def morph():
mf = MorphologyFeatureExtractor()
mf.setThresholdOperation(myBinaryFunc)
a = np.array(mf.extract(watchman.imgbank[0]))
if len(watchman.imgbank) > 1:
b = np.array(mf.extract(watchman.imgbank[1]))
else:
b = np.array(mf.extract(watchman.imgbank[0]))
AandB = np.sum(np.square(a-b))
return AandB
def morph():
mf = MorphologyFeatureExtractor()
mf.setThresholdOperation(myBinaryFunc)
a = np.array(mf.extract(watchman.imgbank[0]))
if len(watchman.imgbank) > 1:
b = np.array(mf.extract(watchman.imgbank[1]))
else:
b = np.array(mf.extract(watchman.imgbank[0]))
AandB = np.sum(np.square(a - b))
return AandB
from SimpleCV import *
from numpy import *
from SimpleCV.Display import Display, pg
from SimpleCV import MorphologyFeatureExtractor
#we pass this methods as our threshold operation
def thresholdOp(in_image):
return in_image.binarize(thresh=70).invert().dilate(2)
# extract only morphology
morph_extractor = MorphologyFeatureExtractor()
morph_extractor.setThresholdOperation(thresholdOp)
extractors = [morph_extractor]
#setup the paths to our data
nuts_path = "./data/nuts/"
bolts_path = "./data/bolts/"
path = [nuts_path, bolts_path]
classes = ['washer', 'bolt']
props = {
'KernelType': 'Linear', #default is a RBF Kernel
'SVMType': 'C', #default is C
'nu': None, # NU for SVM NU
'c': None, #C for SVM C - the slack variable
'degree': None, #degree for poly kernels - defaults to 3
'coef': None, #coef for Poly/Sigmoid defaults to 0
'gamma':
None, #kernel param for poly/rbf/sigma - default is 1/#samples
}
# flag if we need to train the SVM
