$ cd ImageProcessing
--Run This Script as Follows [ObjectExtraction.py] ['FileName.fileExtension']
$ python3 ObjectContourExtraction.py 'Shark 1.PNG'

python 3 [ObjectExtraction.py] ['FileName.fileExtension'] Example: python3 ObjectContourExtraction.py 'Shark 1.PNG'

• Processing Techniques Implemented: -Histogram Equalisation: (Contrast Adjustments) -Colour Space Manipulation (BGR->HSV): Using the 'V' - brightness value -Thresholding: Global Binary Thresholding (Not Adaptive Thresh By Design) -Followed by Contouring to identify all the contours in the image and we select the largest contours as our subject.

• Other Notes: -User input - there is use of ‘cv2.waitKey(0)’ statements to allow user control -The User is in control here, I designed this program to be as ‘hardcode’ independent as possible with sliders for threshold arguments -Tested on Python 3.7.2 - Mac OS Catalina (Not Compatible with Python 2.7)

• From Testing: Shark 1.png works best @ Thresh=97, Equalised= TRUE, Gaussian Blur = 39
• From Testing: Shark 2.png works best @ Thresh=103, Equalised= TRUE, Gaussian Blur = 38

• Jpeg handling to be re-added (PNG Only at the moment)
• GUI Image selector w/ easygui would be ideal but removed due to bugs

• More error handling required around cv2 imreads and imwrites
• Allow User to choose between largest Contours in image so they can select a smaller object in the photo not just the largest subjects of the photo.