Blurred Test Solves Several Cases

Testing the blurred images again i found a human error in my code due to reusing code from elsewhere in the program. It is now fixed and actually shows a good improvement in that all images are classified as Present if they truly are present. I have yet to see a test/sample case where the images have been incorrectly classified.

Also with the new blurred image code the images are now actually picking up previous blurred images too. Showing that a careful selection of varying conditions from illumination to blurred tests will give results that a usable throughout the system. Ideally a larger set of training data would be used rather than the minimal 12 training images currently working with, yet this gives reasonable results to the minimum degree of accuracy required...100% coverage of all target images, yet still allows through a few similar images that are not truly targets (i.e true negatives).

2: 0.119070 Present - Normal
3: 0.128092 Present - Normal
4: 0.082883 Present - Normal
5: 0.131328 Present - Normal
6: 0.072161 Present - Dark
7: 0.053662 Present - Normal
8: 0.159959 Present - Normal
9: 1.000000 Empty
10: 0.113700 Present - Normal
11: 0.156434 Present - Normal
12: 1.000000 Empty
13: 0.045220 Present - Normal
14: 0.077655 Present - Blurred Test        //Old Blurry image now found
15: 0.099331 Present - Blurred Test        //Old Blurry image now found
16: 1.000000 Empty
17: 1.000000 Empty
18: 1.000000 Empty
19: 0.181530 Present - Normal
20: 0.187497 Present - Normal
21: 0.183492 Present - Normal
22: 0.154783 Present - Normal              //Non blurry version of image
23: 0.140616 Present - Normal              //Non blurry version of image
24: 0.163971 Present - Normal              //New Blurry image found as a normal instead
25: 0.095063 Present - Blurred Test        //New Blurry image found