def _make_image_labels_dataset(img,
images_dir,
num_samples=4,
num_objects_per_sample=3):
exts = [".jpg", ".png"]
samples = []
for idx in range(num_samples):
filepath = os.path.join(images_dir,
"%06d%s" % (idx, exts[idx % len(exts)]))
etai.write(img, filepath)
image_labels = etai.ImageLabels()
_label = random.choice(["sun", "rain", "snow"])
image_labels.add_attribute(etad.CategoricalAttribute("label", _label))
for _ in range(num_objects_per_sample):
_label = random.choice(["cat", "dog", "bird", "rabbit"])
_xtl = 0.8 * random.random()
_ytl = 0.8 * random.random()
_bounding_box = etag.BoundingBox.from_coords(
_xtl, _ytl, _xtl + 0.2, _ytl + 0.2)
image_labels.add_object(
etao.DetectedObject(label=_label, bounding_box=_bounding_box))
samples.append(
fo.Sample(
filepath=filepath,
ground_truth=fo.ImageLabels(labels=image_labels),
))
dataset = fo.Dataset()
dataset.add_samples(samples)
return dataset
def to_detected_object(self, name=None):
"""Returns an ``eta.core.objects.DetectedObject`` representation of
this instance.
Args:
name (None): the name of the label field
Returns:
an ``eta.core.objects.DetectedObject``
"""
label = self.label
index = self.index
# pylint: disable=unpacking-non-sequence
tlx, tly, w, h = self.bounding_box
brx = tlx + w
bry = tly + h
bounding_box = etag.BoundingBox.from_coords(tlx, tly, brx, bry)
mask = self.mask
confidence = self.confidence
# pylint: disable=no-member
attrs = _to_eta_attributes(self.attributes)
return etao.DetectedObject(
label=label,
index=index,
bounding_box=bounding_box,
mask=mask,
confidence=confidence,
name=name,
attrs=attrs,
)
def _parse_bdd_annotation(d, frame_size):
image_labels = etai.ImageLabels()
# Frame attributes
frame_attrs = d.get("attributes", {})
image_labels.attrs = _make_attributes(frame_attrs)
# Objects
objects = d.get("labels", [])
for obj in objects:
if "box2d" not in obj:
continue
label = obj["category"]
bbox = obj["box2d"]
bounding_box = etag.BoundingBox.from_abs_coords(
bbox["x1"],
bbox["y1"],
bbox["x2"],
bbox["y2"],
frame_size=frame_size,
)
obj_attrs = obj.get("attributes", {})
attrs = _make_attributes(obj_attrs)
image_labels.add_object(
etao.DetectedObject(
label=label,
bounding_box=bounding_box,
attrs=attrs,
))
return fol.ImageLabels(labels=image_labels)
def to_detected_object(self, name=None):
"""Returns an ``eta.core.objects.DetectedObject`` representation of
this instance.
Args:
name (None): the name of the label field
Returns:
an ``eta.core.objects.DetectedObject``
"""
label = self.label
# pylint: disable=unpacking-non-sequence
tlx, tly, w, h = self.bounding_box
brx = tlx + w
bry = tly + h
bounding_box = etag.BoundingBox.from_coords(tlx, tly, brx, bry)
confidence = self.confidence
# pylint: disable=no-member
attrs = etad.AttributeContainer()
for attr_name, attr in self.attributes.items():
attrs.add(etad.CategoricalAttribute(attr_name, attr.value))
return etao.DetectedObject(
label=label,
bounding_box=bounding_box,
confidence=confidence,
name=name,
attrs=attrs,
)
def _parse_bdd_object(d, frame_size):
label = d["category"]
box2d = d["box2d"]
bounding_box = etag.BoundingBox.from_abs_coords(
box2d["x1"],
box2d["y1"],
box2d["x2"],
box2d["y2"],
frame_size=frame_size,
clamp=False,
)
obj_attrs = d.get("attributes", {})
attrs = _make_attributes(obj_attrs)
return etao.DetectedObject(
label=label, bounding_box=bounding_box, attrs=attrs,
)
def to_detected_object(label, det, image):
''' convert the ObjectDetector detection to an eta DetectedObject.
Args:
label is the label of the object
det is a 5-vector with topleft-x,y bottomright-x,y confidence as values
image is the original image that was use to detect this object
'''
sx, sy, ex, ey, conf = det
return etao.DetectedObject(label,
etag.BoundingBox(
etag.RelativePoint.from_abs(sx,
sy,
img=image),
etag.RelativePoint.from_abs(ex,
ey,
img=image),
),
confidence=conf)
def to_detected_object(self, frame_size):
"""Returns a ``eta.core.objects.DetectedObject`` representation of the
box.
Args:
frame_size: the ``(width, height)`` of the image
Returns:
a ``eta.core.objects.DetectedObject``
"""
label = self.label
bounding_box = etag.BoundingBox.from_abs_coords(self.xtl,
self.ytl,
self.xbr,
self.ybr,
frame_size=frame_size)
attrs = etad.AttributeContainer(
attrs=[a.to_eta_attribute() for a in self.attributes])
return etao.DetectedObject(label=label,
bounding_box=bounding_box,
attrs=attrs)