def getClosestCases(list_of_label_files, list_of_similarity_xml_files, \
similarity, num_closest_cases):
"""Get the closest cases to the test case using some similarity metric and
given a list of files that contain the similarity value.
Parameters
----------
list_of_similarity_xml_files : list of file names (string)
Each file name points to an xml file that contains the similarity value
list_of_label_files : list of file names (string)
Each file name points to a file that has a labeled image
similarity : string
Type of similarity used
num_closest_cases : int
The number of closest cases to return
...
Returns
-------
closest_cases : string 2D list with shape (2, num_cases)
Contains the num_cases cases with highest similarity to the case
being tested, and the associated similarity value
...
"""
#TODO: Account for different similarities
num_training_cases = len(list_of_label_files)
#Read the similarity values
similarity_values = getMISimilarityVec(list_of_similarity_xml_files)
print(similarity_values)
#find highest matches
indexes = []
for i in range(num_training_cases):
indexes.append(i)
if (similarity == "NCC"):
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: abs(float(similarity_values[i]))) #take (from 0 to 10, assuming testint is not in trainng)
else:
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: (similarity_values[i])) #take (from 0 to 10, assuming testint is not in trainng)
print(nlargestvalues)
patient_atlas_labelmaps = [""] * num_closest_cases
patient_atlas_similarity = [1.0] * num_closest_cases
#Now store the num_cases in a 2D list
for i in range(num_closest_cases):
patient_atlas_labelmaps[i] = list_of_label_files[nlargestvalues[i]]
print(patient_atlas_labelmaps[i])
patient_atlas_similarity[i] = similarity_values[nlargestvalues[i]]
closest_cases = np.vstack(
(patient_atlas_labelmaps, patient_atlas_similarity))
return closest_cases
def getClosestCases(list_of_label_files, list_of_similarity_xml_files, \
similarity, num_closest_cases):
"""Get the closest cases to the test case using some similarity metric and
given a list of files that contain the similarity value.
Parameters
----------
list_of_similarity_xml_files : list of file names (string)
Each file name points to an xml file that contains the similarity value
list_of_label_files : list of file names (string)
Each file name points to a file that has a labeled image
similarity : string
Type of similarity used
num_closest_cases : int
The number of closest cases to return
...
Returns
-------
closest_cases : string 2D list with shape (2, num_cases)
Contains the num_cases cases with highest similarity to the case
being tested, and the associated similarity value
...
"""
#TODO: Account for different similarities
num_training_cases = len(list_of_label_files)
#Read the similarity values
similarity_values = getMISimilarityVec(list_of_similarity_xml_files)
print(similarity_values)
#find highest matches
indexes=[]
for i in range(num_training_cases):
indexes.append(i)
if (similarity == "NCC"):
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: abs(float(similarity_values[i]))) #take (from 0 to 10, assuming testint is not in trainng)
else:
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: (similarity_values[i])) #take (from 0 to 10, assuming testint is not in trainng)
print(nlargestvalues)
patient_atlas_labelmaps = [""]*num_closest_cases
patient_atlas_similarity = [1.0]*num_closest_cases
#Now store the num_cases in a 2D list
for i in range(num_closest_cases):
patient_atlas_labelmaps[i] = list_of_label_files[nlargestvalues[i]]
print(patient_atlas_labelmaps[i])
patient_atlas_similarity[i] = similarity_values[nlargestvalues[i]]
closest_cases = np.vstack((patient_atlas_labelmaps, patient_atlas_similarity))
return closest_cases
def getClosestCases(list_of_label_files, list_of_similarity_xml_files, \
similarity, num_closest_cases, threshold = None):
"""Get the closest cases to the test case using some similarity metric and
given a list of files that contain the similarity value.
Parameters
----------
list_of_similarity_xml_files : list of file names (string)
Each file name points to an xml file that contains the similarity value
list_of_label_files : list of file names (string)
Each file name points to a file that has a labeled image
similarity : string
Type of similarity used
num_closest_cases : int
The number of closest cases to return
threshold : float
an optional minimum similarity value constrained on the closest
cases to be returned
...
Returns
-------
closest_cases : string 2D list with shape (2, num_cases)
Contains the num_cases cases with highest similarity to the case
being tested, and the associated similarity value
...
"""
#TODO: Account for different similarities
num_training_cases = len(list_of_label_files)
#Read the similarity values
#print(list_of_similarity_xml_files)
similarity_values = getMISimilarityVec(list_of_similarity_xml_files)
#find highest matches
indexes=[]
for ii in range(num_training_cases):
#heapq.heappush(indexes, (i, similarity_values[i]))
indexes.append(ii)
print("number of training cases "+str(num_training_cases))
if (similarity == "ncc"):
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: (-(similarity_values[i]))) #take (from 0 to 10, assuming testint is not in trainng)
else:
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: (similarity_values[i])) #take (from 0 to 10, assuming testint is not in trainng)
print("n largst values")
print(nlargestvalues)
patient_atlas_labelmaps = [""]*num_closest_cases
patient_atlas_similarity = [0.0]*num_closest_cases
#Now store the num_cases in a 2D list
for i in range(0,num_closest_cases):
print("threashold is "+str(threshold))
if (abs(float(similarity_values[nlargestvalues[i]])) > threshold):
patient_atlas_labelmaps[i] = list_of_label_files[nlargestvalues[i]]
patient_atlas_similarity[i] = abs(float(similarity_values[nlargestvalues[i]]))
if (patient_atlas_similarity[0] == 0.0):
patient_atlas_labelmaps[0] = list_of_label_files[nlargestvalues[0]]
patient_atlas_similarity[0] = abs(float(similarity_values[nlargestvalues[0]]))
closest_cases = np.vstack((patient_atlas_labelmaps, patient_atlas_similarity))
print(closest_cases)
return closest_cases
def getClosestCases(list_of_label_files, list_of_similarity_xml_files, \
similarity, num_closest_cases, threshold = None):
"""Get the closest cases to the test case using some similarity metric and
given a list of files that contain the similarity value.
Parameters
----------
list_of_similarity_xml_files : list of file names (string)
Each file name points to an xml file that contains the similarity value
list_of_label_files : list of file names (string)
Each file name points to a file that has a labeled image
similarity : string
Type of similarity used
num_closest_cases : int
The number of closest cases to return
threshold : float
an optional minimum similarity value constrained on the closest
cases to be returned
...
Returns
-------
closest_cases : string 2D list with shape (2, num_cases)
Contains the num_cases cases with highest similarity to the case
being tested, and the associated similarity value
...
"""
#TODO: Account for different similarities
num_training_cases = len(list_of_label_files)
#Read the similarity values
#print(list_of_similarity_xml_files)
similarity_values = getMISimilarityVec(list_of_similarity_xml_files)
#find highest matches
indexes = []
for ii in range(num_training_cases):
#heapq.heappush(indexes, (i, similarity_values[i]))
indexes.append(ii)
print("number of training cases " + str(num_training_cases))
if (similarity == "ncc"):
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: (-(similarity_values[i]))) #take (from 0 to 10, assuming testint is not in trainng)
else:
nlargestvalues = heapq.nlargest(num_closest_cases, indexes, key=lambda \
i: (similarity_values[i])) #take (from 0 to 10, assuming testint is not in trainng)
print("n largst values")
print(nlargestvalues)
patient_atlas_labelmaps = [""] * num_closest_cases
patient_atlas_similarity = [0.0] * num_closest_cases
#Now store the num_cases in a 2D list
for i in range(0, num_closest_cases):
print("threashold is " + str(threshold))
if (abs(float(similarity_values[nlargestvalues[i]])) > threshold):
patient_atlas_labelmaps[i] = list_of_label_files[nlargestvalues[i]]
patient_atlas_similarity[i] = abs(
float(similarity_values[nlargestvalues[i]]))
if (patient_atlas_similarity[0] == 0.0):
patient_atlas_labelmaps[0] = list_of_label_files[nlargestvalues[0]]
patient_atlas_similarity[0] = abs(
float(similarity_values[nlargestvalues[0]]))
closest_cases = np.vstack(
(patient_atlas_labelmaps, patient_atlas_similarity))
print(closest_cases)
return closest_cases
