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image = bob.ip.color.rgb_to_gray(image)
detections = []
predictions = []
# get the detection scores for the image
for prediction, bounding_box in sampler.iterate_cascade(cascade, image, None):
detections.append(bounding_box)
predictions.append(prediction)
if not detections:
return None
# compute average over the best locations
bb, quality = best_detection(detections, predictions, minimum_overlap, relative_prediction_threshold)
return bb, quality"
1581,"def detect_all_faces(image, cascade = None, sampler = None, threshold = 0, overlaps = 1, minimum_overlap = 0.2, relative_prediction_threshold = 0.25):
""""""detect_all_faces(image, [cascade], [sampler], [threshold], [overlaps], [minimum_overlap], [relative_prediction_threshold]) -> bounding_boxes, qualities
Detects all faces in the given image, whose prediction values are higher than the given threshold.
If the given ``minimum_overlap`` is lower than 1, overlapping bounding boxes are grouped, with the ``minimum_overlap`` being the minimum Jaccard similarity between two boxes to be considered to be overlapping.
Afterwards, all groups which have less than ``overlaps`` elements are discarded (this measure is similar to the Viola-Jones face detector).
Finally, :py:func:`average_detections` is used to compute the average bounding box for each of the groups, including averaging the detection value (which will, hence, usually decrease in value).
**Parameters:**
``image`` : array_like (2D aka gray or 3D aka RGB)
The image to detect a face in.
``cascade`` : str or :py:class:`Cascade` or ``None``
If given, the cascade file name or the loaded cascade to be used to classify image patches.
If not given, the :py:func:`default_cascade` is used.
``sampler`` : :py:class:`Sampler` or ``None``
The sampler that defines the sampling of bounding boxes to search for the face.
If not specified, a default Sampler is instantiated.
``threshold`` : float
The threshold of the quality of detected faces.
Detections with a quality lower than this value will not be considered.
Higher thresholds will not detect all faces, while lower thresholds will generate false detections.
``overlaps`` : int
The number of overlapping boxes that must exist for a bounding box to be considered.
Higher values will remove a lot of false-positives, but might increase the chance of a face to be missed.
The default value ``1`` will not limit the boxes.
``minimum_overlap`` : float between 0 and 1
Groups detections based on the given minimum bounding box overlap, see :py:func:`group_detections`.
``relative_prediction_threshold`` : float between 0 and 1
Limits the bounding boxes to those that have a prediction value higher then ``relative_prediction_threshold * max(predictions)``
**Returns:**
``bounding_boxes`` : [:py:class:`BoundingBox`]
The bounding box containing the detected face.
``qualities`` : [float]
The qualities of the ``bounding_boxes``, values greater than ``threshold``.
""""""
if cascade is None:
cascade = default_cascade()
elif isinstance(cascade, str):
cascade = Cascade(bob.io.base.HDF5File(cascade))
if sampler is None:
sampler = Sampler(patch_size = cascade.extractor.patch_size, distance=2, scale_factor=math.pow(2.,-1./16.), lowest_scale=0.125)
if image.ndim == 3:
image = bob.ip.color.rgb_to_gray(image)
detections = []
predictions = []
# get the detection scores for the image
for prediction, bounding_box in sampler.iterate_cascade(cascade, image, threshold):
detections.append(bounding_box)
predictions.append(prediction)
if not detections:
# No face detected
return None
# group overlapping detections
if minimum_overlap < 1.:
detections, predictions = group_detections(detections, predictions, minimum_overlap, threshold, overlaps)
if not detections:
return None
# average them
detections, predictions = zip(*[average_detections(b, q, relative_prediction_threshold) for b,q in zip(detections, predictions)])
return detections, predictions"
1582,"def cycles_created_by(callable):
""""""
Return graph of cyclic garbage created by the given callable.
Return an :class:`~refcycle.object_graph.ObjectGraph` representing those