def checkCandidates(self):
imageCollections = data_io.get_train_df()
featureGetter = FeatureGetter()
(namesObservations, coordinates,
train) = featureGetter.getTransformedDatasetChecking(imageCollections)
imageNames = namesObservations
currentImage = imageNames[0]
csvArray = Utils.readcsv(imageNames[0])
mitoticPointsDetected = 0
totalMitoticPoints = len(csvArray)
finalTrain = []
for i in range(len(coordinates)):
if imageNames[i] != currentImage:
csvArray = Utils.readcsv(imageNames[i])
totalMitoticPoints += len(csvArray)
currentImage = imageNames[i]
for point in csvArray:
if ((point[0] - coordinates[i][0])**2 +
(point[1] - coordinates[i][1])**2) < 30**2:
mitoticPointsDetected += 1
csvArray.remove(point)
finalTrain.append(train[i])
break
finalTrain = np.array(finalTrain)
allArea = finalTrain[:, 0]
allPerimeter = finalTrain[:, 1]
allRoundness = finalTrain[:, 2]
totalObservations = len(coordinates)
print "Minimum Area: %f" % np.min(allArea)
print "Minimum Perimeter: %f" % np.min(allPerimeter)
print "Minimum Roundness: %f" % np.min(allRoundness)
print "Maximum Area: %f" % np.max(allArea)
print "Maximum Perimeter: %f" % np.max(allPerimeter)
print "Maximum Roundness: %f" % np.max(allRoundness)
print "Total number of candidates: %d" % (totalObservations)
print "Total number of mitotic points: %d" % (totalMitoticPoints)
print "Mitotic points detected: %d" % (mitoticPointsDetected)
print "Mitotic points missed: %d" % (totalMitoticPoints -
mitoticPointsDetected)
def checkCandidates(self):
imageCollections = data_io.get_train_df()
featureGetter = FeatureGetter()
(namesObservations, coordinates, train) = featureGetter.getTransformedDatasetChecking(imageCollections)
imageNames = namesObservations
currentImage = imageNames[0]
csvArray = Utils.readcsv(imageNames[0])
mitoticPointsDetected = 0
totalMitoticPoints = len(csvArray)
finalTrain = []
for i in range(len(coordinates)):
if imageNames[i] != currentImage:
csvArray = Utils.readcsv(imageNames[i])
totalMitoticPoints += len(csvArray)
currentImage = imageNames[i]
for point in csvArray:
if ((point[0]-coordinates[i][0]) ** 2 + (point[1]-coordinates[i][1]) ** 2)< 30**2:
mitoticPointsDetected += 1
csvArray.remove(point)
finalTrain.append(train[i])
break
finalTrain = np.array(finalTrain)
allArea = finalTrain[:,0]
allPerimeter = finalTrain[:,1]
allRoundness = finalTrain[:,2]
totalObservations = len(coordinates)
print "Minimum Area: %f" % np.min(allArea)
print "Minimum Perimeter: %f" % np.min(allPerimeter)
print "Minimum Roundness: %f" % np.min(allRoundness)
print "Maximum Area: %f" % np.max(allArea)
print "Maximum Perimeter: %f" % np.max(allPerimeter)
print "Maximum Roundness: %f" % np.max(allRoundness)
print "Total number of candidates: %d" % (totalObservations)
print "Total number of mitotic points: %d" %(totalMitoticPoints)
print "Mitotic points detected: %d" %(mitoticPointsDetected)
print "Mitotic points missed: %d" %(totalMitoticPoints-mitoticPointsDetected)
