def runtraininganalysis(arr,coor):
left=coor[1]
right=coor[3]
top=coor[0]
bottom=coor[2]
features=[]
if bottom-top>4 and right-left>4:
image=arr[top:bottom,left:right]
image=arrayToImage(image)
g=imageTransforms.gaussblur(image)
gauss={}
gauss[RGB]=Imagetoarray(g)
gauss[grayscale]=Imagetoarray(imageTransforms.grayScale(g))
del g
features.append(calculatefeatures(gauss))
s=imageTransforms.smooth(image)
smooth={}
smooth[RGB]=Imagetoarray(s)
smooth[grayscale]=Imagetoarray(imageTransforms.grayScale(s))
features.append(calculatefeatures(smooth))
del s
s=imageTransforms.sharpen(image)
sharpen={}
sharpen[RGB]=Imagetoarray(s)
sharpen[grayscale]=Imagetoarray(imageTransforms.grayScale(s))
features.append(calculatefeatures(sharpen))
normal={}
normal[RGB]=Imagetoarray(image)
normal[grayscale]=Imagetoarray(imageTransforms.grayScale(image))
features.append(calculatefeatures(normal))
if features:
return features
else:
return None
def getImageClass(w,imgs,size=8):
x,y,step = getWalkerParameters(w,size,factor=1)
p = .9*size*size
z=mode(w.ravel())[0][0]
IC = ImageClass()
for j in x:
for i in y:
try:
img = w[i:i+step,j:j+step]
m = mode(img.ravel())
c=m[1][0]
m=m[0][0]
if c > p and m!=z:
print i,i+step,j,j+step,m,c,p
IC.addVector(int(m),calculatefeatures(imgs,left=j,right=j+step,top=i+step,bottom=i))
else:
print i,i+step,j,j+step,"skipped",m,c,p
except:
print i,i+step,j,j+step,"failed"
return IC
