The task involves the use of several computer vision libraries and dependencies to achieve the objectives of detecting the outer wrap of a droplet, recognising the inner droplet, detecting the centre of the blob as well as counting successfully formed droplets in a video high-speed camera during the droplet 3D printing process. The task is carried out using OpenCV and the code is written in Python Programming Language. Some CV processes such as filtering, dilation and corrosion are employed during the cause of this task to obtain the desired result. Blob detection and the Hough Circle technique were also employed in detecting the blob, centre of mass and outer wrap. A counter was used to count the number of successfully formed droplets in the video and the output displayed. This task shows how applicable Computer Vision is in our everyday life as it was able to identify droplets and correctly count them.
This figure shows the Main Frame is where the visualisation of detection, recognition and counting is done:
The test video is provided as well (VideoCW2).