def sampleAndRunLoop(vidSource):
(major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')
if int(major_ver) < 3:
fps = vidSource.get(cv2.cv.CV_CAP_PROP_FPS)
else:
fps = vidSource.get(cv2.CAP_PROP_FPS)
if fps == 0:
fps = 24
sampleLen = getParam["SampleLength"]
ret, frame = vidSource.read()
sample = np.zeros((sampleLen, frame.shape[0], frame.shape[1], 3),
dtype=np.uint8)
idx = 0
pipeline = PipeLine(fps)
while True:
ret, frame = vidSource.read()
if idx < sampleLen:
sample[idx] = frame
# continue
else:
# Slide sampling window
sample = np.insert(sample[1:], -1, frame, axis=0)
# Perform computation of frequency
respiratoryRate = pipeline.run(sample)
idx += 1
# Display result on the output image
cv2.putText(frame, "Frame: %d, %d bps" % (idx, respiratoryRate),
(50, 50), cv2.FONT_HERSHEY_TRIPLEX, 0.7, (0, 20, 255))
cv2.imshow('output', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
pipeline = PipeLine()
pipeline.add_bg_step(ElasticInitBGStep({'kill_at_end': False}))
pipeline.add_bg_step(VisualizerInitBGStep({'kill_at_end': False}))
pipeline.add_pipe(TweetBinStep(
{
'tweet-frequency': 'daily',
'tweet-format': 'csv',
'ignore-before': '2017-01-01'
}))
pipeline.add_pipe(FeatureExtractStep(
{
'jar-path': '../jars/TextFeatureExtractor.jar',
'tweet-frequency': 'daily',
'query-params': [
'lka',
'sri lanka', 'srilanka',
'flag', 'celebration',
]
}))
pipeline.add_pipe(IKASLStep(
{
'jar-path': '../jars/IKASL.jar',
'tweet-frequency': 'daily',
'additional-args': {'htf': 0.02, '-max-nodes': 8}
}))
pipeline.add_pipe(LayerProcessStep({'tweet-frequency': 'daily'}))
pipeline.add_pipe(ElasticFeedStep({'dataset-name': 'lka'}))
# pipeline.run('../out/pipe_out/feature-extract-out')
# pipeline.run('../out/pipe_out/ikasl-out')
pipeline.run('../data/lka')