def make_prediction(webcam, params, timestamp):
"""
Make a prediction using NearestNeighbors algorithm.
Parameters
----------
webcam : Webcam instance
The source of pictures
params : PredictionParams instance
The parameters to use to compute the prediction
timestamp : int
The Unix-Epoch-timestamp (UTC) of when the prediction is made
Return
------
Prediction instance
"""
cam_id = webcam.webcam_id
result = None
with webcam_fs.get_dataset(cam_id) as dataset:
ex_set = dataset.get_set(params.name)
new_input = dataset.make_input(ex_set, timestamp)
if new_input is not None and len(ex_set.input) > 0:
neighbors = NearestNeighbors()
neighbors.fit(ex_set.input)
output_ref = neighbors.predict(new_input)
output = dataset.output_img(ex_set, output_ref)
# FIXME reference existing image (data and file)
imgpath = webcam_fs.prediction_path(cam_id, params.name, timestamp)
result = Prediction(params=params, path=imgpath)
result.comp_date = datetime.fromtimestamp(timestamp, utc)
result.sci_bytes = output
result.create()
return result
def update_prediction(prediction, real_pic, metric_name='euclidean'):
"""
Update a prediction after receiving the actual picture from the webcam.
Parameters
----------
prediction : Prediction
The model object of the prediction to update
real_pic : Picture
The model object of the actual picture received
Return
------
float : the prediction error
"""
pred_pic = prediction.as_picture()
cam_id = prediction.params.webcam.webcam_id
if metric_name == 'wminkowski-pca':
with webcam_fs.get_dataset(cam_id) as dataset:
if 'pca' not in dataset.imgset.feature_sets:
raise ValueError("""wminkowski-pca cannnot be used
without a PCA feature set""")
pca_extractor = dataset.imgset.feature_sets['pca'].extractor
weights = pca_extractor.pca.explained_variance_ratio_
pred_data = pca_extractor.extract(pred_pic.pixels)
real_data = pca_extractor.extract(real_pic.pixels)
metric = DistanceMetric.get_metric('wminkowski', p=2, w=weights)
else:
pred_data = pred_pic.pixels
real_data = real_pic.pixels
metric = DistanceMetric.get_metric(metric_name)
error = metric.pairwise([pred_data], [real_data])[0]
prediction.error = error
prediction.save()
return error
def store(self):
webcam_fs.add_webcam(self.webcam_id)
with webcam_fs.get_dataset(self.webcam_id) as dataset:
for featureset in dataset.imgset.feature_sets.keys():
db_set = FeatureSet(webcam=self, name=featureset)
db_set.save()
