def testUpthick(self):
annotation = self.training_data.loc[93, 'EncodedPixels']
mask = annotation2Mask(annotation)
[centerX, centerY, alpha, long_side, short_side] = mask2BoxParameters(mask)
self.assertAlmostEqual(centerX, 282.6266530267141)
self.assertAlmostEqual(centerY, 604.9955919287625)
self.assertAlmostEqual(alpha, 1.9295669970654687)
self.assertAlmostEqual(long_side, 215.72667892497674)
self.assertAlmostEqual(short_side, 34.539832078341085)
def testSloping(self):
annotation = self.training_data.loc[2, 'EncodedPixels']
mask = annotation2Mask(annotation)
[centerX, centerY, alpha, long_side, short_side] = mask2BoxParameters(mask)
self.assertAlmostEqual(centerX, 174.0)
self.assertAlmostEqual(centerY, 751.5)
self.assertAlmostEqual(alpha, 0.9827937232473292)
self.assertAlmostEqual(long_side, 36.05551275463989)
self.assertAlmostEqual(short_side, 10.816653826391969)
def testHorizontal(self):
annotation = self.training_data.loc[1, 'EncodedPixels']
mask = annotation2Mask(annotation)
[centerX, centerY, alpha, long_side, short_side] = mask2BoxParameters(mask)
self.assertAlmostEqual(centerX, 447.51483845784924)
self.assertAlmostEqual(centerY, 531.9995614142575)
self.assertAlmostEqual(alpha, 1.541102087187987)
self.assertAlmostEqual(long_side, 102.04410811016969)
self.assertAlmostEqual(short_side, 32.0624390837628)
def createUnitedMask(mask_str_arr: List[List[str]]) -> np.ndarray:
assert (len(mask_str_arr) > 0)
united_mask = None
for mask_str in mask_str_arr:
mask = annotation2Mask(mask_str)
if united_mask is None:
united_mask = mask
else:
united_mask += mask
return united_mask
def testConverter(self):
batch_size = 16
converter = lambda x: annotation2Mask(x).flatten()
ds = imageDataStore(self.fileNames, self.encodedPixels, batch_size,
converter)
images, labels = next(ds)
self.assertIsInstance(images, np.ndarray)
self.assertIsInstance(labels, np.ndarray)
self.assertEquals(images.shape[0], batch_size)
self.assertEquals(labels.shape[0], batch_size)
next(ds)
next(ds)
def testGetABSDDataConverter(self):
batch_size = 16
converter = lambda x: annotation2Mask(x).flatten()
train, dev, test = getABSDData(batch_size, converter, '../data')
def chechBatch(ds):
n_pixels = 768 * 768
images, labels = next(ds)
self.assertIsInstance(images, np.ndarray)
self.assertIsInstance(labels, np.ndarray)
self.assertEquals(images.shape[0], batch_size)
self.assertEquals(labels.shape[0], batch_size)
self.assertEquals(labels.shape[1], n_pixels)
chechBatch(train)
chechBatch(train)
chechBatch(dev)
chechBatch(dev)
chechBatch(test)
chechBatch(test)