The biggest advantages are obviously the range increase, the possibility to use widely available cables, and to u
ABSTRACT The aim of this thesis is to explore different methods for helping computers interpret the real world visually, investigate solutions to those Raspberry pi thesis offered by the open-sourced computer vision library, OpenCV, and implement some of these in a Raspberry Pi based application for detecting and keeping track of objects.
The main focus rests on the practical side of the project. The application also transmits some useful information, such as coordinates and size, to other computers on the network that send an appropriate query.
The source code of the program is documented and can be developed further. Taking into account the relatively high performance requirements of image processing in general and the equipment currently available to the faculty, as a relatively inexpensive and powerful embedded platform the Raspberry Pi was an obvious choice.
The hardware specifications taken into consideration for this work can be seen in Table 1. Partial overview of what OpenCV includes, The application written for this thesis relies heavily on computer vision, image processing and pixel manipulation, for which there exists an open source library named OpenCV Open Source Computer Vision Libraryconsisting of more than optimized algorithms.
Uses range from facial recognition, object identifying, classifications of Raspberry pi thesis actions in videos, achieved with filters, edge mapping, image transformations, detailed feature analysis and more Figure 4.
When ignoring value in the thresholding parameters, it is still possible to detect any colour from an image, given the right hue and saturation values.
Another important characteristic is the circular progression Figure 5 of hue values for different colours, which means that the highest hue value in OpenCV to fit into an unsigned char is simultaneously the lowest, because the space is circular not linear, and should be kept in mind when using image thresholding algorithms in an OpenCV-based application.
The selected hue range in this case was between 0 and 38, which is roughly orange or yellow colour of door on image. The lower boundary of saturation was also raised to accommodate current lighting conditions. The instances of this structure are essentially the objects which the program will be handling.
From the creation of these copies up to the removal or drawing of markers and transmitting information, the objects go through three phases illustrated in Figure Three levels of object existence described using counters as criteria.
Drawing markers on detected objects. Function seen in Listing 8 of Appendix 1. This is mainly for aesthetic purposes, but can be used to check if the user has provided correct parameters e.
It is preferrably run from command line and can be provided parameters by the user currently implemented arguments described in Table 4. It is recommended the user try using the application without any parameters first to get a glimpse of how these settings change what the program is doing, especially using the colour range sliders.
Hue values as a range from 0 to This would provide the application the capability to look for various differently coloured objects and would come in handy when trying to detect objects of colours that are not next to each other e.
Implementing this would require the use of similar methods as seen in the current object detection and tracking functions, but could be extremely CPU-intensive, which a Raspberry Pi might not be able to handle, but could provide more flexibility to a colour-based object detection program.
Colour-based object detection using colours is definitely an effective method, especially when dealing with objects that generally have no constant distinguishable features or corners.
Such objects can be balloons and other round coloured objects, or even spots of paint, to provide a few examples. The main problem with using colours, or more accurately, pixel values, for this purpose is the effects of inconsistent lighting, which a computer can be very sensitive towards, while a human eye can only detect a slight difference.May 23, · Both the Raspberry Pi and Arduino Uno are very powerful devices, good at different things.
The Arduino boards are awesome at . May 30, · Introduction: Analog Sensor Input Raspberry Pi Using a MCP Wiring/installing/basic Program.
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Ultimate guide to Raspberry Pi operating systems, part 1 Since we published a roundup of 10 Raspberry Pi operating systems the number of choices has exploded. Both the Raspberry Pi and Arduino Uno are very powerful devices, good at different things.
The Arduino boards are awesome at reading inputs and outputs from various different things. The Raspberry Pi is basically a mini, open-source Linux computer. If you put these two together, your options are. MATLAB and Simulink Student Suite Rb MATLAB and Simulink Student Suite provides the same tools that professional engineers and scientists use every day.