def should_append_the_returned_focal_points_to_context_request(self, topic):
focal_point1_repr = FocalPoint.from_square(1, 2, 3, 4).to_dict()
focal_point2_repr = FocalPoint.from_square(5, 6, 7, 8).to_dict()
calls = topic.context.request.focal_points.append.call_args_list
first_call_arg_repr = calls[0][0][0].to_dict()
secon_call_arg_repr = calls[1][0][0].to_dict()
expect(first_call_arg_repr).to_equal(focal_point1_repr)
expect(secon_call_arg_repr).to_equal(focal_point2_repr)
def features_to_focal_points(cls, features):
focal_points = []
for (left, top, width, height), neighbors in features:
top = cls.add_hair_offset(top, height)
focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Face Detection"))
return focal_points
def detect(self, context):
features = self.get_features(context)
if features:
for (left, top, width, height), neighbors in features:
context['focal_points'].append(FocalPoint.from_square(left, top, width, height))
else:
self.next(context)
def detect(self, context):
features = self.get_features(context)
if features:
for (left, top, width, height), neighbors in features:
context['focal_points'].append(FocalPoint.from_square(left, top, width, height))
else:
self.next(context)
async def detect(self):
features = self.get_features()
if features:
for square, _ in features:
self.context.request.focal_points.append(
FocalPoint.from_square(*square))
else:
await self.next() # pylint: disable=not-callable
def focal_point(self, overall_width, overall_height):
bbox = self.api_resp['BoundingBox']
x = int(bbox['Left'] * overall_width)
y = int(bbox['Top'] * overall_height)
w = int(bbox['Width'] * overall_width)
h = int(bbox['Height'] * overall_height)
return FocalPoint.from_square(x, y, w, h, origin='RekognitionDetector')
def detect(self, callback):
features = self.get_features()
if features:
for square, neighbors in features:
self.context.request.focal_points.append(FocalPoint.from_square(*square))
callback()
else:
self.next(callback)
def config_context(context):
image_w, image_h = expected.size[0], expected.size[1]
point = FocalPoint.from_square(50,
50,
image_w - 100,
image_h - 100,
origin="Face Detection")
context.request.focal_points = [point]
def features_to_focal_points(cls, features):
focal_points = []
for (left, top, width, height), neighbors in features:
top = cls.add_hair_offset(top, height)
focal_points.append(
FocalPoint.from_square(left,
top,
width,
height,
origin='Face Detection'))
return focal_points
def detect(self, callback):
features = self.get_features()
if features:
for (left, top, width, height), neighbors in features:
top = self.__add_hair_offset(top, height)
self.context.request.focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Face Detection")
)
callback()
else:
self.next(callback)
def detect(self, callback):
features = self.get_features()
if features:
for (left, top, width, height), neighbors in features:
top = self.__add_hair_offset(top, height)
self.context.request.focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Face Detection")
)
callback()
else:
self.next(callback)
def extract_focal(self):
parts = self.parse_url(self.context.request.image_url)
if parts:
image, top, right, left, bottom = parts
top, right, left, bottom = int(top), int(right), int(left), int(bottom)
width = right - left
height = bottom - top
self.context.request.focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Original Extraction")
)
self.context.request.image_url = image
def focal(self, focal_string):
parsed = self.focal_regex.match(focal_string)
if parsed:
left, top, right, bottom = parsed.groups()
left, top, right, bottom = int(left), int(top), int(right), int(bottom)
width = right - left
height = bottom - top
if width and height:
self.context.request.focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Explicit")
)
def focal(self, focal_string):
parsed = self.focal_regex.match(focal_string)
if parsed:
left, top, right, bottom = parsed.groups()
left, top, right, bottom = int(left), int(top), int(right), int(bottom)
width = right - left
height = bottom - top
if width and height:
self.context.request.focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Explicit")
)
def detect(self, callback):
try:
features = self.get_features()
except Exception:
logger.warn('Error during face detection; skipping to next detector')
self.next(callback)
return
if features:
for (left, top, width, height), neighbors in features:
top = self.__add_hair_offset(top, height)
self.context.request.focal_points.append(
FocalPoint.from_square(left, top, width, height, origin="Face Detection")
)
callback()
else:
self.next(callback)
def detect(self, context):
size = context['engine'].size
image_header = cv.CreateImageHeader(size, cv.IPL_DEPTH_8U, 3)
cv.SetData(image_header, Image.open(StringIO(context['buffer'])).tostring())
grayscale = cv.CreateImage(size, 8, 1)
cv.CvtColor(image_header, grayscale, cv.CV_BGR2GRAY)
cv.EqualizeHist(grayscale, grayscale)
faces = cv.HaarDetectObjects(grayscale, Detector.cascade, cv.CreateMemStorage(), 1.1, 3, cv.CV_HAAR_DO_CANNY_PRUNING, (30, 30))
if faces:
for face in faces:
left, top, width, height = face[0]
top = self.__add_hair_offset(top, height)
context['focal_points'].append(FocalPoint.from_square(left, top, width, height))
else:
self.next(context)
async def detect(self):
try:
features = self.get_features()
except Exception as error:
logger.exception(error)
logger.warning("Error during face detection; skipping to next detector")
return await self.next()
if features:
for (left, top, width, height), _ in features:
top = self.__add_hair_offset(top, height)
self.context.request.focal_points.append(
FocalPoint.from_square(
left, top, width, height, origin="Face Detection"
)
)
return
await self.next()
async def detect(self):
if not self.verify_cv():
await self.next()
return
features = self.get_features()
if not features:
await self.next() # pylint: disable=not-callable
for (left, top, width, height), _ in features:
offset = self.get_detection_offset(left, top, width, height)
self.context.request.focal_points.append(
FocalPoint.from_square(
left + offset.get("left", 0.0),
top + offset.get("top", 0.0),
width + offset.get("right", 0.0),
height + offset.get("bottom", 0.0),
origin=self.get_origin(),
))
def test_new_point_square_point(self):
point = FocalPoint.from_square(x=350, y=50, width=110, height=110)
expect(point.x).to_equal(405)
expect(point.y).to_equal(105)
expect(point.weight).to_equal(12100)
def test_new_point_square_point(self):
point = FocalPoint.from_square(x=350, y=50, width=110, height=110)
expect(point.x).to_equal(405)
expect(point.y).to_equal(105)
expect(point.weight).to_equal(12100)
def config_context(context):
image_w, image_h = expected.size[0], expected.size[1]
point = FocalPoint.from_square(50, 50, image_w - 100, image_h - 100, origin='Face Detection')
context.request.focal_points = [point]
def topic(self):
return FocalPoint.from_square(0, 300, 300, 300)
def topic(self):
return FocalPoint.from_square(x=350, y=50, width=110, height=110)
def topic(self):
return FocalPoint.from_square(350, 50, 110, 110)
def test_from_square():
point = FocalPoint.from_square(x=10.0, y=20.0, width=100, height=200)
assert point.x == 55.0
assert point.y == 110.0
assert point.weight == 20000.0
def topic(self):
return FocalPoint.from_square(x=350, y=50, width=110, height=110)
def test_from_square():
point = FocalPoint.from_square(x=10.0, y=20.0, width=100, height=200)
assert point.x == 55.0
assert point.y == 110.0
assert point.weight == 20000.0
