In [43]:

    

%matplotlib inline
import numpy as np
from scipy.ndimage.measurements import label,find_objects,center_of_mass
import PIVutils


    

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S = np.loadtxt('BugData.csv', delimiter=',')


    

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[f, ax] = PIVutils.plotScalarField(S,bound=5)


    

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Thresh = 35


    

In [53]:

    

uSize = S.shape
    
labeled_array, num_features = label(S)

#print(np.unique(labeled_array))
#print(np.unique(labeled_array[:,:,0]))
#print(labeled_array.shape)

print('There are ', num_features, ' features identified')

if Thresh is not None:
    loc = find_objects(labeled_array)
    labeled_array_out = labeled_array.copy()

    counts = np.bincount(labeled_array.ravel())

    ind = np.where(counts>Thresh)[0][1:]
    mask = np.in1d(labeled_array.ravel(), ind).reshape(labeled_array.shape)
    labeled_array_out[~mask] = 0

    [_, labeled_array_out] = np.unique(labeled_array_out,return_inverse=True)
    labeled_array_out = labeled_array_out.reshape(labeled_array.shape)

    num_features_out = len(ind)

    print('A total of ', num_features_out, ' are larger than the threshold size')
else:
    labeled_array_out = labeled_array
    num_features_out = num_features

#a = labeled_array_out[:,:,535]
#b = a.copy()
#c = list(range(uSize[1]))
#b[:,:] = list(range(uSize[1])))
#for i in np.unique(a):
    #print(np.unique(a)[15])
    #print(a[a==i])

#features_per_frame = np.zeros(uSize[2],dtype=int);
#cent = [];
#for i in range(uSize[2]):
    #features_per_frame[i] = len(np.unique(labeled_array_out[:,:,i])[1:])
cent = center_of_mass(S,labeled_array_out,np.unique(labeled_array_out)[1:])


    

    




There are  177  features identified
A total of  13  are larger than the threshold size

In [54]:

    

cent[11]


    

    
Out[54]:





(696.28820197528614, 213.38909911471836)

In [55]:

    

S.shape    #Cent must be less than these numbers


    

    
Out[55]:





(123, 242)

In [56]:

    

np.unique(labeled_array_out)


    

    
Out[56]:





array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13])

In [57]:

    

num_features_out


    

    
Out[57]:





13

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