def pre_analyze(db):
result = dict()
mobile.DB = db
readings = mobile.stream_readings()
start = time.time()
H = cluster.Hierarchy(next(readings))
for i, r in enumerate(readings):
H.append(r)
movements = cluster.segment(H, threshold=0.01)
locs = cluster.locations(movements, threshold=0.5)
return (H, movements, locs)
def make_single_images(image):
for startX, endX in cluster.segment(image, max_threshold=10000):
startY, endY = segment_vertical(image[:, startX:endX])
yield image[startY:endY, startX:endX]
readings = mobile.stream_readings()
# Clustering
start = time.time()
H = cluster.Hierarchy(next(readings))
for i, r in enumerate(readings):
H.append(r)
# if i % 1000 == 0:
# print(i)
print("Clustering duration = %.2f seconds" % (time.time() - start))
# Segmentation
start = time.time()
tops = cluster.segment(H, threshold=0.01)
print("Segmentation duration = %.2f seconds" % (time.time() - start))
# Locations
start = time.time()
locs = cluster.locations(tops, threshold=0.1)
print("Locations duration = %.2f seconds" % (time.time() - start))
def humanize(sec):
if sec < 60:
return "%2.2f s" % sec
min = sec / 60.0
if min < 60:
return "%2.2f m" % min
hr = min / 60.0
return "%2.2f h" % hr
