This project was developed by me in the hopes of being able to use Computer Vision via OpenCV in a way that would be able to not only determine water levels on a pre-recorded video but to also measure the water level on a live video feed, without the use of any onboard sensors and external object tracking.
A team of postgraduate researches at the center of Marine Research at the University of Western Australia needed a software solution that would assist in data analysis of an ROV (Remote Operated Vehicle's) depth measurements without the use of onboard sensors. This was my implementation of a solution that worked and helped reduce errors significantly.
-- registering shadows of water edges [fixed] -- registering different colours (with glass over) [fixed] -- registering different colors of rov model [fixed]
- cv2
- math
- argparse
- imutils
- collections
https://github.com/iftheqhar/opencv2_python
https://github.com/sashagaz/Hand_Detection
https://github.com/iftheqhar/OpenCV2-Python
https://www.geeksforgeeks.org/multiple-color-detection-in-real-time-using-python-opencv/
https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/
https://www.pyimagesearch.com/2018/07/30/opencv-object-tracking/
I practiced detecting liquid (especially transparent ones such as water) using a wide variety of avenues. This resulted in a series of vastly differing performance reports and findings. These methods included Colour Tracking, Frame Change Detection, Gaussian Blur, Canny Edge Detection and a combination of all.
This method involved specifying a range of values that openCV would then start tracking. This method is a commonly used one within the scope of openCV methodologies.
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The latest version of this program runs at a very high efficiency rate, being able to accurately determine the water depth using a live video feed.
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