def getEdges(self, scale, thresholdFactor):
laplacianOfGaussian = ndimage.gaussian_laplace(self._image, scale) # ish 0.57 seconds
zeroCrossings = _findZeroCrossings(laplacianOfGaussian, thresholdFactor) # ish 0.25 sec
result = PointCloud()
for x, y in zip(zeroCrossings[0], zeroCrossings[1]):
result.addXY(float(x), float(y))
return result
def test_simple(self):
encoder = PointCloudJsonEncoder()
cloud = PointCloud()
cloud.addXY(0.0, 1.0)
cloud.addXY(2.0, 3.0)
jsonDict = encoder.default(cloud)
self.assertEqual(jsonDict, {'__type__': 'PointCloud',
'points': [{'x': 0.0, 'y': 1.0}, {'x': 2.0, 'y': 3.0}]})
def dict_to_object(self, d):
if "__type__" not in d:
return d
if d["__type__"] == "PointCloud":
result = PointCloud()
for point in d["points"]:
result.addXY(point["x"], point["y"])
return result
return d
def test_AddPoints(self):
c = PointCloud()
c.addPoint([1.0, 2.0])
c.addXY(3.0, 4.0)
points = [p for p in c]
self.assertEquals(points[0][0], 1.0)
self.assertEquals(points[0][1], 2.0)
self.assertEquals(points[1][0], 3.0)
self.assertEquals(points[1][1], 4.0)
self.assertEquals(c.max(), (3.0, 4.0))
self.assertEquals(c.min(), (1.0, 2.0))
self.assertEquals(c.mean(), (2.0, 3.0))
def test_SimpleImage(self):
simpleImage = np.zeros((12, 12))
simpleImage[1, 1] = 255
expectedPointCloud = PointCloud()
expectedPointCloud.addXY(0.5, 0.0)
expectedPointCloud.addXY(1.5, 2.0)
expectedPointCloud.addXY(0.0, 0.5)
expectedPointCloud.addXY(2.0, 1.5)
e = EdgeDetector(simpleImage)
pointCloud = e.getEdges(1.0, 17.0)
for point, expectedPoint in zip(pointCloud, expectedPointCloud):
self.assertEqual(point[0], expectedPoint[0])
self.assertEqual(point[1], expectedPoint[1])
def test_ToRgb(self):
c = PointCloud()
image = PointCloudToRgbImage(c, 0)
rows, cols, channels = image.shape
self.assertEquals(channels, 3)
for element in np.nditer(image):
self.assertEquals(element, 0.0)
c.addXY(0.0, 0.0)
c.addXY(10.0, 10.0)
image = PointCloudToRgbImage(c, 0)
rows, cols, channels = image.shape
self.assertEquals(rows, 11)
self.assertEquals(cols, 11)
self.assertEquals(image[0, 0, 0], 255)
self.assertEquals(image[0, 0, 1], 0)
self.assertEquals(image[0, 0, 2], 0)
self.assertEquals(image[10, 10, 0], 255)
self.assertEquals(image[10, 10, 1], 0)
self.assertEquals(image[10, 10, 2], 0)
self.assertEquals(np.sum(image), 510)
class KeepInsideRadiusStage:
def __init__(self, radius):
self._radius = radius
self._executionResult = None
def execute(self, pointCloud):
mean = pointCloud.mean()
self._executionResult = PointCloud()
radiusSquare = self._radius**2
for x, y in pointCloud:
squareDistanceFromMean = float((x - mean[0])**2 + (y - mean[1])**2)
if squareDistanceFromMean <= radiusSquare:
self._executionResult.addXY(float(x), float(y))
return self._executionResult
def getImageRepresentation(self):
return PointCloudToRgbImage(self._executionResult, 0)
def __ne__(self, other):
if type(self) != type(other):
return True
return self._radius != other._radius
def test_SimpleWrite(self):
cloud = PointCloud()
cloud.addXY(0.0, 0.0)
cloud.addXY(1.0, 2.0)
writeable = self.StringStorer()
PointCloudHandler.savePointCloudToWriteable(cloud, writeable)
oneString = "".join(writeable.strings)
expectedString = '{\n'\
' "__type__": "PointCloud",\n'\
' "points": [\n'\
' {\n'\
' "x": 0.0,\n'\
' "y": 0.0\n'\
' },\n'\
' {\n'\
' "x": 1.0,\n'\
' "y": 2.0\n'\
' }\n'\
' ]\n'\
'}'
self.assertEquals(oneString, expectedString)
def test_AddNonFloatXY(self):
c = PointCloud()
with self.assertRaises(ValueError):
c.addXY(2.0, "not float")
