Image Processing Applications on Raspberry Pi -From Scratch – Dr. Steven Lawrence Fernandes
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Image Processing Applications on Raspberry Pi -From Scratch – Dr. Steven Lawrence Fernandes Download. You will learn the components of Raspberry Pi, connec…
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Image Processing Applications on Raspberry Pi -From Scratch
Build Real-world Image Processing Applications using Python and OpenCV on Raspberry Pi 3/2/Zero – For Absolute Beginner
Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.
The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.
You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.
This course will take beginners without any coding skills to a level where they can write their own programs.
Basics of Python programming language are well covered in the course.
Building Image Processing applications are taught in the simplest manner which is easy to understand.
Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.
The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing.
Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.
The course will enable you to independently build Image Processing applications using Raspberry Pi.
This course is the easiest way to learn and become familiar with the Raspberry Pi platform.
By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation . User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more.
The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.
Course Curriculum
Introduction
- Components on Raspberry Pi 3 (1:14)
- Components to be connected to Raspberry Pi (1:03)
- Downloading Software to Format SD Card (2:03)
- Formatting SD Card (1:53)
Setting up Raspberry Pi
- Downloading NOOBS Operating System (2:52)
- Copying NOOBS Operating System to SD Card (1:25)
- Flashing NOOBS Operating System to SD Card (2:47)
- Installing Packages (7:07)
- Download Course Codes and Images
Python Basics
- Print (5:46)
- Quiz for Print
- If Condition (4:44)
- Making Decisions (8:50)
- Quiz for Making Decisions
- For loop (10:18)
- While loop (7:33)
- Quiz 3: Quiz for While loop
- Functions (9:44)
- Quiz 4: Quiz for Functions
- Dictionaries (7:08)
- Objects (5:13)
- Class (6:15)
- Modules (2:47)
- Quiz 5: Quiz for Modules
Image Processing Applications
- Load Display Save Images (9:43)
- Scaling (12:59)
- Quiz 6: Quiz for Scaling
- Flipping (9:09)
- Varying Brightness (11:07)
- Quiz 7: Quiz for Varying Brightness
- Bitwise Operations (14:25)
- Blurring and Sharpening (9:18)
- Quiz 8: Quiz for Blurring and Sharpening
- Thresholding (7:15)
- Erosion and Dilation (8:11)
- Quiz 9: Quiz for Erosion and Dilation
- Edge Detection (6:04)
- Image Segmentation (9:50)
- Quiz 10: Quiz for Image Segmentation
Real-world Image Processing Applications
- Real-time Human Face Eyes Nose Detection (18:14)
- Detecting Cars in Video (11:07)
- Pedestrian Detection (10:40)
- Real-time Object Detection (23:36)
- Human Face Recognition -1 (17:00)
- Human Face Recognition – 2 (15:48)
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