interval = float(sys.argv[2])
except:
print 'usage', sys.argv[0], ' <url> <interval>'
sys.exit()
print 'url', url
print 'interval', interval
stamp_begin = -1.0
cf = Classifier('../models/deploy.prototxt',
'../models/pretrained.caffemodel',
'../models/mean.binaryproto',
'../models/labels.txt')
vc = VideoCap(url, (256, 256), "1/3")
curr_encoding = locale.getdefaultlocale()[1]
def cb_image(image):
global cf, quit, stamp_begin
if image is None:
quit = True
return
if stamp_begin < 0.0:
stamp_begin = time.time()
pred = cf.predicate(image)
if __name__ == '__main__':
url = sys.argv[1]
cf = Classifier('../models/deploy.prototxt',
'../models/pretrained.caffemodel',
'../models/mean.binaryproto', '../models/labels.txt')
quit = False
def cb_image(img):
global quit, cf
if img is None:
print 'end'
quit = True
else:
pred = cf.predicate(img)
f = cv2.resize(img, (480, 270))
cv2.imshow('show', f)
key = cv2.waitKey(1)
print pred[0][1].decode('utf-8').encode(
'gbk') + ' score:', pred[0][2]
vc = VideoCap(url, (256, 256), "1/3")
vc.start(cb_image)
while not quit:
time.sleep(1.0)
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
cf = Classifier('../models/deploy.prototxt',
'../models/pretrained.caffemodel',
'../models/mean.binaryproto',
'../models/labels.txt')
quit = False
def cb_image(img):
global quit, cf
if img is None:
print 'end'
quit = True
else:
pred = cf.predicate(img)
f = cv2.resize(img, (480, 270))
cv2.imshow('show', f)
key = cv2.waitKey(1)
print pred[0][1].decode('utf-8').encode('gbk') + ' score:', pred[0][2]
vc = VideoCap(url, (256, 256), "1/3")
vc.start(cb_image)
while not quit:
time.sleep(1.0)
# vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4
