def topic(self):
engine = MockEngine(size=IMAGE_SIZE)
json = JSONEngine(engine=engine, path=IMAGE_PATH)
json.focus([FocalPoint(100, 100), FocalPoint(200, 200)])
return loads(json.read('jpg', 100))
def detect(self, callback):
engine = self.context.modules.engine
sz = engine.size
image = cv.CreateImageHeader(sz, cv.IPL_DEPTH_8U, 3)
cv.SetData(image, engine.get_image_data())
gray_image = cv.CreateImage(engine.size, 8, 1)
convert_mode = getattr(cv, 'CV_%s2GRAY' % engine.get_image_mode())
cv.CvtColor(image, gray_image, convert_mode)
image = gray_image
rows = sz[0]
cols = sz[1]
eig_image = cv.CreateMat(rows, cols, cv.CV_32FC1)
temp_image = cv.CreateMat(rows, cols, cv.CV_32FC1)
points = cv.GoodFeaturesToTrack(image,
eig_image,
temp_image,
20,
0.04,
1.0,
useHarris=False)
if points:
for x, y in points:
self.context.request.focal_points.append(FocalPoint(x, y, 1))
callback()
else:
self.next(callback)
def detect(self, callback):
engine = self.context.modules.engine
try:
img = np.array(
engine.convert_to_grayscale(update_image=False,
with_alpha=False))
except Exception as e:
logger.exception(e)
logger.warn(
'Error during feature detection; skipping to next detector')
self.next(callback)
return
points = cv2.goodFeaturesToTrack(
img,
maxCorners=20,
qualityLevel=0.04,
minDistance=1.0,
useHarrisDetector=False,
)
if points is not None:
for point in points:
x, y = point.ravel()
self.context.request.focal_points.append(
FocalPoint(x.item(), y.item(), 1))
callback()
else:
self.next(callback)
async def detect(self):
engine = self.context.modules.engine
try:
img = np.array(
engine.convert_to_grayscale(update_image=False, alpha=False))
except Exception as error:
logger.exception(error)
logger.warning(
"Error during feature detection; skipping to next detector")
return await self.next() # pylint: disable=not-callable
points = cv2.goodFeaturesToTrack( # pylint: disable=no-member
img,
maxCorners=20,
qualityLevel=0.04,
minDistance=1.0,
useHarrisDetector=False,
)
if points is not None:
for point in points:
x_pos, y_pos = point.ravel()
self.context.request.focal_points.append(
FocalPoint(x_pos.item(), y_pos.item(), 1))
return
await self.next() # pylint: disable=not-callable
def detect(self, callback):
engine = self.context.modules.engine
img = np.array(
engine.convert_to_grayscale(update_image=False, with_alpha=False))
points = cv2.goodFeaturesToTrack(
img,
maxCorners=20,
qualityLevel=0.04,
minDistance=1.0,
useHarrisDetector=False,
)
if points is not None:
for x, y in points.squeeze():
self.context.request.focal_points.append(FocalPoint(x, y, 1))
callback()
else:
self.next(callback)
def test_new_point_weighted(self):
point = FocalPoint(x=10, y=20, height=1.0, width=3.0, weight=3.0)
expect(point.weight).to_equal(3.0)
expect(str(point)).to_equal(
'FocalPoint(x: 10, y: 20, width: 3, height: 1, weight: 3, origin: alignment)'
)
def test_new_point_default_weight(self):
point = FocalPoint(10, 20)
expect(point.x).to_equal(10)
expect(point.y).to_equal(20)
def topic(self):
return FocalPoint(x=10,
y=20,
height=1.0,
width=3.0,
weight=3.0)
def topic(self):
return FocalPoint(10, 20)
def test_default_weight_is_one():
point = FocalPoint(x=10.0, y=20.0)
assert point.weight == 1.0
def test_focal_point():
point = FocalPoint(x=10.0, y=20.0, weight=3.4)
assert point.x == 10.0
assert point.y == 20.0
assert point.weight == 3.4
