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@@ -45,3 +45,12 @@ blue or red
after testing with some other images, i have decided to move circle closeness requirement higher to 35, as i got one very clear picture of th capball that scored a 32%.
the diffrence in regularity is not good enough for distinguishing capball from particle.i
some more testing with the blue ball and one of them was a false negative. 3 more false positives, also one of the particles was below the cut off area. another false negative red ball at 36%, ut it was really covered up by a lot if stuff.
+1/17/2017
+ I am finding a new way to detect holes in the ball by masking the image with the cotour to
+ be differentiated, and then find the area of the countours there, in V values, as a percentage of
+ the area of the main contour.
+ I just found out that ret is te threshhold value found, OTSU automatically finds a threshold value between two peaks in the histogram. I want to use this for the first threshold but the re values for hue are not contigous, I will use i just for the red component of BGR. adaptive threshholding takes into consideration lighting conditions, this will be used in the second level maskin
+g.More info here:http://docs.opencv.org/trunk/d7/d4d/tutorial_py_thresholding.html
+ to make a mask from a contour, we simply make an empty image and draw the contours. we then threshhold the v value with adaptive thresholding, my fears about the 0s outside of the mask are unfounded.actually, it passess the entire black region as white. So i will do percentage of average instead.
+function used: conmasked = cv2.adaptiveThreshold(conmasked, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 3)
+I tried the mean of v in the contour, it only works on close ups, for now i guess we'll use size based ones.