thisisanshgupta/Pycode · Datasets at Hugging Face

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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"
1579,"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)"
1580,"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.

``cascade`` : str or :py:class:`Cascade` or ``None``
If given, the cascade file name or the loaded cascade to be used.
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 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:

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"

1579,"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)"

1580,"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.

``cascade`` : str or :py:class:`Cascade` or ``None``

If given, the cascade file name or the loaded cascade to be used.

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 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: