# Source code for bob.ip.facedetect.detect

```import pkg_resources
import math

from ._library import BoundingBox, prune_detections, group_detections, overlapping_detections

import bob.io.base
import numpy

[docs]def average_detections(detections, predictions, relative_prediction_threshold = 0.25):
"""average_detections(detections, predictions, [relative_prediction_threshold]) -> bounding_box, prediction

Computes the weighted average of the given detections, where the weights are computed based on the prediction values.

**Parameters:**

``detections`` : [:py:class:`BoundingBox`]
The overlapping bounding boxes.

``predictions`` : [float]
The predictions for the ``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_box`` : :py:class:`BoundingBox`
The bounding box which has been merged from the detections

``prediction`` : float
The prediction value of the bounding box, which is a weighted sum of the predictions with minimum overlap
"""
# remove the predictions that are too low
prediction_threshold = relative_prediction_threshold * max(predictions)
detections, predictions = zip(*[[d,p] for d,p in zip(detections, predictions) if p >= prediction_threshold])

# turn remaining predictions into weights
s = sum(predictions)
weights = [p/s for p in predictions]
# compute weighted average of bounding boxes
top = sum(w * b.topleft_f[0] for w, b in zip(weights, detections))
left = sum(w * b.topleft_f[1] for w, b in zip(weights, detections))
bottom = sum(w * b.bottomright_f[0] for w, b in zip(weights, detections))
right = sum(w * b.bottomright_f[1] for w, b in zip(weights, detections))

# compute the average prediction value
value = sum(w*p for w,p in zip(weights, predictions))

# return the average bounding box
return BoundingBox((top, left), (bottom-top, right-left)), value

[docs]def best_detection(detections, predictions, minimum_overlap = 0.2, relative_prediction_threshold = 0.25):
"""best_detection(detections, predictions, [minimum_overlap], [relative_prediction_threshold]) -> bounding_box, prediction

Computes the best detection for the given detections and according predictions.

This is achieved by computing a weighted sum of detections that overlap with the best detection (the one with the highest prediction), where the weights are based on the predictions.
Only detections with according prediction values > 0 are considered.

**Parameters:**

``detections`` : [:py:class:`BoundingBox`]
The detected bounding boxes.

``predictions`` : [float]
The predictions for the ``detections``.

``minimum_overlap`` : float between 0 and 1
The minimum overlap (in terms of Jaccard :py:meth:`BoundingBox.similarity`) of bounding boxes with the best detection to be considered.

``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_box`` : :py:class:`BoundingBox`
The bounding box which has been merged from the detections

``prediction`` : float
The prediction value of the bounding box, which is a weighted sum of the predictions with minimum overlap
"""
# remove all negative predictions since they harm the calculation of the weights
detections = [detections[i] for i in range(len(detections)) if predictions[i] > 0]
predictions = [predictions[i] for i in range(len(predictions)) if predictions[i] > 0]

if not detections:
raise ValueError("No detections with a prediction value > 0 have been found")

# keep only the bounding boxes with the highest overlap
detections, predictions = overlapping_detections(detections, numpy.array(predictions), minimum_overlap)

return average_detections(detections, predictions, relative_prediction_threshold)

[docs]def detect_single_face(image, cascade = None, sampler = None, minimum_overlap=0.2, relative_prediction_threshold = 0.25):
"""detect_single_face(image, [cascade], [sampler], [minimum_overlap], [relative_prediction_threshold]) -> bounding_box, quality

Detects a single face in the given image, i.e., the one with the highest prediction value.

**Parameters:**

``image`` : array_like (2D aka gray or 3D aka RGB)
The image to detect a face in.

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, which will perform a tight sampling.

``minimum_overlap`` : float between 0 and 1
Computes the best detection using the given minimum overlap, see :py:func:`best_detection`

``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_box`` : :py:class:`BoundingBox`
The bounding box containing the detected face.

``quality`` : float
The quality of the detected face, a value greater than 0.
"""

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
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

[docs]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.

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 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
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
```