def setUp(self):
self.cda = pycda.CDA(
detector=_DummyDetector(),
extractor=_DummyExtractor(),
classifier=_DummyClassifier()
)
img_height = np.random.randint(500, 1500)
img_width = np.random.randint(500, 1500)
self.test_image = np.random.rand(img_height, img_width)
self.prediction = pr.Prediction(self.test_image, 'test1', self.cda)
self.cda.predictions.append(self.prediction)
def getImage(self):
layout = [ [sg.Text('Filename')],
[sg.Input(), sg.FileBrowse(key="-IN-")],
[sg.OK(), sg.Cancel()]]
window = sg.Window('Get filename', layout)
event, values = window.read()
window.close()
image = load_image(values["-IN-"])
cda = pycda.CDA()
prediction = cda.get_prediction(image, verbose = True)
prediction.show()
def get_classifier_results():
cda = pycda.CDA(classifier='none')
prediction = cda.get_prediction(get_sample_image())
prediction.known_craters = get_sample_csv()
an = ErrorAnalyzer()
an.analyze(prediction, verbose=False)
proposals, craters = an.return_results()
ground_truth = proposals
cda.classifier = ConvolutionalClassifier()
prediction_2 = cda.get_prediction(get_sample_image())
classification = prediction_2.proposals
Y_true = ground_truth.positive
Y_pred = np.where(classification.likelihood > .5, 1, 0)
return Y_true, Y_pred
def getImage(self):
try:
layout = [ [sg.Text('Filename')],
[sg.Input(), sg.FileBrowse(key="-IN-")],
[sg.OK(), sg.Exit()]]
window = sg.Window('Get filename', layout)
event, values = window.read()
window.close()
if event == 'Exit':
window.close()
else:
image = load_image(values["-IN-"])
cda = pycda.CDA()
prediction = cda.get_prediction(image, verbose = True)
prediction.show()
prediction.to_csv('/home/aurelio/Desktop/CapstoneFinal/CSVs/results1.csv')
except:
print()
def showImage(self):
image = get_sample_image()
#image.show()
cda = pycda.CDA()
prediction = cda.get_prediction(image, verbose = True)
prediction.show()
import cv2 import pycda input_name='NAC_DTM_APOLLO12_M120012135_2M.TIF' img = cv2.imread(input_name) cda=pycda.CDA() detections=cda.predict(img) print(detections.head(10)) predictions=cda.get_prediction(img) predictions.show()