of blue, green, and red channels, how to convert color images to grayscale, and how grayscale images are defined. Finally, you'll perform basic operations on images by investigating how to combine two images using an add operation and make one of the added images more prominent than the other using a weighted addition. Conversely, you'll also perform a subtract operation using two images.
Target
Prerequisites: none
OpenCV: Manipulating Images
Lesson Objectives
OpenCV: Manipulating Images
- discover the key concepts covered in this course
- outline the applications of bitwise AND, OR, NOT, and XOR operations on images
- create masks and inverted masks from a grayscale image
- transform a color image to grayscale and then generate a mask from it
- use the cv2.resize() function to reduce the size of a color image
- apply cv2.resize() function to scale up an image along individual dimensions
- perform translations and rotations in increments of 90 degrees to orient an image
- rotate images by a specific angle around a specific center by generating a rotation matrix and applying an affine transformation
- flip images vertically and horizontally and warp them using a specified perspective
- summarize the key concepts covered in this course
Overview/Description
Images often require to be manipulated to extract meaningful portions of an image or prepare them for a machine learning pipeline. OpenCV can help with this. In this course, you'll investigate a variety of image manipulation operations using OpenCV. You'll begin by recognizing how to filter certain portions of an image using bitwise operations. Next, you'll explore the concept of masks and how to use them while extracting parts of an image. You'll then outline how to apply geometrical operations by resizing an image to specific dimensions and discover challenges that such operations present. You'll finish the course by examining image transformations such as rotations and translations to help orient an image to your requirements. Finally, you'll discover how to flip and warp images to present them from a different perspective.
Images often require to be manipulated to extract meaningful portions of an image or prepare them for a machine learning pipeline. OpenCV can help with this. In this course, you'll investigate a variety of image manipulation operations using OpenCV. You'll begin by recognizing how to filter certain portions of an image using bitwise operations. Next, you'll explore the concept of masks and how to use them while extracting parts of an image. You'll then outline how to apply geometrical operations by resizing an image to specific dimensions and discover challenges that such operations present. You'll finish the course by examining image transformations such as rotations and translations to help orient an image to your requirements. Finally, you'll discover how to flip and warp images to present them from a different perspective.
Target
Prerequisites: none
